Improving entrepreneurial opportunity recognition through web content analytics

NASA Astrophysics Data System (ADS)

Bakar, Muhamad Shahbani Abu; Azmi, Azwiyati

2017-10-01

The ability to recognize and develop an opportunity into a venture defines an entrepreneur. Research in opportunity recognition has been robust and focuses more on explaining the processes involved in opportunity recognition. Factors such as prior knowledge, cognitive and creative capabilities are shown to affect opportunity recognition in entrepreneurs. Prior knowledge in areas such as customer problems, ways to serve the market, and technology has been shows in various studies to be a factor that facilitates entrepreneurs to identify and recognize opportunities. Findings from research also shows that experienced entrepreneurs search and scan for information to discover opportunities. Searching and scanning for information has also been shown to help novice entrepreneurs who lack prior knowledge to narrow this gap and enable them to better identify and recognize opportunities. There is less focus in research on finding empirically proven techniques and methods to develop and enhance opportunity recognition in student entrepreneurs. This is important as the country pushes for more graduate entrepreneurs that can drive the economy. This paper aims to discuss Opportunity Recognition Support System (ORSS), an information support system to help especially student entrepreneurs in identifying and recognizing business opportunities. The ORSS aims to provide the necessary knowledge to student entrepreneurs to be able to better identify and recognize opportunities. Applying design research, theories in opportunity recognition are applied to identify the requirements for the support system and the requirements in turn dictate the design of the support system. The paper proposes the use of web content mining and analytics as two core components and techniques for the support system. Web content mining can mine the vast knowledge repositories available on the internet and analytics can provide entrepreneurs with further insights into the information needed to recognize opportunities in a given market or industry.

Simulation Study of Effects of the Blind Deconvolution on Ultrasound Image

NASA Astrophysics Data System (ADS)

He, Xingwu; You, Junchen

2018-03-01

Ultrasonic image restoration is an essential subject in Medical Ultrasound Imaging. However, without enough and precise system knowledge, some traditional image restoration methods based on the system prior knowledge often fail to improve the image quality. In this paper, we use the simulated ultrasound image to find the effectiveness of the blind deconvolution method for ultrasound image restoration. Experimental results demonstrate that the blind deconvolution method can be applied to the ultrasound image restoration and achieve the satisfactory restoration results without the precise prior knowledge, compared with the traditional image restoration method. And with the inaccurate small initial PSF, the results shows blind deconvolution could improve the overall image quality of ultrasound images, like much better SNR and image resolution, and also show the time consumption of these methods. it has no significant increasing on GPU platform.

The Second Prototype of the Development of a Technological Pedagogical Content Knowledge Based Instructional Design Model: An Implementation Study in a Technology Integration Course

ERIC Educational Resources Information Center

Lee, Chia-Jung; Kim, ChanMin

2014-01-01

This study presents a refined technological pedagogical content knowledge (also known as TPACK) based instructional design model, which was revised using findings from the implementation study of a prior model. The refined model was applied in a technology integration course with 38 preservice teachers. A case study approach was used in this…

Living Animals in the Classroom: A Meta-Analysis on Learning Outcome and a Treatment-Control Study Focusing on Knowledge and Motivation

ERIC Educational Resources Information Center

Hummel, Eberhard; Randler, Christoph

2012-01-01

Prior research states that the use of living animals in the classroom leads to a higher knowledge but those previous studies have methodological and statistical problems. We applied a meta-analysis and developed a treatment-control study in a middle school classroom. The treatments (film vs. living animal) differed only by the presence of the…

Graphic Organizers for Secondary Students with Learning Disabilities

ERIC Educational Resources Information Center

Singleton, Sabrina M.; Filce, Hollie Gabler

2015-01-01

Research suggests students with learning disabilities often have trouble connecting new and prior knowledge, distinguishing essential and nonessential information, and applying comprehension strategies (DiCecco & Gleason, 2002; Vaughn & Edmonds, 2006). Graphic organizers have been suggested as tools educators can use to facilitate critical…

Enhancing Students' Confidence in Employability Skills through the Practice of "Recall, Adapt and Apply"

ERIC Educational Resources Information Center

Sinclair, Alison J.

2017-01-01

The ability to apply prior knowledge to new challenges is a skill that is highly valued by employers, but the confidence to achieve this does not come naturally to all students. An essential step to becoming an independent researcher requires a transition between simply following a fail-safe set of instructions to being able to adapt a known…

Prior Knowledge Facilitates Mutual Gaze Convergence and Head Nodding Synchrony in Face-to-face Communication

PubMed Central

Thepsoonthorn, C.; Yokozuka, T.; Miura, S.; Ogawa, K.; Miyake, Y.

2016-01-01

As prior knowledge is claimed to be an essential key to achieve effective education, we are interested in exploring whether prior knowledge enhances communication effectiveness. To demonstrate the effects of prior knowledge, mutual gaze convergence and head nodding synchrony are observed as indicators of communication effectiveness. We conducted an experiment on lecture task between lecturer and student under 2 conditions: prior knowledge and non-prior knowledge. The students in prior knowledge condition were provided the basic information about the lecture content and were assessed their understanding by the experimenter before starting the lecture while the students in non-prior knowledge had none. The result shows that the interaction in prior knowledge condition establishes significantly higher mutual gaze convergence (t(15.03) = 6.72, p < 0.0001; α = 0.05, n = 20) and head nodding synchrony (t(16.67) = 1.83, p = 0.04; α = 0.05, n = 19) compared to non-prior knowledge condition. This study reveals that prior knowledge facilitates mutual gaze convergence and head nodding synchrony. Furthermore, the interaction with and without prior knowledge can be evaluated by measuring or observing mutual gaze convergence and head nodding synchrony. PMID:27910902

Prior Knowledge Facilitates Mutual Gaze Convergence and Head Nodding Synchrony in Face-to-face Communication.

PubMed

Thepsoonthorn, C; Yokozuka, T; Miura, S; Ogawa, K; Miyake, Y

2016-12-02

As prior knowledge is claimed to be an essential key to achieve effective education, we are interested in exploring whether prior knowledge enhances communication effectiveness. To demonstrate the effects of prior knowledge, mutual gaze convergence and head nodding synchrony are observed as indicators of communication effectiveness. We conducted an experiment on lecture task between lecturer and student under 2 conditions: prior knowledge and non-prior knowledge. The students in prior knowledge condition were provided the basic information about the lecture content and were assessed their understanding by the experimenter before starting the lecture while the students in non-prior knowledge had none. The result shows that the interaction in prior knowledge condition establishes significantly higher mutual gaze convergence (t(15.03) = 6.72, p < 0.0001; α = 0.05, n = 20) and head nodding synchrony (t(16.67) = 1.83, p = 0.04; α = 0.05, n = 19) compared to non-prior knowledge condition. This study reveals that prior knowledge facilitates mutual gaze convergence and head nodding synchrony. Furthermore, the interaction with and without prior knowledge can be evaluated by measuring or observing mutual gaze convergence and head nodding synchrony.

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE PAGES

Oyen, Diane; Anderson, Blake; Sentz, Kari; ...

2017-09-15

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oyen, Diane; Anderson, Blake; Sentz, Kari

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

The effects of activating prior topic and metacognitive knowledge on text comprehension scores.

PubMed

Kostons, Danny; van der Werf, Greetje

2015-09-01

Research on prior knowledge activation has consistently shown that activating learners' prior knowledge has beneficial effects on learning. If learners activate their prior knowledge, this activated knowledge serves as a framework for establishing relationships between the knowledge they already possess and new information provided to them. Thus far, prior knowledge activation has dealt primarily with topic knowledge in specific domains. Students, however, likely also possess at least some metacognitive knowledge useful in those domains, which, when activated, should aid in the deployment of helpful strategies during reading. In this study, we investigated the effects of both prior topic knowledge activation (PTKA) and prior metacognitive knowledge activation (PMKA) on text comprehension scores. Eighty-eight students in primary education were randomly distributed amongst the conditions of the 2 × 2 (PTKA yes/no × PMKA yes/no) designed experiment. Results show that activating prior metacognitive knowledge had a beneficial effect on text comprehension, whereas activating prior topic knowledge, after correcting for the amount of prior knowledge, did not. Most studies deal with explicit instruction of metacognitive knowledge, but our results show that this may not be necessary, specifically in the case of students who already have some metacognitive knowledge. However, existing metacognitive knowledge needs to be activated in order for students to make better use of this knowledge. © 2015 The British Psychological Society.

Fault diagnosis of sensor networked structures with multiple faults using a virtual beam based approach

NASA Astrophysics Data System (ADS)

Wang, H.; Jing, X. J.

2017-07-01

This paper presents a virtual beam based approach suitable for conducting diagnosis of multiple faults in complex structures with limited prior knowledge of the faults involved. The "virtual beam", a recently-proposed concept for fault detection in complex structures, is applied, which consists of a chain of sensors representing a vibration energy transmission path embedded in the complex structure. Statistical tests and adaptive threshold are particularly adopted for fault detection due to limited prior knowledge of normal operational conditions and fault conditions. To isolate the multiple faults within a specific structure or substructure of a more complex one, a 'biased running' strategy is developed and embedded within the bacterial-based optimization method to construct effective virtual beams and thus to improve the accuracy of localization. The proposed method is easy and efficient to implement for multiple fault localization with limited prior knowledge of normal conditions and faults. With extensive experimental results, it is validated that the proposed method can localize both single fault and multiple faults more effectively than the classical trust index subtract on negative add on positive (TI-SNAP) method.

The Influence of Prior Knowledge on the Retrieval-Directed Function of Note Taking in Prior Knowledge Activation

ERIC Educational Resources Information Center

Wetzels, Sandra A. J.; Kester, Liesbeth; van Merrienboer, Jeroen J. G.; Broers, Nick J.

2011-01-01

Background: Prior knowledge activation facilitates learning. Note taking during prior knowledge activation (i.e., note taking directed at retrieving information from memory) might facilitate the activation process by enabling learners to build an external representation of their prior knowledge. However, taking notes might be less effective in…

Sensor fusion II: Human and machine strategies; Proceedings of the Meeting, Philadelphia, PA, Nov. 6-9, 1989

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1990-01-01

Various papers on human and machine strategies in sensor fusion are presented. The general topics addressed include: active vision, measurement and analysis of visual motion, decision models for sensor fusion, implementation of sensor fusion algorithms, applying sensor fusion to image analysis, perceptual modules and their fusion, perceptual organization and object recognition, planning and the integration of high-level knowledge with perception, using prior knowledge and context in sensor fusion.

Figure-ground segmentation based on class-independent shape priors

NASA Astrophysics Data System (ADS)

Li, Yang; Liu, Yang; Liu, Guojun; Guo, Maozu

2018-01-01

We propose a method to generate figure-ground segmentation by incorporating shape priors into the graph-cuts algorithm. Given an image, we first obtain a linear representation of an image and then apply directional chamfer matching to generate class-independent, nonparametric shape priors, which provide shape clues for the graph-cuts algorithm. We then enforce shape priors in a graph-cuts energy function to produce object segmentation. In contrast to previous segmentation methods, the proposed method shares shape knowledge for different semantic classes and does not require class-specific model training. Therefore, the approach obtains high-quality segmentation for objects. We experimentally validate that the proposed method outperforms previous approaches using the challenging PASCAL VOC 2010/2012 and Berkeley (BSD300) segmentation datasets.

The Effect of Lesson Structures on Predication and Inference.

ERIC Educational Resources Information Center

Li, Tiancheng; Jonassen, David H.

Theories of situated learning attempt to overcome the ill-structured nature of some domains of learning, and to use students' tendencies to construct knowledge representation on context and prior experience. Success comes when students apply abstract principles to real life. This study compares the effectiveness of two different lesson structures…

The critical success factors and impact of prior knowledge to nursing students when transferring nursing knowledge during nursing clinical practise.

PubMed

Tsai, Ming-Tien; Tsai, Ling-Long

2005-11-01

Nursing practise plays an important role in transferring nursing knowledge to nursing students. From the related literature review, prior knowledge will affect how learners gain new knowledge. There has been no direct examination of the prior knowledge interaction effect on students' performance and its influence on nursing students when evaluating the knowledge transfer success factors. This study explores (1) the critical success factors in transferring nursing knowledge, (2) the impact of prior knowledge when evaluating the success factors for transferring nursing knowledge. This research utilizes in-depth interviews to probe the initial success factor phase. A total of 422 valid questionnaires were conducted by the authors. The data were analysed by comparing the mean score and t-test between two groups. Seventeen critical success factors were identified by the two groups of students. Twelve items were selected to examine the diversity in the two groups. Students with prior knowledge were more independent than the other group. They also preferred self-directed learning over students without prior knowledge. Students who did not have prior knowledge were eager to take every opportunity to gain experience and more readily adopted new knowledge.

A cautionary note on Bayesian estimation of population size by removal sampling with diffuse priors.

PubMed

Bord, Séverine; Bioche, Christèle; Druilhet, Pierre

2018-05-01

We consider the problem of estimating a population size by removal sampling when the sampling rate is unknown. Bayesian methods are now widespread and allow to include prior knowledge in the analysis. However, we show that Bayes estimates based on default improper priors lead to improper posteriors or infinite estimates. Similarly, weakly informative priors give unstable estimators that are sensitive to the choice of hyperparameters. By examining the likelihood, we show that population size estimates can be stabilized by penalizing small values of the sampling rate or large value of the population size. Based on theoretical results and simulation studies, we propose some recommendations on the choice of the prior. Then, we applied our results to real datasets. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Prefetching of medical imaging data across XDS affinity domains.

PubMed

Helm, Emmanuel; Schuler, Andreas; Krauss, Oliver; Franz, Barbara

2015-01-01

Prior studies as well as medical imaging data are crucial for a radiologist to diagnose a patient. In this paper the radiological workflow is analyzed from a patient's perspective in order to gain knowledge on how possible existing prefetching strategies still can be applied in connection with a standardized distributed health information system conforming to architectures defined by IHE and ELGA. As a result an adaption to such architectures is proposed and further evaluated in a testing environment. Although the approach presented works in terms of prefetching relevant prior studies together with medical imaging data, additional research has to be carried out on how to apply intelligent search strategies in order to narrow retrieved results concerning their possible utilization for a specific diagnosis.

Discovery learning model with geogebra assisted for improvement mathematical visual thinking ability

NASA Astrophysics Data System (ADS)

Juandi, D.; Priatna, N.

2018-05-01

The main goal of this study is to improve the mathematical visual thinking ability of high school student through implementation the Discovery Learning Model with Geogebra Assisted. This objective can be achieved through study used quasi-experimental method, with non-random pretest-posttest control design. The sample subject of this research consist of 62 senior school student grade XI in one of school in Bandung district. The required data will be collected through documentation, observation, written tests, interviews, daily journals, and student worksheets. The results of this study are: 1) Improvement students Mathematical Visual Thinking Ability who obtain learning with applied the Discovery Learning Model with Geogebra assisted is significantly higher than students who obtain conventional learning; 2) There is a difference in the improvement of students’ Mathematical Visual Thinking ability between groups based on prior knowledge mathematical abilities (high, medium, and low) who obtained the treatment. 3) The Mathematical Visual Thinking Ability improvement of the high group is significantly higher than in the medium and low groups. 4) The quality of improvement ability of high and low prior knowledge is moderate category, in while the quality of improvement ability in the high category achieved by student with medium prior knowledge.

Partial knowledge, entropy, and estimation

PubMed Central

MacQueen, James; Marschak, Jacob

1975-01-01

In a growing body of literature, available partial knowledge is used to estimate the prior probability distribution p≡(p1,...,pn) by maximizing entropy H(p)≡-Σpi log pi, subject to constraints on p which express that partial knowledge. The method has been applied to distributions of income, of traffic, of stock-price changes, and of types of brand-article purchases. We shall respond to two justifications given for the method: (α) It is “conservative,” and therefore good, to maximize “uncertainty,” as (uniquely) represented by the entropy parameter. (β) One should apply the mathematics of statistical thermodynamics, which implies that the most probable distribution has highest entropy. Reason (α) is rejected. Reason (β) is valid when “complete ignorance” is defined in a particular way and both the constraint and the estimator's loss function are of certain kinds. PMID:16578733

Memory colours affect colour appearance.

PubMed

Witzel, Christoph; Olkkonen, Maria; Gegenfurtner, Karl R

2016-01-01

Memory colour effects show that colour perception is affected by memory and prior knowledge and hence by cognition. None of Firestone & Scholl's (F&S's) potential pitfalls apply to our work on memory colours. We present a Bayesian model of colour appearance to illustrate that an interaction between perception and memory is plausible from the perspective of vision science.

Applying the Multilevel Framework of Discourse Comprehension to Evaluate the Text Characteristics of General Chemistry Textbooks

ERIC Educational Resources Information Center

Pyburn, Daniel T.; Pazicni, Samuel

2014-01-01

Prior chemistry education research has demonstrated a relationship between student reading skill and general chemistry course performance. In addition to student characteristics, however, the qualities of the learning materials with which students interact also impact student learning. For example, low-knowledge students benefit from texts that…

Participation in Government NR. A Guide for Teachers, Grade 12.

ERIC Educational Resources Information Center

D'Addario, Alice

The "Participation in Government" course is intended to be a culminating activity for social studies students in New York State high schools. Designed to have students apply prior knowledge in the determination of positions regarding vital public issues, the course is particularly crucial for non-regents students, for it will, in many…

Machine learning of frustrated classical spin models. I. Principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Ce; Zhai, Hui

2017-10-01

This work aims at determining whether artificial intelligence can recognize a phase transition without prior human knowledge. If this were successful, it could be applied to, for instance, analyzing data from the quantum simulation of unsolved physical models. Toward this goal, we first need to apply the machine learning algorithm to well-understood models and see whether the outputs are consistent with our prior knowledge, which serves as the benchmark for this approach. In this work, we feed the computer data generated by the classical Monte Carlo simulation for the X Y model in frustrated triangular and union jack lattices, which has two order parameters and exhibits two phase transitions. We show that the outputs of the principal component analysis agree very well with our understanding of different orders in different phases, and the temperature dependences of the major components detect the nature and the locations of the phase transitions. Our work offers promise for using machine learning techniques to study sophisticated statistical models, and our results can be further improved by using principal component analysis with kernel tricks and the neural network method.

Effects of Prior Knowledge on Memory: Implications for Education

ERIC Educational Resources Information Center

Shing, Yee Lee; Brod, Garvin

2016-01-01

The encoding, consolidation, and retrieval of events and facts form the basis for acquiring new skills and knowledge. Prior knowledge can enhance those memory processes considerably and thus foster knowledge acquisition. But prior knowledge can also hinder knowledge acquisition, in particular when the to-be-learned information is inconsistent with…

F-MAP: A Bayesian approach to infer the gene regulatory network using external hints

PubMed Central

Shahdoust, Maryam; Mahjub, Hossein; Sadeghi, Mehdi

2017-01-01

The Common topological features of related species gene regulatory networks suggest reconstruction of the network of one species by using the further information from gene expressions profile of related species. We present an algorithm to reconstruct the gene regulatory network named; F-MAP, which applies the knowledge about gene interactions from related species. Our algorithm sets a Bayesian framework to estimate the precision matrix of one species microarray gene expressions dataset to infer the Gaussian Graphical model of the network. The conjugate Wishart prior is used and the information from related species is applied to estimate the hyperparameters of the prior distribution by using the factor analysis. Applying the proposed algorithm on six related species of drosophila shows that the precision of reconstructed networks is improved considerably compared to the precision of networks constructed by other Bayesian approaches. PMID:28938012

The Effects of Prior Knowledge Activation on Free Recall and Study Time Allocation.

ERIC Educational Resources Information Center

Machiels-Bongaerts, Maureen; And Others

The effects of mobilizing prior knowledge on information processing were studied. Two hypotheses, the cognitive set-point hypothesis and the selective attention hypothesis, try to account for the facilitation effects of prior knowledge activation. These hypotheses predict different recall patterns as a result of mobilizing prior knowledge. In…

Deepening Understanding of Prior Knowledge: What Diverse First-Generation College Students in the U.S. Can Teach Us

ERIC Educational Resources Information Center

Castillo-Montoya, Milagros

2017-01-01

Educational research indicates that teachers revealing and utilizing students' prior knowledge supports students' academic learning. Yet, the variation in students' prior knowledge is not fully known. To better understand students' prior knowledge, I drew on sociocultural learning theories to examine racially and ethnically diverse college…

The implementation of a global fund grant in Lesotho: applying a framework on knowledge absorptive capacity.

PubMed

Biesma, Regien; Makoa, Elsie; Mpemi, Regina; Tsekoa, Lineo; Odonkor, Philip; Brugha, Ruairi

2012-02-01

One of the biggest challenges in scaling up health interventions in sub-Saharan Africa for government recipients is to effectively manage the rapid influx of aid from different donors, each with its own requirements and conditions. However, there is little empirical evidence on how governments absorb knowledge from new donors in order to satisfy their requirements. This case study applies Cuellar and Gallivan's (2006) framework on knowledge absorptive capacity (AC) to illustrate how recipient government organisations in Lesotho identified, assimilated and utilised knowledge on how to meet the disbursement and reporting requirements of Lesotho's Round 5 grant from the Global Fund to Fight AIDS, TB and Malaria (Global Fund). In-depth topic guided interviews with 22 respondents and document reviews were conducted between July 2008 and February 2009. Analysis focused on six organisational determinants that affect an organisation's absorptive capacity: prior-related knowledge, combinative capabilities, motivation, organisational structure, cultural match, and communication channels. Absorptive capacity was mostly evident at the level of the Principal Recipient, the Ministry of Finance, who established a new organisational unit to meet the requirements of Global Fund Grants, while the level of AC was less advanced among the Ministry of Health (Sub-Recipient) and district level implementers. Recipient organisations can increase their absorptive capacity, not only through prior knowledge of donor requirements, but also by deliberately changing their organisational form and through combinative capabilities. The study also revealed how vulnerable African governments are to loss of staff capacity. The application of organisational theory to analyse the interactions of donor agencies with public and non-public country stakeholders illustrates the complexity of the environment that aid recipient governments have to manage. Copyright © 2011 Elsevier Ltd. All rights reserved.

Conceptual change strategies in teaching genetics

NASA Astrophysics Data System (ADS)

Batzli, Laura Elizabeth

The purpose of this study was to evaluate the effectiveness of utilizing conceptual change strategies when teaching high school genetics. The study examined the effects of structuring instruction to provide students with cognitive situations which promote conceptual change, specifically instruction was structured to elicit students' prior knowledge. The goal of the study was that the students would not only be able to solve genetics problems and define basic terminology but they would also have constructed more scientific schemas of the actual processes involved in inheritance. This study is based on the constructivist theory of learning and conceptual change research which suggest that students are actively involved in the process of relating new information to prior knowledge as they construct new knowledge. Two sections of biology II classes received inquiry based instruction and participated in structured cooperative learning groups. However, the unique difference in the treatment group's instruction was the use of structured thought time and the resulting social interaction between the students. The treatment group students' instructional design allowed students to socially construct their cognitive knowledge after elicitation of their prior knowledge. In contrast, the instructional design for the control group students allowed them to socially construct their cognitive knowledge of genetics without the individually structured thought time. The results indicated that the conceptual change strategies with individually structured thought time improved the students' scientific mastery of genetics concepts and they maintained fewer post instructional alternative conceptions. Although all students gained the ability to correctly solve genetics problems, the treatment group students were able to explain the processes involved in terms of meiosis. The treatment group students were also able to better apply their knowledge to novel genetic situations. The implications for genetics instruction from these results were discussed.

Novice and expert teachers' conceptions of learners' prior knowledge

NASA Astrophysics Data System (ADS)

Meyer, Helen

2004-11-01

This study presents comparative case studies of preservice and first-year teachers' and expert teachers' conceptions of the concept of prior knowledge. Kelly's (The Psychology of Personal Construct, New York: W.W. Norton, 1955) theory of personal constructs as discussed by Akerson, Flick, and Lederman (Journal of Research in Science Teaching, 2000, 37, 363-385) in relationship to prior knowledge underpins the study. Six teachers were selected to participate in the case studies based upon their level experience teaching science and their willingness to take part. The comparative case studies of the novice and expert teachers provide insights into (a) how novice and expert teachers understand the concept of prior knowledge and (b) how they use this knowledge to make instructional decisions. Data collection consisted of interviews, classroom observations, and document analysis. Findings suggest that novice teachers hold insufficient conceptions of prior knowledge and its role in instruction to effectively implement constructivist teaching practices. While expert teachers hold a complex conception of prior knowledge and make use of their students' prior knowledge in significant ways during instruction. A second finding was an apparent mismatch between the novice teachers' beliefs about their urban students' life experiences and prior knowledge and the wealth of knowledge the expert teachers found to draw upon.

Use of Elaborative Interrogation to Help Students Acquire Information Consistent with Prior Knowledge and Information Inconsistent with Prior Knowledge.

ERIC Educational Resources Information Center

Woloshyn, Vera E.; And Others

1994-01-01

Thirty-two factual statements, half consistent and half not consistent with subjects' prior knowledge, were processed by 140 sixth and seventh graders. Half were directed to use elaborative interrogation (using prior knowledge) to answer why each statement was true. Across all memory measures, elaborative interrogation subjects performed better…

"Dare I Ask?": Eliciting Prior Knowledge and Its Implications for Teaching and Learning

ERIC Educational Resources Information Center

Dávila, Liv Thorstensson

2015-01-01

This article examines high school teachers' engagement of newcomer English learner students' prior knowledge. Three central research questions guided this study: 1) To what extent do teachers function as mediators of their students' prior knowledge? 2) What goes into teachers' thinking about how and when to elicit prior knowledge? and 3) How do…

Designing worked examples for learning tangent lines to circles

NASA Astrophysics Data System (ADS)

Retnowati, E.; Marissa

2018-03-01

Geometry is a branch of mathematics that deals with shape and space, including the circle. A difficult topic in the circle may be the tangent line to circle. This is considered a complex material since students have to simultaneously apply several principles to solve the problems, these are the property of circle, definition of the tangent, measurement and Pythagorean theorem. This paper discusses designs of worked examples for learning tangent line to circles and how to apply this design to an effective and efficient instructional activity. When students do not have sufficient prior knowledge, solving tangent problems might be clumsy, and as a consequence, the problem-solving activity hinders learning. According to a Cognitive Load Theory, learning occurs when students can construct new knowledge based on the relevant knowledge previously learned. When the relevant knowledge is unavailable, providing students with the worked example is suggested. Worked example may reduce unproductive process during learning that causes extraneous cognitive load. Nevertheless, worked examples must be created in such a way facilitate learning.

Applying Standard Interfaces to a Process-Control Language

NASA Technical Reports Server (NTRS)

Berthold, Richard T.

2005-01-01

A method of applying open-operating-system standard interfaces to the NASA User Interface Language (UIL) has been devised. UIL is a computing language that can be used in monitoring and controlling automated processes: for example, the Timeliner computer program, written in UIL, is a general-purpose software system for monitoring and controlling sequences of automated tasks in a target system. In providing the major elements of connectivity between UIL and the target system, the present method offers advantages over the prior method. Most notably, unlike in the prior method, the software description of the target system can be made independent of the applicable compiler software and need not be linked to the applicable executable compiler image. Also unlike in the prior method, it is not necessary to recompile the source code and relink the source code to a new executable compiler image. Abstraction of the description of the target system to a data file can be defined easily, with intuitive syntax, and knowledge of the source-code language is not needed for the definition.

Predictive top-down integration of prior knowledge during speech perception.

PubMed

Sohoglu, Ediz; Peelle, Jonathan E; Carlyon, Robert P; Davis, Matthew H

2012-06-20

A striking feature of human perception is that our subjective experience depends not only on sensory information from the environment but also on our prior knowledge or expectations. The precise mechanisms by which sensory information and prior knowledge are integrated remain unclear, with longstanding disagreement concerning whether integration is strictly feedforward or whether higher-level knowledge influences sensory processing through feedback connections. Here we used concurrent EEG and MEG recordings to determine how sensory information and prior knowledge are integrated in the brain during speech perception. We manipulated listeners' prior knowledge of speech content by presenting matching, mismatching, or neutral written text before a degraded (noise-vocoded) spoken word. When speech conformed to prior knowledge, subjective perceptual clarity was enhanced. This enhancement in clarity was associated with a spatiotemporal profile of brain activity uniquely consistent with a feedback process: activity in the inferior frontal gyrus was modulated by prior knowledge before activity in lower-level sensory regions of the superior temporal gyrus. In parallel, we parametrically varied the level of speech degradation, and therefore the amount of sensory detail, so that changes in neural responses attributable to sensory information and prior knowledge could be directly compared. Although sensory detail and prior knowledge both enhanced speech clarity, they had an opposite influence on the evoked response in the superior temporal gyrus. We argue that these data are best explained within the framework of predictive coding in which sensory activity is compared with top-down predictions and only unexplained activity propagated through the cortical hierarchy.

Are You Ready to Flip? A New Approach to Staff Development

ERIC Educational Resources Information Center

Burns, Connie S.; Schroeder, Mary M.

2014-01-01

A modified flipped classroom model was used to present content on the 2010 Dietary Guidelines for Americans (DGA) to Community Nutrition Educators (CNEs). CNEs read the DGA prior to discussions at staff meetings. The purpose of the readings and discussions was to increase knowledge of the DGA and offer strategies for applying these concepts…

Under What Circumstances Does External Knowledge about the Correlation Structure Improve Power in Cluster Randomized Designs?

ERIC Educational Resources Information Center

Rhoads, Christopher

2014-01-01

Recent publications have drawn attention to the idea of utilizing prior information about the correlation structure to improve statistical power in cluster randomized experiments. Because power in cluster randomized designs is a function of many different parameters, it has been difficult for applied researchers to discern a simple rule explaining…

Applying Schema Theory to Mass Media Information Processing: Moving toward a Formal Model.

ERIC Educational Resources Information Center

Wicks, Robert H.

Schema theory may be significant in determining if and how news audiences process information. For any given news topic, people have from none to many schemata (cognitive structures that represent organized knowledge about a given concept or type of stimulus abstracted from prior experience) upon which to draw. Models of how schemata are used…

Bayesian hierarchical functional data analysis via contaminated informative priors.

PubMed

Scarpa, Bruno; Dunson, David B

2009-09-01

A variety of flexible approaches have been proposed for functional data analysis, allowing both the mean curve and the distribution about the mean to be unknown. Such methods are most useful when there is limited prior information. Motivated by applications to modeling of temperature curves in the menstrual cycle, this article proposes a flexible approach for incorporating prior information in semiparametric Bayesian analyses of hierarchical functional data. The proposed approach is based on specifying the distribution of functions as a mixture of a parametric hierarchical model and a nonparametric contamination. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. In the motivating application, the contamination component allows unanticipated curve shapes in unhealthy menstrual cycles. Methods are developed for posterior computation, and the approach is applied to data from a European fecundability study.

Bayesian Inference in the Modern Design of Experiments

NASA Technical Reports Server (NTRS)

DeLoach, Richard

2008-01-01

This paper provides an elementary tutorial overview of Bayesian inference and its potential for application in aerospace experimentation in general and wind tunnel testing in particular. Bayes Theorem is reviewed and examples are provided to illustrate how it can be applied to objectively revise prior knowledge by incorporating insights subsequently obtained from additional observations, resulting in new (posterior) knowledge that combines information from both sources. A logical merger of Bayesian methods and certain aspects of Response Surface Modeling is explored. Specific applications to wind tunnel testing, computational code validation, and instrumentation calibration are discussed.

A Hierarchical Bayesian Model for Calibrating Estimates of Species Divergence Times

PubMed Central

Heath, Tracy A.

2012-01-01

In Bayesian divergence time estimation methods, incorporating calibrating information from the fossil record is commonly done by assigning prior densities to ancestral nodes in the tree. Calibration prior densities are typically parametric distributions offset by minimum age estimates provided by the fossil record. Specification of the parameters of calibration densities requires the user to quantify his or her prior knowledge of the age of the ancestral node relative to the age of its calibrating fossil. The values of these parameters can, potentially, result in biased estimates of node ages if they lead to overly informative prior distributions. Accordingly, determining parameter values that lead to adequate prior densities is not straightforward. In this study, I present a hierarchical Bayesian model for calibrating divergence time analyses with multiple fossil age constraints. This approach applies a Dirichlet process prior as a hyperprior on the parameters of calibration prior densities. Specifically, this model assumes that the rate parameters of exponential prior distributions on calibrated nodes are distributed according to a Dirichlet process, whereby the rate parameters are clustered into distinct parameter categories. Both simulated and biological data are analyzed to evaluate the performance of the Dirichlet process hyperprior. Compared with fixed exponential prior densities, the hierarchical Bayesian approach results in more accurate and precise estimates of internal node ages. When this hyperprior is applied using Markov chain Monte Carlo methods, the ages of calibrated nodes are sampled from mixtures of exponential distributions and uncertainty in the values of calibration density parameters is taken into account. PMID:22334343

Relations among conceptual knowledge, procedural knowledge, and procedural flexibility in two samples differing in prior knowledge.

PubMed

Schneider, Michael; Rittle-Johnson, Bethany; Star, Jon R

2011-11-01

Competence in many domains rests on children developing conceptual and procedural knowledge, as well as procedural flexibility. However, research on the developmental relations between these different types of knowledge has yielded unclear results, in part because little attention has been paid to the validity of the measures or to the effects of prior knowledge on the relations. To overcome these problems, we modeled the three constructs in the domain of equation solving as latent factors and tested (a) whether the predictive relations between conceptual and procedural knowledge were bidirectional, (b) whether these interrelations were moderated by prior knowledge, and (c) how both constructs contributed to procedural flexibility. We analyzed data from 2 measurement points each from two samples (Ns = 228 and 304) of middle school students who differed in prior knowledge. Conceptual and procedural knowledge had stable bidirectional relations that were not moderated by prior knowledge. Both kinds of knowledge contributed independently to procedural flexibility. The results demonstrate how changes in complex knowledge structures contribute to competence development.

The Influence of Prior Knowledge on Memory: A Developmental Cognitive Neuroscience Perspective

PubMed Central

Brod, Garvin; Werkle-Bergner, Markus; Shing, Yee Lee

2013-01-01

Across ontogenetic development, individuals gather manifold experiences during which they detect regularities in their environment and thereby accumulate knowledge. This knowledge is used to guide behavior, make predictions, and acquire further new knowledge. In this review, we discuss the influence of prior knowledge on memory from both the psychology and the emerging cognitive neuroscience literature and provide a developmental perspective on this topic. Recent neuroscience findings point to a prominent role of the medial prefrontal cortex (mPFC) and of the hippocampus (HC) in the emergence of prior knowledge and in its application during the processes of successful memory encoding, consolidation, and retrieval. We take the lateral PFC into consideration as well and discuss changes in both medial and lateral PFC and HC across development and postulate how these may be related to the development of the use of prior knowledge for remembering. For future direction, we argue that, to measure age differential effects of prior knowledge on memory, it is necessary to distinguish the availability of prior knowledge from its accessibility and use. PMID:24115923

When generating answers benefits arithmetic skill: the importance of prior knowledge.

PubMed

Rittle-Johnson, Bethany; Kmicikewycz, Alexander Oleksij

2008-09-01

People remember information better if they generate the information while studying rather than read the information. However, prior research has not investigated whether this generation effect extends to related but unstudied items and has not been conducted in classroom settings. We compared third graders' success on studied and unstudied multiplication problems after they spent a class period generating answers to problems or reading the answers from a calculator. The effect of condition interacted with prior knowledge. Students with low prior knowledge had higher accuracy in the generate condition, but as prior knowledge increased, the advantage of generating answers decreased. The benefits of generating answers may extend to unstudied items and to classroom settings, but only for learners with low prior knowledge.

The Effects of the Timing of Isolated FFI on the Explicit Knowledge and Written Accuracy of Learners with Different Prior Knowledge of the Linguistic Target

ERIC Educational Resources Information Center

Shintani, Natsuko

2017-01-01

This study examines the effects of the timing of explicit instruction (EI) on grammatical accuracy. A total of 123 learners were divided into two groups: those with some productive knowledge of past-counterfactual conditionals (+Prior Knowledge) and those without such knowledge (-Prior Knowledge). Each group was divided into four conditions. Two…

Increasing Cognitive Inhibition with a Difficult Prior Task: Implications for Mathematical Thinking

ERIC Educational Resources Information Center

Attridge, Nina; Inglis, Matthew

2015-01-01

Dual-process theories posit two distinct types of cognitive processing: Type 1, which does not use working memory making it fast and automatic, and Type 2, which does use working memory making it slow and effortful. Mathematics often relies on the inhibition of pervasive Type 1 processing to apply new skills or knowledge that require Type 2…

Using comprehension strategies with authentic text in a college chemistry course

NASA Astrophysics Data System (ADS)

Cain, Stephen Daniel

College science students learn important topics by reading textbooks, which contain dense technical prose. Comprehension strategies are known to increase learning from reading. One class of comprehension strategies, called elaboration strategies, is intended to link new information with prior knowledge. Elaboration strategies have an appeal in science courses where new information frequently depends on previously learned information. The purpose of this study was to determine the effectiveness of an elaboration strategy in an authentic college environment. General chemistry students read text about Lewis structures, figures drawn by chemists to depict molecules, while assigned to use either an elaboration strategy, namely elaborative interrogation, or another strategy, rereading, which served as a placebo control. Two texts of equal length were employed in this pretest-posttest experimental design. One was composed by the researcher. The other was an excerpt from a college textbook and contained a procedure for constructing Lewis structures. Students (N = 252) attending a large community college were randomly assigned to one of the two texts and assigned one of the two strategies. The elaborative interrogation strategy was implemented with instructions to answer why-questions posed throughout the reading. Answering why-questions has been hypothesized to activate prior knowledge of a topic, and thus to aid in cognitively connecting new material with prior knowledge. The rereading strategy was implemented with instructions to read text twice. The use of authentic text was one of only a few instances of applying elaborative interrogation with a textbook. In addition, previous studies have generally focused on the learning of facts contained in prose. The application of elaborative interrogation to procedural text has not been previously reported. Results indicated that the more effective strategy was undetermined when reading authentic text in this setting. However, prior knowledge level was identified as a statistically significant factor for learning from authentic text. That is, students with high prior knowledge learned more, regardless of assigned strategy. Another descriptive study was conducted with a separate student sample (N = 34). Previously reported Lewis structure research was replicated. The trend of difficulty for 50 structures in the earlier work was supported.

Pathway-based analyses.

PubMed

Kent, Jack W

2016-02-03

New technologies for acquisition of genomic data, while offering unprecedented opportunities for genetic discovery, also impose severe burdens of interpretation and penalties for multiple testing. The Pathway-based Analyses Group of the Genetic Analysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to aggregation of highdimensional data in pathways informed by prior biological knowledge. Experimental methods testedincluded the use of "synthetic pathways" (random sets of genes) to estimate power and false-positive error rate of methods applied to simulated data; data reduction via independent components analysis, single-nucleotide polymorphism (SNP)-SNP interaction, and use of gene sets to estimate genetic similarity; and general assessment of the efficacy of prior biological knowledge to reduce the dimensionality of complex genomic data. The work of this group explored several promising approaches to managing high-dimensional data, with the caveat that these methods are necessarily constrained by the quality of external bioinformatic annotation.

Knowledge Structures of Entering Computer Networking Students and Their Instructors

ERIC Educational Resources Information Center

DiCerbo, Kristen E.

2007-01-01

Students bring prior knowledge to their learning experiences. This prior knowledge is known to affect how students encode and later retrieve new information learned. Teachers and content developers can use information about students' prior knowledge to create more effective lessons and materials. In many content areas, particularly the sciences,…

Nudging toward Inquiry: Awakening and Building upon Prior Knowledge

ERIC Educational Resources Information Center

Fontichiaro, Kristin, Comp.

2010-01-01

"Prior knowledge" (sometimes called schema or background knowledge) is information one already knows that helps him/her make sense of new information. New learning builds on existing prior knowledge. In traditional reporting-style research projects, students bypass this crucial step and plow right into answer-finding. It's no wonder that many…

Mind wandering during film comprehension: The role of prior knowledge and situational interest.

PubMed

Kopp, Kristopher; Mills, Caitlin; D'Mello, Sidney

2016-06-01

This study assessed the occurrence and factors that influence mind wandering (MW) in the domain of film comprehension. The cascading model of inattention assumes that a stronger mental representation (i.e., a situation model) during comprehension results in less MW. Accordingly, a suppression hypothesis suggests that MW would decrease as a function of having the knowledge of the plot of a film prior to viewing, because the prior-knowledge would help to strengthen the situation model during comprehension. Furthermore, an interest-moderation hypothesis would predict that the suppression effect of prior-knowledge would only emerge when there was interest in viewing the film. In the current experiment, 108 participants either read a short story that depicted the plot (i.e., prior-knowledge condition) or read an unrelated story of equal length (control condition) prior to viewing the short film (32.5 minutes) entitled The Red Balloon. Participants self-reported their interest in viewing the film immediately before the film was presented. MW was tracked using a self-report method targeting instances of MW with metacognitive awareness. Participants in the prior-knowledge condition reported less MW compared with the control condition, thereby supporting the suppression hypothesis. MW also decreased over the duration of the film, but only for those with prior-knowledge of the film. Finally, prior-knowledge effects on MW were only observed when interest was average or high, but not when interest was low.

Assessing the Previous Economic Knowledge of Beginning Students in Germany: Implications for Teaching Economics in Basic Courses

ERIC Educational Resources Information Center

Happ, Roland; Förster, Manuel; Zlatkin-Troitschanskaia, Olga; Carstensen, Vivian

2016-01-01

Study-related prior knowledge plays a decisive role in business and economics degree courses. Prior knowledge has a significant influence on knowledge acquisition in higher education, and teachers need information on it to plan their introductory courses accordingly. Very few studies have been conducted of first-year students' prior economic…

The Effects of Source Credibility in the Presence or Absence of Prior Attitudes: Implications for the Design of Persuasive Communication Campaigns.

PubMed

Kumkale, G Tarcan; Albarracín, Dolores; Seignourel, Paul J

2010-06-01

Most theories of persuasion predict that limited ability and motivation to think about communications should increase the impact of source credibility on persuasion. Furthermore, this effect is assumed to occur, regardless of whether or not the recipients have prior attitudes. In this study, the effects of source credibility, ability, and motivation (knowledge, message repetition, relevance) on persuasion were examined meta-analytically across both attitude formation and change conditions. Findings revealed that the Source Credibility × Ability/Motivation interaction emerged only when participants lacked prior attitudes and were unable to form a new attitude based on the message content. In such settings, the effects of source credibility decayed rapidly. The implications of these findings for applied communication campaigns are discussed.

The Effects of Source Credibility in the Presence or Absence of Prior Attitudes: Implications for the Design of Persuasive Communication Campaigns1

PubMed Central

Kumkale, G. Tarcan; AlbarracÍn, Dolores; Seignourel, Paul J.

2011-01-01

Most theories of persuasion predict that limited ability and motivation to think about communications should increase the impact of source credibility on persuasion. Furthermore, this effect is assumed to occur, regardless of whether or not the recipients have prior attitudes. In this study, the effects of source credibility, ability, and motivation (knowledge, message repetition, relevance) on persuasion were examined meta-analytically across both attitude formation and change conditions. Findings revealed that the Source Credibility × Ability/Motivation interaction emerged only when participants lacked prior attitudes and were unable to form a new attitude based on the message content. In such settings, the effects of source credibility decayed rapidly. The implications of these findings for applied communication campaigns are discussed. PMID:21625405

The Effects of Prior-knowledge and Online Learning Approaches on Students' Inquiry and Argumentation Abilities

NASA Astrophysics Data System (ADS)

Yang, Wen-Tsung; Lin, Yu-Ren; She, Hsiao-Ching; Huang, Kai-Yi

2015-07-01

This study investigated the effects of students' prior science knowledge and online learning approaches (social and individual) on their learning with regard to three topics: science concepts, inquiry, and argumentation. Two science teachers and 118 students from 4 eighth-grade science classes were invited to participate in this research. Students in each class were divided into three groups according to their level of prior science knowledge; they then took either our social- or individual-based online science learning program. The results show that students in the social online argumentation group performed better in argumentation and online argumentation learning. Qualitative analysis indicated that the students' social interactions benefited the co-construction of sound arguments and the accurate understanding of science concepts. In constructing arguments, students in the individual online argumentation group were limited to knowledge recall and self-reflection. High prior-knowledge students significantly outperformed low prior-knowledge students in all three aspects of science learning. However, the difference in inquiry and argumentation performance between low and high prior-knowledge students decreased with the progression of online learning topics.

Explanation and Prior Knowledge Interact to Guide Learning

ERIC Educational Resources Information Center

Williams, Joseph J.; Lombrozo, Tania

2013-01-01

How do explaining and prior knowledge contribute to learning? Four experiments explored the relationship between explanation and prior knowledge in category learning. The experiments independently manipulated whether participants were prompted to explain the category membership of study observations and whether category labels were informative in…

The Importance of Prior Knowledge.

ERIC Educational Resources Information Center

Cleary, Linda Miller

1989-01-01

Recounts a college English teacher's experience of reading and rereading Noam Chomsky, building up a greater store of prior knowledge. Argues that Frank Smith provides a theory for the importance of prior knowledge and Chomsky's work provided a personal example with which to interpret and integrate that theory. (RS)

Exploring expectation effects in EMDR: does prior treatment knowledge affect the degrading effects of eye movements on memories?

PubMed Central

Littel, Marianne; van Schie, Kevin; van den Hout, Marcel A.

2017-01-01

ABSTRACT Background: Eye movement desensitization and reprocessing (EMDR) is an effective psychological treatment for posttraumatic stress disorder. Recalling a memory while simultaneously making eye movements (EM) decreases a memory’s vividness and/or emotionality. It has been argued that non-specific factors, such as treatment expectancy and experimental demand, may contribute to the EMDR’s effectiveness. Objective: The present study was designed to test whether expectations about the working mechanism of EMDR would alter the memory attenuating effects of EM. Two experiments were conducted. In Experiment 1, we examined the effects of pre-existing (non-manipulated) knowledge of EMDR in participants with and without prior knowledge. In Experiment 2, we experimentally manipulated prior knowledge by providing participants without prior knowledge with correct or incorrect information about EMDR’s working mechanism. Method: Participants in both experiments recalled two aversive, autobiographical memories during brief sets of EM (Recall+EM) or keeping eyes stationary (Recall Only). Before and after the intervention, participants scored their memories on vividness and emotionality. A Bayesian approach was used to compare two competing hypotheses on the effects of (existing/given) prior knowledge: (1) Prior (correct) knowledge increases the effects of Recall+EM vs. Recall Only, vs. (2) prior knowledge does not affect the effects of Recall+EM. Results: Recall+EM caused greater reductions in memory vividness and emotionality than Recall Only in all groups, including the incorrect information group. In Experiment 1, both hypotheses were supported by the data: prior knowledge boosted the effects of EM, but only modestly. In Experiment 2, the second hypothesis was clearly supported over the first: providing knowledge of the underlying mechanism of EMDR did not alter the effects of EM. Conclusions: Recall+EM appears to be quite robust against the effects of prior expectations. As Recall+EM is the core component of EMDR, expectancy effects probably contribute little to the effectiveness of EMDR treatment. PMID:29038685

Calculus Instructors' Responses to Prior Knowledge Errors

ERIC Educational Resources Information Center

Talley, Jana Renee

2009-01-01

This study investigates the responses to prior knowledge errors that Calculus I instructors make when assessing students. Prior knowledge is operationalized as any skill or understanding that a student needs to successfully navigate through a Calculus I course. A two part qualitative study consisting of student exams and instructor interviews was…

Signaling Text-Picture Relations in Multimedia Learning: The Influence of Prior Knowledge

ERIC Educational Resources Information Center

Richter, Juliane; Scheiter, Katharina; Eitel, Alexander

2018-01-01

Multimedia integration signals highlight correspondences between text and pictures with the aim of supporting learning from multimedia. A recent meta-analysis revealed that only learners with low domain-specific prior knowledge benefit from multimedia integration signals. To more thoroughly investigate the influence of prior knowledge on the…

Menarche: Prior Knowledge and Experience.

ERIC Educational Resources Information Center

Skandhan, K. P.; And Others

1988-01-01

Recorded menstruation information among 305 young women in India, assessing the differences between those who did and did not have knowledge of menstruation prior to menarche. Those with prior knowledge considered menarche to be a normal physiological function and had a higher rate of regularity, lower rate of dysmenorrhea, and earlier onset of…

Ambulatory Morning Report: A Case-Based Method of Teaching EBM Through Experiential Learning.

PubMed

Luciano, Gina L; Visintainer, Paul F; Kleppel, Reva; Rothberg, Michael B

2016-02-01

Evidence-based medicine (EBM) skills are important to daily practice, but residents generally feel unskilled incorporating EBM into practice. The Kolb experiential learning theory, as applied to curricular planning, offers a unique methodology to help learners build an EBM skill set based on clinical experiences. We sought to blend the learner-centered, case-based merits of the morning report with an experientially based EBM curriculum. We describe and evaluate a patient-centered ambulatory morning report combining the User's Guides to the Medical Literature approach to EBM and experiential learning theory in the internal medicine department at Baystate Medical Center. The Kolb experiential learning theory postulates that experience transforms knowledge; within that premise we designed a curriculum to build EBM skills incorporating residents' patient encounters. By developing structured clinical questions based on recent clinical problems, residents activate prior knowledge. Residents acquire new knowledge through selection and evaluation of an article that addresses the structured clinical questions. Residents then apply and use new knowledge in future patient encounters. To assess the curriculum, we designed an 18-question EBM test, which addressed applied knowledge and EBM skills based on the User's Guides approach. Of the 66 residents who could participate in the curriculum, 61 (92%) completed the test. There was a modest improvement in EBM knowledge, primarily during the first year of training. Our experiential curriculum teaches EBM skills essential to clinical practice. The curriculum differs from traditional EBM curricula in that ours blends experiential learning with an EBM skill set; learners use new knowledge in real time.

Preparing learners with partly incorrect intuitive prior knowledge for learning

PubMed Central

Ohst, Andrea; Fondu, Béatrice M. E.; Glogger, Inga; Nückles, Matthias; Renkl, Alexander

2014-01-01

Learners sometimes have incoherent and fragmented intuitive prior knowledge that is (partly) “incompatible” with the to-be-learned contents. Such knowledge in pieces can cause conceptual disorientation and cognitive overload while learning. We hypothesized that a pre-training intervention providing a generalized schema as a structuring framework for such knowledge in pieces would support (re)organizing-processes of prior knowledge and thus reduce unnecessary cognitive load during subsequent learning. Fifty-six student teachers participated in the experiment. A framework group underwent a pre-training intervention providing a generalized, categorical schema for categorizing primary learning strategies and related but different strategies as a cognitive framework for (re-)organizing their prior knowledge. Our control group received comparable factual information but no framework. Afterwards, all participants learned about primary learning strategies. The framework group claimed to possess higher levels of interest and self-efficacy, achieved higher learning outcomes, and learned more efficiently. Hence, providing a categorical framework can help overcome the barrier of incorrect prior knowledge in pieces. PMID:25071638

Structured feedback on students' concept maps: the proverbial path to learning?

PubMed

Joseph, Conran; Conradsson, David; Nilsson Wikmar, Lena; Rowe, Michael

2017-05-25

Good conceptual knowledge is an essential requirement for health professions students, in that they are required to apply concepts learned in the classroom to a variety of different contexts. However, the use of traditional methods of assessment limits the educator's ability to correct students' conceptual knowledge prior to altering the educational context. Concept mapping (CM) is an educational tool for evaluating conceptual knowledge, but little is known about its use in facilitating the development of richer knowledge frameworks. In addition, structured feedback has the potential to develop good conceptual knowledge. The purpose of this study was to use Kinchin's criteria to assess the impact of structured feedback on the graphical complexity of CM's by observing the development of richer knowledge frameworks. Fifty-eight physiotherapy students created CM's targeting the integration of two knowledge domains within a case-based teaching paradigm. Each student received one round of structured feedback that addressed correction, reinforcement, forensic diagnosis, benchmarking, and longitudinal development on their CM's prior to the final submission. The concept maps were categorized according to Kinchin's criteria as either Spoke, Chain or Net representations, and then evaluated against defined traits of meaningful learning. The inter-rater reliability of categorizing CM's was good. Pre-feedback CM's were predominantly Chain structures (57%), with Net structures appearing least often. There was a significant reduction of the basic Spoke- structured CMs (P = 0.002) and a significant increase of Net-structured maps (P < 0.001) at the final evaluation (post-feedback). Changes in structural complexity of CMs appeared to be indicative of broader knowledge frameworks as assessed against the meaningful learning traits. Feedback on CM's seemed to have contributed towards improving conceptual knowledge and correcting naive conceptions of related knowledge. Educators in medical education could therefore consider using CM's to target individual student development.

Using Hypermedia: Effects of Prior Knowledge and Goal Strength.

ERIC Educational Resources Information Center

Last, David A.; O'Donnell, Angela M.; Kelly, Anthony E.

The influences of a student's prior knowledge and desired goal on the difficulties and benefits associated with using hypertext were examined in this study. Participants, 12 students from an undergraduate course in educational psychology, were assigned to either the low or high prior knowledge category. Within these two groups, subjects were…

The Role of Prior Knowledge in Learning from Analogies in Science Texts

ERIC Educational Resources Information Center

Braasch, Jason L. G.; Goldman, Susan R.

2010-01-01

Two experiments examined whether inconsistent effects of analogies in promoting new content learning from text are related to prior knowledge of the analogy "per se." In Experiment 1, college students who demonstrated little understanding of weather systems and different levels of prior knowledge (more vs. less) of an analogous everyday…

Neural Mechanisms for Integrating Prior Knowledge and Likelihood in Value-Based Probabilistic Inference

PubMed Central

Ting, Chih-Chung; Yu, Chia-Chen; Maloney, Laurence T.

2015-01-01

In Bayesian decision theory, knowledge about the probabilities of possible outcomes is captured by a prior distribution and a likelihood function. The prior reflects past knowledge and the likelihood summarizes current sensory information. The two combined (integrated) form a posterior distribution that allows estimation of the probability of different possible outcomes. In this study, we investigated the neural mechanisms underlying Bayesian integration using a novel lottery decision task in which both prior knowledge and likelihood information about reward probability were systematically manipulated on a trial-by-trial basis. Consistent with Bayesian integration, as sample size increased, subjects tended to weigh likelihood information more compared with prior information. Using fMRI in humans, we found that the medial prefrontal cortex (mPFC) correlated with the mean of the posterior distribution, a statistic that reflects the integration of prior knowledge and likelihood of reward probability. Subsequent analysis revealed that both prior and likelihood information were represented in mPFC and that the neural representations of prior and likelihood in mPFC reflected changes in the behaviorally estimated weights assigned to these different sources of information in response to changes in the environment. Together, these results establish the role of mPFC in prior-likelihood integration and highlight its involvement in representing and integrating these distinct sources of information. PMID:25632152

Unsupervised domain adaptation for early detection of drought stress in hyperspectral images

NASA Astrophysics Data System (ADS)

Schmitter, P.; Steinrücken, J.; Römer, C.; Ballvora, A.; Léon, J.; Rascher, U.; Plümer, L.

2017-09-01

Hyperspectral images can be used to uncover physiological processes in plants if interpreted properly. Machine Learning methods such as Support Vector Machines (SVM) and Random Forests have been applied to estimate development of biomass and detect and predict plant diseases and drought stress. One basic requirement of machine learning implies, that training and testing is done in the same domain and the same distribution. Different genotypes, environmental conditions, illumination and sensors violate this requirement in most practical circumstances. Here, we present an approach, which enables the detection of physiological processes by transferring the prior knowledge within an existing model into a related target domain, where no label information is available. We propose a two-step transformation of the target features, which enables a direct application of an existing model. The transformation is evaluated by an objective function including additional prior knowledge about classification and physiological processes in plants. We have applied the approach to three sets of hyperspectral images, which were acquired with different plant species in different environments observed with different sensors. It is shown, that a classification model, derived on one of the sets, delivers satisfying classification results on the transformed features of the other data sets. Furthermore, in all cases early non-invasive detection of drought stress was possible.

The Effect of Prior Knowledge Activation on Text Recall: An Investigation of Two Conflicting Hypotheses.

ERIC Educational Resources Information Center

Machiels-Bongaerts, Maureen; And Others

Two hypotheses, the cognitive capacity hypothesis and the selective attention hypothesis, try to account for the facilitation effects of prior knowledge activation. They appear to be mutually exclusive since they predict different recall patterns as a result of prior knowledge activation. This study was designed to determine whether the two…

Understanding the Role of Prior Knowledge in a Multimedia Learning Application

ERIC Educational Resources Information Center

Rias, Riaza Mohd; Zaman, Halimah Badioze

2013-01-01

This study looked at the effects that individual differences in prior knowledge have on student understanding in learning with multimedia in a computer science subject. Students were identified as having either low or high prior knowledge from a series of questions asked in a survey conducted at the Faculty of Computer and Mathematical Sciences at…

A Fuzzy-Based Prior Knowledge Diagnostic Model with Multiple Attribute Evaluation

ERIC Educational Resources Information Center

Lin, Yi-Chun; Huang, Yueh-Min

2013-01-01

Prior knowledge is a very important part of teaching and learning, as it affects how instructors and students interact with the learning materials. In general, tests are used to assess students' prior knowledge. Nevertheless, conventional testing approaches usually assign only an overall score to each student, and this may mean that students are…

Prior-knowledge-based feedforward network simulation of true boiling point curve of crude oil.

PubMed

Chen, C W; Chen, D Z

2001-11-01

Theoretical results and practical experience indicate that feedforward networks can approximate a wide class of functional relationships very well. This property is exploited in modeling chemical processes. Given finite and noisy training data, it is important to encode the prior knowledge in neural networks to improve the fit precision and the prediction ability of the model. In this paper, as to the three-layer feedforward networks and the monotonic constraint, the unconstrained method, Joerding's penalty function method, the interpolation method, and the constrained optimization method are analyzed first. Then two novel methods, the exponential weight method and the adaptive method, are proposed. These methods are applied in simulating the true boiling point curve of a crude oil with the condition of increasing monotonicity. The simulation experimental results show that the network models trained by the novel methods are good at approximating the actual process. Finally, all these methods are discussed and compared with each other.

Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries.

PubMed

Li, Jiansen; Song, Ying; Zhu, Zhen; Zhao, Jun

2017-05-01

Dual-dictionary learning (Dual-DL) method utilizes both a low-resolution dictionary and a high-resolution dictionary, which are co-trained for sparse coding and image updating, respectively. It can effectively exploit a priori knowledge regarding the typical structures, specific features, and local details of training sets images. The prior knowledge helps to improve the reconstruction quality greatly. This method has been successfully applied in magnetic resonance (MR) image reconstruction. However, it relies heavily on the training sets, and dictionaries are fixed and nonadaptive. In this research, we improve Dual-DL by using self-adaptive dictionaries. The low- and high-resolution dictionaries are updated correspondingly along with the image updating stage to ensure their self-adaptivity. The updated dictionaries incorporate both the prior information of the training sets and the test image directly. Both dictionaries feature improved adaptability. Experimental results demonstrate that the proposed method can efficiently and significantly improve the quality and robustness of MR image reconstruction.

Refusal bias in HIV prevalence estimates from nationally representative seroprevalence surveys.

PubMed

Reniers, Georges; Eaton, Jeffrey

2009-03-13

To assess the relationship between prior knowledge of one's HIV status and the likelihood to refuse HIV testing in populations-based surveys and explore its potential for producing bias in HIV prevalence estimates. Using longitudinal survey data from Malawi, we estimate the relationship between prior knowledge of HIV-positive status and subsequent refusal of an HIV test. We use that parameter to develop a heuristic model of refusal bias that is applied to six Demographic and Health Surveys, in which refusal by HIV status is not observed. The model only adjusts for refusal bias conditional on a completed interview. Ecologically, HIV prevalence, prior testing rates and refusal for HIV testing are highly correlated. Malawian data further suggest that amongst individuals who know their status, HIV-positive individuals are 4.62 (95% confidence interval, 2.60-8.21) times more likely to refuse testing than HIV-negative ones. On the basis of that parameter and other inputs from the Demographic and Health Surveys, our model predicts downward bias in national HIV prevalence estimates ranging from 1.5% (95% confidence interval, 0.7-2.9) for Senegal to 13.3% (95% confidence interval, 7.2-19.6) for Malawi. In absolute terms, bias in HIV prevalence estimates is negligible for Senegal but 1.6 (95% confidence interval, 0.8-2.3) percentage points for Malawi. Downward bias is more severe in urban populations. Because refusal rates are higher in men, seroprevalence surveys also tend to overestimate the female-to-male ratio of infections. Prior knowledge of HIV status informs decisions to participate in seroprevalence surveys. Informed refusals may produce bias in estimates of HIV prevalence and the sex ratio of infections.

When does prior knowledge disproportionately benefit older adults’ memory?

PubMed Central

Badham, Stephen P.; Hay, Mhairi; Foxon, Natasha; Kaur, Kiran; Maylor, Elizabeth A.

2016-01-01

ABSTRACT Material consistent with knowledge/experience is generally more memorable than material inconsistent with knowledge/experience – an effect that can be more extreme in older adults. Four experiments investigated knowledge effects on memory with young and older adults. Memory for familiar and unfamiliar proverbs (Experiment 1) and for common and uncommon scenes (Experiment 2) showed similar knowledge effects across age groups. Memory for person-consistent and person-neutral actions (Experiment 3) showed a greater benefit of prior knowledge in older adults. For cued recall of related and unrelated word pairs (Experiment 4), older adults benefited more from prior knowledge only when it provided uniquely useful additional information beyond the episodic association itself. The current data and literature suggest that prior knowledge has the age-dissociable mnemonic properties of (1) improving memory for the episodes themselves (age invariant), and (2) providing conceptual information about the tasks/stimuli extrinsically to the actual episodic memory (particularly aiding older adults). PMID:26473767

Is an Illustration Always Worth Ten Thousand Words? Effects of Prior Knowledge, Learning Style and Multimedia Illustrations on Text Comprehension.

ERIC Educational Resources Information Center

Ollerenshaw, Alison; Aidman, Eugene; Kidd, Garry

1997-01-01

This study examined comprehension in four groups of undergraduates under text only, multimedia, and two diagram conditions of text supplementation. Results indicated that effects of text supplementation are mediated by prior knowledge and learning style: multimedia appears more beneficial to surface learners with little prior knowledge and makes…

Effects of Prior Knowledge in Mathematics on Learner-Interface Interactions in a Learning-by-Teaching Intelligent Tutoring System

ERIC Educational Resources Information Center

Bringula, Rex P.; Basa, Roselle S.; Dela Cruz, Cecilio; Rodrigo, Ma. Mercedes T.

2016-01-01

This study attempted to determine the influence of prior knowledge in mathematics of students on learner-interface interactions in a learning-by-teaching intelligent tutoring system. One hundred thirty-nine high school students answered a pretest (i.e., the prior knowledge in mathematics) and a posttest. In between the pretest and posttest, they…

The Effect of the States of Prior Knowledge on Question Answering.

ERIC Educational Resources Information Center

Holmes, Betty C.

A study was conducted to gain insight into the question answering abilities of good and poor readers by comparing how well they answered questions when their prior knowledge was at two different levels (high, low) and in four different states. These states of prior knowledge consisted of the ways in which answers to the questions were stored in…

Learning from Instructional Animations: How Does Prior Knowledge Mediate the Effect of Visual Cues?

ERIC Educational Resources Information Center

Arslan-Ari, I.

2018-01-01

The purpose of this study was to investigate the effects of cueing and prior knowledge on learning and mental effort of students studying an animation with narration. This study employed a 2 (no cueing vs. visual cueing) × 2 (low vs. high prior knowledge) between-subjects factorial design. The results revealed a significant interaction effect…

Global-constrained hidden Markov model applied on wireless capsule endoscopy video segmentation

NASA Astrophysics Data System (ADS)

Wan, Yiwen; Duraisamy, Prakash; Alam, Mohammad S.; Buckles, Bill

2012-06-01

Accurate analysis of wireless capsule endoscopy (WCE) videos is vital but tedious. Automatic image analysis can expedite this task. Video segmentation of WCE into the four parts of the gastrointestinal tract is one way to assist a physician. The segmentation approach described in this paper integrates pattern recognition with statiscal analysis. Iniatially, a support vector machine is applied to classify video frames into four classes using a combination of multiple color and texture features as the feature vector. A Poisson cumulative distribution, for which the parameter depends on the length of segments, models a prior knowledge. A priori knowledge together with inter-frame difference serves as the global constraints driven by the underlying observation of each WCE video, which is fitted by Gaussian distribution to constrain the transition probability of hidden Markov model.Experimental results demonstrated effectiveness of the approach.

Predicting fifth-grade students' understanding of ecological science concepts with motivational and cognitive variables

NASA Astrophysics Data System (ADS)

Alao, Solomon

The need to identify factors that contribute to students' understanding of ecological concepts has been widely expressed in recent literature. The purpose of this study was to investigate the relationship between fifth grade students' prior knowledge, learning strategies, interest, and learning goals and their conceptual understanding of ecological science concepts. Subject were 72 students from three fifth grade classrooms located in a metropolitan area of the eastern United States. Students completed the goal commitment, interest, and strategy use questionnaire (GISQ), and a knowledge test designed to assess their prior knowledge and conceptual understanding of ecological science concepts. The learning goals scale assessed intentions to try to learn and understand ecological concepts. The interest scale assessed the feeling and value-related valences that students ascribed to science and ecological science concepts. The strategy use scale assessed the use of two cognitive strategies (monitoring and elaboration). The knowledge test assessed students' understanding of ecological concepts (the relationship between living organisms and their environment). Scores on all measures were examined for gender differences; no significant gender differences were observed. The motivational and cognitive variables contributed to students' understanding of ecological concepts. After accounting for interest, learning goals, and strategy use, prior knowledge accounted for 28% of the total variance in conceptual understanding. After accounting for prior knowledge, interest, learning goals, and strategy use explained 7%, 6%, and 4% of the total variance in conceptual understanding, respectively. More importantly, these variables were interrelated to each other and to conceptual understanding. After controlling for prior knowledge, learning goals, and strategy use, interest did not predict the variance in conceptual understanding. After controlling for prior knowledge, interest, and strategy use, learning goals did not predict the variance in conceptual understanding. And, after controlling for prior knowledge, interest, and learning goals, strategy use did not predict the variance in conceptual understanding. Results of this study indicated that prior knowledge, interest, learning goals, and strategy use should be included in theoretical models design to explain and to predict fifth grade students' understanding of ecological concepts. Results of this study further suggested that curriculum developers and science teachers need to take fifth grade students' prior knowledge of ecological concepts, interest in science and ecological concepts; intentions to learn and understand ecological concepts, and use of cognitive strategies into account when designing instructional contexts to support these students' understanding of ecological concepts.

The relation between prior knowledge and students' collaborative discovery learning processes

NASA Astrophysics Data System (ADS)

Gijlers, Hannie; de Jong, Ton

2005-03-01

In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction with the environment was logged. Based on students' individual judgments of the truth-value and testability of a series of domain-specific propositions, a detailed description of the knowledge configuration for each dyad was created before they entered the learning environment. Qualitative analyses of two dialogues illustrated that prior knowledge influences the discovery learning processes, and knowledge development in a pair of students. Assessments of student and dyad definitional (domain-specific) knowledge, generic (mathematical and graph) knowledge, and generic (discovery) skills were related to the students' dialogue in different discovery learning processes. Results show that a high level of definitional prior knowledge is positively related to the proportion of communication regarding the interpretation of results. Heterogeneity with respect to generic prior knowledge was positively related to the number of utterances made in the discovery process categories hypotheses generation and experimentation. Results of the qualitative analyses indicated that collaboration between extremely heterogeneous dyads is difficult when the high achiever is not willing to scaffold information and work in the low achiever's zone of proximal development.

The Influence of Self-Regulated Learning and Prior Knowledge on Knowledge Acquisition in Computer-Based Learning Environments

ERIC Educational Resources Information Center

Bernacki, Matthew

2010-01-01

This study examined how learners construct textbase and situation model knowledge in hypertext computer-based learning environments (CBLEs) and documented the influence of specific self-regulated learning (SRL) tactics, prior knowledge, and characteristics of the learner on posttest knowledge scores from exposure to a hypertext. A sample of 160…

Implicit Learning in Science: Activating and Suppressing Scientific Intuitions to Enhance Conceptual Change

NASA Astrophysics Data System (ADS)

Wang, Jeremy Yi-Ming

This dissertation examines the thesis that implicit learning plays a role in learning about scientific phenomena, and subsequently, in conceptual change. Decades of research in learning science demonstrate that a primary challenge of science education is overcoming prior, naive knowledge of natural phenomena in order to gain scientific understanding. Until recently, a key assumption of this research has been that to develop scientific understanding, learners must abandon their prior scientific intuitions and replace them with scientific concepts. However, a growing body of research shows that scientific intuitions persist, even among science experts. This suggests that naive intuitions are suppressed, not supplanted, as learners gain scientific understanding. The current study examines two potential roles of implicit learning processes in the development of scientific knowledge. First, implicit learning is a source of cognitive structures that impede science learning. Second, tasks that engage implicit learning processes can be employed to activate and suppress prior intuitions, enhancing the likelihood that scientific concepts are adopted and applied. This second proposal is tested in two experiments that measure training-induced changes in intuitive and conceptual knowledge related to sinking and floating objects in water. In Experiment 1, an implicit learning task was developed to examine whether implicit learning can induce changes in performance on near and far transfer tasks. The results of this experiment provide evidence that implicit learning tasks activate and suppress scientific intuitions. Experiment 2 examined the effects of combining implicit learning with traditional, direct instruction to enhance explicit learning of science concepts. This experiment demonstrates that sequencing implicit learning task before and after direct instruction has different effects on intuitive and conceptual knowledge. Together, these results suggest a novel approach for enhancing learning for conceptual change in science education.

Collaborative filtering to improve navigation of large radiology knowledge resources.

PubMed

Kahn, Charles E

2005-06-01

Collaborative filtering is a knowledge-discovery technique that can help guide readers to items of potential interest based on the experience of prior users. This study sought to determine the impact of collaborative filtering on navigation of a large, Web-based radiology knowledge resource. Collaborative filtering was applied to a collection of 1,168 radiology hypertext documents available via the Internet. An item-based collaborative filtering algorithm identified each document's six most closely related documents based on 248,304 page views in an 18-day period. Documents were amended to include links to their related documents, and use was analyzed over the next 5 days. The mean number of documents viewed per visit increased from 1.57 to 1.74 (P < 0.0001). Collaborative filtering can increase a radiology information resource's utilization and can improve its usefulness and ease of navigation. The technique holds promise for improving navigation of large Internet-based radiology knowledge resources.

Assessing Teachers' Science Content Knowledge: A Strategy for Assessing Depth of Understanding

NASA Astrophysics Data System (ADS)

McConnell, Tom J.; Parker, Joyce M.; Eberhardt, Jan

2013-06-01

One of the characteristics of effective science teachers is a deep understanding of science concepts. The ability to identify, explain and apply concepts is critical in designing, delivering and assessing instruction. Because some teachers have not completed extensive courses in some areas of science, especially in middle and elementary grades, many professional development programs attempt to strengthen teachers' content knowledge. Assessing this content knowledge is challenging. Concept inventories are reliable and efficient, but do not reveal depth of knowledge. Interviews and observations are time-consuming. The Problem Based Learning Project for Teachers implemented a strategy that includes pre-post instruments in eight content strands that permits blind coding of responses and comparison across teachers and groups of teachers. The instruments include two types of open-ended questions that assess both general knowledge and the ability to apply Big Ideas related to specific science topics. The coding scheme is useful in revealing patterns in prior knowledge and learning, and identifying ideas that are challenging or not addressed by learning activities. The strengths and limitations of the scoring scheme are identified through comparison of the findings to case studies of four participating teachers from middle and elementary schools. The cases include examples of coded pre- and post-test responses to illustrate some of the themes seen in teacher learning. The findings raise questions for future investigation that can be conducted using analyses of the coded responses.

Should we pretreat solid waste prior to anaerobic digestion? An assessment of its environmental cost.

PubMed

Carballa, Marta; Duran, Cecilia; Hospido, Almudena

2011-12-15

Many studies have shown the effectiveness of pretreatments prior to anaerobic digestion of solid wastes, but to our knowledge, none analyzes their environmental consequences/costs. In this work, seven different pretreatments applied to two types of waste (kitchen waste and sewage sludge) have been environmentally evaluated by using life cycle assessment (LCA) methodology. The results show that the environmental burdens associated to the application of pretreatments prior to anaerobic digestion cannot be excluded. Among the options tested, the pressurize-depressurize and chemical (acid or alkaline) pretreatments could be recommended on the basis of their beneficial net environmental performance, while thermal and ozonation alternatives require energy efficiency optimization to reduce their environmental burdens. Reconciling operational, economic and environmental aspects in a holistic approach for the selection of the most sustainable option, mechanical (e.g., pressurize-depressurize) and chemical methods appear to be the most appropriate alternatives at this stage.

Does prior domain-specific content knowledge influence students' recall of arguments surrounding interdisciplinary topics?

PubMed

Schmidt, Hiemke K; Rothgangel, Martin; Grube, Dietmar

2017-12-01

Awareness of various arguments can help interactants present opinions, stress points, and build counterarguments during discussions. At school, some topics are taught in a way that students learn to accumulate knowledge and gather arguments, and later employ them during debates. Prior knowledge may facilitate recalling information on well structured, fact-based topics, but does it facilitate recalling arguments during discussions on complex, interdisciplinary topics? We assessed the prior knowledge in domains related to a bioethical topic of 277 students from Germany (approximately 15 years old), their interest in the topic, and their general knowledge. The students read a text with arguments for and against prenatal diagnostics and tried to recall the arguments one week later and again six weeks later. Prior knowledge in various domains related to the topic individually and separately helped students recall the arguments. These relationships were independent of students' interest in the topic and their general knowledge. Copyright © 2017 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Prior Knowledge Guides Speech Segregation in Human Auditory Cortex.

PubMed

Wang, Yuanye; Zhang, Jianfeng; Zou, Jiajie; Luo, Huan; Ding, Nai

2018-05-18

Segregating concurrent sound streams is a computationally challenging task that requires integrating bottom-up acoustic cues (e.g. pitch) and top-down prior knowledge about sound streams. In a multi-talker environment, the brain can segregate different speakers in about 100 ms in auditory cortex. Here, we used magnetoencephalographic (MEG) recordings to investigate the temporal and spatial signature of how the brain utilizes prior knowledge to segregate 2 speech streams from the same speaker, which can hardly be separated based on bottom-up acoustic cues. In a primed condition, the participants know the target speech stream in advance while in an unprimed condition no such prior knowledge is available. Neural encoding of each speech stream is characterized by the MEG responses tracking the speech envelope. We demonstrate that an effect in bilateral superior temporal gyrus and superior temporal sulcus is much stronger in the primed condition than in the unprimed condition. Priming effects are observed at about 100 ms latency and last more than 600 ms. Interestingly, prior knowledge about the target stream facilitates speech segregation by mainly suppressing the neural tracking of the non-target speech stream. In sum, prior knowledge leads to reliable speech segregation in auditory cortex, even in the absence of reliable bottom-up speech segregation cue.

Contribution of prior semantic knowledge to new episodic learning in amnesia.

PubMed

Kan, Irene P; Alexander, Michael P; Verfaellie, Mieke

2009-05-01

We evaluated whether prior semantic knowledge would enhance episodic learning in amnesia. Subjects studied prices that are either congruent or incongruent with prior price knowledge for grocery and household items and then performed a forced-choice recognition test for the studied prices. Consistent with a previous report, healthy controls' performance was enhanced by price knowledge congruency; however, only a subset of amnesic patients experienced the same benefit. Whereas patients with relatively intact semantic systems, as measured by an anatomical measure (i.e., lesion involvement of anterior and lateral temporal lobes), experienced a significant congruency benefit, patients with compromised semantic systems did not experience a congruency benefit. Our findings suggest that when prior knowledge structures are intact, they can support acquisition of new episodic information by providing frameworks into which such information can be incorporated.

Evaluation of agile designs in first-in-human (FIH) trials--a simulation study.

PubMed

Perlstein, Itay; Bolognese, James A; Krishna, Rajesh; Wagner, John A

2009-12-01

The aim of the investigation was to evaluate alternatives to standard first-in-human (FIH) designs in order to optimize the information gained from such studies by employing novel agile trial designs. Agile designs combine adaptive and flexible elements to enable optimized use of prior information either before and/or during conduct of the study to seamlessly update the study design. A comparison of the traditional 6 + 2 (active + placebo) subjects per cohort design with alternative, reduced sample size, agile designs was performed by using discrete event simulation. Agile designs were evaluated for specific adverse event models and rates as well as dose-proportional, saturated, and steep-accumulation pharmacokinetic profiles. Alternative, reduced sample size (hereafter referred to as agile) designs are proposed for cases where prior knowledge about pharmacokinetics and/or adverse event relationships are available or appropriately assumed. Additionally, preferred alternatives are proposed for a general case when prior knowledge is limited or unavailable. Within the tested conditions and stated assumptions, some agile designs were found to be as efficient as traditional designs. Thus, simulations demonstrated that the agile design is a robust and feasible approach to FIH clinical trials, with no meaningful loss of relevant information, as it relates to PK and AE assumptions. In some circumstances, applying agile designs may decrease the duration and resources required for Phase I studies, increasing the efficiency of early clinical development. We highlight the value and importance of useful prior information when specifying key assumptions related to safety, tolerability, and PK.

The principles of quantification applied to in vivo proton MR spectroscopy.

PubMed

Helms, Gunther

2008-08-01

Following the identification of metabolite signals in the in vivo MR spectrum, quantification is the procedure to estimate numerical values of their concentrations. The two essential steps are discussed in detail: analysis by fitting a model of prior knowledge, that is, the decomposition of the spectrum into the signals of singular metabolites; then, normalization of these signals to yield concentration estimates. Special attention is given to using the in vivo water signal as internal reference.

Using Fuzzy Logic for Performance Evaluation in Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap S.

1992-01-01

Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of dynamic systems and it is demonstrated that it is possible to start with an approximate prior knowledge and learn to refine it through experiments using reinforcement learning.

Scalable Learning for Geostatistics and Speaker Recognition

DTIC Science & Technology

2011-01-01

of prior knowledge of the model or due to improved robustness requirements). Both these methods have their own advantages and disadvantages. The use...application. If the data is well-correlated and low-dimensional, any prior knowledge available on the data can be used to build a parametric model. In the...absence of prior knowledge , non-parametric methods can be used. If the data is high-dimensional, PCA based dimensionality reduction is often the first

Integrative Sparse K-Means With Overlapping Group Lasso in Genomic Applications for Disease Subtype Discovery

PubMed Central

Huo, Zhiguang; Tseng, George

2017-01-01

Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K-means (is-K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare is-K means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency. PMID:28959370

Integrative Sparse K-Means With Overlapping Group Lasso in Genomic Applications for Disease Subtype Discovery.

PubMed

Huo, Zhiguang; Tseng, George

2017-06-01

Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K -means (is- K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare is- K means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency.

Hysteresis as an Implicit Prior in Tactile Spatial Decision Making

PubMed Central

Thiel, Sabrina D.; Bitzer, Sebastian; Nierhaus, Till; Kalberlah, Christian; Preusser, Sven; Neumann, Jane; Nikulin, Vadim V.; van der Meer, Elke; Villringer, Arno; Pleger, Burkhard

2014-01-01

Perceptual decisions not only depend on the incoming information from sensory systems but constitute a combination of current sensory evidence and internally accumulated information from past encounters. Although recent evidence emphasizes the fundamental role of prior knowledge for perceptual decision making, only few studies have quantified the relevance of such priors on perceptual decisions and examined their interplay with other decision-relevant factors, such as the stimulus properties. In the present study we asked whether hysteresis, describing the stability of a percept despite a change in stimulus property and known to occur at perceptual thresholds, also acts as a form of an implicit prior in tactile spatial decision making, supporting the stability of a decision across successively presented random stimuli (i.e., decision hysteresis). We applied a variant of the classical 2-point discrimination task and found that hysteresis influenced perceptual decision making: Participants were more likely to decide ‘same’ rather than ‘different’ on successively presented pin distances. In a direct comparison between the influence of applied pin distances (explicit stimulus property) and hysteresis, we found that on average, stimulus property explained significantly more variance of participants’ decisions than hysteresis. However, when focusing on pin distances at threshold, we found a trend for hysteresis to explain more variance. Furthermore, the less variance was explained by the pin distance on a given decision, the more variance was explained by hysteresis, and vice versa. Our findings suggest that hysteresis acts as an implicit prior in tactile spatial decision making that becomes increasingly important when explicit stimulus properties provide decreasing evidence. PMID:24587045

Comfort and experience with online learning: trends over nine years and associations with knowledge.

PubMed

Cook, David A; Thompson, Warren G

2014-07-01

Some evidence suggests that attitude toward computer-based instruction is an important determinant of success in online learning. We sought to determine how comfort using computers and perceptions of prior online learning experiences have changed over the past decade, and how these associate with learning outcomes. Each year from 2003-2011 we conducted a prospective trial of online learning. As part of each year's study, we asked medicine residents about their comfort using computers and if their previous experiences with online learning were favorable. We assessed knowledge using a multiple-choice test. We used regression to analyze associations and changes over time. 371 internal medicine and family medicine residents participated. Neither comfort with computers nor perceptions of prior online learning experiences showed a significant change across years (p > 0.61), with mean comfort rating 3.96 (maximum 5 = very comfortable) and mean experience rating 4.42 (maximum 6 = strongly agree [favorable]). Comfort showed no significant association with knowledge scores (p = 0.39) but perceptions of prior experiences did, with a 1.56% rise in knowledge score for a 1-point rise in experience score (p = 0.02). Correlations among comfort, perceptions of prior experiences, and number of prior experiences were all small and not statistically significant. Comfort with computers and perceptions of prior experience with online learning remained stable over nine years. Prior good experiences (but not comfort with computers) demonstrated a modest association with knowledge outcomes, suggesting that prior course satisfaction may influence subsequent learning.

Task complexity, student perceptions of vocabulary learning in EFL, and task performance.

PubMed

Wu, Xiaoli; Lowyck, Joost; Sercu, Lies; Elen, Jan

2013-03-01

The study deepened our understanding of how students' self-efficacy beliefs contribute to the context of teaching English as a foreign language in the framework of cognitive mediational paradigm at a fine-tuned task-specific level. The aim was to examine the relationship among task complexity, self-efficacy beliefs, domain-related prior knowledge, learning strategy use, and task performance as they were applied to English vocabulary learning from reading tasks. Participants were 120 second-year university students (mean age 21) from a Chinese university. This experiment had two conditions (simple/complex). A vocabulary level test was first conducted to measure participants' prior knowledge of English vocabulary. Participants were then randomly assigned to one of the learning tasks. Participants were administered task booklets together with the self-efficacy scales, measures of learning strategy use, and post-tests. Data obtained were submitted to multivariate analysis of variance (MANOVA) and path analysis. Results from the MANOVA model showed a significant effect of vocabulary level on self-efficacy beliefs, learning strategy use, and task performance. Task complexity showed no significant effect; however, an interaction effect between vocabulary level and task complexity emerged. Results from the path analysis showed self-efficacy beliefs had an indirect effect on performance. Our results highlighted the mediating role of self-efficacy beliefs and learning strategy use. Our findings indicate that students' prior knowledge plays a crucial role on both self-efficacy beliefs and task performance, and the predictive power of self-efficacy on task performance may lie in its association with learning strategy use. © 2011 The British Psychological Society.

Learning gait of quadruped robot without prior knowledge of the environment

NASA Astrophysics Data System (ADS)

Xu, Tao; Chen, Qijun

2012-09-01

Walking is the basic skill of a legged robot, and one of the promising ways to improve the walking performance and its adaptation to environment changes is to let the robot learn its walking by itself. Currently, most of the walking learning methods are based on robot vision system or some external sensing equipment to estimate the walking performance of certain walking parameters, and therefore are usually only applicable under laboratory condition, where environment can be pre-defined. Inspired by the rhythmic swing movement during walking of legged animals and the behavior of their adjusting their walking gait on different walking surfaces, a concept of walking rhythmic pattern(WRP) is proposed to evaluate the walking specialty of legged robot, which is just based on the walking dynamics of the robot. Based on the onboard acceleration sensor data, a method to calculate WRP using power spectrum in frequency domain and diverse smooth filters is also presented. Since the evaluation of WRP is only based on the walking dynamics data of the robot's body, the proposed method doesn't require prior knowledge of environment and thus can be applied in unknown environment. A gait learning approach of legged robots based on WRP and evolution algorithm(EA) is introduced. By using the proposed approach, a quadruped robot can learn its locomotion by its onboard sensing in an unknown environment, where the robot has no prior knowledge about this place. The experimental result proves proportional relationship exits between WRP match score and walking performance of legged robot, which can be used to evaluate the walking performance in walking optimization under unknown environment.

7 CFR 275.2 - State agency responsibilities.

Code of Federal Regulations, 2014 CFR

2014-01-01

...: (i) Data collection through management evaluation (ME) reviews and quality control (QC) reviews; (ii... knowledge of either the household or the decision under review. Where there is prior knowledge, the reviewer must disqualify her/himself. Prior knowledge is defined as having: (1) Taken any part in the decision...

7 CFR 275.2 - State agency responsibilities.

Code of Federal Regulations, 2011 CFR

2011-01-01

...: (i) Data collection through management evaluation (ME) reviews and quality control (QC) reviews; (ii... knowledge of either the household or the decision under review. Where there is prior knowledge, the reviewer must disqualify her/himself. Prior knowledge is defined as having: (1) Taken any part in the decision...

7 CFR 275.2 - State agency responsibilities.

Code of Federal Regulations, 2013 CFR

2013-01-01

...: (i) Data collection through management evaluation (ME) reviews and quality control (QC) reviews; (ii... knowledge of either the household or the decision under review. Where there is prior knowledge, the reviewer must disqualify her/himself. Prior knowledge is defined as having: (1) Taken any part in the decision...

7 CFR 275.2 - State agency responsibilities.

Code of Federal Regulations, 2012 CFR

2012-01-01

...: (i) Data collection through management evaluation (ME) reviews and quality control (QC) reviews; (ii... knowledge of either the household or the decision under review. Where there is prior knowledge, the reviewer must disqualify her/himself. Prior knowledge is defined as having: (1) Taken any part in the decision...

7 CFR 275.2 - State agency responsibilities.

Code of Federal Regulations, 2010 CFR

2010-01-01

...: (i) Data collection through management evaluation (ME) reviews and quality control (QC) reviews; (ii... knowledge of either the household or the decision under review. Where there is prior knowledge, the reviewer must disqualify her/himself. Prior knowledge is defined as having: (1) Taken any part in the decision...

A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition.

PubMed

Kurashige, Hiroki; Yamashita, Yuichi; Hanakawa, Takashi; Honda, Manabu

2018-01-01

Knowledge acquisition is a process in which one actively selects a piece of information from the environment and assimilates it with prior knowledge. However, little is known about the neural mechanism underlying selectivity in knowledge acquisition. Here we executed a 2-day human experiment to investigate the involvement of characteristic spontaneous activity resembling a so-called "preplay" in selectivity in sentence comprehension, an instance of knowledge acquisition. On day 1, we presented 10 sentences (prior sentences) that were difficult to understand on their own. On the following day, we first measured the resting-state functional magnetic resonance imaging (fMRI). Then, we administered a sentence comprehension task using 20 new sentences (posterior sentences). The posterior sentences were also difficult to understand on their own, but some could be associated with prior sentences to facilitate their understanding. Next, we measured the posterior sentence-induced fMRI to identify the neural representation. From the resting-state fMRI, we extracted the appearances of activity patterns similar to the neural representations for posterior sentences. Importantly, the resting-state fMRI was measured before giving the posterior sentences, and thus such appearances could be considered as preplay-like or prototypical neural representations. We compared the intensities of such appearances with the understanding of posterior sentences. This gave a positive correlation between these two variables, but only if posterior sentences were associated with prior sentences. Additional analysis showed the contribution of the entorhinal cortex, rather than the hippocampus, to the correlation. The present study suggests that prior knowledge-based arrangement of neural activity before an experience contributes to the active selection of information to be learned. Such arrangement prior to an experience resembles preplay activity observed in the rodent brain. In terms of knowledge acquisition, the present study leads to a new view of the brain (or more precisely of the brain's knowledge) as an autopoietic system in which the brain (or knowledge) selects what it should learn by itself, arranges preplay-like activity as a position for the new information in advance, and actively reorganizes itself.

A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition

PubMed Central

Kurashige, Hiroki; Yamashita, Yuichi; Hanakawa, Takashi; Honda, Manabu

2018-01-01

Knowledge acquisition is a process in which one actively selects a piece of information from the environment and assimilates it with prior knowledge. However, little is known about the neural mechanism underlying selectivity in knowledge acquisition. Here we executed a 2-day human experiment to investigate the involvement of characteristic spontaneous activity resembling a so-called “preplay” in selectivity in sentence comprehension, an instance of knowledge acquisition. On day 1, we presented 10 sentences (prior sentences) that were difficult to understand on their own. On the following day, we first measured the resting-state functional magnetic resonance imaging (fMRI). Then, we administered a sentence comprehension task using 20 new sentences (posterior sentences). The posterior sentences were also difficult to understand on their own, but some could be associated with prior sentences to facilitate their understanding. Next, we measured the posterior sentence-induced fMRI to identify the neural representation. From the resting-state fMRI, we extracted the appearances of activity patterns similar to the neural representations for posterior sentences. Importantly, the resting-state fMRI was measured before giving the posterior sentences, and thus such appearances could be considered as preplay-like or prototypical neural representations. We compared the intensities of such appearances with the understanding of posterior sentences. This gave a positive correlation between these two variables, but only if posterior sentences were associated with prior sentences. Additional analysis showed the contribution of the entorhinal cortex, rather than the hippocampus, to the correlation. The present study suggests that prior knowledge-based arrangement of neural activity before an experience contributes to the active selection of information to be learned. Such arrangement prior to an experience resembles preplay activity observed in the rodent brain. In terms of knowledge acquisition, the present study leads to a new view of the brain (or more precisely of the brain’s knowledge) as an autopoietic system in which the brain (or knowledge) selects what it should learn by itself, arranges preplay-like activity as a position for the new information in advance, and actively reorganizes itself. PMID:29662446

Self-Explanation in the Domain of Statistics: An Expertise Reversal Effect

ERIC Educational Resources Information Center

Leppink, Jimmie; Broers, Nick J.; Imbos, Tjaart; van der Vleuten, Cees P. M.; Berger, Martijn P. F.

2012-01-01

This study investigated the effects of four instructional methods on cognitive load, propositional knowledge, and conceptual understanding of statistics, for low prior knowledge students and for high prior knowledge students. The instructional methods were (1) a reading-only control condition, (2) answering open-ended questions, (3) answering…

Immune allied genetic algorithm for Bayesian network structure learning

NASA Astrophysics Data System (ADS)

Song, Qin; Lin, Feng; Sun, Wei; Chang, KC

2012-06-01

Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.

[The segmentation of urinary cells--a first step in the automated processing in urine cytology (author's transl)].

PubMed

Liedtke, C E; Aeikens, B

1980-01-01

By segmentation of cell images we understand the automated decomposition of microscopic cell scenes into nucleus, plasma and background. A segmentation is achieved by using information from the microscope image and prior knowledge about the content of the scene. Different algorithms have been investigated and applied to samples of urothelial cells. A particular algorithm based on a histogram approach which can be easily implemented in hardware is discussed in more detail.

The iSCREEN Electronic Diabetes Dashboard: A Tool to Improve Knowledge and Implementation of Pediatric Clinical Practice Guidelines.

PubMed

Zahanova, Stacy; Tsouka, Alexandra; Palmert, Mark R; Mahmud, Farid H

2017-12-01

Clinical practice guidelines (CPG) provide evidence-based recommendations for patient care but may not be optimally applied in clinical settings. As a pilot study, we evaluated the impact of a computerized, point-of-care decision support system (CDSS) on guideline knowledge and adherence in our diabetes clinic. iSCREEN, a CDSS, integrated with a province-wide electronic health record, was designed based on the Canadian Diabetes Association 2013 Clinical Practice Guidelines for the Prevention and Management of Diabetes in Canada. Evaluation data were gathered by retrospective chart review and clinician questionnaire prior to and after implementation of iSCREEN. Records of patients with type 1 diabetes, 14 to 18 years of age, were assessed for appropriate screening for complications and comorbidities. To assess guideline adherence, 50 charts were reviewed at 2 time periods (25 before and 25 after launch of iSCREEN). Results revealed improved frequency of appropriate screening for diabetic nephropathy (p=0.03) and retinopathy (p=0.04), accompanied by a decrease in under- and overscreening for these outcomes. To assess guideline knowledge, 58 surveys were collected (31 prior to and 27 after the launch of iSCREEN) from care providers in the field of pediatric diabetes. There was a trend toward improved guideline knowledge in all team members (p=0.06). Implementation of a de novo CDSS was associated with improved rates of appropriate screening for diabetes-related complications. A trend toward improvement in health professionals' knowledge of the guidelines was also observed. Evaluation of this point-of-care computerized decision support tool suggests that it may facilitate diabetes care by optimizing complication screening and CPG knowledge, with the potential for broader implementation. Copyright © 2017 Diabetes Canada. Published by Elsevier Inc. All rights reserved.

Comfort and experience with online learning: trends over nine years and associations with knowledge

PubMed Central

2014-01-01

Background Some evidence suggests that attitude toward computer-based instruction is an important determinant of success in online learning. We sought to determine how comfort using computers and perceptions of prior online learning experiences have changed over the past decade, and how these associate with learning outcomes. Methods Each year from 2003–2011 we conducted a prospective trial of online learning. As part of each year’s study, we asked medicine residents about their comfort using computers and if their previous experiences with online learning were favorable. We assessed knowledge using a multiple-choice test. We used regression to analyze associations and changes over time. Results 371 internal medicine and family medicine residents participated. Neither comfort with computers nor perceptions of prior online learning experiences showed a significant change across years (p > 0.61), with mean comfort rating 3.96 (maximum 5 = very comfortable) and mean experience rating 4.42 (maximum 6 = strongly agree [favorable]). Comfort showed no significant association with knowledge scores (p = 0.39) but perceptions of prior experiences did, with a 1.56% rise in knowledge score for a 1-point rise in experience score (p = 0.02). Correlations among comfort, perceptions of prior experiences, and number of prior experiences were all small and not statistically significant. Conclusions Comfort with computers and perceptions of prior experience with online learning remained stable over nine years. Prior good experiences (but not comfort with computers) demonstrated a modest association with knowledge outcomes, suggesting that prior course satisfaction may influence subsequent learning. PMID:24985690

Middle school students' knowledge of autism.

PubMed

Campbell, Jonathan M; Barger, Brian D

2011-06-01

Authors examined 1,015 middle school students' knowledge of autism using a single item of prior awareness and a 10-item Knowledge of Autism (KOA) scale. The KOA scale was designed to assess students' knowledge of the course, etiology, and symptoms associated with autism. Less than half of students (46.1%) reported having heard of autism; however, most students correctly responded that autism was a chronic condition that was not communicable. Students reporting prior awareness of autism scored higher on 9 of 10 KOA scale items when compared to their naïve counterparts. Prior awareness of autism and KOA scores also differed across schools. A more detailed understanding of developmental changes in students' knowledge of autism should improve peer educational interventions.

Rethinking the learning of belief network probabilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Musick, R.

Belief networks are a powerful tool for knowledge discovery that provide concise, understandable probabilistic models of data. There are methods grounded in probability theory to incrementally update the relationships described by the belief network when new information is seen, to perform complex inferences over any set of variables in the data, to incorporate domain expertise and prior knowledge into the model, and to automatically learn the model from data. This paper concentrates on part of the belief network induction problem, that of learning the quantitative structure (the conditional probabilities), given the qualitative structure. In particular, the current practice of rotemore » learning the probabilities in belief networks can be significantly improved upon. We advance the idea of applying any learning algorithm to the task of conditional probability learning in belief networks, discuss potential benefits, and show results of applying neutral networks and other algorithms to a medium sized car insurance belief network. The results demonstrate from 10 to 100% improvements in model error rates over the current approaches.« less

Relationship of prior knowledge and working engineers' learning preferences: implications for designing effective instruction

NASA Astrophysics Data System (ADS)

Baukal, Charles E.; Ausburn, Lynna J.

2017-05-01

Continuing engineering education (CEE) is important to ensure engineers maintain proficiency over the life of their careers. However, relatively few studies have examined designing effective training for working engineers. Research has indicated that both learner instructional preferences and prior knowledge can impact the learning process, but it has not established if these factors are interrelated. The study reported here considered relationships of prior knowledge and three aspects of learning preferences of working engineers at a manufacturing company: learning strategy choices, verbal-visual cognitive styles, and multimedia preferences. Prior knowledge was not found to be significantly related to engineers' learning preferences, indicating independence of effects of these variables on learning. The study also examined relationships of this finding to the Multimedia Cone of Abstraction and implications for its use as an instructional design tool for CEE.

Exploring the Impact of Prior Knowledge and Appropriate Feedback on Students' Perceived Cognitive Load and Learning Outcomes: Animation-based earthquakes instruction

NASA Astrophysics Data System (ADS)

Yeh, Ting-Kuang; Tseng, Kuan-Yun; Cho, Chung-Wen; Barufaldi, James P.; Lin, Mei-Shin; Chang, Chun-Yen

2012-07-01

The aim of this study was to develop an animation-based curriculum and to evaluate the effectiveness of animation-based instruction; the report involved the assessment of prior knowledge and the appropriate feedback approach, for the purpose of reducing perceived cognitive load and improving learning. The curriculum was comprised of five subunits designed to teach the 'Principles of Earthquakes.' Each subunit consisted of three modules: evaluation of prior knowledge with/without in-time feedback; animation-based instruction; and evaluation of learning outcomes with feedback. The 153 participants consisted of 10th grade high-school students. Seventy-eight students participated in the animation-based instruction, involving assessment of prior knowledge and appropriate feedback mechanism (APA group). A total of 75 students participated in animation-based learning that did not take into account their prior knowledge (ANPA group). The effectiveness of the instruction was then evaluated by using a Science Conception Test (SCT), a self-rating cognitive load questionnaire (CLQ), as well as a structured interview. The results indicated that: (1) Students' perceived cognitive load was reduced effectively through improving their prior knowledge by providing appropriate feedback. (2) When students perceived lower levels of cognitive load, they showed better learning outcome. The result of this study revealed that students of the APA group showed better performance than those of the ANPA group in an open-ended question. Furthermore, students' perceived cognitive load was negatively associated with their learning outcomes.

Learning stoichiometry: A comparison of text and multimedia instructional formats

NASA Astrophysics Data System (ADS)

Evans, Karen L.

Even after multiple instructional opportunities, first year college chemistry students are often unable to apply stoichiometry knowledge in equilibrium and acid-base chemistry problem solving. Cognitive research findings suggest that for learning to be meaningful, learners need to actively construct their own knowledge by integrating new information into, and reorganizing, their prior understandings. Scaffolded inquiry in which facts, procedures, and principles are introduced as needed within the context of authentic problem solving may provide the practice and encoding opportunities necessary for construction of a memorable and usable knowledge base. The dynamic and interactive capabilities of online technology may facilitate stoichiometry instruction that promotes this meaningful learning. Entering college freshmen were randomly assigned to either a technology-rich or text-only set of cognitively informed stoichiometry review materials. Analysis of posttest scores revealed a significant but small difference in the performance of the two treatment groups, with the technology-rich group having the advantage. Both SAT and gender, however, explained more of the variability in the scores. Analysis of the posttest scores from the technology-rich treatment group revealed that the degree of interaction with the Virtual Lab simulation was significantly related to posttest performance and subsumed any effect of prior knowledge as measured by SAT scores. Future users of the online course should be encouraged to engage with the problem-solving opportunities provided by the Virtual Lab simulation through either explicit instruction and/or implementation of some level of program control within the course's navigational features.

Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints.

PubMed

Salari, Autoosa; Navarro, Marco A; Milescu, Mirela; Milescu, Lorin S

2018-02-05

To understand how ion channels and other proteins function at the molecular and cellular levels, one must decrypt their kinetic mechanisms. Sophisticated algorithms have been developed that can be used to extract kinetic parameters from a variety of experimental data types. However, formulating models that not only explain new data, but are also consistent with existing knowledge, remains a challenge. Here, we present a two-part study describing a mathematical and computational formalism that can be used to enforce prior knowledge into the model using constraints. In this first part, we focus on constraints that enforce explicit linear relationships involving rate constants or other model parameters. We develop a simple, linear algebra-based transformation that can be applied to enforce many types of model properties and assumptions, such as microscopic reversibility, allosteric gating, and equality and inequality parameter relationships. This transformation converts the set of linearly interdependent model parameters into a reduced set of independent parameters, which can be passed to an automated search engine for model optimization. In the companion article, we introduce a complementary method that can be used to enforce arbitrary parameter relationships and any constraints that quantify the behavior of the model under certain conditions. The procedures described in this study can, in principle, be coupled to any of the existing methods for solving molecular kinetics for ion channels or other proteins. These concepts can be used not only to enforce existing knowledge but also to formulate and test new hypotheses. © 2018 Salari et al.

Investigating the Effectiveness of Inquiry-Based Instruction on Students with Different Prior Knowledge and Reading Abilities

ERIC Educational Resources Information Center

Wang, Jing-Ru; Wang, Yuh-Chao; Tai, Hsin-Jung; Chen, Wen-Ju

2010-01-01

This study examined the differential impacts of an inquiry-based instruction on conceptual changes across levels of prior knowledge and reading ability. The instrument emphasized four simultaneously important components: conceptual knowledge, reading ability, attitude toward science, and learning environment. Although the learning patterns and…

The Relation between Prior Knowledge and Students' Collaborative Discovery Learning Processes

ERIC Educational Resources Information Center

Gijlers, Hannie; de Jong, Ton

2005-01-01

In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction…

Prior Knowledge and Story Processing: Integration, Selection, and Variation. Technical Report No. 138.

ERIC Educational Resources Information Center

Spiro, Rand J.

Psychological research concerning several aspects of the relationship between existing knowledge schemata and the processing of text is summarized in this report. The first section is concerned with dynamic processes of story understanding, with emphasis on the integration of information. The role of prior knowledge in accommodating parts of…

Preparation for College General Chemistry: More than Just a Matter of Content Knowledge Acquisition

ERIC Educational Resources Information Center

Cracolice, Mark S.; Busby, Brittany D.

2015-01-01

This study investigates the potential of five factors that may be predictive of success in college general chemistry courses: prior knowledge of common alternate conceptions, intelligence, scientific reasoning ability, proportional reasoning ability, and attitude toward chemistry. We found that both prior knowledge and scientific reasoning ability…

Third-Grade Students' Mental Models of Energy Expenditure during Exercise

ERIC Educational Resources Information Center

Pasco, Denis; Ennis, Catherine D.

2015-01-01

Background: Students' prior knowledge plays an important role in learning new knowledge. In physical education (PE) and physical activity settings, studies have confirmed the role of students' prior knowledge. According to Placek and Griffin, these studies demonstrate that: "our students are not empty balls waiting to be filled with knowledge…

Requirements analysis, domain knowledge, and design

NASA Technical Reports Server (NTRS)

Potts, Colin

1988-01-01

Two improvements to current requirements analysis practices are suggested: domain modeling, and the systematic application of analysis heuristics. Domain modeling is the representation of relevant application knowledge prior to requirements specification. Artificial intelligence techniques may eventually be applicable for domain modeling. In the short term, however, restricted domain modeling techniques, such as that in JSD, will still be of practical benefit. Analysis heuristics are standard patterns of reasoning about the requirements. They usually generate questions of clarification or issues relating to completeness. Analysis heuristics can be represented and therefore systematically applied in an issue-based framework. This is illustrated by an issue-based analysis of JSD's domain modeling and functional specification heuristics. They are discussed in the context of the preliminary design of simple embedded systems.

Art, science, and the existential focus of clinical medicine.

PubMed

Warsop, A

2002-12-01

The continuing debate over the status of medicine as an art or a science remains far from resolved. The aim of this paper is to clarify what is meant by the art of medicine. In the following interpretation I contrast two current perspectives of the medical art. I argue that the art of medicine is best understood in terms of the Aristotelian notion of techne. It consists of listening skills directed to the lived experience of the patient in such a way that knowledge (principally scientific knowledge) can be applied in a therapeutic way. This constitutes what I call medicine's existential focus. The art of medicine is prior to and independent of medical science which plays an important but subordinate role.

Quantifying Uncertainty in Inverse Models of Geologic Data from Shear Zones

NASA Astrophysics Data System (ADS)

Davis, J. R.; Titus, S.

2016-12-01

We use Bayesian Markov chain Monte Carlo simulation to quantify uncertainty in inverse models of geologic data. Although this approach can be applied to many tectonic settings, field areas, and mathematical models, we focus on transpressional shear zones. The underlying forward model, either kinematic or dynamic, produces a velocity field, which predicts the dikes, foliation-lineations, crystallographic preferred orientation (CPO), shape preferred orientation (SPO), and other geologic data that should arise in the shear zone. These predictions are compared to data using modern methods of geometric statistics, including the Watson (for lines such as dike poles), isotropic matrix Fisher (for orientations such as foliation-lineations and CPO), and multivariate normal (for log-ellipsoids such as SPO) distributions. The result of the comparison is a likelihood, which is a key ingredient in the Bayesian approach. The other key ingredient is a prior distribution, which reflects the geologist's knowledge of the parameters before seeing the data. For some parameters, such as shear zone strike and dip, we identify realistic informative priors. For other parameters, where the geologist has no prior knowledge, we identify useful uninformative priors.We investigate the performance of this approach through numerical experiments on synthetic data sets. A fundamental issue is that many models of deformation exhibit asymptotic behavior (e.g., flow apophyses, fabric attractors) or periodic behavior (e.g., SPO when the clasts are rigid), which causes the likelihood to be too uniform. Based on our experiments, we offer rules of thumb for how many data, of which types, are needed to constrain deformation.

Toward a dose reduction strategy using model-based reconstruction with limited-angle tomosynthesis

NASA Astrophysics Data System (ADS)

Haneda, Eri; Tkaczyk, J. E.; Palma, Giovanni; Iordache, Rǎzvan; Zelakiewicz, Scott; Muller, Serge; De Man, Bruno

2014-03-01

Model-based iterative reconstruction (MBIR) is an emerging technique for several imaging modalities and appli- cations including medical CT, security CT, PET, and microscopy. Its success derives from an ability to preserve image resolution and perceived diagnostic quality under impressively reduced signal level. MBIR typically uses a cost optimization framework that models system geometry, photon statistics, and prior knowledge of the recon- structed volume. The challenge of tomosynthetic geometries is that the inverse problem becomes more ill-posed due to the limited angles, meaning the volumetric image solution is not uniquely determined by the incom- pletely sampled projection data. Furthermore, low signal level conditions introduce additional challenges due to noise. A fundamental strength of MBIR for limited-views and limited-angle is that it provides a framework for constraining the solution consistent with prior knowledge of expected image characteristics. In this study, we analyze through simulation the capability of MBIR with respect to prior modeling components for limited-views, limited-angle digital breast tomosynthesis (DBT) under low dose conditions. A comparison to ground truth phantoms shows that MBIR with regularization achieves a higher level of fidelity and lower level of blurring and streaking artifacts compared to other state of the art iterative reconstructions, especially for high contrast objects. The benefit of contrast preservation along with less artifacts may lead to detectability improvement of microcalcification for more accurate cancer diagnosis.

Endoscopic submucosal dissection for removal of superficial gastrointestinal neoplasms: A technical review

PubMed Central

Matsui, Noriaki; Akahoshi, Kazuya; Nakamura, Kazuhiko; Ihara, Eikichi; Kita, Hiroto

2012-01-01

Endoscopic submucosal dissection (ESD) is now the most common endoscopic treatment in Japan for intramucosal gastrointestinal neoplasms (non-metastatic). ESD is an invasive endoscopic surgical procedure, requiring extensive knowledge, skill, and specialized equipment. ESD starts with evaluation of the lesion, as accurate assessment of the depth and margin of the lesion is essential. The devices and strategies used in ESD vary, depending on the nature of the lesion. Prior to the procedure, the operator must be knowledgeable about the treatment strategy(ies), the device(s) to use, the electrocautery machine settings, the substances to inject, and other aspects. In addition, the operator must be able to manage complications, should they arise, including immediate recognition of the complication(s) and its treatment. Finally, in case the ESD treatment is not successful, the operator should be prepared to apply alternative treatments. Thus, adequate knowledge and training are essential to successfully perform ESD. PMID:22523613

Mathematics understanding and anxiety in collaborative teaching

NASA Astrophysics Data System (ADS)

Ansari, B. I.; Wahyu, N.

2017-12-01

This study aims to examine students’ mathematical understanding and anxiety using collaborative teaching. The sample consists of 51 students in the 7th-grade of MTs N Jeureula, one of the Islamic public junior high schools in Jeureula, Aceh, Indonesia. A test of mathematics understanding was administered to the students twice during the period of two months. The result suggests that there is a significant increase in mathematical understanding in the pre-test and post-test. We categorized the students into the high, intermediate, and low level of prior mathematics knowledge. In the high-level prior knowledge, there is no difference of mathematical understanding between the experiment and control group. Meanwhile, in the intermediate and low level of prior knowledge, there is a significant difference of mathematical understanding between the experiment and control group. The mathematics anxiety is at an intermediate level in the experiment class and at a high level in the control group. There is no interaction between the learning model and the students’ prior knowledge towards the mathematical understanding, but there are interactions towards the mathematics anxiety. It indicates that the collaborative teaching model and the students’ prior knowledge do not simultaneously impacts on the mathematics understanding but the mathematics anxiety.

Developmental Change in the Influence of Domain-General Abilities and Domain-Specific Knowledge on Mathematics Achievement: An Eight-Year Longitudinal Study

PubMed Central

Geary, David C.; Nicholas, Alan; Li, Yaoran; Sun, Jianguo

2016-01-01

The contributions of domain-general abilities and domain-specific knowledge to subsequent mathematics achievement were longitudinally assessed (n = 167) through 8th grade. First grade intelligence and working memory and prior grade reading achievement indexed domain-general effects and domain-specific effects were indexed by prior grade mathematics achievement and mathematical cognition measures of prior grade number knowledge, addition skills, and fraction knowledge. Use of functional data analysis enabled grade-by-grade estimation of overall domain-general and domain-specific effects on subsequent mathematics achievement, the relative importance of individual domain-general and domain-specific variables on this achievement, and linear and non-linear across-grade estimates of these effects. The overall importance of domain-general abilities for subsequent achievement was stable across grades, with working memory emerging as the most important domain-general ability in later grades. The importance of prior mathematical competencies on subsequent mathematics achievement increased across grades, with number knowledge and arithmetic skills critical in all grades and fraction knowledge in later grades. Overall, domain-general abilities were more important than domain-specific knowledge for mathematics learning in early grades but general abilities and domain-specific knowledge were equally important in later grades. PMID:28781382

Prior schemata transfer as an account for assessing the intuitive use of new technology.

PubMed

Fischer, Sandrine; Itoh, Makoto; Inagaki, Toshiyuki

2015-01-01

New devices are considered intuitive when they allow users to transfer prior knowledge. Drawing upon fundamental psychology experiments that distinguish prior knowledge transfer from new schema induction, a procedure was specified for assessing intuitive use. This procedure was tested with 31 participants who, prior to using an on-board computer prototype, studied its screenshots in reading vs. schema induction conditions. Distinct patterns of transfer or induction resulted for features of the prototype whose functions were familiar or unfamiliar, respectively. Though moderated by participants' cognitive style, these findings demonstrated a means for quantitatively assessing transfer of prior knowledge as the operation that underlies intuitive use. Implications for interface evaluation and design, as well as potential improvements to the procedure, are discussed. Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.

How Prior Knowledge and Colour Contrast Interfere Visual Search Processes in Novice Learners: An Eye Tracking Study

ERIC Educational Resources Information Center

Sonmez, Duygu; Altun, Arif; Mazman, Sacide Guzin

2012-01-01

This study investigates how prior content knowledge and prior exposure to microscope slides on the phases of mitosis effect students' visual search strategies and their ability to differentiate cells that are going through any phases of mitosis. Two different sets of microscope slide views were used for this purpose; with high and low colour…

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge

PubMed Central

Wagner, Florian

2015-01-01

Method Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. Results I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets. PMID:26575370

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.

PubMed

Wagner, Florian

2015-01-01

Genome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping. I first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.

Executive Functions as Moderators of the Worked Example Effect: When Shifting Is More Important than Working Memory Capacity

ERIC Educational Resources Information Center

Schwaighofer, Matthias; Bühner, Markus; Fischer, Frank

2016-01-01

Worked examples have proven to be effective for knowledge acquisition compared with problem solving, particularly when prior knowledge is low (e.g., Kalyuga, 2007). However, in addition to prior knowledge, executive functions and fluid intelligence might be potential moderators of the effectiveness of worked examples. The present study examines…

Can Prior Knowledge Hurt Text Comprehension? An Answer Borrowed from Plato, Aristotle, and Descartes.

ERIC Educational Resources Information Center

Friedman, Lawrence B.

Taking a philosophical approach based on what Plato, Aristotle, and Descartes said about knowledge, this paper addresses some of the murkiness in the conceptual space surrounding the issue of whether prior knowledge does or does not facilitate text comprehension. Specifically, the paper first develops a non-exhaustive typology of cases in which…

Effects of Activation of Prior Knowledge on the Recall of a Clinical Case.

ERIC Educational Resources Information Center

Schmidt, Henk G.; Boshuizen, Henny P. A.

A study investigated the known phenomenon of "intermediate effect" in which medical students with an intermediate amount of knowledge and experience demonstrate higher amounts of recall of the text of a medical case than either experienced clinicians or novices. In this study the amount of activation of prior knowledge was controlled by…

Effect of Altered Prior Knowledge on Passage Recall.

ERIC Educational Resources Information Center

Langer, Judith A.; Nicolich, Mark

A study was conducted to determine: (1) the relationships between prior knowledge and passage recall; (2) the effect of a prereading activity (PReP) on available knowledge; and (3) the effect of the PReP activity on total comprehension scores. The subjects were 161 sixth grade students from a middle class suburban Long Island, New York, public…

Does Teaching Experience Matter? Examining Biology Teachers' Prior Knowledge for Teaching in an Alternative Certification Program

ERIC Educational Resources Information Center

Friedrichsen, Patricia J.; Abell, Sandra K.; Pareja, Enrique M.; Brown, Patrick L.; Lankford, Deanna M.; Volkmann, Mark J.

2009-01-01

Alternative certification programs (ACPs) have been proposed as a viable way to address teacher shortages, yet we know little about how teacher knowledge develops within such programs. The purpose of this study was to investigate prior knowledge for teaching among students entering an ACP, comparing individuals with teaching experience to those…

The Importance of Prior Knowledge when Comparing Examples: Influences on Conceptual and Procedural Knowledge of Equation Solving

ERIC Educational Resources Information Center

Rittle-Johnson, Bethany; Star, Jon R.; Durkin, Kelley

2009-01-01

Comparing multiple examples typically supports learning and transfer in laboratory studies and is considered a key feature of high-quality mathematics instruction. This experimental study investigated the importance of prior knowledge in learning from comparison. Seventh- and 8th-grade students (N = 236) learned to solve equations by comparing…

Multiple network-constrained regressions expand insights into influenza vaccination responses.

PubMed

Avey, Stefan; Mohanty, Subhasis; Wilson, Jean; Zapata, Heidi; Joshi, Samit R; Siconolfi, Barbara; Tsang, Sui; Shaw, Albert C; Kleinstein, Steven H

2017-07-15

Systems immunology leverages recent technological advancements that enable broad profiling of the immune system to better understand the response to infection and vaccination, as well as the dysregulation that occurs in disease. An increasingly common approach to gain insights from these large-scale profiling experiments involves the application of statistical learning methods to predict disease states or the immune response to perturbations. However, the goal of many systems studies is not to maximize accuracy, but rather to gain biological insights. The predictors identified using current approaches can be biologically uninterpretable or present only one of many equally predictive models, leading to a narrow understanding of the underlying biology. Here we show that incorporating prior biological knowledge within a logistic modeling framework by using network-level constraints on transcriptional profiling data significantly improves interpretability. Moreover, incorporating different types of biological knowledge produces models that highlight distinct aspects of the underlying biology, while maintaining predictive accuracy. We propose a new framework, Logistic Multiple Network-constrained Regression (LogMiNeR), and apply it to understand the mechanisms underlying differential responses to influenza vaccination. Although standard logistic regression approaches were predictive, they were minimally interpretable. Incorporating prior knowledge using LogMiNeR led to models that were equally predictive yet highly interpretable. In this context, B cell-specific genes and mTOR signaling were associated with an effective vaccination response in young adults. Overall, our results demonstrate a new paradigm for analyzing high-dimensional immune profiling data in which multiple networks encoding prior knowledge are incorporated to improve model interpretability. The R source code described in this article is publicly available at https://bitbucket.org/kleinstein/logminer . steven.kleinstein@yale.edu or stefan.avey@yale.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

What You Know Can Hurt You: Effects of Age and Prior Knowledge on the Accuracy of Judgments of Learning

PubMed Central

Toth, Jeffrey P.; Daniels, Karen A.; Solinger, Lisa A.

2011-01-01

How do aging and prior knowledge affect memory and metamemory? We explored this question in the context of a dual-process approach to Judgments of Learning (JOLs) which require people to predict their ability to remember information at a later time. Young and older adults (n's = 36, mean ages = 20.2 & 73.1) studied the names of actors that were famous in the 1950s or 1990s, providing a JOL for each. Recognition memory for studied and unstudied actors was then assessed using a Recollect/Know/No-Memory (R/K/N) judgment task. Results showed that prior knowledge increased recollection in both age groups such that older adults recollected significantly more 1950s actors than younger adults. Also, for both age groups and both decades, actors judged R at test garnered significantly higher JOLs at study than actors judged K or N. However, while the young showed benefits of prior knowledge on relative JOL accuracy, older adults did not, showing lower levels of JOL accuracy for 1950s actors despite having higher recollection for, and knowledge about, those actors. Overall, the data suggest that prior knowledge can be a double-edged sword, increasing the availability of details that can support later recollection, but also increasing non-diagnostic feelings of familiarity that can reduce the accuracy of memory predictions. PMID:21480715

Effects of Teacher Use of Analogies on Achievement of High School Biology Students with Varying Levels of Cognitive Ability and Prior Knowledge.

ERIC Educational Resources Information Center

Burns, Joseph C.; Okey, James R.

This study investigated the effects of analogy-based and conventional lecture-based instructional strategies on the achievement of four classes of high school biology students (N=123). Prior to treatment, students were assessed for cognitive ability and prior knowledge of the analogy vehicle. The analogy-based treatment consisted of teacher…

The impact of curiosity on learning during a school field trip to the zoo

NASA Astrophysics Data System (ADS)

Carlin, Kerry Ann

1999-11-01

This study was designed to examine (a) differences in cognitive learning as a result of a zoo field trip, (b) if the trip to the zoo had an impact on epistemic curiosity, (c) the role epistemic curiosity plays in learning, (d) the effect of gender, race, prior knowledge and prior visitation to the zoo on learning and epistemic curiosity, (e) participants' affect for the zoo animals, and (f) if prior visitation to the zoo contributes to prior knowledge. Ninety-six fourth and fifth grade children completed curiosity, cognitive, and affective written tests before and after a field trip to the Lowery Park Zoo in Tampa, Florida. The data showed that students were very curious about zoo animals. Dependent T-tests indicated no significant difference between pretest and posttest curiosity levels. The trip did not influence participants' curiosity levels. Multiple regression analysis was used to determine the relationship between the dependent variable, curiosity, and the independent variables, gender, race, prior knowledge, and prior visitation. No significant differences were found. Dependent T-tests indicated no significant difference between pretest and posttest cognitive scores. The field trip to the zoo did not cause an increase in participants' knowledge. However, participants did learn on the trip. After the field trip, participants identified more animals displayed by the zoo than they did before. Also, more animals were identified by species and genus names after the trip than before. These differences were significant (alpha = .05). Multiple regression analysis was used to determine the relationship between the dependent variable, posttest cognitive performance, and the independent variables, curiosity, gender, race, prior knowledge, and prior visitation. A significant difference was found for prior knowledge (alpha = .05). No significant differences were found for the other independent variables. Chi-square tests of significance indicated significant differences (alpha = .05) in preferences for types of animals and preference for animals by gender. Significant differences (alpha = .05) were also found between the reasons why animals were preferred. Differences occurred between animals that were liked and disliked, between genders, and between the pretest and the posttest.

An investigation of multitasking information behavior and the influence of working memory and flow

NASA Astrophysics Data System (ADS)

Alexopoulou, Peggy; Hepworth, Mark; Morris, Anne

2015-02-01

This study explored the multitasking information behaviour of Web users and how this is influenced by working memory, flow and Personal, Artefact and Task characteristics, as described in the PAT model. The research was exploratory using a pragmatic, mixed method approach. Thirty University students participated; 10 psychologists, 10 accountants and 10 mechanical engineers. The data collection tools used were: pre and post questionnaires, a working memory test, a flow state scale test, audio-visual data, web search logs, think aloud data, observation, and the critical decision method. All participants searched information on the Web for four topics: two for which they had prior knowledge and two more without prior knowledge. Perception of task complexity was found to be related to working memory. People with low working memory reported a significant increase in task complexity after they had completed information searching tasks for which they had no prior knowledge, this was not the case for tasks with prior knowledge. Regarding flow and task complexity, the results confirmed the suggestion of the PAT model (Finneran and Zhang, 2003), which proposed that a complex task can lead to anxiety and low flow levels as well as to perceived challenge and high flow levels. However, the results did not confirm the suggestion of the PAT model regarding the characteristics of web search systems and especially perceived vividness. All participants experienced high vividness. According to the PAT model, however, only people with high flow should experience high levels of vividness. Flow affected the degree of change of knowledge of the participants. People with high flow gained more knowledge for tasks without prior knowledge rather than people with low flow. Furthermore, accountants felt that tasks without prior knowledge were less complex at the end of the web seeking procedure than psychologists and mechanical engineers. Finally, the three disciplines appeared to differ regarding the multitasking information behaviour characteristics such as queries, web search sessions and opened tabs/windows.

Dealing with difficult deformations: construction of a knowledge-based deformation atlas

NASA Astrophysics Data System (ADS)

Thorup, S. S.; Darvann, T. A.; Hermann, N. V.; Larsen, P.; Ólafsdóttir, H.; Paulsen, R. R.; Kane, A. A.; Govier, D.; Lo, L.-J.; Kreiborg, S.; Larsen, R.

2010-03-01

Twenty-three Taiwanese infants with unilateral cleft lip and palate (UCLP) were CT-scanned before lip repair at the age of 3 months, and again after lip repair at the age of 12 months. In order to evaluate the surgical result, detailed point correspondence between pre- and post-surgical images was needed. We have previously demonstrated that non-rigid registration using B-splines is able to provide automated determination of point correspondences in populations of infants without cleft lip. However, this type of registration fails when applied to the task of determining the complex deformation from before to after lip closure in infants with UCLP. The purpose of the present work was to show that use of prior information about typical deformations due to lip closure, through the construction of a knowledge-based atlas of deformations, could overcome the problem. Initially, mean volumes (atlases) for the pre- and post-surgical populations, respectively, were automatically constructed by non-rigid registration. An expert placed corresponding landmarks in the cleft area in the two atlases; this provided prior information used to build a knowledge-based deformation atlas. We model the change from pre- to post-surgery using thin-plate spline warping. The registration results are convincing and represent a first move towards an automatic registration method for dealing with difficult deformations due to this type of surgery.

[Application of text mining approach to pre-education prior to clinical practice].

PubMed

Koinuma, Masayoshi; Koike, Katsuya; Nakamura, Hitoshi

2008-06-01

We developed a new survey analysis technique to understand students' actual aims for effective pretraining prior to clinical practice. We asked third-year undergraduate students to write fixed-style complete and free sentences on "preparation of drug dispensing." Then, we converted their sentence data in to text style and performed Japanese-language morphologic analysis on the data using language analysis software. We classified key words, which were created on the basis of the word class information of the Japanese language morphologic analysis, into categories based on causes and characteristics. In addition to this, we classified the characteristics into six categories consisting of those concepts including "knowledge," "skill and attitude," "image," etc. with the KJ method technique. The results showed that the awareness of students of "preparation of drug dispensing" tended to be approximately three-fold more frequent in "skill and attitude," "risk," etc. than in "knowledge." Regarding the characteristics in the category of the "image," words like "hard," "challenging," "responsibility," "life," etc. frequently occurred. The results of corresponding analysis showed that the characteristics of the words "knowledge" and "skills and attitude" were independent. As the result of developing a cause-and-effect diagram, it was demonstrated that the phase "hanging tough" described most of the various factors. We thus could understand students' actual feelings by applying text-mining as a new survey analysis technique.

Powerful Learning Tools for ELLs: Using Native Language, Familiar Examples, and Concept Mapping to Teach English Language Learners

ERIC Educational Resources Information Center

Dong, Yu Ren

2013-01-01

This article highlights how English language learners' (ELLs) prior knowledge can be used to help learn science vocabulary. The article explains that the concept of prior knowledge needs to encompass the ELL student's native language, previous science learning, native literacy skills, and native cultural knowledge and life experiences.…

Teachers' Beliefs about the Role of Prior Language Knowledge in Learning and How These Influence Teaching Practices

ERIC Educational Resources Information Center

De Angelis, Gessica

2011-01-01

The present study was developed to assess teachers' beliefs on (1) the role of prior language knowledge in language learning; (2) the perceived usefulness of language knowledge in modern society; and (3) the teaching practices to be used with multilingual students. Subjects were 176 secondary schoolteachers working in Italy (N = 103), Austria (N =…

Stochastic formulation of patient positioning using linac-mounted cone beam imaging with prior knowledge

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoegele, W.; Loeschel, R.; Dobler, B.

2011-02-15

Purpose: In this work, a novel stochastic framework for patient positioning based on linac-mounted CB projections is introduced. Based on this formulation, the most probable shifts and rotations of the patient are estimated, incorporating interfractional deformations of patient anatomy and other uncertainties associated with patient setup. Methods: The target position is assumed to be defined by and is stochastically determined from positions of various features such as anatomical landmarks or markers in CB projections, i.e., radiographs acquired with a CB-CT system. The patient positioning problem of finding the target location from CB projections is posed as an inverse problem withmore » prior knowledge and is solved using a Bayesian maximum a posteriori (MAP) approach. The prior knowledge is three-fold and includes the accuracy of an initial patient setup (such as in-room laser and skin marks), the plasticity of the body (relative shifts between target and features), and the feature detection error in CB projections (which may vary depending on specific detection algorithm and feature type). For this purpose, MAP estimators are derived and a procedure of using them in clinical practice is outlined. Furthermore, a rule of thumb is theoretically derived, relating basic parameters of the prior knowledge (initial setup accuracy, plasticity of the body, and number of features) and the parameters of CB data acquisition (number of projections and accuracy of feature detection) to the expected estimation accuracy. Results: MAP estimation can be applied to arbitrary features and detection algorithms. However, to experimentally demonstrate its applicability and to perform the validation of the algorithm, a water-equivalent, deformable phantom with features represented by six 1 mm chrome balls were utilized. These features were detected in the cone beam projections (XVI, Elekta Synergy) by a local threshold method for demonstration purposes only. The accuracy of estimation (strongly varying for different plasticity parameters of the body) agreed with the rule of thumb formula. Moreover, based on this rule of thumb formula, about 20 projections for 6 detectable features seem to be sufficient for a target estimation accuracy of 0.2 cm, even for relatively large feature detection errors with standard deviation of 0.5 cm and spatial displacements of the features with standard deviation of 0.5 cm. Conclusions: The authors have introduced a general MAP-based patient setup algorithm accounting for different sources of uncertainties, which are utilized as the prior knowledge in a transparent way. This new framework can be further utilized for different clinical sites, as well as theoretical developments in the field of patient positioning for radiotherapy.« less

Experiential knowledge of expert coaches can help identify informational constraints on performance of dynamic interceptive actions.

PubMed

Greenwood, Daniel; Davids, Keith; Renshaw, Ian

2014-01-01

Coordination of dynamic interceptive movements is predicated on cyclical relations between an individual's actions and information sources from the performance environment. To identify dynamic informational constraints, which are interwoven with individual and task constraints, coaches' experiential knowledge provides a complementary source to support empirical understanding of performance in sport. In this study, 15 expert coaches from 3 sports (track and field, gymnastics and cricket) participated in a semi-structured interview process to identify potential informational constraints which they perceived to regulate action during run-up performance. Expert coaches' experiential knowledge revealed multiple information sources which may constrain performance adaptations in such locomotor pointing tasks. In addition to the locomotor pointing target, coaches' knowledge highlighted two other key informational constraints: vertical reference points located near the locomotor pointing target and a check mark located prior to the locomotor pointing target. This study highlights opportunities for broadening the understanding of perception and action coupling processes, and the identified information sources warrant further empirical investigation as potential constraints on athletic performance. Integration of experiential knowledge of expert coaches with theoretically driven empirical knowledge represents a promising avenue to drive future applied science research and pedagogical practice.

Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature.

PubMed

Radhakrishnan, Srinivasan; Erbis, Serkan; Isaacs, Jacqueline A; Kamarthi, Sagar

2017-01-01

Systematic reviews of scientific literature are important for mapping the existing state of research and highlighting further growth channels in a field of study, but systematic reviews are inherently tedious, time consuming, and manual in nature. In recent years, keyword co-occurrence networks (KCNs) are exploited for knowledge mapping. In a KCN, each keyword is represented as a node and each co-occurrence of a pair of words is represented as a link. The number of times that a pair of words co-occurs in multiple articles constitutes the weight of the link connecting the pair. The network constructed in this manner represents cumulative knowledge of a domain and helps to uncover meaningful knowledge components and insights based on the patterns and strength of links between keywords that appear in the literature. In this work, we propose a KCN-based approach that can be implemented prior to undertaking a systematic review to guide and accelerate the review process. The novelty of this method lies in the new metrics used for statistical analysis of a KCN that differ from those typically used for KCN analysis. The approach is demonstrated through its application to nano-related Environmental, Health, and Safety (EHS) risk literature. The KCN approach identified the knowledge components, knowledge structure, and research trends that match with those discovered through a traditional systematic review of the nanoEHS field. Because KCN-based analyses can be conducted more quickly to explore a vast amount of literature, this method can provide a knowledge map and insights prior to undertaking a rigorous traditional systematic review. This two-step approach can significantly reduce the effort and time required for a traditional systematic literature review. The proposed KCN-based pre-systematic review method is universal. It can be applied to any scientific field of study to prepare a knowledge map.

Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature

PubMed Central

Isaacs, Jacqueline A.

2017-01-01

Systematic reviews of scientific literature are important for mapping the existing state of research and highlighting further growth channels in a field of study, but systematic reviews are inherently tedious, time consuming, and manual in nature. In recent years, keyword co-occurrence networks (KCNs) are exploited for knowledge mapping. In a KCN, each keyword is represented as a node and each co-occurrence of a pair of words is represented as a link. The number of times that a pair of words co-occurs in multiple articles constitutes the weight of the link connecting the pair. The network constructed in this manner represents cumulative knowledge of a domain and helps to uncover meaningful knowledge components and insights based on the patterns and strength of links between keywords that appear in the literature. In this work, we propose a KCN-based approach that can be implemented prior to undertaking a systematic review to guide and accelerate the review process. The novelty of this method lies in the new metrics used for statistical analysis of a KCN that differ from those typically used for KCN analysis. The approach is demonstrated through its application to nano-related Environmental, Health, and Safety (EHS) risk literature. The KCN approach identified the knowledge components, knowledge structure, and research trends that match with those discovered through a traditional systematic review of the nanoEHS field. Because KCN-based analyses can be conducted more quickly to explore a vast amount of literature, this method can provide a knowledge map and insights prior to undertaking a rigorous traditional systematic review. This two-step approach can significantly reduce the effort and time required for a traditional systematic literature review. The proposed KCN-based pre-systematic review method is universal. It can be applied to any scientific field of study to prepare a knowledge map. PMID:28328983

Modifying Cookbook Labs.

ERIC Educational Resources Information Center

Clark, Robert, L.; Clough, Michael P.; Berg, Craig A.

2000-01-01

Modifies an extended lab activity from a cookbook approach for determining the percent mass of water in copper sulfate pentahydrate crystals to one which incorporates students' prior knowledge, engenders active mental struggling with prior knowledge and new experiences, and encourages metacognition. (Contains 12 references.) (ASK)

Knowledge Modeling in Prior Art Search

NASA Astrophysics Data System (ADS)

Graf, Erik; Frommholz, Ingo; Lalmas, Mounia; van Rijsbergen, Keith

This study explores the benefits of integrating knowledge representations in prior art patent retrieval. Key to the introduced approach is the utilization of human judgment available in the form of classifications assigned to patent documents. The paper first outlines in detail how a methodology for the extraction of knowledge from such an hierarchical classification system can be established. Further potential ways of integrating this knowledge with existing Information Retrieval paradigms in a scalable and flexible manner are investigated. Finally based on these integration strategies the effectiveness in terms of recall and precision is evaluated in the context of a prior art search task for European patents. As a result of this evaluation it can be established that in general the proposed knowledge expansion techniques are particularly beneficial to recall and, with respect to optimizing field retrieval settings, further result in significant precision gains.

Multiplexed fluctuation-dissipation-theorem calibration of optical tweezers inside living cells

NASA Astrophysics Data System (ADS)

Yan, Hao; Johnston, Jessica F.; Cahn, Sidney B.; King, Megan C.; Mochrie, Simon G. J.

2017-11-01

In order to apply optical tweezers-based force measurements within an uncharacterized viscoelastic medium such as the cytoplasm of a living cell, a quantitative calibration method that may be applied in this complex environment is needed. We describe an improved version of the fluctuation-dissipation-theorem calibration method, which has been developed to perform in situ calibration in viscoelastic media without prior knowledge of the trapped object. Using this calibration procedure, it is possible to extract values of the medium's viscoelastic moduli as well as the force constant describing the optical trap. To demonstrate our method, we calibrate an optical trap in water, in polyethylene oxide solutions of different concentrations, and inside living fission yeast (S. pombe).

Bayesian methodology incorporating expert judgment for ranking countermeasure effectiveness under uncertainty: example applied to at grade railroad crossings in Korea.

PubMed

Washington, Simon; Oh, Jutaek

2006-03-01

Transportation professionals are sometimes required to make difficult transportation safety investment decisions in the face of uncertainty. In particular, an engineer may be expected to choose among an array of technologies and/or countermeasures to remediate perceived safety problems when: (1) little information is known about the countermeasure effects on safety; (2) information is known but from different regions, states, or countries where a direct generalization may not be appropriate; (3) where the technologies and/or countermeasures are relatively untested, or (4) where costs prohibit the full and careful testing of each of the candidate countermeasures via before-after studies. The importance of an informed and well-considered decision based on the best possible engineering knowledge and information is imperative due to the potential impact on the numbers of human injuries and deaths that may result from these investments. This paper describes the formalization and application of a methodology to evaluate the safety benefit of countermeasures in the face of uncertainty. To illustrate the methodology, 18 countermeasures for improving safety of at grade railroad crossings (AGRXs) in the Republic of Korea are considered. Akin to "stated preference" methods in travel survey research, the methodology applies random selection and laws of large numbers to derive accident modification factor (AMF) densities from expert opinions. In a full Bayesian analysis framework, the collective opinions in the form of AMF densities (data likelihood) are combined with prior knowledge (AMF density priors) for the 18 countermeasures to obtain 'best' estimates of AMFs (AMF posterior credible intervals). The countermeasures are then compared and recommended based on the largest safety returns with minimum risk (uncertainty). To the author's knowledge the complete methodology is new and has not previously been applied or reported in the literature. The results demonstrate that the methodology is able to discern anticipated safety benefit differences across candidate countermeasures. For the 18 at grade railroad crossings considered in this analysis, it was found that the top three performing countermeasures for reducing crashes are in-vehicle warning systems, obstacle detection systems, and constant warning time systems.

Inference of Time-Evolving Coupled Dynamical Systems in the Presence of Noise

NASA Astrophysics Data System (ADS)

Stankovski, Tomislav; Duggento, Andrea; McClintock, Peter V. E.; Stefanovska, Aneta

2012-07-01

A new method is introduced for analysis of interactions between time-dependent coupled oscillators, based on the signals they generate. It distinguishes unsynchronized dynamics from noise-induced phase slips and enables the evolution of the coupling functions and other parameters to be followed. It is based on phase dynamics, with Bayesian inference of the time-evolving parameters achieved by shaping the prior densities to incorporate knowledge of previous samples. The method is tested numerically and applied to reveal and quantify the time-varying nature of cardiorespiratory interactions.

Nonlinear differential equations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dresner, L.

1988-01-01

This report is the text of a graduate course on nonlinear differential equations given by the author at the University of Wisconsin-Madison during the summer of 1987. The topics covered are: direction fields of first-order differential equations; the Lie (group) theory of ordinary differential equations; similarity solutions of second-order partial differential equations; maximum principles and differential inequalities; monotone operators and iteration; complementary variational principles; and stability of numerical methods. The report should be of interest to graduate students, faculty, and practicing scientists and engineers. No prior knowledge is required beyond a good working knowledge of the calculus. The emphasis ismore » on practical results. Most of the illustrative examples are taken from the fields of nonlinear diffusion, heat and mass transfer, applied superconductivity, and helium cryogenics.« less

Best Practice Strategies for Effective Use of Questions as a Teaching Tool

PubMed Central

Elsner, Jamie; Haines, Stuart T.

2013-01-01

Questions have long been used as a teaching tool by teachers and preceptors to assess students’ knowledge, promote comprehension, and stimulate critical thinking. Well-crafted questions lead to new insights, generate discussion, and promote the comprehensive exploration of subject matter. Poorly constructed questions can stifle learning by creating confusion, intimidating students, and limiting creative thinking. Teachers most often ask lower-order, convergent questions that rely on students’ factual recall of prior knowledge rather than asking higher-order, divergent questions that promote deep thinking, requiring students to analyze and evaluate concepts. This review summarizes the taxonomy of questions, provides strategies for formulating effective questions, and explores practical considerations to enhance student engagement and promote critical thinking. These concepts can be applied in the classroom and in experiential learning environments. PMID:24052658

Best practice strategies for effective use of questions as a teaching tool.

PubMed

Tofade, Toyin; Elsner, Jamie; Haines, Stuart T

2013-09-12

Questions have long been used as a teaching tool by teachers and preceptors to assess students' knowledge, promote comprehension, and stimulate critical thinking. Well-crafted questions lead to new insights, generate discussion, and promote the comprehensive exploration of subject matter. Poorly constructed questions can stifle learning by creating confusion, intimidating students, and limiting creative thinking. Teachers most often ask lower-order, convergent questions that rely on students' factual recall of prior knowledge rather than asking higher-order, divergent questions that promote deep thinking, requiring students to analyze and evaluate concepts. This review summarizes the taxonomy of questions, provides strategies for formulating effective questions, and explores practical considerations to enhance student engagement and promote critical thinking. These concepts can be applied in the classroom and in experiential learning environments.

An interactive Bayesian geostatistical inverse protocol for hydraulic tomography

USGS Publications Warehouse

Fienen, Michael N.; Clemo, Tom; Kitanidis, Peter K.

2008-01-01

Hydraulic tomography is a powerful technique for characterizing heterogeneous hydrogeologic parameters. An explicit trade-off between characterization based on measurement misfit and subjective characterization using prior information is presented. We apply a Bayesian geostatistical inverse approach that is well suited to accommodate a flexible model with the level of complexity driven by the data and explicitly considering uncertainty. Prior information is incorporated through the selection of a parameter covariance model characterizing continuity and providing stability. Often, discontinuities in the parameter field, typically caused by geologic contacts between contrasting lithologic units, necessitate subdivision into zones across which there is no correlation among hydraulic parameters. We propose an interactive protocol in which zonation candidates are implied from the data and are evaluated using cross validation and expert knowledge. Uncertainty introduced by limited knowledge of dynamic regional conditions is mitigated by using drawdown rather than native head values. An adjoint state formulation of MODFLOW-2000 is used to calculate sensitivities which are used both for the solution to the inverse problem and to guide protocol decisions. The protocol is tested using synthetic two-dimensional steady state examples in which the wells are located at the edge of the region of interest.

Effects of Prior Economic Education, Native Language, and Gender on Economic Knowledge of First-Year Students in Higher Education. A Comparative Study between Germany and the USA

ERIC Educational Resources Information Center

Brückner, Sebastian; Förster, Manuel; Zlatkin-Troitschanskaia, Olga; Walstad, William B.

2015-01-01

The assessment of university students' economic knowledge has become an increasingly important research area within and across countries. Particularly, the different influences of prior education, native language, and gender as some of the main prerequisites on students' economic knowledge have been highlighted since long. However, the findings…

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

PubMed Central

Mikut, Ralf

2017-01-01

Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. The functionality of the proposed approach is further validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. The generality of the concept makes it applicable to practically any field with processing strategies that are arranged as linear pipelines. The automated analysis of terabyte-scale microscopy data will especially benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout. PMID:29095927

Language knowledge and event knowledge in language use.

PubMed

Willits, Jon A; Amato, Michael S; MacDonald, Maryellen C

2015-05-01

This paper examines how semantic knowledge is used in language comprehension and in making judgments about events in the world. We contrast knowledge gleaned from prior language experience ("language knowledge") and knowledge coming from prior experience with the world ("world knowledge"). In two corpus analyses, we show that previous research linking verb aspect and event representations have confounded language and world knowledge. Then, using carefully chosen stimuli that remove this confound, we performed four experiments that manipulated the degree to which language knowledge or world knowledge should be salient and relevant to performing a task, finding in each case that participants use the type of knowledge most appropriate to the task. These results provide evidence for a highly context-sensitive and interactionist perspective on how semantic knowledge is represented and used during language processing. Copyright © 2015. Published by Elsevier Inc.

Incorporating linguistic knowledge for learning distributed word representations.

PubMed

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining.

Incorporating Linguistic Knowledge for Learning Distributed Word Representations

PubMed Central

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining. PMID:25874581

Probabilistic models for neural populations that naturally capture global coupling and criticality

PubMed Central

2017-01-01

Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality. PMID:28926564

Estimating the effect of lay knowledge and prior contact with pulmonary TB patients, on health-belief model in a high-risk pulmonary TB transmission population.

PubMed

Zein, Rizqy Amelia; Suhariadi, Fendy; Hendriani, Wiwin

2017-01-01

The research aimed to investigate the effect of lay knowledge of pulmonary tuberculosis (TB) and prior contact with pulmonary TB patients on a health-belief model (HBM) as well as to identify the social determinants that affect lay knowledge. Survey research design was conducted, where participants were required to fill in a questionnaire, which measured HBM and lay knowledge of pulmonary TB. Research participants were 500 residents of Semampir, Asemrowo, Bubutan, Pabean Cantian, and Simokerto districts, where the risk of pulmonary TB transmission is higher than other districts in Surabaya. Being a female, older in age, and having prior contact with pulmonary TB patients significantly increase the likelihood of having a higher level of lay knowledge. Lay knowledge is a substantial determinant to estimate belief in the effectiveness of health behavior and personal health threat. Prior contact with pulmonary TB patients is able to explain the belief in the effectiveness of a health behavior, yet fails to estimate participants' belief in the personal health threat. Health authorities should prioritize males and young people as their main target groups in a pulmonary TB awareness campaign. The campaign should be able to reconstruct people's misconception about pulmonary TB, thereby bringing around the health-risk perception so that it is not solely focused on improving lay knowledge.

The positive and negative consequences of multiple-choice testing.

PubMed

Roediger, Henry L; Marsh, Elizabeth J

2005-09-01

Multiple-choice tests are commonly used in educational settings but with unknown effects on students' knowledge. The authors examined the consequences of taking a multiple-choice test on a later general knowledge test in which students were warned not to guess. A large positive testing effect was obtained: Prior testing of facts aided final cued-recall performance. However, prior testing also had negative consequences. Prior reading of a greater number of multiple-choice lures decreased the positive testing effect and increased production of multiple-choice lures as incorrect answers on the final test. Multiple-choice testing may inadvertently lead to the creation of false knowledge.

Incorporating Functional Genomic Information in Genetic Association Studies Using an Empirical Bayes Approach.

PubMed

Spencer, Amy V; Cox, Angela; Lin, Wei-Yu; Easton, Douglas F; Michailidou, Kyriaki; Walters, Kevin

2016-04-01

There is a large amount of functional genetic data available, which can be used to inform fine-mapping association studies (in diseases with well-characterised disease pathways). Single nucleotide polymorphism (SNP) prioritization via Bayes factors is attractive because prior information can inform the effect size or the prior probability of causal association. This approach requires the specification of the effect size. If the information needed to estimate a priori the probability density for the effect sizes for causal SNPs in a genomic region isn't consistent or isn't available, then specifying a prior variance for the effect sizes is challenging. We propose both an empirical method to estimate this prior variance, and a coherent approach to using SNP-level functional data, to inform the prior probability of causal association. Through simulation we show that when ranking SNPs by our empirical Bayes factor in a fine-mapping study, the causal SNP rank is generally as high or higher than the rank using Bayes factors with other plausible values of the prior variance. Importantly, we also show that assigning SNP-specific prior probabilities of association based on expert prior functional knowledge of the disease mechanism can lead to improved causal SNPs ranks compared to ranking with identical prior probabilities of association. We demonstrate the use of our methods by applying the methods to the fine mapping of the CASP8 region of chromosome 2 using genotype data from the Collaborative Oncological Gene-Environment Study (COGS) Consortium. The data we analysed included approximately 46,000 breast cancer case and 43,000 healthy control samples. © 2016 The Authors. *Genetic Epidemiology published by Wiley Periodicals, Inc.

Processing and memory of information presented in narrative or expository texts.

PubMed

Wolfe, Michael B W; Woodwyk, Joshua M

2010-09-01

Previous research suggests that narrative and expository texts differ in the extent to which they prompt students to integrate to-be-learned content with relevant prior knowledge during comprehension. We expand on previous research by examining on-line processing and representation in memory of to-be-learned content that is embedded in narrative or expository texts. We are particularly interested in how differences in the use of relevant prior knowledge leads to differences in terms of levels of discourse representation (textbase vs. situation model). A total of 61 university undergraduates in Expt 1, and 160 in Expt 2. In Expt 1, subjects thought out loud while comprehending circulatory system content embedded in a narrative or expository text, followed by free recall of text content. In Expt 2, subjects read silently and completed a sentence recognition task to assess memory. In Expt 1, subjects made more associations to prior knowledge while reading the expository text, and recalled more content. Content recall was also correlated with amount of relevant prior knowledge for subjects who read the expository text but not the narrative text. In Expt 2, subjects reading the expository text (compared to the narrative text) had a weaker textbase representation of the to-be-learned content, but a marginally stronger situation model. Results suggest that in terms of to-be-learned content, expository texts trigger students to utilize relevant prior knowledge more than narrative texts.

Problem-based Learning Using the Online Medicare Part D Plan Finder Tool

PubMed Central

Stebbins, Marilyn R.; Lai, Eric; Smith, Amanda R.; Lipton, Helene Levens

2008-01-01

Objectives To implement didactic and problem-based learning curricular innovations aimed at increasing students' knowledge of Medicare Part D, improving their ability to apply the online Medicare Prescription Drug Plan Finder tool to a patient case, and improving their attitudes toward patient advocacy for Medicare beneficiaries. Methods A survey instrument and a case-based online Medicare Prescription Drug Plan Finder tool exercise were administered to a single group (n = 120) of second-year pharmacy graduate students prior to and following completion of a course on health policy. Three domains (knowledge, skill mastery and attitudes) were measured before and after two 90-minute lectures on Medicare Part D. Results The online Medicare Prescription Drug Plan Finder exercise and Medicare Part D didactic lectures had positive effects on students' knowledge of Part D, attitudes toward patient advocacy, and ability to accurately use the Medicare Prescription Drug Plan Finder tool. Conclusions The success of these didactic and problem-based curricular innovations in improving pharmacy students' knowledge, skills, and attitudes regarding Part D warrants further evaluation to determine their portability to clinical settings and other pharmacy schools. PMID:18698399

Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering

PubMed Central

Nan, Zhixiong; Wei, Ping; Xu, Linhai; Zheng, Nanning

2016-01-01

Lane boundary detection technology has progressed rapidly over the past few decades. However, many challenges that often lead to lane detection unavailability remain to be solved. In this paper, we propose a spatial-temporal knowledge filtering model to detect lane boundaries in videos. To address the challenges of structure variation, large noise and complex illumination, this model incorporates prior spatial-temporal knowledge with lane appearance features to jointly identify lane boundaries. The model first extracts line segments in video frames. Two novel filters—the Crossing Point Filter (CPF) and the Structure Triangle Filter (STF)—are proposed to filter out the noisy line segments. The two filters introduce spatial structure constraints and temporal location constraints into lane detection, which represent the spatial-temporal knowledge about lanes. A straight line or curve model determined by a state machine is used to fit the line segments to finally output the lane boundaries. We collected a challenging realistic traffic scene dataset. The experimental results on this dataset and other standard dataset demonstrate the strength of our method. The proposed method has been successfully applied to our autonomous experimental vehicle. PMID:27529248

Language knowledge and event knowledge in language use

PubMed Central

Willits, Jon A.; Amato, Michael S.; MacDonald, Maryellen C.

2018-01-01

This paper examines how semantic knowledge is used in language comprehension and in making judgments about events in the world. We contrast knowledge gleaned from prior language experience (“language knowledge”) and knowledge coming from prior experience with the world (“world knowledge”). In two corpus analyses, we show that previous research linking verb aspect and event representations have confounded language and world knowledge. Then, using carefully chosen stimuli that remove this confound, we performed four experiments that manipulated the degree to which language knowledge or world knowledge should be salient and relevant to performing a task, finding in each case that participants use the type of knowledge most appropriate to the task. These results provide evidence for a highly context-sensitive and interactionist perspective on how semantic knowledge is represented and used during language processing. PMID:25791750

An empirical Bayes approach to network recovery using external knowledge.

PubMed

Kpogbezan, Gino B; van der Vaart, Aad W; van Wieringen, Wessel N; Leday, Gwenaël G R; van de Wiel, Mark A

2017-09-01

Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior knowledge on the network topology. In the case of gene interaction networks such knowledge may come for instance from pathway repositories like KEGG, or be inferred from data of a pilot study. The Bayesian framework provides a natural means of including such prior knowledge. Based on a Bayesian Simultaneous Equation Model, we develop an appealing Empirical Bayes (EB) procedure that automatically assesses the agreement of the used prior knowledge with the data at hand. We use variational Bayes method for posterior densities approximation and compare its accuracy with that of Gibbs sampling strategy. Our method is computationally fast, and can outperform known competitors. In a simulation study, we show that accurate prior data can greatly improve the reconstruction of the network, but need not harm the reconstruction if wrong. We demonstrate the benefits of the method in an analysis of gene expression data from GEO. In particular, the edges of the recovered network have superior reproducibility (compared to that of competitors) over resampled versions of the data. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

78 FR 29071 - Assessment of Mediation and Arbitration Procedures

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-17

... proceeding. Program participants in the new arbitration program will have prior knowledge of the issues to be... final rules, all parties opting into the arbitration program will have full prior knowledge that these... including discovery, the submission of evidence, and the treatment of confidential information, and the...

Teaching Practice: A Perspective on Inter-Text and Prior Knowledge

ERIC Educational Resources Information Center

Costley, Kevin C.; West, Howard G.

2012-01-01

The use of teaching practices that involve intertextual relationship discovery in today's elementary classrooms is increasingly essential to the success of young learners of reading. Teachers must constantly strive to expand their perspective of how to incorporate the dialogue included in prior knowledge assessment. Teachers must also consider how…

Elaborative-Interrogation and Prior-Knowledge Effects on Learning of Facts.

ERIC Educational Resources Information Center

Woloshyn, Vera E.; And Others

1992-01-01

The differences among elaborative-interrogation, reading-to-understand, and no-exposure control conditions with familiar domain material in contrast to unfamiliar domain material were studied for 50 Canadian and 50 west German undergraduates. Results provide evidence of effects of both elaborative interrogation and prior knowledge on learning.…

Effects of Example Variability and Prior Knowledge in How Students Learn to Solve Equations

ERIC Educational Resources Information Center

Guo, Jian-Peng; Yang, Ling-Yan; Ding, Yi

2014-01-01

Researchers have consistently demonstrated that multiple examples are better than one example in facilitating learning because the comparison evoked by multiple examples supports learning and transfer. However, research outcomes are unclear regarding the effects of example variability and prior knowledge on learning from comparing multiple…

Relationship of Students' Prior Knowledge and Order of Questions on Tests to Students' Test Scores.

ERIC Educational Resources Information Center

Papp, Klara K.; And Others

1987-01-01

A study examined whether students beginning a cell biology course with prior knowledge of its three areas (genetics, histology, and biochemistry) would retain that advantage throughout the course and whether achievement was influenced by the order of questions in a test. (MSE)

The Impact of Prior Programming Knowledge on Lecture Attendance and Final Exam

ERIC Educational Resources Information Center

Veerasamy, Ashok Kumar; D'Souza, Daryl; Lindén, Rolf; Laakso, Mikko-Jussi

2018-01-01

In this article, we report the results of the impact of prior programming knowledge (PPK) on lecture attendance (LA) and on subsequent final programming exam performance in a university level introductory programming course. This study used Spearman's rank correlation coefficient, multiple regression, Kruskal-Wallis, and Bonferroni correction…

Composing Knowledge: Writing, Rhetoric, and Reflection in Prior Learning Assessment

ERIC Educational Resources Information Center

Leaker, Cathy; Ostman, Heather

2010-01-01

In this article, we argue that prior learning assessment (PLA) essays manifest a series of issues central to composition research and practice: they foreground the "contact zone" between the unauthorized writer, institutional power, and the articulation of knowledge claims; they reinforce the central role of a multifaceted approach to…

Using Analogies to Facilitate Conceptual Change in Mathematics Learning

ERIC Educational Resources Information Center

Vamvakoussi, Xenia

2017-01-01

The problem of adverse effects of prior knowledge in mathematics learning has been amply documented and theorized by mathematics educators as well as cognitive/developmental psychologists. This problem emerges when students' prior knowledge about a mathematical notion comes in contrast with new information coming from instruction, giving rise to…

Specific Previous Experience Affects Perception of Harmony and Meter

ERIC Educational Resources Information Center

Creel, Sarah C.

2011-01-01

Prior knowledge shapes our experiences, but which prior knowledge shapes which experiences? This question is addressed in the domain of music perception. Three experiments were used to determine whether listeners activate specific musical memories during music listening. Each experiment provided listeners with one of two musical contexts that was…

Pre-Test Assessment

ERIC Educational Resources Information Center

Berry, Thomas

2008-01-01

Pre-tests are a non-graded assessment tool used to determine pre-existing subject knowledge. Typically pre-tests are administered prior to a course to determine knowledge baseline, but here they are used to test students prior to topical material coverage throughout the course. While counterintuitive, the pre-tests cover material the student is…

Integrated Analysis of Pharmacologic, Clinical, and SNP Microarray Data using Projection onto the Most Interesting Statistical Evidence with Adaptive Permutation Testing

PubMed Central

Pounds, Stan; Cao, Xueyuan; Cheng, Cheng; Yang, Jun; Campana, Dario; Evans, William E.; Pui, Ching-Hon; Relling, Mary V.

2010-01-01

Powerful methods for integrated analysis of multiple biological data sets are needed to maximize interpretation capacity and acquire meaningful knowledge. We recently developed Projection Onto the Most Interesting Statistical Evidence (PROMISE). PROMISE is a statistical procedure that incorporates prior knowledge about the biological relationships among endpoint variables into an integrated analysis of microarray gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to the integrated analysis of pharmacologic, clinical, and genome-wide genotype data that incorporating knowledge about the biological relationships among pharmacologic and clinical response data. An efficient permutation-testing algorithm is introduced so that statistical calculations are computationally feasible in this higher-dimension setting. The new method is applied to a pediatric leukemia data set. The results clearly indicate that PROMISE is a powerful statistical tool for identifying genomic features that exhibit a biologically meaningful pattern of association with multiple endpoint variables. PMID:21516175

The Interpretation of a Knowledge Claim in the Recognition of Prior Learning (RPL) and the Impact of This on RPL Practice

ERIC Educational Resources Information Center

de Graaff, Frederika

2014-01-01

The question addressed in this paper is: what does a knowledge claim consist of in the context of the Recognition of Prior Learning (RPL)? The research comprises a case study of RPL applicants' entry into a postgraduate diploma (a fourth-year programme) in project management. The focus is on the knowledge claims made as part of the RPL application…

Creating illusions of knowledge: learning errors that contradict prior knowledge.

PubMed

Fazio, Lisa K; Barber, Sarah J; Rajaram, Suparna; Ornstein, Peter A; Marsh, Elizabeth J

2013-02-01

Most people know that the Pacific is the largest ocean on Earth and that Edison invented the light bulb. Our question is whether this knowledge is stable, or if people will incorporate errors into their knowledge bases, even if they have the correct knowledge stored in memory. To test this, we asked participants general-knowledge questions 2 weeks before they read stories that contained errors (e.g., "Franklin invented the light bulb"). On a later general-knowledge test, participants reproduced story errors despite previously answering the questions correctly. This misinformation effect was found even for questions that were answered correctly on the initial test with the highest level of confidence. Furthermore, prior knowledge offered no protection against errors entering the knowledge base; the misinformation effect was equivalent for previously known and unknown facts. Errors can enter the knowledge base even when learners have the knowledge necessary to catch the errors. 2013 APA, all rights reserved

Age differences in suggestibility to contradictions of demonstrated knowledge: the influence of prior knowledge.

PubMed

Umanath, Sharda

2016-11-01

People maintain intact general knowledge into very old age and use it to support remembering. Interestingly, when older and younger adults encounter errors that contradict general knowledge, older adults suffer fewer memorial consequences: Older adults use fewer recently-encountered errors as answers for later knowledge questions. Why do older adults show this reduced suggestibility, and what role does their intact knowledge play? In three experiments, I examined suggestibility following exposure to errors in fictional stories that contradict general knowledge. Older adults consistently demonstrated more prior knowledge than younger adults but also gained access to even more across time. Additionally, they did not show a reduction in new learning from the stories, indicating lesser involvement of episodic memory failures. Critically, when knowledge was stably accessible, older adults relied more heavily on that knowledge compared to younger adults, resulting in reduced suggestibility. Implications for the broader role of knowledge in aging are discussed.

Fast multi-dimensional NMR by minimal sampling

NASA Astrophysics Data System (ADS)

Kupče, Ēriks; Freeman, Ray

2008-03-01

A new scheme is proposed for very fast acquisition of three-dimensional NMR spectra based on minimal sampling, instead of the customary step-wise exploration of all of evolution space. The method relies on prior experiments to determine accurate values for the evolving frequencies and intensities from the two-dimensional 'first planes' recorded by setting t1 = 0 or t2 = 0. With this prior knowledge, the entire three-dimensional spectrum can be reconstructed by an additional measurement of the response at a single location (t1∗,t2∗) where t1∗ and t2∗ are fixed values of the evolution times. A key feature is the ability to resolve problems of overlap in the acquisition dimension. Applied to a small protein, agitoxin, the three-dimensional HNCO spectrum is obtained 35 times faster than systematic Cartesian sampling of the evolution domain. The extension to multi-dimensional spectroscopy is outlined.

Teaching Newton's Laws with the iPod Touch in Conceptual Physics

NASA Astrophysics Data System (ADS)

Kelly, Angela M.

2011-04-01

One of the greatest challenges in teaching physics is helping students achieve a conceptual understanding of Newton's laws. I find that students fresh from middle school can sometimes recite the laws verbatim ("An object in motion stays in motion…" and "For every action…"), but they rarely demonstrate a working knowledge of how to apply them to observable phenomena. As a firm believer in inquiry-based teaching methods, I like to develop activities where students can experiment and construct understandings based on relevant personal experiences. Consequently, I am always looking for exciting new technologies that can readily demonstrate how physics affects everyday things. In a conceptual physics class designed for ninth-graders, I created a structured activity where students applied Newton's laws to a series of free applications downloaded on iPod Touches. The laws had been introduced during the prior class session with textual descriptions and graphical representations. The course is offered as part of the Enlace Latino Collegiate Society, a weekend enrichment program for middle and high school students in the Bronx. The majority of students had limited or no prior exposure to physics concepts, and many attended high schools where physics was not offered at all.

Radiometrie recalibration procedure for landsat-5 thematic mapper data

USGS Publications Warehouse

Chander, G.; Micijevic, E.; Hayes, R.W.; Barsi, J.A.

2008-01-01

The Landsat-5 (L5) satellite was launched on March 01, 1984, with a design life of three years. Incredibly, the L5 Thematic Mapper (TM) has collected data for 23 years. Over this time, the detectors have aged, and its radiometric characteristics have changed since launch. The calibration procedures and parameters have also changed with time. Revised radiometric calibrations have improved the radiometric accuracy of recently processed data; however, users with data that were processed prior to the calibration update do not benefit from the revisions. A procedure has been developed to give users the ability to recalibrate their existing Level 1 (L1) products without having to purchase reprocessed data from the U.S. Geological Survey (USGS). The accuracy of the recalibration is dependent on the knowledge of the prior calibration applied to the data. The ""Work Order" file, included with standard National Land Archive Production System (NLAFS) data products, gives parameters that define the applied calibration. These are the Internal Calibrator (IC) calibration parameters or the default prelaunch calibration, if there were problems with the IC calibration. This paper details the recalibration procedure for data processed using IC, in which users have the Work Order file. ?? 2001 IEEE.

MRAC Control with Prior Model Knowledge for Asymmetric Damaged Aircraft

PubMed Central

Zhang, Jing

2015-01-01

This paper develops a novel state-tracking multivariable model reference adaptive control (MRAC) technique utilizing prior knowledge of plant models to recover control performance of an asymmetric structural damaged aircraft. A modification of linear model representation is given. With prior knowledge on structural damage, a polytope linear parameter varying (LPV) model is derived to cover all concerned damage conditions. An MRAC method is developed for the polytope model, of which the stability and asymptotic error convergence are theoretically proved. The proposed technique reduces the number of parameters to be adapted and thus decreases computational cost and requires less input information. The method is validated by simulations on NASA generic transport model (GTM) with damage. PMID:26180839

Learning and inference using complex generative models in a spatial localization task.

PubMed

Bejjanki, Vikranth R; Knill, David C; Aslin, Richard N

2016-01-01

A large body of research has established that, under relatively simple task conditions, human observers integrate uncertain sensory information with learned prior knowledge in an approximately Bayes-optimal manner. However, in many natural tasks, observers must perform this sensory-plus-prior integration when the underlying generative model of the environment consists of multiple causes. Here we ask if the Bayes-optimal integration seen with simple tasks also applies to such natural tasks when the generative model is more complex, or whether observers rely instead on a less efficient set of heuristics that approximate ideal performance. Participants localized a "hidden" target whose position on a touch screen was sampled from a location-contingent bimodal generative model with different variances around each mode. Over repeated exposure to this task, participants learned the a priori locations of the target (i.e., the bimodal generative model), and integrated this learned knowledge with uncertain sensory information on a trial-by-trial basis in a manner consistent with the predictions of Bayes-optimal behavior. In particular, participants rapidly learned the locations of the two modes of the generative model, but the relative variances of the modes were learned much more slowly. Taken together, our results suggest that human performance in a more complex localization task, which requires the integration of sensory information with learned knowledge of a bimodal generative model, is consistent with the predictions of Bayes-optimal behavior, but involves a much longer time-course than in simpler tasks.

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Potentiation in young infants: The origin of the prior knowledge effect?

PubMed Central

Barr, Rachel; Rovee-Collier, Carolyn; Learmonth, Amy

2011-01-01

In two experiments with 6-month-old infants, we found that prior learning of an operant task (remembered for 2 weeks) mediated new learning of a modeling event (remembered for only 1 day) and increased its recall. Infants first learned to associate lever pressing with moving a toy train housed in a large box. One or 2 weeks later, three target actions were modeled on a hand puppet while the train box (a retrieval cue) was in view. Merely retrieving the train memory strengthened it, and simultaneously pairing its retrieved memory with the modeled actions potentiated their learning and recall. When paired 1 week later, deferred imitation increased from 1 day to 4 weeks; when paired 2 weeks later, it increased from 1 day to 6 weeks. The striking parallels between potentiated learning in infants and the prior knowledge effect in adults suggests that the prior knowledge effect originates in early infancy. PMID:21264602

When Relationships Depicted Diagrammatically Conflict with Prior Knowledge: An Investigation of Students' Interpretations of Evolutionary Trees

ERIC Educational Resources Information Center

Novick, Laura R.; Catley, Kefyn M.

2014-01-01

Science is an important domain for investigating students' responses to information that contradicts their prior knowledge. In previous studies of this topic, this information was communicated verbally. The present research used diagrams, specifically trees (cladograms) depicting evolutionary relationships among taxa. Effects of college…

Building Knowledge through Portfolio Learning in Prior Learning Assessment and Recognition

ERIC Educational Resources Information Center

Conrad, Dianne

2008-01-01

It is important for academic credibility that the process of prior learning assessment and recognition (PLAR) keeps learning and knowledge as its foundational tenets. Doing so ensures PLAR's recognition as a fertile ground for learners' cognitive and personal growth. In many postsecondary venues, PLAR is often misunderstood and confused with…

Temporal Learning in 4 1/2- and 6-Year-Old Children: Role of Instructions and Prior Knowledge.

ERIC Educational Resources Information Center

Droit, Sylvie; And Others

1990-01-01

Examined the role of prior temporal knowledge of 4 1/2- and 6-year-olds through the use of high-rate, interval, and minimal instructions in a fixed-interval training schedule. Determined that the subjects' learning depended on their verbal self-control skills. (BC)

Understanding the Complexities of Prior Knowledge

ERIC Educational Resources Information Center

Soiferman, L. Karen

2014-01-01

The purpose of the study was to gain an understanding of the kinds of prior knowledge students bring with them from high school as it relates to the conventions of writing that they are expected to follow in ARTS 1110 Introduction to University. The research questions were "Can first-year students taking the Arts 1110 Introduction to…

An Effectiveness Index and Profile for Instructional Media.

ERIC Educational Resources Information Center

Bond, Jack H.

A scale was developed for judging the relative value of various media in teaching children. Posttest scores were partitioned into several components: error, prior knowledge, guessing, and gain from the learning exercise. By estimating the amounts of prior knowledge, guessing, and error, and then subtracting these from the total score, an index of…

Making Connections in Math: Activating a Prior Knowledge Analogue Matters for Learning

ERIC Educational Resources Information Center

Sidney, Pooja G.; Alibali, Martha W.

2015-01-01

This study investigated analogical transfer of conceptual structure from a prior-knowledge domain to support learning in a new domain of mathematics: division by fractions. Before a procedural lesson on division by fractions, fifth and sixth graders practiced with a surface analogue (other operations on fractions) or a structural analogue (whole…

An Investigation of the Relationships between Prior Knowledge and Vocabulary Development with Culturally Diverse Students.

ERIC Educational Resources Information Center

Karbon, Jacqueline C.

Using a semantic mapping technique for vocabulary instruction, a study explored how children of diverse groups bring different cultural backgrounds and prior knowledge to tasks involved in learning new words. The study was conducted in three sixth-grade classrooms--one containing rural Native American (especially Menominee) children, another…

The Influence of Prior Knowledge on Perception and Action: Relationships to Autistic Traits

ERIC Educational Resources Information Center

Buckingham, Gavin; Michelakakis, Elizabeth Evgenia; Rajendran, Gnanathusharan

2016-01-01

Autism is characterised by a range of perceptual and sensorimotor deficits, which might be related to abnormalities in how autistic individuals use prior knowledge. We investigated this proposition in a large non-clinical population in the context of the size-weight illusion, where individual's expectations about object weight influence their…

Interplay of Prior Knowledge, Self-Regulation and Motivation in Complex Multimedia Learning Environments

ERIC Educational Resources Information Center

Song, H. S.; Kalet, A. L.; Plass, J. L.

2016-01-01

This study examined the direct and indirect effects of medical clerkship students' prior knowledge, self-regulation and motivation on learning performance in complex multimedia learning environments. The data from 386 medical clerkship students from six medical schools were analysed using structural equation modeling. The structural model revealed…

Effects of Students' Prior Knowledge on Scientific Reasoning in Density.

ERIC Educational Resources Information Center

Yang, Il-Ho; Kwon, Yong-Ju; Kim, Young-Shin; Jang, Myoung-Duk; Jeong, Jin-Woo; Park, Kuk-Tae

2002-01-01

Investigates the effects of students' prior knowledge on the scientific reasoning processes of performing the task of controlling variables with computer simulation and identifies a number of problems that students encounter in scientific discovery. Involves (n=27) 5th grade students and (n=33) 7th grade students. Indicates that students' prior…

Relationship of Prior Knowledge and Working Engineers' Learning Preferences: Implications for Designing Effective Instruction

ERIC Educational Resources Information Center

Baukal, Charles E.; Ausburn, Lynna J.

2017-01-01

Continuing engineering education (CEE) is important to ensure engineers maintain proficiency over the life of their careers. However, relatively few studies have examined designing effective training for working engineers. Research has indicated that both learner instructional preferences and prior knowledge can impact the learning process, but it…

The Influence of Prior Knowledge and Viewing Repertoire on Learning from Video

ERIC Educational Resources Information Center

de Boer, Jelle; Kommers, Piet A. M.; de Brock, Bert; Tolboom, Jos

2016-01-01

Video is increasingly used as an instructional tool. It is therefore becoming more important to improve learning of students from video. We investigated whether student learning effects are influenced through an instruction about other viewing behaviours, and whether these learning effects depend on their prior knowledge. In a controlled…

Prior Knowledge and Online Inquiry-Based Science Reading: Evidence from Eye Tracking

ERIC Educational Resources Information Center

Ho, Hsin Ning Jessie; Tsai, Meng-Jung; Wang, Ching-Yeh; Tsai, Chin-Chung

2014-01-01

This study employed eye-tracking technology to examine how students with different levels of prior knowledge process text and data diagrams when reading a web-based scientific report. Students' visual behaviors were tracked and recorded when they read a report demonstrating the relationship between the greenhouse effect and global climate…

Students' Achievement in Relation to Reasoning Ability, Prior Knowledge and Gender

ERIC Educational Resources Information Center

Yenilmez, Ayse; Sungur, Semra; Tekkaya, Ceren

2006-01-01

This study investigated students' achievement regarding photosynthesis and respiration in plants in relation to reasoning ability, prior knowledge and gender. A total of 117 eighth-grade students participated in the study. Test of logical thinking and the two-tier multiple choice tests were administered to determine students' reasoning ability and…

The Effectiveness of Using Incorrect Examples to Support Learning about Decimal Magnitude

ERIC Educational Resources Information Center

Durkin, Kelley; Rittle-Johnson, Bethany

2012-01-01

Comparing common mathematical errors to correct examples may facilitate learning, even for students with limited prior domain knowledge. We examined whether studying incorrect and correct examples was more effective than studying two correct examples across prior knowledge levels. Fourth- and fifth-grade students (N = 74) learned about decimal…

Students' inductive reasoning skills and the relevance of prior knowledge: an exploratory study with a computer-based training course on the topic of acne vulgaris.

PubMed

Horn-Ritzinger, Sabine; Bernhardt, Johannes; Horn, Michael; Smolle, Josef

2011-04-01

The importance of inductive instruction in medical education is increasingly growing. Little is known about the relevance of prior knowledge regarding students' inductive reasoning abilities. The purpose is to evaluate this inductive teaching method as a means of fostering higher levels of learning and to explore how individual differences in prior knowledge (high [HPK] vs. low [LPK]) contribute to students' inductive reasoning skills. Twenty-six LPK and 18 HPK students could train twice with an interactive computer-based training object to discover the underlying concept before doing the final comprehension check. Students had a median of 76.9% of correct answers in the first, 90.9% in the second training, and answered 92% of the final assessment questions correctly. More important, 86% of all students succeeded with inductive learning, among them 83% of the HPK students and 89% of the LPK students. Prior knowledge did not predict performance on overall comprehension. This inductive instructional strategy fostered students' deep approaches to learning in a time-effective way.

Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio Transients

NASA Technical Reports Server (NTRS)

Thompson, David R.; Majid, Walid A.; Reed, Colorado J.; Wagstaff, Kiri L.

2011-01-01

We present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses adaptive eigenbases to combine 1) prior knowledge about uninteresting signals with 2) online estimation of the current data properties to enable highly sensitive and precise detection of novel signals. We apply the method to the problem of detecting fast transient radio anomalies and compare it to current alternative algorithms. Tests based on observations from the Parkes Multibeam Survey show both effective detection of interesting rare events and robustness to known false alarm anomalies.

Relationship of resident characteristics, attitudes, prior training and clinical knowledge to communication skills performance.

PubMed

Laidlaw, Toni Suzuki; Kaufman, David M; MacLeod, Heather; van Zanten, Sander; Simpson, David; Wrixon, William

2006-01-01

A substantial body of literature demonstrates that communication skills in medicine can be taught and retained through teaching and practice. Considerable evidence also reveals that characteristics such as gender, age, language and attitudes affect communication skills performance. Our study examined the characteristics, attitudes and prior communication skills training of residents to determine the relationship of each to patient-doctor communication. The relationship between communication skills proficiency and clinical knowledge application (biomedical and ethical) was also examined through the use of doctor-developed clinical content checklists, as very little research has been conducted in this area. A total of 78 first- and second-year residents across all departments at Dalhousie Medical School participated in a videotaped 4-station objective structured clinical examination presenting a range of communication and clinical knowledge challenges. A variety of instruments were used to gather information and assess performance. Two expert raters evaluated the videotapes. Significant relationships were observed between resident characteristics, prior communication skills training, clinical knowledge and communication skills performance. Females, younger residents and residents with English as first language scored significantly higher, as did residents with prior communication skills training. A significant positive relationship was found between the clinical content checklist and communication performance. Gender was the only characteristic related significantly to attitudes. Gender, age, language and prior communication skills training are related to communication skills performance and have implications for resident education. The positive relationship between communication skills proficiency and clinical knowledge application is important and should be explored further.

Prior Knowledge Activation: How Different Concept Mapping Tasks Lead to Substantial Differences in Cognitive Processes, Learning Outcomes, and Perceived Self-Efficacy

ERIC Educational Resources Information Center

Gurlitt, Johannes; Renkl, Alexander

2010-01-01

Two experiments investigated the effects of characteristic features of concept mapping used for prior knowledge activation. Characteristic demands of concept mapping include connecting lines representing the relationships between concepts and labeling these lines, specifying the type of the semantic relationships. In the first experiment,…

Concept Development and Meaningful Learning among Electrical Engineering Students Engaged in a Problem-Based Laboratory Experience

ERIC Educational Resources Information Center

Bledsoe, Karen E.; Flick, Lawrence

2012-01-01

This phenomenographic study documented changes in student-held electrical concepts the development of meaningful learning among students with both low and high prior knowledge within a problem-based learning (PBL) undergraduate electrical engineering course. This paper reports on four subjects: two with high prior knowledge and two with low prior…

Formative Assessment Pre-Test to Identify College Students' Prior Knowledge, Misconceptions and Learning Difficulties in Biology

ERIC Educational Resources Information Center

Lazarowitz, Reuven; Lieb, Carl

2006-01-01

A formative assessment pretest was administered to undergraduate students at the beginning of a science course in order to find out their prior knowledge, misconceptions and learning difficulties on the topic of the human respiratory system and energy issues. Those findings could provide their instructors with the valuable information required in…

The Influence of Prior Knowledge, Peer Review, Age, and Gender in Online Philosophy Discussions

ERIC Educational Resources Information Center

Cuddy, Lucas Stebbins

2016-01-01

Using a primarily experimental design, this study investigated whether discussion boards in online community college philosophy classes can be designed in the Blackboard course management system to lead to higher order thinking. Discussions were designed using one of two teaching techniques: the activation of prior knowledge or the use of peer…

Thai University Students' Prior Knowledge about P-Waves Generated during Particle Motion

ERIC Educational Resources Information Center

Rakkapao, Suttida; Arayathanikul, Kwan; Pananont, Passakorn

2009-01-01

The goal of this study is to identify Thai students' prior knowledge about particle motion when P-waves arrive. This existing idea significantly influences what and how students learn in the classroom. The data were collected via conceptual open-ended questions designed by the researchers and through explanatory follow-up interviews. Participants…

The Interpretation of Cellular Transport Graphics by Students with Low and High Prior Knowledge

ERIC Educational Resources Information Center

Cook, Michelle; Carter, Glenda; Wiebe, Eric N.

2008-01-01

The purpose of this study was to examine how prior knowledge of cellular transport influenced how high school students in the USA viewed and interpreted graphic representations of this topic. The participants were Advanced Placement Biology students (n = 65); each participant had previously taken a biology course in high school. After assessing…

The Effects of Prior-Knowledge and Online Learning Approaches on Students' Inquiry and Argumentation Abilities

ERIC Educational Resources Information Center

Yang, Wen-Tsung; Lin, Yu-Ren; She, Hsiao-Ching; Huang, Kai-Yi

2015-01-01

This study investigated the effects of students' prior science knowledge and online learning approaches (social and individual) on their learning with regard to three topics: science concepts, inquiry, and argumentation. Two science teachers and 118 students from 4 eighth-grade science classes were invited to participate in this research. Students…

Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data

PubMed Central

Shah, Abhik; Woolf, Peter

2009-01-01

Summary In this paper, we introduce pebl, a Python library and application for learning Bayesian network structure from data and prior knowledge that provides features unmatched by alternative software packages: the ability to use interventional data, flexible specification of structural priors, modeling with hidden variables and exploitation of parallel processing. PMID:20161541

The Instructional Effectiveness of Animated Signaling among Learners with High and Low Prior Knowledge

ERIC Educational Resources Information Center

Li, Shanshan

2012-01-01

The purpose of this study was to investigate the instructional effectiveness of animated signals among learners with high and low prior knowledge. Each of the two treatments was presented with animated instruction either with signals or without signals on the content of how an airplane achieves lift. Subjects were eighty-seven undergraduate…

A Fair and Balanced Look at the News: What Affects Memory for Controversial Arguments?

ERIC Educational Resources Information Center

Wiley, J.

2005-01-01

This research demonstrates how prior knowledge may allow for qualitative differences in representation of texts about controversial issues. People often experience a memory bias in favor of information with which they agree. In several experiments it was found that individuals with high prior knowledge about the topic were better able to recall…

Prior Knowledge Influence on Self-Explanation Effectiveness when Solving Problems: An Exploratory Study in Science Learning

ERIC Educational Resources Information Center

Ionas, Ioan Gelu; Cernusca, Dan; Collier, Harvest L.

2012-01-01

This exploratory study presents the outcomes of using self-explanation to improve learners' performance in solving basic chemistry problems. The results of the randomized experiment show the existence of a moderation effect between prior knowledge and the level of support self-explanation provides to learners, suggestive of a synergistic effect…

The Impact of Learner's Prior Knowledge on Their Use of Chemistry Computer Simulations: A Case Study

ERIC Educational Resources Information Center

Liu, Han-Chin; Andre, Thomas; Greenbowe, Thomas

2008-01-01

It is complicated to design a computer simulation that adapts to students with different characteristics. This study documented cases that show how college students' prior chemistry knowledge level affected their interaction with peers and their approach to solving problems with the use of computer simulations that were designed to learn…

Feedback Both Helps and Hinders Learning: The Causal Role of Prior Knowledge

ERIC Educational Resources Information Center

Fyfe, Emily R.; Rittle-Johnson, Bethany

2016-01-01

Feedback can be a powerful learning tool, but its effects vary widely. Research has suggested that learners' prior knowledge may moderate the effects of feedback; however, no causal link has been established. In Experiment 1, we randomly assigned elementary school children (N = 108) to a condition based on a crossing of 2 factors: induced strategy…

The Effect of Prior Knowledge and Feedback Type Design on Student Achievement and Satisfaction in Introductory Accounting

ERIC Educational Resources Information Center

Campbell, Donald P.

2013-01-01

This study investigated the effect of student prior knowledge and feedback type on student achievement and satisfaction in an introductory managerial accounting course using computer-based formative assessment tools. The study involved a redesign of the existing Job Order Costing unit using the ADDIE model of instructional design. The…

Activating Junior Secondary School Students' Prior Knowledge for the Development of Vocabulary, Concepts and Mathematics through Instructional Strategies

ERIC Educational Resources Information Center

Oyinloye, Olu; Popoola, Abiodun A.

2013-01-01

This paper investigates the activation of students' prior knowledge for the development of vocabulary, concepts and mathematics. It has been observed that many secondary school students are not performing well in the examination conducted by the West African Examinations Council and National Examinations Council of Nigeria. The situation became…

Mechanisms underlying comprehension of health information in adulthood: the roles of prior knowledge and working memory capacity.

PubMed

Soederberg Miller, Lisa M; Gibson, Tanja N; Applegate, Elizabeth A; de Dios, Jeannette

2011-07-01

Prior knowledge, working memory capacity (WMC), and conceptual integration (attention allocated to integrating concepts in text) are critical within many contexts; however, their impact on the acquisition of health information (i.e. learning) is relatively unexplored.We examined how these factors impact learning about nutrition within a cross-sectional study of adults ages 18 to 81. Results showed that conceptual integration mediated the effects of knowledge and WMC on learning, confirming that attention to concepts while reading is important for learning about health. We also found that when knowledge was controlled, age declines in learning increased, suggesting that knowledge mitigates the effects of age on learning about nutrition.

Cognitive patterns of neuroanatomy concepts: Knowledge organizations that emerge from problem solving versus information gathering

NASA Astrophysics Data System (ADS)

Weidner, Jeanne Margaret O'malley

2000-10-01

This study was motivated by some of the claims that are found in the literature on Problem-Based Learning (PBL). This instructional technique, which uses case studies as its primary instructional tool, has been advanced as an alternative to traditional instruction in order to foster more meaningful, integrative learning of scientific concepts. Several of the advantages attributed to Problem-Based Learning are that it (1) is generally preferred by students because it appears to foster a more nurturing and enjoyable learning experience, (2) fosters greater retention of knowledge and concepts acquired, and (3) results in increased ability to apply this knowledge toward solving new problems. This study examines the differences that result when students learn neuroanatomy concepts under two instructional contexts: problem solving vs. information gathering. The technological resource provided to students to support learning under each of these contexts was the multimedia program BrainStorm: An Interactive Neuroanatomy Atlas (Coppa & Tancred, 1995). The study explores the influence of context with regard to subjects' performance on objective post-tests, organization of knowledge as measured by Pathfinder Networks, differential use of the multimedia software and discourse differences emerging from the transcripts. The findings support previous research in the literature that problem-solving results in less knowledge acquisition in the short term, greater retention of material over time, and a subjects' preference for the method. However, both the degree of retention and preference were influenced by subjects' prior knowledge of the material in the exercises, as there was a significant difference in performance between the two exercises: for the exercise about which subjects appeared to have greater background information, memory decay was less, and subject attitude toward the problem solving instructional format was more favorable, than for the exercise for which subjects had less prior knowledge. Subjects also used the software differently under each format with regard to modules accessed, time spent in modules, and types of information sought. In addition, analyses of the transcripts showed more numerous occurrences of explanations and summarizations in the problem-solving context, compared to the information gathering context. The attempts to show significant differences between the contexts by means of Pathfinder analyses were less than successful.

Students entering internship show readiness in the nutrition care process.

PubMed

Baker, S D; Cotugna, N

2013-10-01

The British Dietetic Association and the International Confederation of Dietetic Associations are developing an international model for dietetics practice as an aid in providing evidence-based practice. In the USA, undergraduate programmes are mandated by the Academy of Nutrition and Dietetics (formerly the American Dietetic Association) to incorporate the nutrition care process (NCP) into the curriculum so that students can use the process during their dietetic internship and later practice. The present study aimed to assess interns' readiness in the NCP prior to beginning a dietetic internship. Before starting the internship, the 40 interns in the 2009-2010 class of a university-based internship were sent an e-mail requesting they complete an online survey. Questions inquired about their NCP background with respect to: academic preparation, work or volunteer experiences, knowledge and confidence in ability to apply the NCP. Survey results were analysed with SPSS statistical software (SPSS Inc., Chicago, IL, USA). The 39 interns completing the survey indicated they had prior exposure to the NCP. All but one reported that their academic coursework covered the NCP. Approximately half of the interns worked or volunteered in settings that used the NCP. Overall, students correctly answered most of the questions assessing their basic knowledge in the NCP. Thirty-seven of the 39 interns had some confidence or felt confident in their ability to apply the NCP during internship rotations. This distance internship attracts students from all over the USA, and so the findings of the present study shed light on current undergraduate preparation in the NCP. © 2013 The Authors Journal of Human Nutrition and Dietetics © 2013 The British Dietetic Association Ltd.

Perceptual learning of degraded speech by minimizing prediction error.

PubMed

Sohoglu, Ediz; Davis, Matthew H

2016-03-22

Human perception is shaped by past experience on multiple timescales. Sudden and dramatic changes in perception occur when prior knowledge or expectations match stimulus content. These immediate effects contrast with the longer-term, more gradual improvements that are characteristic of perceptual learning. Despite extensive investigation of these two experience-dependent phenomena, there is considerable debate about whether they result from common or dissociable neural mechanisms. Here we test single- and dual-mechanism accounts of experience-dependent changes in perception using concurrent magnetoencephalographic and EEG recordings of neural responses evoked by degraded speech. When speech clarity was enhanced by prior knowledge obtained from matching text, we observed reduced neural activity in a peri-auditory region of the superior temporal gyrus (STG). Critically, longer-term improvements in the accuracy of speech recognition following perceptual learning resulted in reduced activity in a nearly identical STG region. Moreover, short-term neural changes caused by prior knowledge and longer-term neural changes arising from perceptual learning were correlated across subjects with the magnitude of learning-induced changes in recognition accuracy. These experience-dependent effects on neural processing could be dissociated from the neural effect of hearing physically clearer speech, which similarly enhanced perception but increased rather than decreased STG responses. Hence, the observed neural effects of prior knowledge and perceptual learning cannot be attributed to epiphenomenal changes in listening effort that accompany enhanced perception. Instead, our results support a predictive coding account of speech perception; computational simulations show how a single mechanism, minimization of prediction error, can drive immediate perceptual effects of prior knowledge and longer-term perceptual learning of degraded speech.

Perceptual learning of degraded speech by minimizing prediction error

PubMed Central

Sohoglu, Ediz

2016-01-01

Human perception is shaped by past experience on multiple timescales. Sudden and dramatic changes in perception occur when prior knowledge or expectations match stimulus content. These immediate effects contrast with the longer-term, more gradual improvements that are characteristic of perceptual learning. Despite extensive investigation of these two experience-dependent phenomena, there is considerable debate about whether they result from common or dissociable neural mechanisms. Here we test single- and dual-mechanism accounts of experience-dependent changes in perception using concurrent magnetoencephalographic and EEG recordings of neural responses evoked by degraded speech. When speech clarity was enhanced by prior knowledge obtained from matching text, we observed reduced neural activity in a peri-auditory region of the superior temporal gyrus (STG). Critically, longer-term improvements in the accuracy of speech recognition following perceptual learning resulted in reduced activity in a nearly identical STG region. Moreover, short-term neural changes caused by prior knowledge and longer-term neural changes arising from perceptual learning were correlated across subjects with the magnitude of learning-induced changes in recognition accuracy. These experience-dependent effects on neural processing could be dissociated from the neural effect of hearing physically clearer speech, which similarly enhanced perception but increased rather than decreased STG responses. Hence, the observed neural effects of prior knowledge and perceptual learning cannot be attributed to epiphenomenal changes in listening effort that accompany enhanced perception. Instead, our results support a predictive coding account of speech perception; computational simulations show how a single mechanism, minimization of prediction error, can drive immediate perceptual effects of prior knowledge and longer-term perceptual learning of degraded speech. PMID:26957596

Optimizing Multiple Analyte Injections in Surface Plasmon Resonance Biosensors with Analytes having Different Refractive Index Increments

PubMed Central

Mehand, Massinissa Si; Srinivasan, Bala; De Crescenzo, Gregory

2015-01-01

Surface plasmon resonance-based biosensors have been successfully applied to the study of the interactions between macromolecules and small molecular weight compounds. In an effort to increase the throughput of these SPR-based experiments, we have already proposed to inject multiple compounds simultaneously over the same surface. When specifically applied to small molecular weight compounds, such a strategy would however require prior knowledge of the refractive index increment of each compound in order to correctly interpret the recorded signal. An additional experiment is typically required to obtain this information. In this manuscript, we show that through the introduction of an additional global parameter corresponding to the ratio of the saturating signals associated with each molecule, the kinetic parameters could be identified with similar confidence intervals without any other experimentation. PMID:26515024

In-camera automation of photographic composition rules.

PubMed

Banerjee, Serene; Evans, Brian L

2007-07-01

At the time of image acquisition, professional photographers apply many rules of thumb to improve the composition of their photographs. This paper develops a joint optical-digital processing framework for automating composition rules during image acquisition for photographs with one main subject. Within the framework, we automate three photographic composition rules: repositioning the main subject, making the main subject more prominent, and making objects that merge with the main subject less prominent. The idea is to provide to the user alternate pictures obtained by applying photographic composition rules in addition to the original picture taken by the user. The proposed algorithms do not depend on prior knowledge of the indoor/outdoor setting or scene content. The proposed algorithms are also designed to be amenable to software implementation on fixed-point programmable digital signal processors available in digital still cameras.

The Role of Specificity, Targeted Learning Activities, and Prior Knowledge for the Effects of Relevance Instructions

ERIC Educational Resources Information Center

Roelle, Julian; Lehmkuhl, Nina; Beyer, Martin-Uwe; Berthold, Kirsten

2015-01-01

In 2 experiments we examined the role of (a) specificity, (b) the type of targeted learning activities, and (c) learners' prior knowledge for the effects of relevance instructions on learning from instructional explanations. In Experiment 1, we recruited novices regarding the topic of atomic structure (N = 80) and found that "specific"…

Effects of Prior Knowledge of Topics and the Instructional Objectives on Students' Achievement in Literature-in-English

ERIC Educational Resources Information Center

Mbah, Blessing Akaraka

2015-01-01

This study investigated the effects of prior knowledge of topics with their instructional objectives on senior secondary school class two (SS II) students. The study was carried out in Abakaliki Education Zone of Ebonyi State, Nigeria. The design of the study is quasi experimental of pretest-posttest of non-equivalent control group. Two research…

Polite Web-Based Intelligent Tutors: Can They Improve Learning in Classrooms?

ERIC Educational Resources Information Center

McLaren, Bruce M.; DeLeeuw, Krista E.; Mayer, Richard E.

2011-01-01

Should an intelligent software tutor be polite, in an effort to motivate and cajole students to learn, or should it use more direct language? If it should be polite, under what conditions? In a series of studies in different contexts (e.g., lab versus classroom) with a variety of students (e.g., low prior knowledge versus high prior knowledge),…

"She Has to Drink Blood of the Snake": Culture and Prior Knowledge in Science|Health Education

ERIC Educational Resources Information Center

Bricker, Leah A.; Reeve, Suzanne; Bell, Philip

2014-01-01

In this analysis, we argue that science education should attend more deeply to youths' cultural resources and practices (e.g. material, social, and intellectual). Inherent in our argument is a call for revisiting conceptions of "prior knowledge" to theorize how people make sense of the complex ecologies of experience, ideas, and cultural…

Effects of Different Types of True-False Questions on Memory Awareness and Long-Term Retention

ERIC Educational Resources Information Center

Schaap, Lydia; Verkoeijen, Peter; Schmidt, Henk

2014-01-01

This study investigated the effects of two different true-false questions on memory awareness and long-term retention of knowledge. Participants took four subsequent knowledge tests on curriculum learning material that they studied at different retention intervals prior to the start of this study (i.e. prior to the first test). At the first and…

Effects of Prior Knowledge and Concept-Map Structure on Disorientation, Cognitive Load, and Learning

ERIC Educational Resources Information Center

Amadieu, Franck; van Gog, Tamara; Paas, Fred; Tricot, Andre; Marine, Claudette

2009-01-01

This study explored the effects of prior knowledge (high vs. low; HPK and LPK) and concept-map structure (hierarchical vs. network; HS and NS) on disorientation, cognitive load, and learning from non-linear documents on "the infection process of a retrograde virus (HIV)". Participants in the study were 24 adults. Overall subjective ratings of…

Relevant Prior Knowledge Moderates the Effect of Elaboration during Small Group Discussion on Academic Achievement

ERIC Educational Resources Information Center

Van Blankenstein, Floris M.; Dolmans, Diana H. J. M.; Van der Vleuten, Cees P. M.; Schmidt, Henk G.

2013-01-01

This study set out to test whether relevant prior knowledge would moderate a positive effect on academic achievement of elaboration during small-group discussion. In a 2 × 2 experimental design, 66 undergraduate students observed a video showing a small-group problem-based discussion about thunder and lightning. In the video, a teacher asked…

The Mediation Effect of In-Game Performance between Prior Knowledge and Posttest Score. CRESST Report 819

ERIC Educational Resources Information Center

Kerr, Deirdre; Chung, Gregory K. W. K.

2012-01-01

Though video games are commonly considered to hold great potential as learning environments, their effectiveness as a teaching tool has yet to be determined. One reason for this is that researchers often run into the problem of multicollinearity between prior knowledge, in-game performance, and posttest scores, thereby making the determination of…

Developmental Change in the Influence of Domain-General Abilities and Domain-Specific Knowledge on Mathematics Achievement: An Eight-Year Longitudinal Study

ERIC Educational Resources Information Center

Geary, David C.; Nicholas, Alan; Li, Yaoran; Sun, Jianguo

2017-01-01

The contributions of domain-general abilities and domain-specific knowledge to subsequent mathematics achievement were longitudinally assessed (n = 167) through 8th grade. First grade intelligence and working memory and prior grade reading achievement indexed domain-general effects, and domain-specific effects were indexed by prior grade…

Blended Learning Based on Schoology: Effort of Improvement Learning Outcome and Practicum Chance in Vocational High School

ERIC Educational Resources Information Center

Irawan, Vincentius Tjandra; Sutadji, Eddy; Widiyanti

2017-01-01

The aims of this study were to determine: (1) the differences in learning outcome between Blended Learning based on Schoology and Problem-Based Learning, (2) the differences in learning outcome between students with prior knowledge of high, medium, and low, and (3) the interaction between Blended Learning based on Schoology and prior knowledge to…

Why is a Pomegranate an Apple? The Role of Shape, Taxonomic Relatedness, and Prior Lexical Knowledge in Children's Overextensions of "Apple" and "Dog."

ERIC Educational Resources Information Center

Gelman, Susan A.; Croft, William; Fu, Panfang; Clausner, Timothy; Gottfried, Gail

1998-01-01

Examined how object shape, taxonomic relatedness, and prior lexical knowledge influenced children's overextensions (e.g., referring to pomegranates as apples). Researchers presented items that disentangled the three factors and used a novel comprehension task where children could indicate negative exemplars. Error patterns differed by task and by…

The impact of group membership on collaborative learning with wikis.

PubMed

Matschke, Christina; Moskaliuk, Johannes; Kimmerle, Joachim

2013-02-01

The social web stimulates learning through collaboration. However, information in the social web is often associated with information about its author. Based on previous evidence that ingroup information is preferred to outgroup information, the current research investigates whether group memberships of wiki authors affect learning. In an experimental study, we manipulated the group memberships (ingroup vs. outgroup) of wiki authors by using nicknames. The designated group memberships (being fans of a soccer team or not) were completely irrelevant for the domain of the wiki (the medical disorder fibromyalgia). Nevertheless, wiki information from the ingroup led to more integration of information into prior knowledge as well as more increase of factual knowledge than information from the outgroup. The results demonstrate that individuals apply social selection strategies when considering information from wikis, which may foster, but also hinder, learning and collaboration. Practical implications for collaborative learning in the social web are discussed.

International Dengue Vaccine Communication and Advocacy: Challenges and Way Forward.

PubMed

Carvalho, Ana; Van Roy, Rebecca; Andrus, Jon

2016-01-01

Dengue vaccine introduction will likely occur soon. However, little has been published on international dengue vaccine communication and advocacy. More effort at the international level is required to review, unify and strategically disseminate dengue vaccine knowledge to endemic countries' decision makers and potential donors. Waiting to plan for the introduction of new vaccines until licensure may delay access in developing countries. Concerted efforts to communicate and advocate for vaccines prior to licensure are likely challenged by unknowns of the use of dengue vaccines and the disease, including uncertainties of vaccine impact, vaccine access and dengue's complex pathogenesis and epidemiology. Nevertheless, the international community has the opportunity to apply previous best practices for vaccine communication and advocacy. The following key strategies will strengthen international dengue vaccine communication and advocacy: consolidating existing coalitions under one strategic umbrella, urgently convening stakeholders to formulate the roadmap for integrated dengue prevention and control, and improving the dissemination of dengue scientific knowledge.

Knowledge acquisition is governed by striatal prediction errors.

PubMed

Pine, Alex; Sadeh, Noa; Ben-Yakov, Aya; Dudai, Yadin; Mendelsohn, Avi

2018-04-26

Discrepancies between expectations and outcomes, or prediction errors, are central to trial-and-error learning based on reward and punishment, and their neurobiological basis is well characterized. It is not known, however, whether the same principles apply to declarative memory systems, such as those supporting semantic learning. Here, we demonstrate with fMRI that the brain parametrically encodes the degree to which new factual information violates expectations based on prior knowledge and beliefs-most prominently in the ventral striatum, and cortical regions supporting declarative memory encoding. These semantic prediction errors determine the extent to which information is incorporated into long-term memory, such that learning is superior when incoming information counters strong incorrect recollections, thereby eliciting large prediction errors. Paradoxically, by the same account, strong accurate recollections are more amenable to being supplanted by misinformation, engendering false memories. These findings highlight a commonality in brain mechanisms and computational rules that govern declarative and nondeclarative learning, traditionally deemed dissociable.

Optimizing physicians' instruction of PACS through e-learning: cognitive load theory applied.

PubMed

Devolder, P; Pynoo, B; Voet, T; Adang, L; Vercruysse, J; Duyck, P

2009-03-01

This article outlines the strategy used by our hospital to maximize the knowledge transfer to referring physicians on using a picture archiving and communication system (PACS). We developed an e-learning platform underpinned by the cognitive load theory (CLT) so that in depth knowledge of PACS' abilities becomes attainable regardless of the user's prior experience with computers. The application of the techniques proposed by CLT optimizes the learning of the new actions necessary to obtain and manipulate radiological images. The application of cognitive load reducing techniques is explained with several examples. We discuss the need to safeguard the physicians' main mental processes to keep the patient's interests in focus. A holistic adoption of CLT techniques both in teaching and in configuration of information systems could be adopted to attain this goal. An overview of the advantages of this instruction method is given both on the individual and organizational level.

The Impact of Group Membership on Collaborative Learning with Wikis

PubMed Central

Matschke, Christina; Moskaliuk, Johannes

2013-01-01

Abstract The social web stimulates learning through collaboration. However, information in the social web is often associated with information about its author. Based on previous evidence that ingroup information is preferred to outgroup information, the current research investigates whether group memberships of wiki authors affect learning. In an experimental study, we manipulated the group memberships (ingroup vs. outgroup) of wiki authors by using nicknames. The designated group memberships (being fans of a soccer team or not) were completely irrelevant for the domain of the wiki (the medical disorder fibromyalgia). Nevertheless, wiki information from the ingroup led to more integration of information into prior knowledge as well as more increase of factual knowledge than information from the outgroup. The results demonstrate that individuals apply social selection strategies when considering information from wikis, which may foster, but also hinder, learning and collaboration. Practical implications for collaborative learning in the social web are discussed. PMID:23113690

Building dynamical models from data and prior knowledge: the case of the first period-doubling bifurcation.

PubMed

Aguirre, Luis Antonio; Furtado, Edgar Campos

2007-10-01

This paper reviews some aspects of nonlinear model building from data with (gray box) and without (black box) prior knowledge. The model class is very important because it determines two aspects of the final model, namely (i) the type of nonlinearity that can be accurately approximated and (ii) the type of prior knowledge that can be taken into account. Such features are usually in conflict when it comes to choosing the model class. The problem of model structure selection is also reviewed. It is argued that such a problem is philosophically different depending on the model class and it is suggested that the choice of model class should be performed based on the type of a priori available. A procedure is proposed to build polynomial models from data on a Poincaré section and prior knowledge about the first period-doubling bifurcation, for which the normal form is also polynomial. The final models approximate dynamical data in a least-squares sense and, by design, present the first period-doubling bifurcation at a specified value of parameters. The procedure is illustrated by means of simulated examples.

Mixture class recovery in GMM under varying degrees of class separation: frequentist versus Bayesian estimation.

PubMed

Depaoli, Sarah

2013-06-01

Growth mixture modeling (GMM) represents a technique that is designed to capture change over time for unobserved subgroups (or latent classes) that exhibit qualitatively different patterns of growth. The aim of the current article was to explore the impact of latent class separation (i.e., how similar growth trajectories are across latent classes) on GMM performance. Several estimation conditions were compared: maximum likelihood via the expectation maximization (EM) algorithm and the Bayesian framework implementing diffuse priors, "accurate" informative priors, weakly informative priors, data-driven informative priors, priors reflecting partial-knowledge of parameters, and "inaccurate" (but informative) priors. The main goal was to provide insight about the optimal estimation condition under different degrees of latent class separation for GMM. Results indicated that optimal parameter recovery was obtained though the Bayesian approach using "accurate" informative priors, and partial-knowledge priors showed promise for the recovery of the growth trajectory parameters. Maximum likelihood and the remaining Bayesian estimation conditions yielded poor parameter recovery for the latent class proportions and the growth trajectories. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem.

PubMed

Lu, Qiang; Ren, Jun; Wang, Zhiguang

2016-01-01

A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalized to accommodate different fields of knowledge. However, since GP has to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge and GP (PFK-GP) is proposed to reduce the space of GP searching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population, PFK-GP finds the right formulas quickly by exploring the search space of data features. We have compared PFK-GP with Pareto GP on regression of eight benchmark problems. The experimental results confirm that the PFK-GP can reduce the search space and obtain the significant improvement in the quality of SR.

Effects of argument quality, source credibility and self-reported diabetes knowledge on message attitudes: an experiment using diabetes related messages.

PubMed

Lin, Tung-Cheng; Hwang, Lih-Lian; Lai, Yung-Jye

2017-05-17

Previous studies have reported that credibility and content (argument quality) are the most critical factors affecting the quality of health information and its acceptance and use; however, this causal relationship merits further investigation in the context of health education. Moreover, message recipients' prior knowledge may moderate these relationships. This study used the elaboration likelihood model to determine the main effects of argument quality, source credibility and the moderating effect of self-reported diabetes knowledge on message attitudes. A between-subjects experimental design using an educational message concerning diabetes for manipulation was applied to validate the effects empirically. A total of 181 participants without diabetes were recruited from the Department of Health, Taipei City Government. Four group messages were manipulated in terms of argument quality (high and low) × source credibility (high and low). Argument quality and source credibility of health information significantly influenced the attitude of message recipients. The participants with high self-reported knowledge participants exhibited significant disapproval for messages with low argument quality. Effective health information should provide objective descriptions and cite reliable sources; in addition, it should provide accurate, customised messages for recipients who have high background knowledge level and ability to discern message quality. © 2017 Health Libraries Group Health Information & Libraries Journal.

Medicine is not science: guessing the future, predicting the past.

PubMed

Miller, Clifford

2014-12-01

Irregularity limits human ability to know, understand and predict. A better understanding of irregularity may improve the reliability of knowledge. Irregularity and its consequences for knowledge are considered. Reliable predictive empirical knowledge of the physical world has always been obtained by observation of regularities, without needing science or theory. Prediction from observational knowledge can remain reliable despite some theories based on it proving false. A naïve theory of irregularity is outlined. Reducing irregularity and/or increasing regularity can increase the reliability of knowledge. Beyond long experience and specialization, improvements include implementing supporting knowledge systems of libraries of appropriately classified prior cases and clinical histories and education about expertise, intuition and professional judgement. A consequence of irregularity and complexity is that classical reductionist science cannot provide reliable predictions of the behaviour of complex systems found in nature, including of the human body. Expertise, expert judgement and their exercise appear overarching. Diagnosis involves predicting the past will recur in the current patient applying expertise and intuition from knowledge and experience of previous cases and probabilistic medical theory. Treatment decisions are an educated guess about the future (prognosis). Benefits of the improvements suggested here are likely in fields where paucity of feedback for practitioners limits development of reliable expert diagnostic intuition. Further analysis, definition and classification of irregularity is appropriate. Observing and recording irregularities are initial steps in developing irregularity theory to improve the reliability and extent of knowledge, albeit some forms of irregularity present inherent difficulties. © 2014 John Wiley & Sons, Ltd.

PRIOR-WK&E: Social Software for Policy Making in the Knowledge Society

NASA Astrophysics Data System (ADS)

Turón, Alberto; Aguarón, Juan; Escobar, María Teresa; Gallardo, Carolina; Moreno-Jiménez, José María; Salazar, José Luis

This paper presents a social software application denominated as PRIOR-WK&E. It has been developed by the Zaragoza Multicriteria Decision Making Group (GDMZ) with the aim of responding to the challenges of policy making in the Knowledge Society. Three specific modules have been added to PRIOR, the collaborative tool used by the research group (GDMZ) for considering the multicriteria selection of a discrete set of alternatives. The first module (W), that deals with multiactor decision making through the Web, and the second (K), that concerns the extraction and diffusion of knowledge related to the scientific resolution of the problem, were explained in [1]. The new application strengthens securitization and includes a third module (E) that evaluates the effectiveness of public administrations policy making.

WE-G-207-07: Iterative CT Shading Correction Method with No Prior Information

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, P; Mao, T; Niu, T

2015-06-15

Purpose: Shading artifacts are caused by scatter contamination, beam hardening effects and other non-ideal imaging condition. Our Purpose is to propose a novel and general correction framework to eliminate low-frequency shading artifacts in CT imaging (e.g., cone-beam CT, low-kVp CT) without relying on prior information. Methods: Our method applies general knowledge of the relatively uniform CT number distribution in one tissue component. Image segmentation is applied to construct template image where each structure is filled with the same CT number of that specific tissue. By subtracting the ideal template from CT image, the residual from various error sources are generated.more » Since the forward projection is an integration process, the non-continuous low-frequency shading artifacts in the image become continuous and low-frequency signals in the line integral. Residual image is thus forward projected and its line integral is filtered using Savitzky-Golay filter to estimate the error. A compensation map is reconstructed on the error using standard FDK algorithm and added to the original image to obtain the shading corrected one. Since the segmentation is not accurate on shaded CT image, the proposed scheme is iterated until the variation of residual image is minimized. Results: The proposed method is evaluated on a Catphan600 phantom, a pelvic patient and a CT angiography scan for carotid artery assessment. Compared to the one without correction, our method reduces the overall CT number error from >200 HU to be <35 HU and increases the spatial uniformity by a factor of 1.4. Conclusion: We propose an effective iterative algorithm for shading correction in CT imaging. Being different from existing algorithms, our method is only assisted by general anatomical and physical information in CT imaging without relying on prior knowledge. Our method is thus practical and attractive as a general solution to CT shading correction. This work is supported by the National Science Foundation of China (NSFC Grant No. 81201091), National High Technology Research and Development Program of China (863 program, Grant No. 2015AA020917), and Fund Project for Excellent Abroad Scholar Personnel in Science and Technology.« less

Markov Logic Networks in the Analysis of Genetic Data

PubMed Central

Sakhanenko, Nikita A.

2010-01-01

Abstract Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics. PMID:20958249

Measuring Knowledge Elaboration Based on a Computer-Assisted Knowledge Map Analytical Approach to Collaborative Learning

ERIC Educational Resources Information Center

Zheng, Lanqin; Huang, Ronghuai; Hwang, Gwo-Jen; Yang, Kaicheng

2015-01-01

The purpose of this study is to quantitatively measure the level of knowledge elaboration and explore the relationships between prior knowledge of a group, group performance, and knowledge elaboration in collaborative learning. Two experiments were conducted to investigate the level of knowledge elaboration. The collaborative learning objective in…

Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality

USGS Publications Warehouse

Walsh, Daniel P.; Norton, Andrew S.; Storm, Daniel J.; Van Deelen, Timothy R.; Heisy, Dennis M.

2018-01-01

Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause-specific mortality provide an example of implicit use of expert knowledge when causes-of-death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause-specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause-of-death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event-time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause-of-death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause-of-death assignment in modeling of cause-specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause-specific survival data for white-tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause-of-death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference.

Superposing pure quantum states with partial prior information

NASA Astrophysics Data System (ADS)

Dogra, Shruti; Thomas, George; Ghosh, Sibasish; Suter, Dieter

2018-05-01

The principle of superposition is an intriguing feature of quantum mechanics, which is regularly exploited in many different circumstances. A recent work [M. Oszmaniec et al., Phys. Rev. Lett. 116, 110403 (2016), 10.1103/PhysRevLett.116.110403] shows that the fundamentals of quantum mechanics restrict the process of superimposing two unknown pure states, even though it is possible to superimpose two quantum states with partial prior knowledge. The prior knowledge imposes geometrical constraints on the choice of input states. We discuss an experimentally feasible protocol to superimpose multiple pure states of a d -dimensional quantum system and carry out an explicit experimental realization for two single-qubit pure states with partial prior information on a two-qubit NMR quantum information processor.

The Impacts of Virtual Manipulatives and Prior Knowledge on Geometry Learning Performance in Junior High School

ERIC Educational Resources Information Center

Lee, Chun-Yi; Chen, Ming-Jang

2014-01-01

Previous studies on the effects of virtual and physical manipulatives have failed to consider the impact of prior knowledge on the efficacy of manipulatives. This study focuses on the learning of plane geometry in junior high schools, including the sum of interior angles in polygons, the sum of exterior angles in polygons, and the properties of…

Exploring the Impact of Prior Knowledge and Appropriate Feedback on Students' Perceived Cognitive Load and Learning Outcomes: Animation-Based Earthquakes Instruction

ERIC Educational Resources Information Center

Yeh, Ting-Kuang; Tseng, Kuan-Yun; Cho, Chung-Wen; Barufaldi, James P.; Lin, Mei-Shin; Chang, Chun-Yen

2012-01-01

The aim of this study was to develop an animation-based curriculum and to evaluate the effectiveness of animation-based instruction; the report involved the assessment of prior knowledge and the appropriate feedback approach, for the purpose of reducing perceived cognitive load and improving learning. The curriculum was comprised of five subunits…

How Word Decoding, Vocabulary and Prior Topic Knowledge Predict Reading Comprehension. A Study of Language-Minority Students in Norwegian Fifth Grade Classrooms

ERIC Educational Resources Information Center

Rydland, Veslemoy; Aukrust, Vibeke Grover; Fulland, Helene

2012-01-01

This study examined the contribution of word decoding, first-language (L1) and second-language (L2) vocabulary and prior topic knowledge to L2 reading comprehension. For measuring reading comprehension we employed two different reading tasks: Woodcock Passage Comprehension and a researcher-developed content-area reading assignment (the Global…

The Effectiveness of Worked Examples Associated with Presentation Format and Prior Knowledge: A Web-Based Experiment

ERIC Educational Resources Information Center

Hsiao, E-Ling

2010-01-01

The aim of this study is to explore whether presentation format and prior knowledge affect the effectiveness of worked examples. The experiment was conducted through a specially designed online instrument. A 2X2X3 factorial before-and-after design was conducted. Three-way ANOVA was employed for data analysis. The result showed first, that prior…

Case-based reasoning for space applications: Utilization of prior experience in knowledge-based systems

NASA Technical Reports Server (NTRS)

King, James A.

1987-01-01

The goal is to explain Case-Based Reasoning as a vehicle to establish knowledge-based systems based on experimental reasoning for possible space applications. This goal will be accomplished through an examination of reasoning based on prior experience in a sample domain, and also through a presentation of proposed space applications which could utilize Case-Based Reasoning techniques.

Effects of Reading Ability, Prior Knowledge, Topic Interest, and Locus of Control on At-Risk College Students' Use of Graphic Organizers and Summarizing.

ERIC Educational Resources Information Center

Balajthy, Ernest; Weisberg, Renee

A study investigated the influence of key factors (general comprehension ability, prior knowledge of passage topic, interest in passage topic, and locus of control) on training at-risk college students in the use of graphic organizers as a cognitive learning strategy. Subjects, 60 college freshmen required to take a developmental reading/study…

Bayesian inference for disease prevalence using negative binomial group testing

PubMed Central

Pritchard, Nicholas A.; Tebbs, Joshua M.

2011-01-01

Group testing, also known as pooled testing, and inverse sampling are both widely used methods of data collection when the goal is to estimate a small proportion. Taking a Bayesian approach, we consider the new problem of estimating disease prevalence from group testing when inverse (negative binomial) sampling is used. Using different distributions to incorporate prior knowledge of disease incidence and different loss functions, we derive closed form expressions for posterior distributions and resulting point and credible interval estimators. We then evaluate our new estimators, on Bayesian and classical grounds, and apply our methods to a West Nile Virus data set. PMID:21259308

Self-Monitoring and Knowledge-Building in Learning by Teaching

ERIC Educational Resources Information Center

Roscoe, Rod D.

2014-01-01

Prior research has established that learning by teaching depends upon peer tutors' engagement in knowledge-building, in which tutors integrate their knowledge and generate new knowledge through reasoning. However, many tutors adopt a "knowledge-telling bias" defined by shallow summarizing of source materials and didactic lectures.…

Lending a Helping Hand: Voluntary Engagement in Knowledge Sharing

ERIC Educational Resources Information Center

Mergel, Ines; Lazer, David; Binz-Scharf, Maria Christina

2008-01-01

Knowledge is essential for the functioning of every social system, especially for professionals in knowledge-intensive organisations. Since individuals do not possess all the work-related knowledge that they require, they turn to others in search for that knowledge. While prior research has mainly focused on antecedents and consequences of…

Science Literacy and Prior Knowledge of Astronomy MOOC Students

NASA Astrophysics Data System (ADS)

Impey, Chris David; Buxner, Sanlyn; Wenger, Matthew; Formanek, Martin

2018-01-01

Many of science classes offered on Coursera fall into fall into the category of general education or general interest classes for lifelong learners, including our own, Astronomy: Exploring Time and Space. Very little is known about the backgrounds and prior knowledge of these students. In this talk we present the results of a survey of our Astronomy MOOC students. We also compare these results to our previous work on undergraduate students in introductory astronomy courses. Survey questions examined student demographics and motivations as well as their science and information literacy (including basic science knowledge, interest, attitudes and beliefs, and where they get their information about science). We found that our MOOC students are different than the undergraduate students in more ways than demographics. Many MOOC students demonstrated high levels of science and information literacy. With a more comprehensive understanding of our students’ motivations and prior knowledge about science and how they get their information about science, we will be able to develop more tailored learning experiences for these lifelong learners.

A dynamic developmental link between verbal comprehension-knowledge (Gc) and reading comprehension: verbal comprehension-knowledge drives positive change in reading comprehension.

PubMed

Reynolds, Matthew R; Turek, Joshua J

2012-12-01

Intelligence and general academic achievement have a well-established relation, but the interrelated development of the two constructs over time is less well-known. In this study, the dynamic developmental relation between verbal comprehension-knowledge (Gc) and reading comprehension was examined by applying bivariate dual change score models (McArdle, 2009) to longitudinal data collected from children aged 9 through 15 who were part of the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (NICHD SECCYD). A unidirectional dynamic link was found in which higher levels of prior Gc led to increased positive change in reading comprehension scores. This unidirectional link was not altered by including intelligence measured at 24-months, SES, sex, basic reading, and reading volume as time-invariant covariates. Gc is a leading indicator of reading comprehension and should be considered when developing and monitoring long-term reading comprehension interventions for children. Copyright © 2012 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

A risk-based classification scheme for genetically modified foods. II: Graded testing.

PubMed

Chao, Eunice; Krewski, Daniel

2008-12-01

This paper presents a graded approach to the testing of crop-derived genetically modified (GM) foods based on concern levels in a proposed risk-based classification scheme (RBCS) and currently available testing methods. A graded approach offers the potential for more efficient use of testing resources by focusing less on lower concern GM foods, and more on higher concern foods. In this proposed approach to graded testing, products that are classified as Level I would have met baseline testing requirements that are comparable to what is widely applied to premarket assessment of GM foods at present. In most cases, Level I products would require no further testing, or very limited confirmatory analyses. For products classified as Level II or higher, additional testing would be required, depending on the type of the substance, prior dietary history, estimated exposure level, prior knowledge of toxicity of the substance, and the nature of the concern related to unintended changes in the modified food. Level III testing applies only to the assessment of toxic and antinutritional effects from intended changes and is tailored to the nature of the substance in question. Since appropriate test methods are not currently available for all effects of concern, future research to strengthen the testing of GM foods is discussed.

A Chemical Route to Activation of Open Metal Sites in the Copper-Based Metal-Organic Framework Materials HKUST-1 and Cu-MOF-2.

PubMed

Kim, Hong Ki; Yun, Won Seok; Kim, Min-Bum; Kim, Jeung Yoon; Bae, Youn-Sang; Lee, JaeDong; Jeong, Nak Cheon

2015-08-12

Open coordination sites (OCSs) in metal-organic frameworks (MOFs) often function as key factors in the potential applications of MOFs, such as gas separation, gas sorption, and catalysis. For these applications, the activation process to remove the solvent molecules coordinated at the OCSs is an essential step that must be performed prior to use of the MOFs. To date, the thermal method performed by applying heat and vacuum has been the only method for such activation. In this report, we demonstrate that methylene chloride (MC) itself can perform the activation role: this process can serve as an alternative "chemical route" for the activation that does not require applying heat. To the best of our knowledge, no previous study has demonstrated this function of MC, although MC has been popularly used in the pretreatment step prior to the thermal activation process. On the basis of a Raman study, we propose a plausible mechanism for the chemical activation, in which the function of MC is possibly due to its coordination with the Cu(2+) center and subsequent spontaneous decoordination. Using HKUST-1 film, we further demonstrate that this chemical activation route is highly suitable for activating large-area MOF films.

Robust interferometric imaging via prior-less phase recovery: redundant spacing calibration with generalized-closure phases

NASA Astrophysics Data System (ADS)

Kurien, Binoy G.; Ashcom, Jonathan B.; Shah, Vinay N.; Rachlin, Yaron; Tarokh, Vahid

2017-01-01

Atmospheric turbulence presents a fundamental challenge to Fourier phase recovery in optical interferometry. Typical reconstruction algorithms employ Bayesian inference techniques which rely on prior knowledge of the scene under observation. In contrast, redundant spacing calibration (RSC) algorithms employ redundancy in the baselines of the interferometric array to directly expose the contribution of turbulence, thereby enabling phase recovery for targets of arbitrary and unknown complexity. Traditionally RSC algorithms have been applied directly to single-exposure measurements, which are reliable only at high photon flux in general. In scenarios of low photon flux, such as those arising in the observation of dim objects in space, one must instead rely on time-averaged, atmosphere-invariant quantities such as the bispectrum. In this paper, we develop a novel RSC-based algorithm for prior-less phase recovery in which we generalize the bispectrum to higher order atmosphere-invariants (n-spectra) for improved sensitivity. We provide a strategy for selection of a high-signal-to-noise ratio set of n-spectra using the graph-theoretic notion of the minimum cycle basis. We also discuss a key property of this set (wrap-invariance), which then enables reliable application of standard linear estimation techniques to recover the Fourier phases from the 2π-wrapped n-spectra phases. For validation, we analyse the expected shot-noise-limited performance of our algorithm for both pairwise and Fizeau interferometric architectures, and corroborate this analysis with simulation results showing performance near an atmosphere-oracle Cramer-Rao bound. Lastly, we apply techniques from the field of compressed sensing to perform image reconstruction from the estimated complex visibilities.

Assessment of cognitive factors that impact on student knowledge of genetics

NASA Astrophysics Data System (ADS)

Gerow, Tracy Nelson

1999-12-01

Attaining an understanding of basic principles of inheritance and their implications is crucial for all people as society is confronted with a variety of ethical, sociological and ecological questions generated by the rapid growth of genetic knowledge. College level students are burdened by terminology, have difficulty making associations among related ideas, and often possess misconceptions or fragmented ideas about how traits are inherited. Subject comprehension is evaluated mostly with objective testing techniques that don't show how well students truly understand concepts. This research was done to determine how prior subject knowledge in biology and general cognitive ability affected community college students' understanding of several genetic principles both before and after completing a one-semester college biology course. Understanding of genetic principles was determined with a videotape assessment that evaluated student written explanations of experimental events. The evaluations were then used to place students into three categories: descriptive, transitional, and relational type learners. A subset of students was interviewed to better determine how thoroughly genetic concepts depicted in the videotape program were understood. Prior subject matter knowledge and cognitive level were discovered to be moderately correlated with ability to explain genetic phenomena. Most students in this study were categorized as either descriptive or transitional learners. Descriptive type students gave less detailed explanations, employed less successful problem solving methods, had more misconceptions and used feedback less effectively than did transitional type learners. The study results show that science teachers need to be aware of the heterogeneity existing in their students' background knowledge and cognitive skills. It demonstrated that a large contingency of students, descriptive learners, lack a framework of knowledge upon which to build new concepts or change old ones. Science teachers need to apply meaningful assessment methods as students progress through learning new concepts so that errors in thinking can be diagnosed and remedied with appropriate teaching strategies. It is anticipated that assessment methods that engage students in explaining their understanding of concepts will bring about significant changes in how students learn subjects like genetics and also impact instruction in this crucial area of biological science.

Atypical combinations and scientific impact.

PubMed

Uzzi, Brian; Mukherjee, Satyam; Stringer, Michael; Jones, Ben

2013-10-25

Novelty is an essential feature of creative ideas, yet the building blocks of new ideas are often embodied in existing knowledge. From this perspective, balancing atypical knowledge with conventional knowledge may be critical to the link between innovativeness and impact. Our analysis of 17.9 million papers spanning all scientific fields suggests that science follows a nearly universal pattern: The highest-impact science is primarily grounded in exceptionally conventional combinations of prior work yet simultaneously features an intrusion of unusual combinations. Papers of this type were twice as likely to be highly cited works. Novel combinations of prior work are rare, yet teams are 37.7% more likely than solo authors to insert novel combinations into familiar knowledge domains.

Automatized spleen segmentation in non-contrast-enhanced MR volume data using subject-specific shape priors

NASA Astrophysics Data System (ADS)

Gloger, Oliver; Tönnies, Klaus; Bülow, Robin; Völzke, Henry

2017-07-01

To develop the first fully automated 3D spleen segmentation framework derived from T1-weighted magnetic resonance (MR) imaging data and to verify its performance for spleen delineation and volumetry. This approach considers the issue of low contrast between spleen and adjacent tissue in non-contrast-enhanced MR images. Native T1-weighted MR volume data was performed on a 1.5 T MR system in an epidemiological study. We analyzed random subsamples of MR examinations without pathologies to develop and verify the spleen segmentation framework. The framework is modularized to include different kinds of prior knowledge into the segmentation pipeline. Classification by support vector machines differentiates between five different shape types in computed foreground probability maps and recognizes characteristic spleen regions in axial slices of MR volume data. A spleen-shape space generated by training produces subject-specific prior shape knowledge that is then incorporated into a final 3D level set segmentation method. Individually adapted shape-driven forces as well as image-driven forces resulting from refined foreground probability maps steer the level set successfully to the segment the spleen. The framework achieves promising segmentation results with mean Dice coefficients of nearly 0.91 and low volumetric mean errors of 6.3%. The presented spleen segmentation approach can delineate spleen tissue in native MR volume data. Several kinds of prior shape knowledge including subject-specific 3D prior shape knowledge can be used to guide segmentation processes achieving promising results.

Resiliency of the Multiscale Retinex Image Enhancement Algorithm

NASA Technical Reports Server (NTRS)

Rahman, Zia-Ur; Jobson, Daniel J.; Woodell, Glenn A.

1998-01-01

The multiscale retinex with color restoration (MSRCR) continues to prove itself in extensive testing to be very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition, However, issues remain with regard to the resiliency of the MSRCR to different image sources and arbitrary image manipulations which may have been applied prior to retinex processing. In this paper we define these areas of concern, provide experimental results, and, examine the effects of commonly occurring image manipulation on retinex performance. In virtually all cases the MSRCR is highly resilient to the effects of both the image source variations and commonly encountered prior image-processing. Significant artifacts are primarily observed for the case of selective color channel clipping in large dark zones in a image. These issues are of concerning the processing of digital image archives and other applications where there is neither control over the image acquisition process, nor knowledge about any processing done on th data beforehand.

Real time MRI prostate segmentation based on wavelet multiscale products flow tracking.

PubMed

Flores-Tapia, Daniel; Venugopal, Niranjan; Thomas, Gabriel; McCurdy, Boyd; Ryner, Lawrence; Pistorius, Stephen

2010-01-01

Currently, prostate cancer is the third leading cause of cancer-related deaths among men in North America. As with many others types of cancer, early detection and treatment greatly increases the patient's chance of survival. Combined Magnetic Resonance Imaging and Spectroscopic Imaging (MRI/MRSI) techniques have became a reliable tool for early stage prostate cancer detection. Nevertheless, their performance is strongly affected by the determination of the region of interest (ROI) prior to data acquisition process. The process of executing prostate MRI/MRSI techniques can be significantly enhanced by segmenting the whole prostate. A novel method for segmentation of the prostate in MRI datasets is presented. This method exploits the different behavior presented by signal singularities and noise in the wavelet domain in order to accurately detect the borders around the prostate. The prostate contour is then traced by using a set of spatially variant rules that are based on prior knowledge about the general shape of the prostate. The proposed method yielded promising results when applied to clinical datasets.

The altitudinal temperature lapse rates applied to high elevation rockfalls studies in the Western European Alps

NASA Astrophysics Data System (ADS)

Nigrelli, Guido; Fratianni, Simona; Zampollo, Arianna; Turconi, Laura; Chiarle, Marta

2018-02-01

Temperature is one of the most important aspects of mountain climates. The relationships between air temperature and rockfalls at high-elevation sites are very important to know, but are also very difficult to study. In relation to this, a reliable method to estimate air temperatures at high-elevation sites is to apply the altitudinal temperature lapse rates (ATLR). The aims of this work are to quantify the values and the variability of the hourly ATLR and to apply this to estimated temperatures at high-elevation sites for rockfalls studies. To calculate ATLR prior the rockfalls, we used data acquired from two automatic weather stations that are located at an elevation above 2500 m. The sensors/instruments of these two stations are reliable because subjected to an accurate control and calibration once for year and the raw data have passed two automatic quality controls. Our study has yielded the following main results: (i) hourly ATLR increases slightly with increasing altitude, (ii) it is possible to estimate temperature at high-elevation sites with a good level of accuracy using ATLR, and (iii) temperature plays an important role on slope failures that occur at high-elevation sites and its importance is much more evident if the values oscillate around 0 °C with an amplitude of ±5 °C during the previous time-period. For these studies, it is not enough to improve the knowledge on air temperature, but it is necessary to develop an integrated knowledge of the thermal conditions of different materials involved in these processes (rock, debris, ice, water). Moreover, this integrated knowledge must be acquired by means of sensors and acquisition chains with known metrological traceability and uncertainty of measurements.

The Role of "Creative Transfer" in Professional Transitions

ERIC Educational Resources Information Center

Triantafyllaki, Angeliki

2016-01-01

This paper discusses the concept of "knowledge transfer" in terms of expansion of prior knowledge, creativity and approaches to generating new knowledge. It explores professional transitions in which knowledge restructuring and identity reformation are pathways into greater work flexibility and adjustment. Two studies, exploring…

Tensions in learning professional identities - nursing students' narratives and participation in practical skills during their clinical practice: an ethnographic study.

PubMed

Ewertsson, Mona; Bagga-Gupta, Sangeeta; Allvin, Renée; Blomberg, Karin

2017-01-01

Clinical practice is a pivotal part of nursing education. It provides students with the opportunity to put the knowledge and skills they have acquired from lectures into practice with real patients, under the guidance of registered nurses. Clinical experience is also essential for shaping the nursing students' identity as future professional nurses. There is a lack of knowledge and understanding of the ways in which students learn practical skills and apply knowledge within and across different contexts, i.e. how they apply clinical skills, learnt in the laboratory in university settings, in the clinical setting. The aim of this study was therefore to explore how nursing students describe, and use, their prior experiences related to practical skills during their clinical practice. An ethnographic case study design was used. Fieldwork included participant observations (82 h), informal conversations, and interviews ( n  = 7) that were conducted during nursing students' ( n  = 17) clinical practice at an emergency department at a university hospital in Sweden. The overarching theme identified was "Learning about professional identities with respect to situated power". This encompasses tensions in students' learning when they are socialized into practical skills in the nursing profession. This overarching theme consists of three sub-themes: "Embodied knowledge", "Divergent ways of assessing and evaluating knowledge" and "Balancing approaches". Nursing students do not automatically possess the ability to transfer knowledge from one setting to another; rather, their development is shaped by their experiences and interactions with others when they meet real patients. The study revealed different ways in which students navigated tensions related to power differentials. Reflecting on actions is a prerequisite for developing and learning practical skills and professional identities. This highlights the importance of both educators' and the preceptors' roles for socializing students in this process.

Effects of Process-Oriented and Product-Oriented Worked Examples and Prior Knowledge on Learner Problem Solving and Attitude: A Study in the Domain of Microeconomics

ERIC Educational Resources Information Center

Brooks, Christopher Darren

2009-01-01

The purpose of this study was to investigate the effectiveness of process-oriented and product-oriented worked example strategies and the mediating effect of prior knowledge (high versus low) on problem solving and learner attitude in the domain of microeconomics. In addition, the effect of these variables on learning efficiency as well as the…

Influence of Prior Knowledge and Interest on Fourth- and Fifth-Grade Passage Comprehension on the Qualitative Reading Inventory-4

ERIC Educational Resources Information Center

Clark, Mary Kristen; Kamhi, Alan G.

2014-01-01

Purpose: In 2 experiments, we examined the influence of prior knowledge and interest on 4th- and 5th-grade students' passage comprehension scores on the Qualitative Reading Inventory-4 (QRI-4) and 2 experimenter constructed passages. Method: In Experiment 1, 4th- and 5th-grade students were administered 4 Level 4 passages or 4 Level 5…

Effects of Type of Exploratory Strategy and Prior Knowledge on Middle School Students' Learning of Chemical Formulas from a 3D Role-Playing Game

ERIC Educational Resources Information Center

Chen, Ming-Puu; Wong, Yu-Ting; Wang, Li-Chun

2014-01-01

The purpose of this study was to examine the effects of the type of exploratory strategy and level of prior knowledge on middle school students' performance and motivation in learning chemical formulas via a 3D role-playing game (RPG). Two types of exploratory strategies-RPG exploratory with worked-example and RPG exploratory without…

Counting-On, Trading and Partitioning: Effects of Training and Prior Knowledge on Performance on Base-10 Tasks

ERIC Educational Resources Information Center

Saxton, Matthew; Cakir, Kadir

2006-01-01

Factors affecting performance on base-10 tasks were investigated in a series of four studies with a total of 453 children aged 5-7 years. Training in counting-on was found to enhance child performance on base-10 tasks (Studies 2, 3, and 4), while prior knowledge of counting-on (Study 1), trading (Studies 1 and 3), and partitioning (Studies 1 and…

Evaluation of graduate nursing students' information literacy self-efficacy and applied skills.

PubMed

Robertson, D Susie; Felicilda-Reynaldo, Rhea Faye D

2015-03-01

Maintaining evidence-based nursing practice requires information literacy (IL) skills that should be established prior to completing an undergraduate nursing degree. Based on Bandura's social cognitive theory, this cross-sectional descriptive correlational study assessed the perceived and applied IL skills of graduate nursing students from two family nurse practitioner (FNP) programs in the midwestern United States. Results showed that although the 26 newly admitted FNP students demonstrated a high level of confidence in their IL skills, the students did not perform well in the actual IL skills test. According to Bandura, the students' confidence in their IL knowledge should allow students to be engaged in course activities requiring IL skills. Nurse educators teaching in undergraduate or graduate programs are in key positions to incorporate IL experiences into class activities to allow for skill assessment and further practice. Further research is needed on nursing students' IL self-efficacy and performance. Copyright 2015, SLACK Incorporated.

Fingerprinting microbiomes towards screening for microbial antibiotic resistance.

PubMed

Jin, Naifu; Zhang, Dayi; Martin, Francis L

2017-05-22

There is an increasing need to investigate microbiomes in their entirety in a variety of contexts ranging from environmental to human health scenarios. This requirement is becoming increasingly important with the emergence of antibiotic resistance. In general, more conventional approaches are too expensive and/or time-consuming and often predicated on prior knowledge of the microorganisms one wishes to study. Herein, we propose the use of biospectroscopy tools as relatively high-throughput, non-destructive approaches to profile microbiomes under study. Fourier-transform infrared (FTIR) or Raman spectroscopy both generate fingerprint spectra of biological material and such spectra can readily be subsequently classed according to biochemical changes in the microbiota, such as emergence of antibiotic resistance. FTIR spectroscopy techniques generally can only be applied to desiccated material whereas Raman approaches can be applied to more hydrated samples. The ability to readily fingerprint microbiomes could lend itself to new approaches in determining microbial behaviours and emergence of antibiotic resistance.

A mathematical model to describe the nonlinear elastic properties of the gastrocnemius tendon of chickens.

PubMed

Foutz, T L

1991-03-01

A phenomenological model was developed to describe the nonlinear elastic behavior of the avian gastrocnemius tendon. Quasistatic uniaxial tensile tests were used to apply a deformation and resulting load on the tendon at a deformation rate of 5 mm/min. Plots of deformation versus load indicated a nonlinear loading response. By calculating engineering stress and engineering strain, the experimental data were normalized for tendon shape. The elastic response was determined from stress-strain curves and was found to vary with engineering strain. The response to the applied engineering strain could best be described by a mathematical model that combined a linear function and a nonlinear function. Three parameters in the model were developed to represent the nonlinear elastic behavior of the tendon, thereby allowing analysis of elasticity without prior knowledge of engineering strain. This procedure reduced the amount of data needed for the statistical analysis of nonlinear elasticity.

Illustrating anticipatory life cycle assessment for emerging photovoltaic technologies.

PubMed

Wender, Ben A; Foley, Rider W; Prado-Lopez, Valentina; Ravikumar, Dwarakanath; Eisenberg, Daniel A; Hottle, Troy A; Sadowski, Jathan; Flanagan, William P; Fisher, Angela; Laurin, Lise; Bates, Matthew E; Linkov, Igor; Seager, Thomas P; Fraser, Matthew P; Guston, David H

2014-09-16

Current research policy and strategy documents recommend applying life cycle assessment (LCA) early in research and development (R&D) to guide emerging technologies toward decreased environmental burden. However, existing LCA practices are ill-suited to support these recommendations. Barriers related to data availability, rapid technology change, and isolation of environmental from technical research inhibit application of LCA to developing technologies. Overcoming these challenges requires methodological advances that help identify environmental opportunities prior to large R&D investments. Such an anticipatory approach to LCA requires synthesis of social, environmental, and technical knowledge beyond the capabilities of current practices. This paper introduces a novel framework for anticipatory LCA that incorporates technology forecasting, risk research, social engagement, and comparative impact assessment, then applies this framework to photovoltaic (PV) technologies. These examples illustrate the potential for anticipatory LCA to prioritize research questions and help guide environmentally responsible innovation of emerging technologies.

Artificial neural networks for document analysis and recognition.

PubMed

Marinai, Simone; Gori, Marco; Soda, Giovanni; Society, Computer

2005-01-01

Artificial neural networks have been extensively applied to document analysis and recognition. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document processing tasks, like preprocessing, layout analysis, character segmentation, word recognition, and signature verification, have been effectively faced with very promising results. This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied. Similarities and differences between approaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysis on the reviewed approaches and depicts the most promising research guidelines in the field. In particular, a second generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment.

Image quality improvement in MDCT cardiac imaging via SMART-RECON method

NASA Astrophysics Data System (ADS)

Li, Yinsheng; Cao, Ximiao; Xing, Zhanfeng; Sun, Xuguang; Hsieh, Jiang; Chen, Guang-Hong

2017-03-01

Coronary CT angiography (CCTA) is a challenging imaging task currently limited by the achievable temporal resolution of modern Multi-Detector CT (MDCT) scanners. In this paper, the recently proposed SMARTRECON method has been applied in MDCT-based CCTA imaging to improve the image quality without any prior knowledge of cardiac motion. After the prospective ECG-gated data acquisition from a short-scan angular span, the acquired data were sorted into several sub-sectors of view angles; each corresponds to a 1/4th of the short-scan angular range. Information of the cardiac motion was thus encoded into the data in each view angle sub-sector. The SMART-RECON algorithm was then applied to jointly reconstruct several image volumes, each of which is temporally consistent with the data acquired in the corresponding view angle sub-sector. Extensive numerical simulations were performed to validate the proposed technique and investigate the performance dependence.

Interactive web-based learning modules prior to general medicine advanced pharmacy practice experiences.

PubMed

Isaacs, Alex N; Walton, Alison M; Nisly, Sarah A

2015-04-25

To implement and evaluate interactive web-based learning modules prior to advanced pharmacy practice experiences (APPEs) on inpatient general medicine. Three clinical web-based learning modules were developed for use prior to APPEs in 4 health care systems. The aim of the interactive modules was to strengthen baseline clinical knowledge before the APPE to enable the application of learned material through the delivery of patient care. For the primary endpoint, postassessment scores increased overall and for each individual module compared to preassessment scores. Postassessment scores were similar among the health care systems. The survey demonstrated positive student perceptions of this learning experience. Prior to inpatient general medicine APPEs, web-based learning enabled the standardization and assessment of baseline student knowledge across 4 health care systems.

Prior-knowledge Fitting of Accelerated Five-dimensional Echo Planar J-resolved Spectroscopic Imaging: Effect of Nonlinear Reconstruction on Quantitation.

PubMed

Iqbal, Zohaib; Wilson, Neil E; Thomas, M Albert

2017-07-24

1 H Magnetic Resonance Spectroscopic imaging (SI) is a powerful tool capable of investigating metabolism in vivo from mul- tiple regions. However, SI techniques are time consuming, and are therefore difficult to implement clinically. By applying non-uniform sampling (NUS) and compressed sensing (CS) reconstruction, it is possible to accelerate these scans while re- taining key spectral information. One recently developed method that utilizes this type of acceleration is the five-dimensional echo planar J-resolved spectroscopic imaging (5D EP-JRESI) sequence, which is capable of obtaining two-dimensional (2D) spectra from three spatial dimensions. The prior-knowledge fitting (ProFit) algorithm is typically used to quantify 2D spectra in vivo, however the effects of NUS and CS reconstruction on the quantitation results are unknown. This study utilized a simulated brain phantom to investigate the errors introduced through the acceleration methods. Errors (normalized root mean square error >15%) were found between metabolite concentrations after twelve-fold acceleration for several low concentra- tion (<2 mM) metabolites. The Cramér Rao lower bound% (CRLB%) values, which are typically used for quality control, were not reflective of the increased quantitation error arising from acceleration. Finally, occipital white (OWM) and gray (OGM) human brain matter were quantified in vivo using the 5D EP-JRESI sequence with eight-fold acceleration.

Interplay between Content Knowledge and Scientific Argumentation

ERIC Educational Resources Information Center

Hakyolu, Hanife; Ogan-Bekiroglu, Feral

2016-01-01

This research study aimed to analyze the relationship between content knowledge and argumentation by examining students' prior subject matter knowledge and their production of arguments as well as by comparing students' arguments with their knowledge-in-use during scientific argumentation sessions. A correlational research design was carried out…

Creating Illusions of Knowledge: Learning Errors that Contradict Prior Knowledge

ERIC Educational Resources Information Center

Fazio, Lisa K.; Barber, Sarah J.; Rajaram, Suparna; Ornstein, Peter A.; Marsh, Elizabeth J.

2013-01-01

Most people know that the Pacific is the largest ocean on Earth and that Edison invented the light bulb. Our question is whether this knowledge is stable, or if people will incorporate errors into their knowledge bases, even if they have the correct knowledge stored in memory. To test this, we asked participants general-knowledge questions 2 weeks…

Suicide-Related Knowledge and Confidence Among Behavioral Health Care Staff in Seven States.

PubMed

Silva, Caroline; Smith, April R; Dodd, Dorian R; Covington, David W; Joiner, Thomas E

2016-11-01

Death by suicide is a serious and growing public health concern in the United States. This noncontrolled, naturalistic study examined professionals' knowledge about suicide and confidence in working with suicidal individuals, comparing those who had received either of two gatekeeper trainings-Question, Persuade, and Refer (QPR) or Applied Suicide Intervention Skills Training (ASIST)-or other suicide-relevant training or no training. Participants (N=16,693) were individuals in various professional roles in the field of behavioral health care in Indiana, Kentucky, New York, Pennsylvania, Tennessee, Texas, and Utah. Participants completed a survey assessing suicide knowledge and skills confidence. Most participants (52.9%) reported no previous suicide prevention or assessment training. Individuals with suicide-relevant training demonstrated greater suicide knowledge and confidence than those with no such training. Among those who had received any training, no differences were found in suicide knowledge; however, individuals who had received ASIST reported greater confidence in working with suicidal individuals, compared with those who had received other training. Professional role and prior experience with a client who had died by suicide had significant positive relationships with suicide knowledge and confidence. Regional differences emerged between states and are examined within the context of statewide suicide prevention initiatives. Increasing access to and incentives for participating in suicide-relevant training among behavioral health care staff may foster a more knowledgeable and confident group of gatekeepers. Future research should examine whether increases in knowledge and confidence among staff translate into actual changes in practice that help protect and serve at-risk individuals.

Profiles of Inconsistent Knowledge in Children's Pathways of Conceptual Change

ERIC Educational Resources Information Center

Schneider, Michael; Hardy, Ilonca

2013-01-01

Conceptual change requires learners to restructure parts of their conceptual knowledge base. Prior research has identified the fragmentation and the integration of knowledge as 2 important component processes of knowledge restructuring but remains unclear as to their relative importance and the time of their occurrence during development. Previous…

49 CFR 240.209 - Procedures for making the determination on knowledge.

Code of Federal Regulations, 2010 CFR

2010-10-01

... knowledge. 240.209 Section 240.209 Transportation Other Regulations Relating to Transportation (Continued... determination on knowledge. (a) Each railroad, prior to initially certifying or recertifying any person as an... with the requirements of § 240.125 of this part, demonstrated sufficient knowledge of the railroad's...

Activation of Background Knowledge for Inference Making: Effects on Reading Comprehension

ERIC Educational Resources Information Center

Elbro, Carsten; Buch-Iversen, Ida

2013-01-01

Failure to "activate" relevant, existing background knowledge may be a cause of poor reading comprehension. This failure may cause particular problems with inferences that depend heavily on prior knowledge. Conversely, teaching how to use background knowledge in the context of gap-filling inferences could improve reading comprehension in…

Building on prior knowledge without building it in.

PubMed

Hansen, Steven S; Lampinen, Andrew K; Suri, Gaurav; McClelland, James L

2017-01-01

Lake et al. propose that people rely on "start-up software," "causal models," and "intuitive theories" built using compositional representations to learn new tasks more efficiently than some deep neural network models. We highlight the many drawbacks of a commitment to compositional representations and describe our continuing effort to explore how the ability to build on prior knowledge and to learn new tasks efficiently could arise through learning in deep neural networks.

Smoothing Spline ANOVA Decomposition of Arbitrary Splines: An Application to Eye Movements in Reading

PubMed Central

Matuschek, Hannes; Kliegl, Reinhold; Holschneider, Matthias

2015-01-01

The Smoothing Spline ANOVA (SS-ANOVA) requires a specialized construction of basis and penalty terms in order to incorporate prior knowledge about the data to be fitted. Typically, one resorts to the most general approach using tensor product splines. This implies severe constraints on the correlation structure, i.e. the assumption of isotropy of smoothness can not be incorporated in general. This may increase the variance of the spline fit, especially if only a relatively small set of observations are given. In this article, we propose an alternative method that allows to incorporate prior knowledge without the need to construct specialized bases and penalties, allowing the researcher to choose the spline basis and penalty according to the prior knowledge of the observations rather than choosing them according to the analysis to be done. The two approaches are compared with an artificial example and with analyses of fixation durations during reading. PMID:25816246

Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data

PubMed Central

2014-01-01

Background High-throughput omics technologies have enabled the measurement of many genes or metabolites simultaneously. The resulting high dimensional experimental data poses significant challenges to transcriptomics and metabolomics data analysis methods, which may lead to spurious instead of biologically relevant results. One strategy to improve the results is the incorporation of prior biological knowledge in the analysis. This strategy is used to reduce the solution space and/or to focus the analysis on biological meaningful regions. In this article, we review a selection of these methods used in transcriptomics and metabolomics. We combine the reviewed methods in three groups based on the underlying mathematical model: exploratory methods, supervised methods and estimation of the covariance matrix. We discuss which prior knowledge has been used, how it is incorporated and how it modifies the mathematical properties of the underlying methods. PMID:25033193

Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles

NASA Astrophysics Data System (ADS)

Cook, Michelle Patrick

2006-11-01

Visual representations are essential for communicating ideas in the science classroom; however, the design of such representations is not always beneficial for learners. This paper presents instructional design considerations providing empirical evidence and integrating theoretical concepts related to cognitive load. Learners have a limited working memory, and instructional representations should be designed with the goal of reducing unnecessary cognitive load. However, cognitive architecture alone is not the only factor to be considered; individual differences, especially prior knowledge, are critical in determining what impact a visual representation will have on learners' cognitive structures and processes. Prior knowledge can determine the ease with which learners can perceive and interpret visual representations in working memory. Although a long tradition of research has compared experts and novices, more research is necessary to fully explore the expert-novice continuum and maximize the potential of visual representations.

Sleep Spindle Density Predicts the Effect of Prior Knowledge on Memory Consolidation

PubMed Central

Lambon Ralph, Matthew A.; Kempkes, Marleen; Cousins, James N.; Lewis, Penelope A.

2016-01-01

Information that relates to a prior knowledge schema is remembered better and consolidates more rapidly than information that does not. Another factor that influences memory consolidation is sleep and growing evidence suggests that sleep-related processing is important for integration with existing knowledge. Here, we perform an examination of how sleep-related mechanisms interact with schema-dependent memory advantage. Participants first established a schema over 2 weeks. Next, they encoded new facts, which were either related to the schema or completely unrelated. After a 24 h retention interval, including a night of sleep, which we monitored with polysomnography, participants encoded a second set of facts. Finally, memory for all facts was tested in a functional magnetic resonance imaging scanner. Behaviorally, sleep spindle density predicted an increase of the schema benefit to memory across the retention interval. Higher spindle densities were associated with reduced decay of schema-related memories. Functionally, spindle density predicted increased disengagement of the hippocampus across 24 h for schema-related memories only. Together, these results suggest that sleep spindle activity is associated with the effect of prior knowledge on memory consolidation. SIGNIFICANCE STATEMENT Episodic memories are gradually assimilated into long-term memory and this process is strongly influenced by sleep. The consolidation of new information is also influenced by its relationship to existing knowledge structures, or schemas, but the role of sleep in such schema-related consolidation is unknown. We show that sleep spindle density predicts the extent to which schemas influence the consolidation of related facts. This is the first evidence that sleep is associated with the interaction between prior knowledge and long-term memory formation. PMID:27030764

45 CFR 1616.3 - Qualifications.

Code of Federal Regulations, 2010 CFR

2010-10-01

...) Academic training and performance; (b) The nature and extent of prior legal experience; (c) Knowledge and understanding of the legal problems and needs of the poor; (d) Prior working experience in the client community...

Educational intervention assessment aiming the hearing preservation of workers at a hospital laundry.

PubMed

Fontoura, Francisca Pinheiro; Gonçalves, Cláudia Giglio de Oliveira; Willig, Mariluci Hautsch; Lüders, Debora

2018-02-19

Evaluate the effectiveness of educational interventions on hearing health developed at a hospital laundry. Quantitative assessment conducted at a hospital laundry. The study sample comprised 80 workers of both genders divided into two groups: Study Group (SG) and Control Group (CG). The educational interventions in hearing preservation were evaluated based on a theoretical approach using the Participatory Problem-based Methodology in five workshops. To assess the results of the workshops, an instrument containing 36 questions on knowledge, attitudes, and practices in hearing preservation at work was used. Questionnaires A and B were applied prior to and one month after intervention, respectively. The answers to both questionnaires were analyzed by group according to gender and schooling. Results of the pre-intervention phase showed low scores regarding knowledge about hearing health in the work setting for both groups, but significant improvement in knowledge was observed after intervention in the SG, with 77.7% of the answers presenting significant difference between the groups. There was also an improvement in the mean scores, with 35 responses (95.22%) presenting scores >4 (considered adequate). The women presented lower knowledge scores than the men; however, these differences were not observed in the SG after the workshops. Schooling was not a relevant factor in the assessment. The educational proposal grounded in the Participatory Problem-based Methodology expanded knowledge about hearing health at work among the participants.

Dynamic modelling of microRNA regulation during mesenchymal stem cell differentiation.

PubMed

Weber, Michael; Sotoca, Ana M; Kupfer, Peter; Guthke, Reinhard; van Zoelen, Everardus J

2013-11-12

Network inference from gene expression data is a typical approach to reconstruct gene regulatory networks. During chondrogenic differentiation of human mesenchymal stem cells (hMSCs), a complex transcriptional network is active and regulates the temporal differentiation progress. As modulators of transcriptional regulation, microRNAs (miRNAs) play a critical role in stem cell differentiation. Integrated network inference aimes at determining interrelations between miRNAs and mRNAs on the basis of expression data as well as miRNA target predictions. We applied the NetGenerator tool in order to infer an integrated gene regulatory network. Time series experiments were performed to measure mRNA and miRNA abundances of TGF-beta1+BMP2 stimulated hMSCs. Network nodes were identified by analysing temporal expression changes, miRNA target gene predictions, time series correlation and literature knowledge. Network inference was performed using NetGenerator to reconstruct a dynamical regulatory model based on the measured data and prior knowledge. The resulting model is robust against noise and shows an optimal trade-off between fitting precision and inclusion of prior knowledge. It predicts the influence of miRNAs on the expression of chondrogenic marker genes and therefore proposes novel regulatory relations in differentiation control. By analysing the inferred network, we identified a previously unknown regulatory effect of miR-524-5p on the expression of the transcription factor SOX9 and the chondrogenic marker genes COL2A1, ACAN and COL10A1. Genome-wide exploration of miRNA-mRNA regulatory relationships is a reasonable approach to identify miRNAs which have so far not been associated with the investigated differentiation process. The NetGenerator tool is able to identify valid gene regulatory networks on the basis of miRNA and mRNA time series data.

Crossing borders: High school science teachers learning to teach the specialized language of science

NASA Astrophysics Data System (ADS)

Patrick, Jennifer Drake

The highly specialized language of science is both challenging and alienating to adolescent readers. This study investigated how secondary science teachers learn to teach the specialized language of science in their classrooms. Three research questions guided this study: (a) what do science teachers know about teaching reading in science? (b) what understanding about the unique language demands of science reading do they construct through professional development? and (c) how do they integrate what they have learned about these specialized features of science language into their teaching practices? This study investigated the experience of seven secondary science teachers as they participated in a professional development program designed to teach them about the specialized language of science. Data sources included participant interviews, audio-taped professional development sessions, field notes from classroom observations, and a prior knowledge survey. Results from this study suggest that science teachers (a) were excited to learn about disciplinary reading practices, (b) developed an emergent awareness of the specialized features of science language and the various genres of science writing, and (c) recognized that the challenges of science reading goes beyond vocabulary. These teachers' efforts to understand and address the language of science in their teaching practices were undermined by their lack of basic knowledge of grammar, availability of time and resources, their prior knowledge and experiences, existing curriculum, and school structure. This study contributes to our understanding of how secondary science teachers learn about disciplinary literacy and apply that knowledge in their classroom instruction. It has important implications for literacy educators and science educators who are interested in using language and literacy practices in the service of science teaching and learning. (Full text of this dissertation may be available via the University of Florida Libraries web site. Please check http://www.uflib.ufl.edu/etd.html)

A Study about Placement Support Using Semantic Similarity

ERIC Educational Resources Information Center

Katz, Marco; van Bruggen, Jan; Giesbers, Bas; Waterink, Wim; Eshuis, Jannes; Koper, Rob

2014-01-01

This paper discusses Latent Semantic Analysis (LSA) as a method for the assessment of prior learning. The Accreditation of Prior Learning (APL) is a procedure to offer learners an individualized curriculum based on their prior experiences and knowledge. The placement decisions in this process are based on the analysis of student material by domain…

The Effects of Feedback during Exploratory Mathematics Problem Solving: Prior Knowledge Matters

ERIC Educational Resources Information Center

Fyfe, Emily R.; Rittle-Johnson, Bethany; DeCaro, Marci S.

2012-01-01

Providing exploratory activities prior to explicit instruction can facilitate learning. However, the level of guidance provided during the exploration has largely gone unstudied. In this study, we examined the effects of 1 form of guidance, feedback, during exploratory mathematics problem solving for children with varying levels of prior domain…

Learners' strategies for reconstructing cognitive frameworks and navigating conceptual change from prior conception to consensual genetics knowledge

NASA Astrophysics Data System (ADS)

Parrott, Annette M.

Problem. Science teachers are charged with preparing students to become scientifically literate individuals. Teachers are given curriculum that specifies the knowledge that students should come away with; however, they are not necessarily aware of the knowledge with which the student arrives or how best to help them navigate between the two knowledge states. Educators must be aware, not only of where their students are conceptually, but how their students move from their prior knowledge and naive theories, to scientifically acceptable theories. The understanding of how students navigate this course has the potential to revolutionize educational practices. Methods. This study explored how five 9th grade biology students reconstructed their cognitive frameworks and navigated conceptual change from prior conception to consensual genetics knowledge. The research questions investigated were: (1) how do students in the process of changing their naive science theories to accepted science theories describe their journey from prior knowledge to current conception, and (2) what are the methods that students utilize to bridge the gap between alternate and consensual science conceptions to effect conceptual change. Qualitative and quantitative methods were employed to gather and analyze the data. In depth, semi-structured interviews formed the primary data for probing the context and details of students' conceptual change experience. Primary interview data was coded by thematic analysis. Results and discussion. This study revealed information about students' perceived roles in learning, the role of articulation in the conceptual change process, and ways in which a community of learners aids conceptual change. It was ascertained that students see their role in learning primarily as repeating information until they could add that information to their knowledge. Students are more likely to consider challenges to their conceptual frameworks and be more motivated to become active participants in constructing their knowledge when they are working collaboratively with peers instead of receiving instruction from their teacher. Articulation was found to be instrumental in aiding learners in identifying their alternate conceptions as well as in revisiting, investigating and reconstructing their conceptual frameworks. Based on the assumptions generated, suggestions were offered to inform pedagogical practice in support of the conceptual change process.

From science to action: Principles for undertaking environmental research that enables knowledge exchange and evidence-based decision-making.

PubMed

Cvitanovic, C; McDonald, J; Hobday, A J

2016-12-01

Effective conservation requires knowledge exchange among scientists and decision-makers to enable learning and support evidence-based decision-making. Efforts to improve knowledge exchange have been hindered by a paucity of empirically-grounded guidance to help scientists and practitioners design and implement research programs that actively facilitate knowledge exchange. To address this, we evaluated the Ningaloo Research Program (NRP), which was designed to generate new scientific knowledge to support evidence-based decisions about the management of the Ningaloo Marine Park in north-western Australia. Specifically, we evaluated (1) outcomes of the NRP, including the extent to which new knowledge informed management decisions; (2) the barriers that prevented knowledge exchange among scientists and managers; (3) the key requirements for improving knowledge exchange processes in the future; and (4) the core capacities that are required to support knowledge exchange processes. While the NRP generated expansive and multidisciplinary science outputs directly relevant to the management of the Ningaloo Marine Park, decision-makers are largely unaware of this knowledge and little has been integrated into decision-making processes. A range of barriers prevented efficient and effective knowledge exchange among scientists and decision-makers including cultural differences among the groups, institutional barriers within decision-making agencies, scientific outputs that were not translated for decision-makers and poor alignment between research design and actual knowledge needs. We identify a set of principles to be implemented routinely as part of any applied research program, including; (i) stakeholder mapping prior to the commencement of research programs to identify all stakeholders, (ii) research questions to be co-developed with stakeholders, (iii) implementation of participatory research approaches, (iv) use of a knowledge broker, and (v) tailored knowledge management systems. Finally, we articulate the individual, institutional and financial capacities that must be developed to underpin successful knowledge exchange strategies. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Reconstruction of normal forms by learning informed observation geometries from data.

PubMed

Yair, Or; Talmon, Ronen; Coifman, Ronald R; Kevrekidis, Ioannis G

2017-09-19

The discovery of physical laws consistent with empirical observations is at the heart of (applied) science and engineering. These laws typically take the form of nonlinear differential equations depending on parameters; dynamical systems theory provides, through the appropriate normal forms, an "intrinsic" prototypical characterization of the types of dynamical regimes accessible to a given model. Using an implementation of data-informed geometry learning, we directly reconstruct the relevant "normal forms": a quantitative mapping from empirical observations to prototypical realizations of the underlying dynamics. Interestingly, the state variables and the parameters of these realizations are inferred from the empirical observations; without prior knowledge or understanding, they parametrize the dynamics intrinsically without explicit reference to fundamental physical quantities.

Compressive Sampling Based Interior Reconstruction for Dynamic Carbon Nanotube Micro-CT

PubMed Central

Yu, Hengyong; Cao, Guohua; Burk, Laurel; Lee, Yueh; Lu, Jianping; Santago, Pete; Zhou, Otto; Wang, Ge

2010-01-01

In the computed tomography (CT) field, one recent invention is the so-called carbon nanotube (CNT) based field emission x-ray technology. On the other hand, compressive sampling (CS) based interior tomography is a new innovation. Combining the strengths of these two novel subjects, we apply the interior tomography technique to local mouse cardiac imaging using respiration and cardiac gating with a CNT based micro-CT scanner. The major features of our method are: (1) it does not need exact prior knowledge inside an ROI; and (2) two orthogonal scout projections are employed to regularize the reconstruction. Both numerical simulations and in vivo mouse studies are performed to demonstrate the feasibility of our methodology. PMID:19923686

Frequency domain phase-shifted confocal microscopy (FDPCM) with array detection

NASA Astrophysics Data System (ADS)

Ge, Baoliang; Huang, Yujia; Fang, Yue; Kuang, Cuifang; Xiu, Peng; Liu, Xu

2017-09-01

We proposed a novel method to reconstruct images taken by array detected confocal microscopy without prior knowledge about its detector distribution. The proposed frequency domain phase-shifted confocal microscopy (FDPCM) shifts the image from each detection channel to its corresponding place by substituting the phase information in Fourier domain. Theoretical analysis shows that our method could approach the resolution nearly twofold of wide-field microscopy. Simulation and experiment results are also shown to verify the applicability and effectiveness of our method. Compared to Airyscan, our method holds the advantage of simplicity and convenience to be applied to array detectors with different structure, which makes FDPCM have great potential in the application of biomedical observation in the future.

Temporal dynamics of the knowledge-mediated visual disambiguation process in humans: a magnetoencephalography study.

PubMed

Urakawa, Tomokazu; Ogata, Katsuya; Kimura, Takahiro; Kume, Yuko; Tobimatsu, Shozo

2015-01-01

Disambiguation of a noisy visual scene with prior knowledge is an indispensable task of the visual system. To adequately adapt to a dynamically changing visual environment full of noisy visual scenes, the implementation of knowledge-mediated disambiguation in the brain is imperative and essential for proceeding as fast as possible under the limited capacity of visual image processing. However, the temporal profile of the disambiguation process has not yet been fully elucidated in the brain. The present study attempted to determine how quickly knowledge-mediated disambiguation began to proceed along visual areas after the onset of a two-tone ambiguous image using magnetoencephalography with high temporal resolution. Using the predictive coding framework, we focused on activity reduction for the two-tone ambiguous image as an index of the implementation of disambiguation. Source analysis revealed that a significant activity reduction was observed in the lateral occipital area at approximately 120 ms after the onset of the ambiguous image, but not in preceding activity (about 115 ms) in the cuneus when participants perceptually disambiguated the ambiguous image with prior knowledge. These results suggested that knowledge-mediated disambiguation may be implemented as early as approximately 120 ms following an ambiguous visual scene, at least in the lateral occipital area, and provided an insight into the temporal profile of the disambiguation process of a noisy visual scene with prior knowledge. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Mitigating Information Overload: The Impact of Context-Based Approach to the Design of Tools for Intelligence Analysts

DTIC Science & Technology

2008-03-01

amount of arriving data, extract actionable information, and integrate it with prior knowledge. Add to that the pressures of today’s fusion center...information, and integrate it with prior knowledge. Add to that the pressures of today’s fusion center climate and it becomes clear that analysts, police... fusion centers, including specifics about how these problems manifest at the Illinois State Police (ISP) Statewide Terrorism and Intelligence Center

The dynamics of fidelity over the time course of long-term memory.

PubMed

Persaud, Kimele; Hemmer, Pernille

2016-08-01

Bayesian models of cognition assume that prior knowledge about the world influences judgments. Recent approaches have suggested that the loss of fidelity from working to long-term (LT) memory is simply due to an increased rate of guessing (e.g. Brady, Konkle, Gill, Oliva, & Alvarez, 2013). That is, recall is the result of either remembering (with some noise) or guessing. This stands in contrast to Bayesian models of cognition while assume that prior knowledge about the world influences judgments, and that recall is a combination of expectations learned from the environment and noisy memory representations. Here, we evaluate the time course of fidelity in LT episodic memory, and the relative contribution of prior category knowledge and guessing, using a continuous recall paradigm. At an aggregate level, performance reflects a high rate of guessing. However, when aggregate data is partitioned by lag (i.e., the number of presentations from study to test), or is un-aggregated, performance appears to be more complex than just remembering with some noise and guessing. We implemented three models: the standard remember-guess model, a three-component remember-guess model, and a Bayesian mixture model and evaluated these models against the data. The results emphasize the importance of taking into account the influence of prior category knowledge on memory. Copyright © 2016 Elsevier Inc. All rights reserved.

Design Guide for Earth System Science Education: Common Student Learning Objectives and Special Pedagogical Approaches

NASA Astrophysics Data System (ADS)

Baker, D.

2006-12-01

As part of the NASA-supported undergraduate Earth System Science Education (ESSE) program, fifty-seven institutions have developed and implemented a wide range of Earth system science (ESS) courses, pedagogies, and evaluation tools. The Teaching, Learning, and Evaluation section of USRA's online ESSE Design Guide showcases these ESS learning environments. This Design Guide section also provides resources for faculty who wish to develop ESS courses. It addresses important course design issues including prior student knowledge and interests, student learning objectives, learning resources, pedagogical approaches, and assessments tied to student learning objectives. The ESSE Design Guide provides links to over 130 ESS course syllabi at introductory, senior, and graduate levels. ESS courses over the past 15 years exhibit common student learning objectives and unique pedagogical approaches. From analysis of ESS course syllabi, seven common student learning objectives emerged: 1) demonstrate systems thinking, 2) develop an ESS knowledge base, 3) apply ESS to the human dimension, 4) expand and apply analytical skills, 5) improve critical thinking skills, 6) build professional/career skills, and 7) acquire an enjoyment and appreciation for science. To meet these objectives, ESSE often requires different ways of teaching than in traditional scientific disciplines. This presentation will highlight some especially successful pedagogical approaches for creating positive and engaging ESS learning environments.

Everyday ethics and help-seeking in early rheumatoid arthritis

PubMed Central

Townsend, A.; Adam, P.; Cox, S.M.; Li, L.C.

2018-01-01

Background Sociological understandings of chronic illness have revealed tensions and complexities around help-seeking. Although ethics underpins healthcare, its application in the area of chronic illness is limited. Here we apply an ethical framework to interview accounts and identify ethical challenges in the early rheumatoid arthritis (RA) experience. Methods In-depth interviews were conducted with eight participants who had been diagnosed with RA in the 12 months prior to recruitment. Applying the concepts of autonomous decision-making and procedural justice highlighted ethical concerns which arose throughout the help-seeking process. Analysis was based on the constant-comparison approach. Results Individuals described decision-making, illness actions and the medical encounter. The process was complicated by inadequate knowledge about symptoms, common-sense understandings about the GP appointment, difficulties concerning access to specialists, and patient–practitioner interactions. Autonomous decision-making and procedural justice were compromised. The accounts revealed contradictions between the policy ideals of active self-management, patient-centred care and shared decision-making, and the everyday experiences of individuals. Conclusions For ethical healthcare there is a need for: public knowledge about early RA symptoms; more effective patient–practitioner communication; and increased support during the wait between primary and secondary care. Healthcare facilities and the government may consider different models to deliver services to people requiring rheumatology consults. PMID:20610465

Toward a Blended Ontology: Applying Knowledge Systems to ...

EPA Pesticide Factsheets

Bionanomedicine and environmental research share need common terms and ontologies. This study applied knowledge systems, data mining, and bibliometrics used in nano-scale ADME research from 1991 to 2011. The prominence of nano-ADME in environmental research began to exceed the publication rate in medical research in 2006. That trend appears to continue as a result of the growing products in commerce using nanotechnology, that is, 5-fold growth in number of countries with nanomaterials research centers. Funding for this research virtually did not exist prior to 2002, whereas today both medical and environmental research is funded globally. Key nanoparticle research began with pharmacology and therapeutic drug-delivery and contrasting agents, but the advances have found utility in the environmental research community. As evidence ultrafine aerosols and aquatic colloids research increased 6-fold, indicating a new emphasis on environmental nanotoxicology. User-directed expert elicitation from the engineering and chemical/ADME domains can be combined with appropriate Boolean logic and queries to define the corpus of nanoparticle interest. The study combined pharmacological expertise and informatics to identify the corpus by building logical conclusions and observations. Publication records informatics can lead to an enhanced understanding the connectivity between fields, as well as overcoming the differences in ontology between the fields. The National Exposure Resea

Assessment of Knowledge Transfer in the Context of Biomechanics

ERIC Educational Resources Information Center

Hutchison, Randolph E.

2011-01-01

The dynamic act of knowledge transfer, or the connection of a student's prior knowledge to features of a new problem, could be considered one of the primary goals of education. Yet studies highlight more instances of failure than success. This dissertation focuses on how knowledge transfer takes place during individual problem solving, in…

New Knowledge Derived from Learned Knowledge: Functional-Anatomic Correlates of Stimulus Equivalence

ERIC Educational Resources Information Center

Schlund, Michael W.; Hoehn-Saric, Rudolf; Cataldo, Michael F.

2007-01-01

Forming new knowledge based on knowledge established through prior learning is a central feature of higher cognition that is captured in research on stimulus equivalence (SE). Numerous SE investigations show that reinforcing behavior under control of distinct sets of arbitrary conditional relations gives rise to stimulus control by new, "derived"…

They're Lovin' It: How Preschool Children Mediated Their Funds of Knowledge into Dramatic Play

ERIC Educational Resources Information Center

Karabon, Anne

2017-01-01

The funds of knowledge framework promotes connecting community contexts with curriculum aimed to activate children's prior knowledge. Typically, teachers determine what knowledge sources harmonise best with their existing programming, potentially omitting particular resources that may not align. Young children, on the other hand, can act as agents…

Is knowledge important? Empirical research on nuclear risk communication in two countries.

PubMed

Perko, Tanja; Zeleznik, Nadja; Turcanu, Catrinel; Thijssen, Peter

2012-06-01

Increasing audience knowledge is often set as a primary objective of risk communication efforts. But is it worthwhile focusing risk communication strategies solely on enhancing specific knowledge? The main research questions tackled in this paper were: (1) if prior audience knowledge related to specific radiation risks is influential for the perception of these risks and the acceptance of communicated messages and (2) if gender, attitudes, risk perception of other radiation risks, confidence in authorities, and living in the vicinity of nuclear/radiological installations may also play an important role in this matter. The goal of this study was to test empirically the mentioned predictors in two independent case studies in different countries. The first case study was an information campaign for iodine pre-distribution in Belgium (N = 1035). The second was the information campaign on long-term radioactive waste disposal in Slovenia (N = 1,200). In both cases, recurrent and intensive communication campaigns were carried out by the authorities aiming, among other things, at increasing specific audience knowledge. Results show that higher prior audience knowledge leads to more willingness to accept communicated messages, but it does not affect people’s perception of the specific risk communicated. In addition, the influence of prior audience knowledge on the acceptance of communicated messages is shown to be no stronger than that of general radiation risk perception. The results in both case studies suggest that effective risk communication has to focus not only on knowledge but also on other more heuristic predictors, such as risk perception or attitudes toward communicated risks.

Relations among Conceptual Knowledge, Procedural Knowledge, and Procedural Flexibility in Two Samples Differing in Prior Knowledge

ERIC Educational Resources Information Center

Schneider, Michael; Rittle-Johnson, Bethany; Star, Jon R.

2011-01-01

Competence in many domains rests on children developing conceptual and procedural knowledge, as well as procedural flexibility. However, research on the developmental relations between these different types of knowledge has yielded unclear results, in part because little attention has been paid to the validity of the measures or to the effects of…

Knowledge exchange in the UK CLAHRCs.

PubMed

Racko, Girts

2018-04-09

Purpose The purpose of this paper is to examine how knowledge exchange between academics and clinicians in Collaborations for Leadership in Applied Health Research and Care (CLAHRCs) is influenced by their social position based on their symbolic and social capitals, that is, their personal professional status and connections to high-status professional peers, knowledge brokers, and unfamiliar professional peers. Design/methodology/approach Using an online survey, the author triangulates the cross-sectional measurement of the effects of academic and clinicians' social position in the initial and later phases of CLAHRCs with the longitudinal measurement of these effects over a two-year period. Findings First, academics and clinicians with a higher personal professional status are more likely to develop joint networks and decision making both in the early and later phases of a CLAHRC. Second, academics and clinicians who are more connected to higher status occupational peers are more likely to develop joint networks in the early phase of a knowledge exchange partnership but are less likely to become engaged in joint networks over time. Third, involvement of knowledge brokers in the networks of academics and clinicians is likely to facilitate their inter-professional networking only in the later partnership phase. Practical implications Academics and clinicians' capitals have a distinctive influence on knowledge exchange in the early and later phases of CLAHRCs and on a change in knowledge exchange over a two-year period. Originality/value Prior research on CLAHRCs has examined how knowledge exchange between academics and clinicians can be encouraged by the creation of shared governance mechanisms. The author advances this research by highlighting the role of their social position in facilitating knowledge exchange.

On-site comprehensive curriculum to teach reproductive health to female adolescents in Kenya.

PubMed

Gaughran, Margaret; Asgary, Ramin

2014-04-01

Rates of sexually transmitted infections (STIs) and unplanned pregnancy are high in Kenya, and limited reproductive health education exists in schools. We designed and implemented a 6-week reproductive health curriculum in Laikipia District, Kenya, in 2011, which included didactic sessions, educational games, and open discussions. We applied a mixed quantitative and qualitative methods to evaluate this curriculum including a comprehensive 35-item survey to assess pre- and post-training knowledge, attitudes, and practices of female teenagers regarding STIs/HIV and family planning using paired t-test as well as complementary focus groups (n=42) and individual interviews (n=20). Average age for 42 female teenagers was 16.5 (± 1.31) years. Pre-test questionnaires revealed lack of knowledge about different types of STIs, specifically chlamydia, but adequate knowledge of basic contraception including abstinence and condom use. By the conclusion of the study, we observed improvement in following educational domains: general knowledge of HIV/AIDS (85% ± 7.5% to 94% ± 5.6%) (p<0.001); general knowledge of teen pregnancy and STIs (57% ± 19% to 82% ± 13%) (p<0.001); and overall scores of knowledge, attitude, and self-efficacy (81% ± 6.6% to 90% ± 5%) (p<0.001). Focus group discussions, however, revealed persistent misconceptions and knowledge gaps with themes regarding HIV transmission risk factors, perceived difficulty negotiating condom use, masturbation and its perceived consequences, and issues surrounding female circumcision. Important misconceptions and gaps in reproductive practices were identified and addressed using a mixed methods approach. Despite prior basic knowledge and positive attitudes on STI prevention and family planning, complementary teaching approaches were instrumental in improving overall knowledge of STIs other than HIV as well as family planning. The curriculum was feasible, well received, and achieved its educational goals.

An investigation of prior knowledge in Automatic Music Transcription systems.

PubMed

Cazau, Dorian; Revillon, Guillaume; Krywyk, Julien; Adam, Olivier

2015-10-01

Automatic transcription of music is a long-studied research field with many operational systems available commercially. In this paper, a generic transcription system able to host various prior knowledge parameters has been developed, followed by an in-depth investigation of their impact on music transcription. Explicit links between musical knowledge and algorithmic formalism have been made. Musical knowledge covers classes of timbre, musicology, and playing style of an instrument repertoire. An evaluation sound corpus gathering musical pieces played by human performers from three different instrument repertoires, namely, classical piano, steel-string acoustic guitar, and the marovany zither from Madagascar, has been developed. The different components of musical knowledge have been successively incorporated in a complete transcription system, consisting mainly of a Probabilistic Latent Component Analysis algorithm post-processed with a Hidden Markov Model, and their impact on transcription results have been comparatively evaluated.

Analytical Method to Evaluate Failure Potential During High-Risk Component Development

NASA Technical Reports Server (NTRS)

Tumer, Irem Y.; Stone, Robert B.; Clancy, Daniel (Technical Monitor)

2001-01-01

Communicating failure mode information during design and manufacturing is a crucial task for failure prevention. Most processes use Failure Modes and Effects types of analyses, as well as prior knowledge and experience, to determine the potential modes of failures a product might encounter during its lifetime. When new products are being considered and designed, this knowledge and information is expanded upon to help designers extrapolate based on their similarity with existing products and the potential design tradeoffs. This paper makes use of similarities and tradeoffs that exist between different failure modes based on the functionality of each component/product. In this light, a function-failure method is developed to help the design of new products with solutions for functions that eliminate or reduce the potential of a failure mode. The method is applied to a simplified rotating machinery example in this paper, and is proposed as a means to account for helicopter failure modes during design and production, addressing stringent safety and performance requirements for NASA applications.

Adult educators' core competences

NASA Astrophysics Data System (ADS)

Wahlgren, Bjarne

2016-06-01

Which competences do professional adult educators need? This research note discusses the topic from a comparative perspective, finding that adult educators' required competences are wide-ranging, heterogeneous and complex. They are subject to context in terms of national and cultural environment as well as the kind of adult education concerned (e.g. basic education, work-related education etc.). However, it seems that it is possible to identify certain competence requirements which transcend national, cultural and functional boundaries. This research note summarises these common or "core" requirements, organising them into four thematic subcategories: (1) communicating subject knowledge; (2) taking students' prior learning into account; (3) supporting a learning environment; and (4) the adult educator's reflection on his or her own performance. At the end of his analysis of different competence profiles, the author notes that adult educators' ability to train adult learners in a way which then enables them to apply and use what they have learned in practice (thus performing knowledge transfer) still seems to be overlooked.

Prior knowledge-based approach for associating ...

EPA Pesticide Factsheets

Evaluating the potential human health and/or ecological risks associated with exposures to complex chemical mixtures in the ambient environment is one of the central challenges of chemical safety assessment and environmental protection. There is a need for approaches that can help to integrate chemical monitoring and bio-effects data to evaluate risks associated with chemicals present in the environment. We used prior knowledge about chemical-gene interactions to develop a knowledge assembly model for detected chemicals at five locations near two wastewater treatment plants. The assembly model was used to generate hypotheses about the biological impacts of the chemicals at each location. The hypotheses were tested using empirical hepatic gene expression data from fathead minnows exposed for 12 d at each location. Empirical gene expression data was also mapped to the assembly models to statistically evaluate the likelihood of a chemical contributing to the observed biological responses. The prior knowledge approach was able reasonably hypothesize the biological impacts at one site but not the other. Chemicals most likely contributing to the observed biological responses were identified at each location. Despite limitations to the approach, knowledge assembly models have strong potential for associating chemical occurrence with potential biological effects and providing a foundation for hypothesis generation to guide research and/or monitoring efforts relat

A blind deconvolution method based on L1/L2 regularization prior in the gradient space

NASA Astrophysics Data System (ADS)

Cai, Ying; Shi, Yu; Hua, Xia

2018-02-01

In the process of image restoration, the result of image restoration is very different from the real image because of the existence of noise, in order to solve the ill posed problem in image restoration, a blind deconvolution method based on L1/L2 regularization prior to gradient domain is proposed. The method presented in this paper first adds a function to the prior knowledge, which is the ratio of the L1 norm to the L2 norm, and takes the function as the penalty term in the high frequency domain of the image. Then, the function is iteratively updated, and the iterative shrinkage threshold algorithm is applied to solve the high frequency image. In this paper, it is considered that the information in the gradient domain is better for the estimation of blur kernel, so the blur kernel is estimated in the gradient domain. This problem can be quickly implemented in the frequency domain by fast Fast Fourier Transform. In addition, in order to improve the effectiveness of the algorithm, we have added a multi-scale iterative optimization method. This paper proposes the blind deconvolution method based on L1/L2 regularization priors in the gradient space can obtain the unique and stable solution in the process of image restoration, which not only keeps the edges and details of the image, but also ensures the accuracy of the results.

WE-A-BRD-01: Innovation in Radiation Therapy Planning I: Knowledge Guided Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Q; Olsen, L

2014-06-15

Intensity modulated radiation therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) offer the capability of normal tissues and organs sparing. However, the exact amount of sparing is often unknown until the plan is complete. This lack of prior guidance has led to the iterative, trial and-error approach in current planning practice. Even with this effort the search for patient-specific optimal organ sparing is still strongly influenced by planner's experience. While experience generally helps in maximizing the dosimetric advantages of IMRT/VMAT, there have been several reports showing unnecessarily high degree of plan quality variability at individual institutions and amongst different institutions,more » even with a large amount of experience and the best available tools. Further, when physician and physicist evaluate a plan, the dosimetric quality of the plan is often compared with a standard protocol that ignores individual patient anatomy and tumor characteristic variations. In recent years, developments of knowledge models for clinical IMRT/VMAT planning guidance have shown promising clinical potentials. These knowledge models extract past expert clinical experience into mathematical models that predict dose sparing references at patient-specific level. For physicians and planners, these references provide objective values that reflect best achievable dosimetric constraints. For quality assurance, applying patient-specific dosimetry requirements will enable more quantitative and objective assessment of protocol compliance for complex IMRT planning. Learning Objectives: Modeling and representation of knowledge for knowledge-guided treatment planning. Demonstrations of knowledge-guided treatment planning with a few clinical caanatomical sites. Validation and evaluation of knowledge models for cost and quality effective standardization of plan optimization.« less

Some Simple Formulas for Posterior Convergence Rates

PubMed Central

2014-01-01

We derive some simple relations that demonstrate how the posterior convergence rate is related to two driving factors: a “penalized divergence” of the prior, which measures the ability of the prior distribution to propose a nonnegligible set of working models to approximate the true model and a “norm complexity” of the prior, which measures the complexity of the prior support, weighted by the prior probability masses. These formulas are explicit and involve no essential assumptions and are easy to apply. We apply this approach to the case with model averaging and derive some useful oracle inequalities that can optimize the performance adaptively without knowing the true model. PMID:27379278

Analysis of Cricoid Pressure Force and Technique Among Anesthesiologists, Nurse Anesthetists, and Registered Nurses.

PubMed

Lefave, Melissa; Harrell, Brad; Wright, Molly

2016-06-01

The purpose of this project was to assess the ability of anesthesiologists, nurse anesthetists, and registered nurses to correctly identify anatomic landmarks of cricoid pressure and apply the correct amount of force. The project included an educational intervention with one group pretest-post-test design. Participants demonstrated cricoid pressure on a laryngotracheal model. After an educational intervention video, participants were asked to repeat cricoid pressure on the model. Participants with a nurse anesthesia background applied more appropriate force pretest than other participants; however, post-test results, while improved, showed no significant difference among providers. Participant identification of the correct anatomy of the cricoid cartilage and application of correct force were significantly improved after education. This study revealed that participants lacked prior knowledge of correct cricoid anatomy and pressure as well as the ability to apply correct force to the laryngotracheal model before an educational intervention. The intervention used in this study proved successful in educating health care providers. Copyright © 2016 American Society of PeriAnesthesia Nurses. Published by Elsevier Inc. All rights reserved.

Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology

PubMed Central

2012-01-01

Background An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data. Results We put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for pathway- and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian and frequentist penalised-likelihood methods for incorporating network-based information. Conclusions The empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge. PMID:22578440

What are they up to? The role of sensory evidence and prior knowledge in action understanding.

PubMed

Chambon, Valerian; Domenech, Philippe; Pacherie, Elisabeth; Koechlin, Etienne; Baraduc, Pierre; Farrer, Chlöé

2011-02-18

Explaining or predicting the behaviour of our conspecifics requires the ability to infer the intentions that motivate it. Such inferences are assumed to rely on two types of information: (1) the sensory information conveyed by movement kinematics and (2) the observer's prior expectations--acquired from past experience or derived from prior knowledge. However, the respective contribution of these two sources of information is still controversial. This controversy stems in part from the fact that "intention" is an umbrella term that may embrace various sub-types each being assigned different scopes and targets. We hypothesized that variations in the scope and target of intentions may account for variations in the contribution of visual kinematics and prior knowledge to the intention inference process. To test this hypothesis, we conducted four behavioural experiments in which participants were instructed to identify different types of intention: basic intentions (i.e. simple goal of a motor act), superordinate intentions (i.e. general goal of a sequence of motor acts), or social intentions (i.e. intentions accomplished in a context of reciprocal interaction). For each of the above-mentioned intentions, we varied (1) the amount of visual information available from the action scene and (2) participant's prior expectations concerning the intention that was more likely to be accomplished. First, we showed that intentional judgments depend on a consistent interaction between visual information and participant's prior expectations. Moreover, we demonstrated that this interaction varied according to the type of intention to be inferred, with participant's priors rather than perceptual evidence exerting a greater effect on the inference of social and superordinate intentions. The results are discussed by appealing to the specific properties of each type of intention considered and further interpreted in the light of a hierarchical model of action representation.

Cognitive assessment and health education in children from two different cultures.

PubMed

Sivaramakrishnan, M; Arocha, J F; Patel, V L

1998-09-01

This paper presents research aimed at investigating high level comprehension and problem solving processes in children in two different countries, India and Colombia. To this end, we use a series of health-related cognitive tasks as assessment tools. In one study, we also examine children's performance on these cognitive tasks, in relation to their nutritional status and parasitic load. The ages of the children tested ranged from 2 through 14 years. The tasks were designed to assess comprehension of sequences, organization of concepts, understanding of health routines (hygiene practices) and evaluation of hypothesis and evidence. The results show that children approach the different tasks with a baggage of beliefs and local knowledge of the world which determines their reasoning process, their comprehension and their problems solving. The results are discussed in terms of cognitive assessment approaches, as applied to classroom instruction. Given that children construct their understanding of reality based on what they already know and that education does not take this into account, we recommend that assessment tools should be devised that can tap prior knowledge and understanding, such that this can be analyzed and understood in relation to knowledge taught in the classroom. Current educational assessment fails in such an endeavor.

The Role of Frontal Cortical and Medial-Temporal Lobe Brain Areas in Learning a Bayesian Prior Belief on Reversals

PubMed Central

Jang, Anthony I.; Costa, Vincent D.; Rudebeck, Peter H.; Chudasama, Yogita; Murray, Elisabeth A.

2015-01-01

Reversal learning has been extensively studied across species as a task that indexes the ability to flexibly make and reverse deterministic stimulus–reward associations. Although various brain lesions have been found to affect performance on this task, the behavioral processes affected by these lesions have not yet been determined. This task includes at least two kinds of learning. First, subjects have to learn and reverse stimulus–reward associations in each block of trials. Second, subjects become more proficient at reversing choice preferences as they experience more reversals. We have developed a Bayesian approach to separately characterize these two learning processes. Reversal of choice behavior within each block is driven by a combination of evidence that a reversal has occurred, and a prior belief in reversals that evolves with experience across blocks. We applied the approach to behavior obtained from 89 macaques, comprising 12 lesion groups and a control group. We found that animals from all of the groups reversed more quickly as they experienced more reversals, and correspondingly they updated their prior beliefs about reversals at the same rate. However, the initial values of the priors that the various groups of animals brought to the task differed significantly, and it was these initial priors that led to the differences in behavior. Thus, by taking a Bayesian approach we find that variability in reversal-learning performance attributable to different neural systems is primarily driven by different prior beliefs about reversals that each group brings to the task. SIGNIFICANCE STATEMENT The ability to use prior knowledge to adapt choice behavior is critical for flexible decision making. Reversal learning is often studied as a form of flexible decision making. However, prior studies have not identified which brain regions are important for the formation and use of prior beliefs to guide choice behavior. Here we develop a Bayesian approach that formally characterizes learning set as a concept, and we show that, in macaque monkeys, the amygdala and medial prefrontal cortex have a role in establishing an initial belief about the stability of the reward environment. PMID:26290251

Collaborative Knowledge Building with Wikis: The Impact of Redundancy and Polarity

ERIC Educational Resources Information Center

Moskaliuk, Johannes; Kimmerle, Joachim; Cress, Ulrike

2012-01-01

Wikis as shared digital artifacts may enable users to participate in processes of knowledge building. To what extent and with which quality knowledge building can take place is assumed to depend on the interrelation between people's prior knowledge and the information available in a wiki. In two experimental studies we examined the impact on…

The Effects of Prior Knowledge on Children's Memory and Suggestibility

ERIC Educational Resources Information Center

Elischberger, Holger B.

2005-01-01

In this study, 5- and 6-year-olds were read a story and asked to recall its details. Two independent factors-prestory knowledge and poststory suggestions-were crossed to examine the effects on children's story recall. The results indicated that prestory social knowledge about the story protagonist as well as academic knowledge relating to the…

Approaching Multidimensional Forms of Knowledge through Personal Meaning Mapping in Science Integrating Teaching outside the Classroom

ERIC Educational Resources Information Center

Hartmeyer, Rikke; Bølling, Mads; Bentsen, Peter

2017-01-01

Current research points to Personal Meaning Mapping (PMM) as a method useful in investigating students' prior and current science knowledge. However, studies investigating PMM as a method for exploring specific knowledge dimensions are lacking. Ensuring that students are able to access specific knowledge dimensions is important, especially in…

Topic-Specific Pedagogical Content Knowledge (TSPCK) in Redox and Electrochemistry of Experienced Teachers

ERIC Educational Resources Information Center

O'Brien, Stephanie

2017-01-01

Topic specific pedagogical content knowledge (TSPCK) is the basis by which knowledge of subject matter of a particular topic is conveyed to students. This includes students' prior knowledge, curricular saliency, what makes a topic easy or difficult to teach, representations, and teaching strategies. The goal of this study is to assess the…

Creating Connections: Using the Internet to Support Struggling Readers' Background Knowledge. Issues in Technology

ERIC Educational Resources Information Center

Karchmer, Rachel A.

2004-01-01

Background knowledge plays an important role in one?s ability to learn. We learn new knowledge by relating it to our prior knowledge, which in turn provides concrete understanding (Piaget, 1969). Rosenblatt (1996) explained, "The reader brings to the work personality traits, memories of past events, present needs and preoccupations, a…

Interplay of Secondary Pre-Service Teacher Content Knowledge (CK), Pedagogical Content Knowledge (PCK) and Attitudes Regarding Scientific Inquiry Teaching within Teacher Training

ERIC Educational Resources Information Center

Smit, Robbert; Weitzel, Holger; Blank, Robert; Rietz, Florian; Tardent, Josiane; Robin, Nicolas

2017-01-01

Background: Beginning teachers encounter several constraints with respect to scientific inquiry. Depending on their prior beliefs, knowledge and understanding, these constraints affect their teaching of inquiry. Purpose: To investigate quantitatively the longitudinal relationship between pre-service teachers' knowledge and attitudes on scientific…

Optimal marker placement in hadrontherapy: intelligent optimization strategies with augmented Lagrangian pattern search.

PubMed

Altomare, Cristina; Guglielmann, Raffaella; Riboldi, Marco; Bellazzi, Riccardo; Baroni, Guido

2015-02-01

In high precision photon radiotherapy and in hadrontherapy, it is crucial to minimize the occurrence of geometrical deviations with respect to the treatment plan in each treatment session. To this end, point-based infrared (IR) optical tracking for patient set-up quality assessment is performed. Such tracking depends on external fiducial points placement. The main purpose of our work is to propose a new algorithm based on simulated annealing and augmented Lagrangian pattern search (SAPS), which is able to take into account prior knowledge, such as spatial constraints, during the optimization process. The SAPS algorithm was tested on data related to head and neck and pelvic cancer patients, and that were fitted with external surface markers for IR optical tracking applied for patient set-up preliminary correction. The integrated algorithm was tested considering optimality measures obtained with Computed Tomography (CT) images (i.e. the ratio between the so-called target registration error and fiducial registration error, TRE/FRE) and assessing the marker spatial distribution. Comparison has been performed with randomly selected marker configuration and with the GETS algorithm (Genetic Evolutionary Taboo Search), also taking into account the presence of organs at risk. The results obtained with SAPS highlight improvements with respect to the other approaches: (i) TRE/FRE ratio decreases; (ii) marker distribution satisfies both marker visibility and spatial constraints. We have also investigated how the TRE/FRE ratio is influenced by the number of markers, obtaining significant TRE/FRE reduction with respect to the random configurations, when a high number of markers is used. The SAPS algorithm is a valuable strategy for fiducial configuration optimization in IR optical tracking applied for patient set-up error detection and correction in radiation therapy, showing that taking into account prior knowledge is valuable in this optimization process. Further work will be focused on the computational optimization of the SAPS algorithm toward fast point-of-care applications. Copyright © 2014 Elsevier Inc. All rights reserved.

Empirical Bayes estimation of proportions with application to cowbird parasitism rates

USGS Publications Warehouse

Link, W.A.; Hahn, D.C.

1996-01-01

Bayesian models provide a structure for studying collections of parameters such as are considered in the investigation of communities, ecosystems, and landscapes. This structure allows for improved estimation of individual parameters, by considering them in the context of a group of related parameters. Individual estimates are differentially adjusted toward an overall mean, with the magnitude of their adjustment based on their precision. Consequently, Bayesian estimation allows for a more credible identification of extreme values in a collection of estimates. Bayesian models regard individual parameters as values sampled from a specified probability distribution, called a prior. The requirement that the prior be known is often regarded as an unattractive feature of Bayesian analysis and may be the reason why Bayesian analyses are not frequently applied in ecological studies. Empirical Bayes methods provide an alternative approach that incorporates the structural advantages of Bayesian models while requiring a less stringent specification of prior knowledge. Rather than requiring that the prior distribution be known, empirical Bayes methods require only that it be in a certain family of distributions, indexed by hyperparameters that can be estimated from the available data. This structure is of interest per se, in addition to its value in allowing for improved estimation of individual parameters; for example, hypotheses regarding the existence of distinct subgroups in a collection of parameters can be considered under the empirical Bayes framework by allowing the hyperparameters to vary among subgroups. Though empirical Bayes methods have been applied in a variety of contexts, they have received little attention in the ecological literature. We describe the empirical Bayes approach in application to estimation of proportions, using data obtained in a community-wide study of cowbird parasitism rates for illustration. Since observed proportions based on small sample sizes are heavily adjusted toward the mean, extreme values among empirical Bayes estimates identify those species for which there is the greatest evidence of extreme parasitism rates. Applying a subgroup analysis to our data on cowbird parasitism rates, we conclude that parasitism rates for Neotropical Migrants as a group are no greater than those of Resident/Short-distance Migrant species in this forest community. Our data and analyses demonstrate that the parasitism rates for certain Neotropical Migrant species are remarkably low (Wood Thrush and Rose-breasted Grosbeak) while those for others are remarkably high (Ovenbird and Red-eyed Vireo).

Prior Conceptual Knowledge and Textbook Search.

ERIC Educational Resources Information Center

Byrnes, James P.; Guthrie, John T.

1992-01-01

The role of a subject's conceptual knowledge in the procedural task of searching a text for information was studied for 51 college undergraduates in 2 experiments involving knowledge of anatomy. Students with more anatomical information were able to search a text more quickly. Educational implications are discussed. (SLD)

How Knowledge Powers Reading

ERIC Educational Resources Information Center

Lemov, Doug

2017-01-01

Recent research shows that reading comprehension relies heavily on prior knowledge. Far more than generic "reading skills" like drawing inferences, making predictions, and knowing the function of subheads, how well students learn from a nonfiction text depends on their background knowledge of the text's subject matter. And in a cyclical…

Employees and Creativity: Social Ties and Access to Heterogeneous Knowledge

ERIC Educational Resources Information Center

Huang, Chiung-En; Liu, Chih-Hsing Sam

2015-01-01

This study dealt with employee social ties, knowledge heterogeneity contacts, and the generation of creativity. Although prior studies demonstrated a relationship between network position and creativity, inadequate attention has been paid to network ties and heterogeneity knowledge contacts. This study considered the social interaction processes…

Computer Experiences, Self-Efficacy and Knowledge of Students Enrolled in Introductory University Agriculture Courses.

ERIC Educational Resources Information Center

Johnson, Donald M.; Ferguson, James A.; Lester, Melissa L.

1999-01-01

Of 175 freshmen agriculture students, 74% had prior computer courses, 62% owned computers. The number of computer topics studied predicted both computer self-efficacy and computer knowledge. A substantial positive correlation was found between self-efficacy and computer knowledge. (SK)

Fracture characterization by hybrid enumerative search and Gauss-Newton least-squares inversion methods

NASA Astrophysics Data System (ADS)

Alkharji, Mohammed N.

Most fracture characterization methods provide a general description of the fracture parameters as part of the reservoirs parameters; the fracture interaction and geometry within the reservoir is given less attention. T-Matrix and Linear Slip effective medium fracture models are implemented to invert the elastic tensor for the parameters and geometries of the fractures within the reservoir. The fracture inverse problem has an ill-posed, overdetermined, underconstrained rank-deficit system of equations. Least-squares inverse methods are used to solve the problem. A good starting initial model for the parameters is a key factor in the reliability of the inversion. Most methods assume that the starting parameters are close to the solution to avoid inaccurate local minimum solutions. The prior knowledge of the fracture parameters and their geometry is not available. We develop a hybrid, enumerative and Gauss-Newton, method that estimates the fracture parameters and geometry from the elastic tensor with no prior knowledge of the initial parameter values. The fracture parameters are separated into two groups. The first group contains the fracture parameters with no prior information, and the second group contains the parameters with known prior information. Different models are generated from the first group parameters by sampling the solution space over a predefined range of possible solutions for each parameter. Each model generated by the first group is fixed and used as a starting model to invert for the second group of parameters using the Gauss-Newton method. The least-squares residual between the observed elastic tensor and the estimated elastic tensor is calculated for each model. The model parameters that yield the least-squares residual corresponds to the correct fracture reservoir parameters and geometry. Two synthetic examples of fractured reservoirs with oil and gas saturations were inverted with no prior information about the fracture properties. The results showed that the hybrid algorithm successfully predicted the fracture parametrization, geometry, and the fluid content within the modeled reservoir. The method was also applied on an elastic tensor extracted from the Weyburn field in Saskatchewan, Canada. The solution suggested no presence of fractures but only a VTI system caused by the shale layering in the targeted reservoir, this interpretation is supported by other Weyburn field data.

Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding.

PubMed

Olsher, Daniel

2014-10-01

Noise-resistant and nuanced, COGBASE makes 10 million pieces of commonsense data and a host of novel reasoning algorithms available via a family of semantically-driven prior probability distributions. Machine learning, Big Data, natural language understanding/processing, and social AI can draw on COGBASE to determine lexical semantics, infer goals and interests, simulate emotion and affect, calculate document gists and topic models, and link commonsense knowledge to domain models and social, spatial, cultural, and psychological data. COGBASE is especially ideal for social Big Data, which tends to involve highly implicit contexts, cognitive artifacts, difficult-to-parse texts, and deep domain knowledge dependencies. Copyright © 2014 Elsevier Ltd. All rights reserved.

SFM: A novel sequence-based fusion method for disease genes identification and prioritization.

PubMed

Yousef, Abdulaziz; Moghadam Charkari, Nasrollah

2015-10-21

The identification of disease genes from human genome is of great importance to improve diagnosis and treatment of disease. Several machine learning methods have been introduced to identify disease genes. However, these methods mostly differ in the prior knowledge used to construct the feature vector for each instance (gene), the ways of selecting negative data (non-disease genes) where there is no investigational approach to find them and the classification methods used to make the final decision. In this work, a novel Sequence-based fusion method (SFM) is proposed to identify disease genes. In this regard, unlike existing methods, instead of using a noisy and incomplete prior-knowledge, the amino acid sequence of the proteins which is universal data has been carried out to present the genes (proteins) into four different feature vectors. To select more likely negative data from candidate genes, the intersection set of four negative sets which are generated using distance approach is considered. Then, Decision Tree (C4.5) has been applied as a fusion method to combine the results of four independent state-of the-art predictors based on support vector machine (SVM) algorithm, and to make the final decision. The experimental results of the proposed method have been evaluated by some standard measures. The results indicate the precision, recall and F-measure of 82.6%, 85.6% and 84, respectively. These results confirm the efficiency and validity of the proposed method. Copyright © 2015 Elsevier Ltd. All rights reserved.

Incorporating prior information into differential network analysis using non-paranormal graphical models.

PubMed

Zhang, Xiao-Fei; Ou-Yang, Le; Yan, Hong

2017-08-15

Understanding how gene regulatory networks change under different cellular states is important for revealing insights into network dynamics. Gaussian graphical models, which assume that the data follow a joint normal distribution, have been used recently to infer differential networks. However, the distributions of the omics data are non-normal in general. Furthermore, although much biological knowledge (or prior information) has been accumulated, most existing methods ignore the valuable prior information. Therefore, new statistical methods are needed to relax the normality assumption and make full use of prior information. We propose a new differential network analysis method to address the above challenges. Instead of using Gaussian graphical models, we employ a non-paranormal graphical model that can relax the normality assumption. We develop a principled model to take into account the following prior information: (i) a differential edge less likely exists between two genes that do not participate together in the same pathway; (ii) changes in the networks are driven by certain regulator genes that are perturbed across different cellular states and (iii) the differential networks estimated from multi-view gene expression data likely share common structures. Simulation studies demonstrate that our method outperforms other graphical model-based algorithms. We apply our method to identify the differential networks between platinum-sensitive and platinum-resistant ovarian tumors, and the differential networks between the proneural and mesenchymal subtypes of glioblastoma. Hub nodes in the estimated differential networks rediscover known cancer-related regulator genes and contain interesting predictions. The source code is at https://github.com/Zhangxf-ccnu/pDNA. szuouyl@gmail.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

“Super-deblended” Dust Emission in Galaxies. I. The GOODS-North Catalog and the Cosmic Star Formation Rate Density out to Redshift 6

NASA Astrophysics Data System (ADS)

Liu, Daizhong; Daddi, Emanuele; Dickinson, Mark; Owen, Frazer; Pannella, Maurilio; Sargent, Mark; Béthermin, Matthieu; Magdis, Georgios; Gao, Yu; Shu, Xinwen; Wang, Tao; Jin, Shuowen; Inami, Hanae

2018-02-01

We present a new technique to measure multi-wavelength “super-deblended” photometry from highly confused images, which we apply to Herschel and ground-based far-infrared (FIR) and (sub-)millimeter (mm) data in the northern field of the Great Observatories Origins Deep Survey. There are two key novelties. First, starting with a large database of deep Spitzer 24 μm and VLA 20 cm detections that are used to define prior positions for fitting the FIR/submm data, we perform an active selection of useful priors independently at each frequency band, moving from less to more confused bands. Exploiting knowledge of redshift and all available photometry, we identify hopelessly faint priors that we remove from the fitting pool. This approach significantly reduces blending degeneracies and allows reliable photometry to be obtained for galaxies in FIR+mm bands. Second, we obtain well-behaved, nearly Gaussian flux density uncertainties, individually tailored to all fitted priors for each band. This is done by exploiting extensive simulations that allow us to calibrate the conversion of formal fitting uncertainties to realistic uncertainties, depending on directly measurable quantities. We achieve deeper detection limits with high fidelity measurements and uncertainties at FIR+mm bands. As an illustration of the utility of these measurements, we identify 70 galaxies with z≥slant 3 and reliable FIR+mm detections. We present new constraints on the cosmic star formation rate density at 3< z< 6, finding a significant contribution from z≥slant 3 dusty galaxies that are missed by optical-to-near-infrared color selection. Photometric measurements for 3306 priors, including more than 1000 FIR+mm detections, are released publicly with our catalog.

Enhancing meaningful learning and self-efficacy through collaboration between dental hygienist and physiotherapist students - a scholarship project.

PubMed

Johannsen, A; Bolander-Laksov, K; Bjurshammar, N; Nordgren, B; Fridén, C; Hagströmer, M

2012-11-01

Within the field of Dental Hygiene (DH) and Physiotherapy (PT), students are taught to use an evidence-based approach. Educators need to consider the nature of evidence-based practice from the perspective of content knowledge and learning strategies. Such effort to seek best available evidence and to apply a systematic and scholarly approach to teaching and learning is called scholarship of teaching and learning. To evaluate the application of the scholarship model including an evidence-based approach to enhance meaningful learning and self-efficacy among DH and PT students. Based on the research on student learning, three central theories were identified (constructivism, meaningful learning and self-efficacy). These were applied in our context to support learner engagement and the application of prior knowledge in a new situation. The DH students performed an oral health examination on the PT students, and the PT students performed an individual health test on the DH students; both groups used motivational interviewing. Documentation of student's learning experience was carried out through seminars and questionnaires. The students were overall satisfied with the learning experience. Most appreciated are that it reflected a 'real' professional situation and that it also reinforced important learning from their seminars. The scholarship model made the teachers aware of the importance of evidence-based teaching. Furthermore, the indicators for meaningful learning and increased self-efficacy were high, and the students became more engaged by practising in a real situation, more aware of other health professions and reflected about tacit knowledge. © 2012 John Wiley & Sons A/S.

Agricultural mapping using Support Vector Machine-Based Endmember Extraction (SVM-BEE)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Archibald, Richard K; Filippi, Anthony M; Bhaduri, Budhendra L

Extracting endmembers from remotely sensed images of vegetated areas can present difficulties. In this research, we applied a recently developed endmember-extraction algorithm based on Support Vector Machines (SVMs) to the problem of semi-autonomous estimation of vegetation endmembers from a hyperspectral image. This algorithm, referred to as Support Vector Machine-Based Endmember Extraction (SVM-BEE), accurately and rapidly yields a computed representation of hyperspectral data that can accommodate multiple distributions. The number of distributions is identified without prior knowledge, based upon this representation. Prior work established that SVM-BEE is robustly noise-tolerant and can semi-automatically and effectively estimate endmembers; synthetic data and a geologicmore » scene were previously analyzed. Here we compared the efficacies of the SVM-BEE and N-FINDR algorithms in extracting endmembers from a predominantly agricultural scene. SVM-BEE was able to estimate vegetation and other endmembers for all classes in the image, which N-FINDR failed to do. Classifications based on SVM-BEE endmembers were markedly more accurate compared with those based on N-FINDR endmembers.« less

Modeling Protein Expression and Protein Signaling Pathways

PubMed Central

Telesca, Donatello; Müller, Peter; Kornblau, Steven M.; Suchard, Marc A.; Ji, Yuan

2015-01-01

High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case, inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The proposed approach exploits available expert knowledge about the target pathway to define an informative prior on the hidden conditional dependence structure. An important feature of this prior is that it provides an instrument to explicitly anchor the model space to a set of interactions of interest, favoring a local search approach to model determination. We apply our model to reverse-phase protein array data from a study on acute myeloid leukemia. Our inference identifies relevant subpathways in relation to the unfolding of the biological process under study. PMID:26246646

Do Runner Beans Really Make You Run Fast? Young Children Learning About Science-Related Food Concepts in Informal Settings

NASA Astrophysics Data System (ADS)

Cumming, Jenny

2003-08-01

Early years practitioners acknowledge that much learning takes place in a family context. Science educators, in particular, recognise the importance of children's prior knowledge, both as a foundation on which to build and as a possible source of misconceptions. However, little work has been done to discover what young children learn outside school. This study utilised parent diaries and questionnaires to elucidate the experiences of children aged four to seven which might contribute to their knowledge about the origin of food and its destiny after being eaten. The findings indicate that children learn more scientifically correct information with friends and family than teachers might realise. Awareness of children's informal knowledge can assist teachers when planning activities. As well as this, children's prior knowledge can be utilised in classroom discourse to promote understanding.

Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks.

PubMed

Khan, Taimoor; De, Asok

2014-01-01

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

PubMed Central

De, Asok

2014-01-01

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results. PMID:27382616

Tacit knowledge.

PubMed

Walker, Alexander Muir

2017-04-01

Information that is not made explicit is nonetheless embedded in most of our standard procedures. In its simplest form, embedded information may take the form of prior knowledge held by the researcher and presumed to be agreed to by consumers of the research product. More interesting are the settings in which the prior information is held unconsciously by both researcher and reader, or when the very form of an "effective procedure" incorporates its creator's (unspoken) understanding of a problem. While it may not be productive to exhaustively detail the embedded or tacit knowledge that manifests itself in creative scientific work, at least at the beginning, we may want to routinize methods for extracting and documenting the ways of thinking that make "experts" expert. We should not back away from both expecting and respecting the tacit knowledge the pervades our work and the work of others.

Preservice Agricultural Education Teachers' Experiences in and Anticipation of Content Knowledge Preparation

ERIC Educational Resources Information Center

Rice, Amber H.; Kitchel, Tracy

2015-01-01

This study explored the experiences of preservice agriculture teachers in content knowledge preparation for pedagogical content knowledge (PCK) development. The researchers employed a phenomenological approach in which six preservice teachers were interviewed the semester prior to student teaching. The researchers found there was general…

Conditional Reasoning in Autism: Activation and Integration of Knowledge and Belief

ERIC Educational Resources Information Center

McKenzie, Rebecca; Evans, Jonathan St. B. T.; Handley, Simon J.

2010-01-01

Everyday conditional reasoning is typically influenced by prior knowledge and belief in the form of specific exceptions known as counterexamples. This study explored whether adolescents with autism spectrum disorder (ASD; N = 26) were less influenced by background knowledge than typically developing adolescents (N = 38) when engaged in conditional…

The Effects of Prior Knowledge and Instruction on Understanding Image Formation.

ERIC Educational Resources Information Center

Galili, Igal; And Others

1993-01-01

Reports a study (n=27) concerning the knowledge about image formation exhibited by students following instruction in geometrical optics in an activity-based college physics course for prospective elementary teachers. Student diagrams and verbal comments indicate their knowledge can be described as an intermediate state: a hybridization of…

Designing Knowledge Scaffolds to Support Mathematical Problem Solving

ERIC Educational Resources Information Center

Rittle-Johnson, Bethany; Koedinger, Kenneth R.

2005-01-01

We present a methodology for designing better learning environments. In Phase 1, 6th-grade students' (n = 223) prior knowledge was assessed using a difficulty factors assessment (DFA). The assessment revealed that scaffolds designed to elicit contextual, conceptual, or procedural knowledge each improved students' ability to add and subtract…

Uncovering and Informing Preservice Teachers' Prior Knowledge about Poverty

ERIC Educational Resources Information Center

Mundy, Charlotte Anne; Leko, Melinda Marie

2015-01-01

This study explored 30 preservice teachers' knowledge on issues related to poverty. In an open-ended questionnaire, preservice teachers' perceptions of poverty and how teachers should respond to students from poverty were explored. Results indicated that preservice teachers' knowledge was nonspecific and lacked focus on the relationship among…

Using expert knowledge for test linking.

PubMed

Bolsinova, Maria; Hoijtink, Herbert; Vermeulen, Jorine Adinda; Béguin, Anton

2017-12-01

Linking and equating procedures are used to make the results of different test forms comparable. In the cases where no assumption of random equivalent groups can be made some form of linking design is used. In practice the amount of data available to link the two tests is often very limited due to logistic and security reasons, which affects the precision of linking procedures. This study proposes to enhance the quality of linking procedures based on sparse data by using Bayesian methods which combine the information in the linking data with background information captured in informative prior distributions. We propose two methods for the elicitation of prior knowledge about the difference in difficulty of two tests from subject-matter experts and explain how these results can be used in the specification of priors. To illustrate the proposed methods and evaluate the quality of linking with and without informative priors, an empirical example of linking primary school mathematics tests is presented. The results suggest that informative priors can increase the precision of linking without decreasing the accuracy. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Critical Appraisal Skills Among Canadian Obstetrics and Gynaecology Residents: How Do They Fare?

PubMed

Bougie, Olga; Posner, Glenn; Black, Amanda Y

2015-07-01

Evidence-based medicine has become the standard of care in clinical practice. In this study, our objectives were to (1) determine the type of epidemiology and/or biostatistical training being given in Canadian obstetrics and gynaecology post-graduate programs, (2) determine obstetrics and gynaecology residents' level of confidence with critical appraisal, and (3) assess knowledge of fundamental biostatistical and epidemiological principles among Canadian obstetrics and gynaecology trainees. During a national standardized in-training examination, all Canadian obstetrics and gynaecology residents were invited to complete an anonymous cross-sectional survey to determine their levels of confidence with critical appraisal. Fifteen critical appraisal questions were integrated into the standardized examination to assess critical appraisal skills objectively. Primary outcomes were the residents' level of confidence interpreting biostatistical results and applying research findings to clinical practice, their desire for more biostatistics/epidemiological training in residency, and their performance on knowledge questions. A total of 301 of 355 residents completed the survey (response rate=84.8%). Most (76.7%) had little/no confidence interpreting research statistics. Confidence was significantly higher in those with increased seniority (OR=1.93), in those who had taken a previous epidemiology/statistics course (OR=2.65), and in those who had prior publications (OR=1.82). Many (68%) had little/no confidence applying research findings to clinical practice. Confidence increased significantly with increasing training year (P<0.001) and with formal epidemiology training during residency (OR=2.01). The mean score of the 355 residents on the knowledge assessment questions was 69.8%. Increasing seniority was associated with improved overall test performance (P=0.02). Poorer performance topics included analytical study method (9.9%), study design (36.9%), and sample size (42.0%). Most (84.4%) wanted more epidemiology teaching. Canadian obstetrics and gynaecology residents may have the biostatistical and epidemiological knowledge to interpret results published in the literature, but lack confidence applying these skills in clinical settings. Most residents want additional training in these areas, and residency programs should include training in formal curriculums to improve their confidence and prepare them for a lifelong practice of evidence-based medicine.

Absorptive capacity as a guiding concept for effective public sector management and conservation of freshwater ecosystems.

PubMed

Murray, K; Roux, D J; Nel, J L; Driver, A; Freimund, W

2011-05-01

The ability of an organisation to recognise the value of new external information, acquire it, assimilate it, transform, and exploit it, namely its absorptive capacity (AC), has been much researched in the context of commercial organisations and even applied to national innovation. This paper considers four key AC-related concepts and their relevance to public sector organisations with mandates to manage and conserve freshwater ecosystems for the common good. The concepts are the importance of in-house prior related knowledge, the importance of informal knowledge transfer, the need for motivation and intensity of effort, and the importance of gatekeepers. These concepts are used to synthesise guidance for a way forward in respect of such freshwater management and conservation, using the imminent release of a specific scientific conservation planning and management tool in South Africa as a case study. The tool comprises a comprehensive series of maps that depict national freshwater ecosystem priority areas for South Africa. Insights for implementing agencies relate to maintaining an internal science, rather than research capacity; making unpublished and especially tacit knowledge available through informal knowledge transfer; not underestimating the importance of intensity of effort required to create AC, driven by focussed motivation; and the potential use of a gatekeeper at national level (external to the implementing organisations), possibly playing a more general 'bridging' role, and multiple internal (organisational) gatekeepers playing the more limited role of 'knowledge translators'. The role of AC as a unifying framework is also proposed.

A pilot educational intervention for headache and concussion: The headache and arts program.

PubMed

Minen, Mia T; Boubour, Alexandra

2018-05-15

Using a science, technology, engineering, arts, and mathematics (STEAM) curriculum, we developed, piloted, and tested the Headache and Arts Program. This program seeks to increase knowledge and awareness of migraine and concussion among high school students through a visual arts-based curriculum. We developed a 2-week Headache and Arts Program with lesson plans and art assignments for high school visual arts classes and an age-appropriate assessment to assess students' knowledge of migraine and concussion. We assessed students' knowledge through (1) the creation of artwork that depicted the experience of a migraine or concussion, (2) the conception and implementation of methods to transfer knowledge gained through the program, and (3) preassessment and postassessment results. The assessment was distributed to all students prior to the Headache and Arts Program. In a smaller sample, we distributed the assessment 3 months after the program to assess longitudinal effects. Descriptive analyses and p values were calculated using SPSS V.24 and Microsoft Excel. Forty-eight students participated in the research program. Students created artwork that integrated STEAM knowledge learned through the program and applied creative methods to teach others about migraine and concussion. At baseline, students' total scores averaged 67.6% correct. Total scores for the longitudinal preassessment, immediate postassessment, and delayed 3-month postassessment averaged 69.4%, 72.8%, and 80.0% correct, respectively. The use of a visual arts-based curriculum may be effective for migraine and concussion education among high school students. © 2018 American Academy of Neurology.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Yitan; Xu, Yanxun; Helseth, Donald L.

Background: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. Methods: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data. It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood modelmore » derived by Bayesian graphical model based on TCGA data, and producing a posterior graph as updated and data-enhanced knowledge. In short, Zodiac realizes “Prior interaction map + TCGA data → Posterior interaction map.” Results: Zodiac provides molecular interactions for about 200 million pairs of genes. All the results are generated from a big-data analysis and organized into a comprehensive database allowing customized search. In addition, Zodiac provides data processing and analysis tools that allow users to customize the prior networks and update the genetic pathways of their interest. Zodiac is publicly available at www.compgenome.org/ZODIAC. Conclusions: Zodiac recapitulates and extends existing knowledge of molecular interactions in cancer. It can be used to explore novel gene-gene interactions, transcriptional regulation, and other types of molecular interplays in cancer.« less

Fostering Upper Secondary Students' Ability to Engage in Practices of Scientific Investigation: a Comparative Analysis of an Explicit and an Implicit Instructional Approach

NASA Astrophysics Data System (ADS)

Vorholzer, Andreas; von Aufschnaiter, Claudia; Boone, William J.

2018-02-01

Inquiry-based teaching is considered as contributing to content-related, procedural, and epistemic learning goals of science education. In this study, a quasi-experimental research design was utilized to investigate to what extent embedding inquiry activities in an explicit and an implicit instructional approach fosters students' ability to engage in three practices of scientific investigation (POSI): (1) formulating questions and hypotheses, (2) planning investigations, (3) analyzing and interpreting data. Both approaches were implemented in a classroom-based intervention conducted in a German upper secondary school (N = 222). Students' procedural knowledge of the three POSI was assessed with a paper-pencil test prior and post to the intervention, their content knowledge and dispositional factors (e.g., cognitive abilities) were gathered once. Results show that not only explicit but also implicit instruction fosters students' knowledge of POSI. While overall explicit instruction was found to be more effective, the findings indicate that the effectiveness depends considerably on the practice addressed. Moreover, findings suggest that both approaches were equally beneficial for all students regardless of their prior content knowledge and their prior procedural knowledge of POSI. Potential conditions for the success of explicit and implicit approaches as well as implications for instruction on POSI in science classrooms and for future research are discussed.

Conceptualisation of knowledge construction in community service-learning programmes in nursing education.

PubMed

Mthembu, Sindi Z; Mtshali, Fikile G

2013-01-01

Practices in higher education have been criticised for not developing and preparing students for the expertise required in real environments. Literature reports that educational programmes tend to favour knowledge conformation rather than knowledge construction; however, community service learning (CSL) is a powerful pedagogical strategy that encourages students to make meaningful connections between the content in the classroom and real-life experiences as manifested by the communities. Through CSL, learning is achieved by the active construction of knowledge supported by multiple perspectives within meaningful real contexts, and the social interactions amongst students are seen to play a critical role in the processes of learning and cognition. This article reflects facilitators’ perspective of the knowledge construction process as used with students doing community service learning in basic nursing programmes. The aim of this article was to conceptualise the phenomenon of knowledge construction and thereby provide educators with a shared meaning and common understanding, and to analyse the interaction strategies utilised by nurse educators in the process of knowledge construction in community service-learning programmes in basic nursing education. A qualitative research approach based on a grounded theory research design was used in this article. Two nursing education institutions were purposively selected. Structured interviews were conducted with 16 participants. The results revealed that the knowledge construction in community service-learning programmes is conceptualised as having specific determinants, including the use of authentic health-related problems, academic coaching through scaffolding, academic discourse-dialogue, interactive learning in communities of learners, active learning, continuous reflection as well as collaborative and inquiry-based learning. Upon completion of an experience, students create and test generated knowledge in different contextual health settings. It was concluded that knowledge is constructed by students as a result of their interaction with the communities in their socio-cultural context and is mediated by their prior concrete experiences. The implication of this is that students construct knowledge that can be applied in their future work places.

A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research

PubMed Central

van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B; Neyer, Franz J; van Aken, Marcel AG

2014-01-01

Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First, the ingredients underlying Bayesian methods are introduced using a simplified example. Thereafter, the advantages and pitfalls of the specification of prior knowledge are discussed. To illustrate Bayesian methods explained in this study, in a second example a series of studies that examine the theoretical framework of dynamic interactionism are considered. In the Discussion the advantages and disadvantages of using Bayesian statistics are reviewed, and guidelines on how to report on Bayesian statistics are provided. PMID:24116396

A controls engineering approach for analyzing airplane input-output characteristics

NASA Technical Reports Server (NTRS)

Arbuckle, P. Douglas

1991-01-01

An engineering approach for analyzing airplane control and output characteristics is presented. State-space matrix equations describing the linear perturbation dynamics are transformed from physical coordinates into scaled coordinates. The scaling is accomplished by applying various transformations to the system to employ prior engineering knowledge of the airplane physics. Two different analysis techniques are then explained. Modal analysis techniques calculate the influence of each system input on each fundamental mode of motion and the distribution of each mode among the system outputs. The optimal steady state response technique computes the blending of steady state control inputs that optimize the steady state response of selected system outputs. Analysis of an example airplane model is presented to demonstrate the described engineering approach.

Does genomic selection have a future in plant breeding?

PubMed

Jonas, Elisabeth; de Koning, Dirk-Jan

2013-09-01

Plant breeding largely depends on phenotypic selection in plots and only for some, often disease-resistance-related traits, uses genetic markers. The more recently developed concept of genomic selection, using a black box approach with no need of prior knowledge about the effect or function of individual markers, has also been proposed as a great opportunity for plant breeding. Several empirical and theoretical studies have focused on the possibility to implement this as a novel molecular method across various species. Although we do not question the potential of genomic selection in general, in this Opinion, we emphasize that genomic selection approaches from dairy cattle breeding cannot be easily applied to complex plant breeding. Copyright © 2013 Elsevier Ltd. All rights reserved.

Remote photoacoustic detection of liquid contamination of a surface.

PubMed

Perrett, Brian; Harris, Michael; Pearson, Guy N; Willetts, David V; Pitter, Mark C

2003-08-20

A method for the remote detection and identification of liquid chemicals at ranges of tens of meters is presented. The technique uses pulsed indirect photoacoustic spectroscopy in the 10-microm wavelength region. Enhanced sensitivity is brought about by three main system developments: (1) increased laser-pulse energy (150 microJ/pulse), leading to increased strength of the generated photoacoustic signal; (2) increased microphone sensitivity and improved directionality by the use of a 60-cm-diameter parabolic dish; and (3) signal processing that allows improved discrimination of the signal from noise levels through prior knowledge of the pulse shape and pulse-repetition frequency. The practical aspects of applying the technique in a field environment are briefly examined, and possible applications of this technique are discussed.

Data modeling of network dynamics

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Faucheux, Jeffery P.; Harris, Brad

2004-01-01

This paper highlights Data Modeling theory and its use for text data mining as a graphical network search engine. Data Modeling is then used to create a real-time filter capable of monitoring network traffic down to the port level for unusual dynamics and changes in business as usual. This is accomplished in an unsupervised fashion without a priori knowledge of abnormal characteristics. Two novel methods for converting streaming binary data into a form amenable to graphics based search and change detection are introduced. These techniques are then successfully applied to 1999 KDD Cup network attack data log-on sessions to demonstrate that Data Modeling can detect attacks without prior training on any form of attack behavior. Finally, two new methods for data encryption using these ideas are proposed.

A statistical learning strategy for closed-loop control of fluid flows

NASA Astrophysics Data System (ADS)

Guéniat, Florimond; Mathelin, Lionel; Hussaini, M. Yousuff

2016-12-01

This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz'63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.

Promoting knowledge integration of scientific principles and environmental stewardship: Assessing an issue-based approach to teaching evolution and marine conservation

NASA Astrophysics Data System (ADS)

Zimmerman, Timothy David

2005-11-01

Students and citizens need to apply science to important issues every day. Yet the design of science curricula that foster integration of science and everyday decisions is not well understood. For example, can curricula be designed that help learners apply scientific reasons for choosing only environmentally sustainable seafood for dinner? Learners must develop integrated understandings of scientific principles, prior experiences, and current decisions in order to comprehend how everyday decisions impact environmental resources. In order to investigate how such integrated understandings can be promoted within school science classes, research was conducted with an inquiry-oriented curriculum that utilizes technology and a visit to an informal learning environment (aquarium) to promote the integration of scientific principles (adaptation) with environmental stewardship. This research used a knowledge integration approach to teaching and learning that provided a framework for promoting the application of science to environmental issues. Marine biology, often forsaken in classrooms for terrestrial biology, served as the scientific context for the curriculum. The curriculum design incorporated a three-phase pedagogical strategy and new technology tools to help students integrate knowledge and experiences across the classroom and aquarium learning environments. The research design and assessment protocols included comparisons among and within student populations using two versions of the curriculum: an issue-based version and a principle-based version. These inquiry curricula were tested with sophomore biology students attending a marine-focused academy within a coastal California high school. Pretest-posttest outcomes were compared between and within the curricular treatments. Additionally, comparisons were made between the inquiry groups and seniors in an Advanced Placement biology course who attend the same high school. Results indicate that the inquiry curricula enabled students to integrate and apply knowledge of evolutionary biology to real-world environmental stewardship issues. Over the course of the curriculum, students' ideas became more scientifically normative and tended to focus around concepts of natural selection. Students using the inquiry curricula outperformed the Advanced Placement biology students on several measures, including knowledge of evolutionary biology. These results have implications for designing science curricula that seek to promote the application of science to environmental stewardship and integrate formal and informal learning environments.

Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Real-Time Parameter Estimation Method Applied to a MIMO Process and its Comparison with an Offline Identification Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaplanoglu, Erkan; Safak, Koray K.; Varol, H. Selcuk

2009-01-12

An experiment based method is proposed for parameter estimation of a class of linear multivariable systems. The method was applied to a pressure-level control process. Experimental time domain input/output data was utilized in a gray-box modeling approach. Prior knowledge of the form of the system transfer function matrix elements is assumed to be known. Continuous-time system transfer function matrix parameters were estimated in real-time by the least-squares method. Simulation results of experimentally determined system transfer function matrix compare very well with the experimental results. For comparison and as an alternative to the proposed real-time estimation method, we also implemented anmore » offline identification method using artificial neural networks and obtained fairly good results. The proposed methods can be implemented conveniently on a desktop PC equipped with a data acquisition board for parameter estimation of moderately complex linear multivariable systems.« less

A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images.

PubMed

Gloger, Oliver; Kühn, Jens; Stanski, Adam; Völzke, Henry; Puls, Ralf

2010-07-01

Automatic 3D liver segmentation in magnetic resonance (MR) data sets has proven to be a very challenging task in the domain of medical image analysis. There exist numerous approaches for automatic 3D liver segmentation on computer tomography data sets that have influenced the segmentation of MR images. In contrast to previous approaches to liver segmentation in MR data sets, we use all available MR channel information of different weightings and formulate liver tissue and position probabilities in a probabilistic framework. We apply multiclass linear discriminant analysis as a fast and efficient dimensionality reduction technique and generate probability maps then used for segmentation. We develop a fully automatic three-step 3D segmentation approach based upon a modified region growing approach and a further threshold technique. Finally, we incorporate characteristic prior knowledge to improve the segmentation results. This novel 3D segmentation approach is modularized and can be applied for normal and fat accumulated liver tissue properties. Copyright 2010 Elsevier Inc. All rights reserved.

An analysis of the optimal multiobjective inventory clustering decision with small quantity and great variety inventory by applying a DPSO.

PubMed

Wang, Shen-Tsu; Li, Meng-Hua

2014-01-01

When an enterprise has thousands of varieties in its inventory, the use of a single management method could not be a feasible approach. A better way to manage this problem would be to categorise inventory items into several clusters according to inventory decisions and to use different management methods for managing different clusters. The present study applies DPSO (dynamic particle swarm optimisation) to a problem of clustering of inventory items. Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number. The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate. This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO. In comparison with other clustering methods, the proposed method can consider different objectives and obtain an overall better solution to obtain better convergence results and inventory decisions.

Activists within the Academy: The Role of Prior Experience in Adult Learners' Acquisition of Postgraduate Literacies in a Postapartheid South African University

ERIC Educational Resources Information Center

Cooper, Linda

2011-01-01

The article takes as a case study a group of disability rights activists who were given access to a master's program via Recognition of Prior Learning. The question explored is "Can adult learners' prior experiential knowledge act as a resource for the successful acquisition of postgraduate academic literacy practices?" The analysis is…

Gradient-based reliability maps for ACM-based segmentation of hippocampus.

PubMed

Zarpalas, Dimitrios; Gkontra, Polyxeni; Daras, Petros; Maglaveras, Nicos

2014-04-01

Automatic segmentation of deep brain structures, such as the hippocampus (HC), in MR images has attracted considerable scientific attention due to the widespread use of MRI and to the principal role of some structures in various mental disorders. In this literature, there exists a substantial amount of work relying on deformable models incorporating prior knowledge about structures' anatomy and shape information. However, shape priors capture global shape characteristics and thus fail to model boundaries of varying properties; HC boundaries present rich, poor, and missing gradient regions. On top of that, shape prior knowledge is blended with image information in the evolution process, through global weighting of the two terms, again neglecting the spatially varying boundary properties, causing segmentation faults. An innovative method is hereby presented that aims to achieve highly accurate HC segmentation in MR images, based on the modeling of boundary properties at each anatomical location and the inclusion of appropriate image information for each of those, within an active contour model framework. Hence, blending of image information and prior knowledge is based on a local weighting map, which mixes gradient information, regional and whole brain statistical information with a multi-atlas-based spatial distribution map of the structure's labels. Experimental results on three different datasets demonstrate the efficacy and accuracy of the proposed method.

An Optimization-Based Framework for the Transformation of Incomplete Biological Knowledge into a Probabilistic Structure and Its Application to the Utilization of Gene/Protein Signaling Pathways in Discrete Phenotype Classification.

PubMed

Esfahani, Mohammad Shahrokh; Dougherty, Edward R

2015-01-01

Phenotype classification via genomic data is hampered by small sample sizes that negatively impact classifier design. Utilization of prior biological knowledge in conjunction with training data can improve both classifier design and error estimation via the construction of the optimal Bayesian classifier. In the genomic setting, gene/protein signaling pathways provide a key source of biological knowledge. Although these pathways are neither complete, nor regulatory, with no timing associated with them, they are capable of constraining the set of possible models representing the underlying interaction between molecules. The aim of this paper is to provide a framework and the mathematical tools to transform signaling pathways to prior probabilities governing uncertainty classes of feature-label distributions used in classifier design. Structural motifs extracted from the signaling pathways are mapped to a set of constraints on a prior probability on a Multinomial distribution. Being the conjugate prior for the Multinomial distribution, we propose optimization paradigms to estimate the parameters of a Dirichlet distribution in the Bayesian setting. The performance of the proposed methods is tested on two widely studied pathways: mammalian cell cycle and a p53 pathway model.

Reflective Assessment in Knowledge Building by Students with Low Academic Achievement

ERIC Educational Resources Information Center

Yang, Yuqin; van Aalst, Jan; Chan, Carol K. K.; Tian, Wen

2016-01-01

This study investigated whether and how students with low prior achievement can carry out and benefit from reflective assessment supported by the Knowledge Connections Analyzer (KCA) to collaboratively improve their knowledge-building discourse. Participants were a class of 20 Grade 11 students with low achievement taking visual art from an…

Knowledge of Algebra for Teaching: A Framework of Knowledge and Practices

ERIC Educational Resources Information Center

McCrory, Raven; Floden, Robert; Ferrini-Mundy, Joan; Reckase, Mark D.; Senk, Sharon L.

2012-01-01

Defining what teachers need to know to teach algebra successfully is important for informing teacher preparation and professional development efforts. Based on prior research, analysis of video, interviews with teachers, and analysis of textbooks, we define categories of knowledge and practices of teaching for understanding and assessing teachers'…

An Investigation of Preservice Teachers' Beliefs about the Certainty of Teaching Knowledge

ERIC Educational Resources Information Center

Ferguson, Leila E.; Brownlee, Jo Lunn

2018-01-01

Beliefs about the certainty of teaching knowledge may influence how preservice teachers engage with and learn from knowledge sources in teacher education, and their subsequent practice. In light of inconsistencies in prior findings that mainly employ epistemic questionnaires, we extended research focusing on a contextual analysis. Sixty-six…

Utilizing knowledge from prior plans in the evaluation of quality assurance

NASA Astrophysics Data System (ADS)

Stanhope, Carl; Wu, Q. Jackie; Yuan, Lulin; Liu, Jianfei; Hood, Rodney; Yin, Fang-Fang; Adamson, Justus

2015-06-01

Increased interest regarding sensitivity of pre-treatment intensity modulated radiotherapy and volumetric modulated arc radiotherapy (VMAT) quality assurance (QA) to delivery errors has led to the development of dose-volume histogram (DVH) based analysis. This paradigm shift necessitates a change in the acceptance criteria and action tolerance for QA. Here we present a knowledge based technique to objectively quantify degradations in DVH for prostate radiotherapy. Using machine learning, organ-at-risk (OAR) DVHs from a population of 198 prior patients’ plans were adapted to a test patient’s anatomy to establish patient-specific DVH ranges. This technique was applied to single arc prostate VMAT plans to evaluate various simulated delivery errors: systematic single leaf offsets, systematic leaf bank offsets, random normally distributed leaf fluctuations, systematic lag in gantry angle of the mutli-leaf collimators (MLCs), fluctuations in dose rate, and delivery of each VMAT arc with a constant rather than variable dose rate. Quantitative Analyses of Normal Tissue Effects in the Clinic suggests V75Gy dose limits of 15% for the rectum and 25% for the bladder, however the knowledge based constraints were more stringent: 8.48   ±   2.65% for the rectum and 4.90   ±   1.98% for the bladder. 19   ±   10 mm single leaf and 1.9   ±   0.7 mm single bank offsets resulted in rectum DVHs worse than 97.7% (2σ) of clinically accepted plans. PTV degradations fell outside of the acceptable range for 0.6   ±   0.3 mm leaf offsets, 0.11   ±   0.06 mm bank offsets, 0.6   ±   1.3 mm of random noise, and 1.0   ±   0.7° of gantry-MLC lag. Utilizing a training set comprised of prior treatment plans, machine learning is used to predict a range of achievable DVHs for the test patient’s anatomy. Consequently, degradations leading to statistical outliers may be identified. A knowledge based QA evaluation enables customized QA criteria per treatment site, institution and/or physician and can often be more sensitive to errors than criteria based on organ complication rates.

Pacific Research Platform - Creation of a West Coast Big Data Freeway System Applied to the CONNected objECT (CONNECT) Data Mining Framework for Earth Science Knowledge Discovery

NASA Astrophysics Data System (ADS)

Sellars, S. L.; Nguyen, P.; Tatar, J.; Graham, J.; Kawsenuk, B.; DeFanti, T.; Smarr, L.; Sorooshian, S.; Ralph, M.

2017-12-01

A new era in computational earth sciences is within our grasps with the availability of ever-increasing earth observational data, enhanced computational capabilities, and innovative computation approaches that allow for the assimilation, analysis and ability to model the complex earth science phenomena. The Pacific Research Platform (PRP), CENIC and associated technologies such as the Flash I/O Network Appliance (FIONA) provide scientists a unique capability for advancing towards this new era. This presentation reports on the development of multi-institutional rapid data access capabilities and data pipeline for applying a novel image characterization and segmentation approach, CONNected objECT (CONNECT) algorithm to study Atmospheric River (AR) events impacting the Western United States. ARs are often associated with torrential rains, swollen rivers, flash flooding, and mudslides. CONNECT is computationally intensive, reliant on very large data transfers, storage and data mining techniques. The ability to apply the method to multiple variables and datasets located at different University of California campuses has previously been challenged by inadequate network bandwidth and computational constraints. The presentation will highlight how the inter-campus CONNECT data mining framework improved from our prior download speeds of 10MB/s to 500MB/s using the PRP and the FIONAs. We present a worked example using the NASA MERRA data to describe how the PRP and FIONA have provided researchers with the capability for advancing knowledge about ARs. Finally, we will discuss future efforts to expand the scope to additional variables in earth sciences.

Definition of sampling units begets conclusions in ecology: the case of habitats for plant communities.

PubMed

Mörsdorf, Martin A; Ravolainen, Virve T; Støvern, Leif Einar; Yoccoz, Nigel G; Jónsdóttir, Ingibjörg Svala; Bråthen, Kari Anne

2015-01-01

In ecology, expert knowledge on habitat characteristics is often used to define sampling units such as study sites. Ecologists are especially prone to such approaches when prior sampling frames are not accessible. Here we ask to what extent can different approaches to the definition of sampling units influence the conclusions that are drawn from an ecological study? We do this by comparing a formal versus a subjective definition of sampling units within a study design which is based on well-articulated objectives and proper methodology. Both approaches are applied to tundra plant communities in mesic and snowbed habitats. For the formal approach, sampling units were first defined for each habitat in concave terrain of suitable slope using GIS. In the field, these units were only accepted as the targeted habitats if additional criteria for vegetation cover were fulfilled. For the subjective approach, sampling units were defined visually in the field, based on typical plant communities of mesic and snowbed habitats. For each approach, we collected information about plant community characteristics within a total of 11 mesic and seven snowbed units distributed between two herding districts of contrasting reindeer density. Results from the two approaches differed significantly in several plant community characteristics in both mesic and snowbed habitats. Furthermore, differences between the two approaches were not consistent because their magnitude and direction differed both between the two habitats and the two reindeer herding districts. Consequently, we could draw different conclusions on how plant diversity and relative abundance of functional groups are differentiated between the two habitats depending on the approach used. We therefore challenge ecologists to formalize the expert knowledge applied to define sampling units through a set of well-articulated rules, rather than applying it subjectively. We see this as instrumental for progress in ecology as only rules based on expert knowledge are transparent and lead to results reproducible by other ecologists.

Can Clinical Scenario Videos Improve Dental Students' Perceptions of the Basic Sciences and Ability to Apply Content Knowledge?

PubMed

Miller, Cynthia Jayne; Metz, Michael James

2015-12-01

Dental students often have difficulty understanding the importance of basic science classes, such as physiology, for their future careers. To help alleviate this problem, the aim of this study was to create and evaluate a series of video modules using simulated patients and custom-designed animations that showcase medical emergencies in the dental practice. First-year students in a dental physiology course formatively assessed their knowledge using embedded questions in each of the three videos; 108 to 114 of the total 120 first-year students answered the questions, for a 90-95% response rate. These responses indicated that while the students could initially recognize the cause of the medical emergency, they had difficulty in applying their knowledge of physiology to the scenario. In two of the three videos, students drastically improved their ability to answer high-level clinical questions at the conclusion of the video. Additionally, when compared to the previous year of the course, there was a significant improvement in unit exam scores on clinically related questions (6.2% increase). Surveys were administered to the first-year students who participated in the video modules and fourth-year students who had completed the course prior to implementation of any clinical material. The response rate for the first-year students was 96% (115/120) and for the fourth-year students was 57% (68/120). The first-year students indicated a more positive perception of the physiology course and its importance for success on board examinations and their dental career than the fourth-year students. The students perceived that the most positive aspects of the modules were the clear applications of physiology to real-life dental situations, the interactive nature of the videos, and the improved student comprehension of course concepts. These results suggest that online modules may be used successfully to improve students' perceptions of the basic sciences and enhance their ability to apply basic science content to clinically important scenarios.

Prior Knowledge Assessment Guide

DTIC Science & Technology

2014-12-01

marksmanship, advanced rifle marksmanship, and even specialized shooting courses. A comparison of the means on the test for the two groups showed that the...hands- on evaluations of student knowledge and/or skills. Pretests however, determine how much knowledge a student currently possesses of the course...content; thus, questions on pretests assess knowledge about what is to be taught in the course. Also, most pretests will include test items

The Interaction of Knowledge and Text Structure on the Ability to Identify Main Ideas in Texts. Content Knowledge and Reading Comprehension.

ERIC Educational Resources Information Center

Day, Jeanne D.; Engelhardt, Jean

Two studies examined how the factors of content-relevant knowledge and text organization influence students' abilities to study and to remember text information. The first experiment examined the effect of prior content knowledge on students' ability to identify important information in the text. Forty 7th- and forty 11th-grade students, experts…

Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

PubMed Central

Roos, Marco; Marshall, M Scott; Gibson, Andrew P; Schuemie, Martijn; Meij, Edgar; Katrenko, Sophia; van Hage, Willem Robert; Krommydas, Konstantinos; Adriaans, Pieter W

2009-01-01

Background Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. Results We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. Conclusion We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation. PMID:19796406

Nutrition Knowledge of Teen-Agers.

ERIC Educational Resources Information Center

Skinner, Jean D.; Woodburn, Margy J.

1984-01-01

Nutrition knowledge tests were administered to 1,193 adolescents in Oregon prior to instructional units on nutrition in health and home economics classes. Mean scores on the tests were low. Guidelines for nutrition educators of adolescents are presented. (Author/CJB)

Vegetable Response to Herbicides Applied to Low-Density Polyethylene Mulch Prior to Transplant

USDA-ARS?s Scientific Manuscript database

Few herbicides are available for weed control in vegetables. The elimination of methyl bromide increases the need for herbicides. An experiment was conducted to evaluate crop injury from herbicides applied to LDPE mulch prior to transplant. Irrigation (1 cm) or no irrigation following crop transplan...

Protection Motivation Theory in Predicting Intention to Engage in Protective Behaviors against Schistosomiasis among Middle School Students in Rural China

PubMed Central

Chen, Xinguang; Yu, Bin; Gao, Mengting; Yan, Hong; Okafor, Chukwuemeka N.

2014-01-01

Background Among millions of people who suffer from schistosomiasis in China, adolescents are at increased risk to be infected. However, there is a lack of theory-guided behavioral prevention intervention programs to protect these adolescents. This study attempted to apply the Protection Motivation Theory (PMT) in predicting intentions to engage in protective behaviors against schistosomiasis infection. Methods The participants were selected using the stratified cluster sampling method. Survey data were collected using anonymous self-reported questionnaire. The advanced structural equation modeling (SEM) method was utilized to assess the complex relationship among schistosomiasis knowledge, previous risk exposure and protective measures in predicting intentions to engage in protective behavior through the PMT constructs. Principal Findings Approximately 70% of participants reported they were always aware of schistosomiasis before exposure to water with endemic schistosomiasis, 6% of the participants reported frequency of weekly or monthly prior exposure to snail-conditioned water. 74% of participants reported having always engaged in protective behaviors in the past three months. Approximately 7% were unlikely or very unlikely to avoid contact with snail-conditioned water, and to use protective behaviors before exposure. Results from SEM analysis indicated that both schistosomiasis knowledge and prior exposure to schistosomiasis were indirectly related to behavior intentions through intrinsic rewards and self-efficacy; prior protective behaviors were indirectly related to behavior intentions through severity, intrinsic rewards and self-efficacy, while awareness had an indirect relationship with behavior intentions through self-efficacy. Among the seven PMT constructs, severity, intrinsic rewards and self-efficacy were significantly associated with behavior intentions. Conclusions The PMT can be used to predict the intention to engage in protective behaviors against schistosomiasis. Schistosomiasis intervention programs should focus on the severity, intrinsic rewards and self-efficacy of protection motivation, and also increase the awareness of infection, and enrich the contents of schistosomiasis education. PMID:25329829

Protection motivation theory in predicting intention to engage in protective behaviors against schistosomiasis among middle school students in rural China.

PubMed

Xiao, Han; Li, Shiyue; Chen, Xinguang; Yu, Bin; Gao, Mengting; Yan, Hong; Okafor, Chukwuemeka N

2014-10-01

Among millions of people who suffer from schistosomiasis in China, adolescents are at increased risk to be infected. However, there is a lack of theory-guided behavioral prevention intervention programs to protect these adolescents. This study attempted to apply the Protection Motivation Theory (PMT) in predicting intentions to engage in protective behaviors against schistosomiasis infection. The participants were selected using the stratified cluster sampling method. Survey data were collected using anonymous self-reported questionnaire. The advanced structural equation modeling (SEM) method was utilized to assess the complex relationship among schistosomiasis knowledge, previous risk exposure and protective measures in predicting intentions to engage in protective behavior through the PMT constructs. Approximately 70% of participants reported they were always aware of schistosomiasis before exposure to water with endemic schistosomiasis, 6% of the participants reported frequency of weekly or monthly prior exposure to snail-conditioned water. 74% of participants reported having always engaged in protective behaviors in the past three months. Approximately 7% were unlikely or very unlikely to avoid contact with snail-conditioned water, and to use protective behaviors before exposure. Results from SEM analysis indicated that both schistosomiasis knowledge and prior exposure to schistosomiasis were indirectly related to behavior intentions through intrinsic rewards and self-efficacy; prior protective behaviors were indirectly related to behavior intentions through severity, intrinsic rewards and self-efficacy, while awareness had an indirect relationship with behavior intentions through self-efficacy. Among the seven PMT constructs, severity, intrinsic rewards and self-efficacy were significantly associated with behavior intentions. The PMT can be used to predict the intention to engage in protective behaviors against schistosomiasis. Schistosomiasis intervention programs should focus on the severity, intrinsic rewards and self-efficacy of protection motivation, and also increase the awareness of infection, and enrich the contents of schistosomiasis education.

Advances in Pediatric Cardiology Boot Camp: Boot Camp Training Promotes Fellowship Readiness and Enables Retention of Knowledge.

PubMed

Ceresnak, Scott R; Axelrod, David M; Sacks, Loren D; Motonaga, Kara S; Johnson, Emily R; Krawczeski, Catherine D

2017-03-01

We previously demonstrated that a pediatric cardiology boot camp can improve knowledge acquisition and decrease anxiety for trainees. We sought to determine if boot camp participants entered fellowship with a knowledge advantage over fellows who did not attend and if there was moderate-term retention of that knowledge. A 2-day training program was provided for incoming pediatric cardiology fellows from eight fellowship programs in April 2016. Hands-on, immersive experiences and simulations were provided in all major areas of pediatric cardiology. Knowledge-based examinations were completed by each participant prior to boot camp (PRE), immediately post-training (POST), and prior to the start of fellowship in June 2016 (F/U). A control group of fellows who did not attend boot camp also completed an examination prior to fellowship (CTRL). Comparisons of scores were made for individual participants and between participants and controls. A total of 16 participants and 16 control subjects were included. Baseline exam scores were similar between participants and controls (PRE 47 ± 11% vs. CTRL 52 ± 10%; p = 0.22). Participants' knowledge improved with boot camp training (PRE 47 ± 11% vs. POST 70 ± 8%; p < 0.001) and there was excellent moderate-term retention of the information taught at boot camp (PRE 47 ± 11% vs. F/U 71 ± 8%; p < 0.001). Testing done at the beginning of fellowship demonstrated significantly better scores in participants versus controls (F/U 71 ± 8% vs. CTRL 52 ± 10%; p < 0.001). Boot camp participants demonstrated a significant improvement in basic cardiology knowledge after the training program and had excellent moderate-term retention of that knowledge. Participants began fellowship with a larger fund of knowledge than those fellows who did not attend.

Development of uncertainty-based work injury model using Bayesian structural equation modelling.

PubMed

Chatterjee, Snehamoy

2014-01-01

This paper proposed a Bayesian method-based structural equation model (SEM) of miners' work injury for an underground coal mine in India. The environmental and behavioural variables for work injury were identified and causal relationships were developed. For Bayesian modelling, prior distributions of SEM parameters are necessary to develop the model. In this paper, two approaches were adopted to obtain prior distribution for factor loading parameters and structural parameters of SEM. In the first approach, the prior distributions were considered as a fixed distribution function with specific parameter values, whereas, in the second approach, prior distributions of the parameters were generated from experts' opinions. The posterior distributions of these parameters were obtained by applying Bayesian rule. The Markov Chain Monte Carlo sampling in the form Gibbs sampling was applied for sampling from the posterior distribution. The results revealed that all coefficients of structural and measurement model parameters are statistically significant in experts' opinion-based priors, whereas, two coefficients are not statistically significant when fixed prior-based distributions are applied. The error statistics reveals that Bayesian structural model provides reasonably good fit of work injury with high coefficient of determination (0.91) and less mean squared error as compared to traditional SEM.

Elapsed decision time affects the weighting of prior probability in a perceptual decision task

PubMed Central

Hanks, Timothy D.; Mazurek, Mark E.; Kiani, Roozbeh; Hopp, Elizabeth; Shadlen, Michael N.

2012-01-01

Decisions are often based on a combination of new evidence with prior knowledge of the probable best choice. Optimal combination requires knowledge about the reliability of evidence, but in many realistic situations, this is unknown. Here we propose and test a novel theory: the brain exploits elapsed time during decision formation to combine sensory evidence with prior probability. Elapsed time is useful because (i) decisions that linger tend to arise from less reliable evidence, and (ii) the expected accuracy at a given decision time depends on the reliability of the evidence gathered up to that point. These regularities allow the brain to combine prior information with sensory evidence by weighting the latter in accordance with reliability. To test this theory, we manipulated the prior probability of the rewarded choice while subjects performed a reaction-time discrimination of motion direction using a range of stimulus reliabilities that varied from trial to trial. The theory explains the effect of prior probability on choice and reaction time over a wide range of stimulus strengths. We found that prior probability was incorporated into the decision process as a dynamic bias signal that increases as a function of decision time. This bias signal depends on the speed-accuracy setting of human subjects, and it is reflected in the firing rates of neurons in the lateral intraparietal cortex (LIP) of rhesus monkeys performing this task. PMID:21525274

Elapsed decision time affects the weighting of prior probability in a perceptual decision task.

PubMed

Hanks, Timothy D; Mazurek, Mark E; Kiani, Roozbeh; Hopp, Elisabeth; Shadlen, Michael N

2011-04-27

Decisions are often based on a combination of new evidence with prior knowledge of the probable best choice. Optimal combination requires knowledge about the reliability of evidence, but in many realistic situations, this is unknown. Here we propose and test a novel theory: the brain exploits elapsed time during decision formation to combine sensory evidence with prior probability. Elapsed time is useful because (1) decisions that linger tend to arise from less reliable evidence, and (2) the expected accuracy at a given decision time depends on the reliability of the evidence gathered up to that point. These regularities allow the brain to combine prior information with sensory evidence by weighting the latter in accordance with reliability. To test this theory, we manipulated the prior probability of the rewarded choice while subjects performed a reaction-time discrimination of motion direction using a range of stimulus reliabilities that varied from trial to trial. The theory explains the effect of prior probability on choice and reaction time over a wide range of stimulus strengths. We found that prior probability was incorporated into the decision process as a dynamic bias signal that increases as a function of decision time. This bias signal depends on the speed-accuracy setting of human subjects, and it is reflected in the firing rates of neurons in the lateral intraparietal area (LIP) of rhesus monkeys performing this task.

Levels of line graph question interpretation with intermediate elementary students of varying scientific and mathematical knowledge and ability: A think aloud study

NASA Astrophysics Data System (ADS)

Keller, Stacy Kathryn

This study examined how intermediate elementary students' mathematics and science background knowledge affected their interpretation of line graphs and how their interpretations were affected by graph question levels. A purposive sample of 14 6th-grade students engaged in think aloud interviews (Ericsson & Simon, 1993) while completing an excerpted Test of Graphing in Science (TOGS) (McKenzie & Padilla, 1986). Hand gestures were video recorded. Student performance on the TOGS was assessed using an assessment rubric created from previously cited factors affecting students' graphing ability. Factors were categorized using Bertin's (1983) three graph question levels. The assessment rubric was validated by Padilla and a veteran mathematics and science teacher. Observational notes were also collected. Data were analyzed using Roth and Bowen's semiotic process of reading graphs (2001). Key findings from this analysis included differences in the use of heuristics, self-generated questions, science knowledge, and self-motivation. Students with higher prior achievement used a greater number and variety of heuristics and more often chose appropriate heuristics. They also monitored their understanding of the question and the adequacy of their strategy and answer by asking themselves questions. Most used their science knowledge spontaneously to check their understanding of the question and the adequacy of their answers. Students with lower and moderate prior achievement favored one heuristic even when it was not useful for answering the question and rarely asked their own questions. In some cases, if students with lower prior achievement had thought about their answers in the context of their science knowledge, they would have been able to recognize their errors. One student with lower prior achievement motivated herself when she thought the questions were too difficult. In addition, students answered the TOGS in one of three ways: as if they were mathematics word problems, science data to be analyzed, or they were confused and had to guess. A second set of findings corroborated how science background knowledge affected graph interpretation: correct science knowledge supported students' reasoning, but it was not necessary to answer any question correctly; correct science knowledge could not compensate for incomplete mathematics knowledge; and incorrect science knowledge often distracted students when they tried to use it while answering a question. Finally, using Roth and Bowen's (2001) two-stage semiotic model of reading graphs, representative vignettes showed emerging patterns from the study. This study added to our understanding of the role of science content knowledge during line graph interpretation, highlighted the importance of heuristics and mathematics procedural knowledge, and documented the importance of perception attentions, motivation, and students' self-generated questions. Recommendations were made for future research in line graph interpretation in mathematics and science education and for improving instruction in this area.

Knowledge-based nonuniform sampling in multidimensional NMR.

PubMed

Schuyler, Adam D; Maciejewski, Mark W; Arthanari, Haribabu; Hoch, Jeffrey C

2011-07-01

The full resolution afforded by high-field magnets is rarely realized in the indirect dimensions of multidimensional NMR experiments because of the time cost of uniformly sampling to long evolution times. Emerging methods utilizing nonuniform sampling (NUS) enable high resolution along indirect dimensions by sampling long evolution times without sampling at every multiple of the Nyquist sampling interval. While the earliest NUS approaches matched the decay of sampling density to the decay of the signal envelope, recent approaches based on coupled evolution times attempt to optimize sampling by choosing projection angles that increase the likelihood of resolving closely-spaced resonances. These approaches employ knowledge about chemical shifts to predict optimal projection angles, whereas prior applications of tailored sampling employed only knowledge of the decay rate. In this work we adapt the matched filter approach as a general strategy for knowledge-based nonuniform sampling that can exploit prior knowledge about chemical shifts and is not restricted to sampling projections. Based on several measures of performance, we find that exponentially weighted random sampling (envelope matched sampling) performs better than shift-based sampling (beat matched sampling). While shift-based sampling can yield small advantages in sensitivity, the gains are generally outweighed by diminished robustness. Our observation that more robust sampling schemes are only slightly less sensitive than schemes highly optimized using prior knowledge about chemical shifts has broad implications for any multidimensional NMR study employing NUS. The results derived from simulated data are demonstrated with a sample application to PfPMT, the phosphoethanolamine methyltransferase of the human malaria parasite Plasmodium falciparum.

Application of Bayesian informative priors to enhance the transferability of safety performance functions.

PubMed

Farid, Ahmed; Abdel-Aty, Mohamed; Lee, Jaeyoung; Eluru, Naveen

2017-09-01

Safety performance functions (SPFs) are essential tools for highway agencies to predict crashes, identify hotspots and assess safety countermeasures. In the Highway Safety Manual (HSM), a variety of SPFs are provided for different types of roadway facilities, crash types and severity levels. Agencies, lacking the necessary resources to develop own localized SPFs, may opt to apply the HSM's SPFs for their jurisdictions. Yet, municipalities that want to develop and maintain their regional SPFs might encounter the issue of the small sample bias. Bayesian inference is being conducted to address this issue by combining the current data with prior information to achieve reliable results. It follows that the essence of Bayesian statistics is the application of informative priors, obtained from other SPFs or experts' experiences. In this study, we investigate the applicability of informative priors for Bayesian negative binomial SPFs for rural divided multilane highway segments in Florida and California. An SPF with non-informative priors is developed for each state and its parameters' distributions are assigned to the other state's SPF as informative priors. The performances of SPFs are evaluated by applying each state's SPFs to the other state. The analysis is conducted for both total (KABCO) and severe (KAB) crashes. As per the results, applying one state's SPF with informative priors, which are the other state's SPF independent variable estimates, to the latter state's conditions yields better goodness of fit (GOF) values than applying the former state's SPF with non-informative priors to the conditions of the latter state. This is for both total and severe crash SPFs. Hence, for localities where it is not preferred to develop own localized SPFs and adopt SPFs from elsewhere to cut down on resources, application of informative priors is shown to facilitate the process. Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.

What Are They Up To? The Role of Sensory Evidence and Prior Knowledge in Action Understanding

PubMed Central

Chambon, Valerian; Domenech, Philippe; Pacherie, Elisabeth; Koechlin, Etienne; Baraduc, Pierre; Farrer, Chlöé

2011-01-01

Explaining or predicting the behaviour of our conspecifics requires the ability to infer the intentions that motivate it. Such inferences are assumed to rely on two types of information: (1) the sensory information conveyed by movement kinematics and (2) the observer's prior expectations – acquired from past experience or derived from prior knowledge. However, the respective contribution of these two sources of information is still controversial. This controversy stems in part from the fact that “intention” is an umbrella term that may embrace various sub-types each being assigned different scopes and targets. We hypothesized that variations in the scope and target of intentions may account for variations in the contribution of visual kinematics and prior knowledge to the intention inference process. To test this hypothesis, we conducted four behavioural experiments in which participants were instructed to identify different types of intention: basic intentions (i.e. simple goal of a motor act), superordinate intentions (i.e. general goal of a sequence of motor acts), or social intentions (i.e. intentions accomplished in a context of reciprocal interaction). For each of the above-mentioned intentions, we varied (1) the amount of visual information available from the action scene and (2) participant's prior expectations concerning the intention that was more likely to be accomplished. First, we showed that intentional judgments depend on a consistent interaction between visual information and participant's prior expectations. Moreover, we demonstrated that this interaction varied according to the type of intention to be inferred, with participant's priors rather than perceptual evidence exerting a greater effect on the inference of social and superordinate intentions. The results are discussed by appealing to the specific properties of each type of intention considered and further interpreted in the light of a hierarchical model of action representation. PMID:21364992

Schema-driven facilitation of new hierarchy learning in the transitive inference paradigm

PubMed Central

Kumaran, Dharshan

2013-01-01

Prior knowledge, in the form of a mental schema or framework, is viewed to facilitate the learning of new information in a range of experimental and everyday scenarios. Despite rising interest in the cognitive and neural mechanisms underlying schema-driven facilitation of new learning, few paradigms have been developed to examine this issue in humans. Here we develop a multiphase experimental scenario aimed at characterizing schema-based effects in the context of a paradigm that has been very widely used across species, the transitive inference task. We show that an associative schema, comprised of prior knowledge of the rank positions of familiar items in the hierarchy, has a marked effect on transitivity performance and the development of relational knowledge of the hierarchy that cannot be accounted for by more general changes in task strategy. Further, we show that participants are capable of deploying prior knowledge to successful effect under surprising conditions (i.e., when corrective feedback is totally absent), but only when the associative schema is robust. Finally, our results provide insights into the cognitive mechanisms underlying such schema-driven effects, and suggest that new hierarchy learning in the transitive inference task can occur through a contextual transfer mechanism that exploits the structure of associative experiences. PMID:23782509

Schema-driven facilitation of new hierarchy learning in the transitive inference paradigm.

PubMed

Kumaran, Dharshan

2013-06-19

Prior knowledge, in the form of a mental schema or framework, is viewed to facilitate the learning of new information in a range of experimental and everyday scenarios. Despite rising interest in the cognitive and neural mechanisms underlying schema-driven facilitation of new learning, few paradigms have been developed to examine this issue in humans. Here we develop a multiphase experimental scenario aimed at characterizing schema-based effects in the context of a paradigm that has been very widely used across species, the transitive inference task. We show that an associative schema, comprised of prior knowledge of the rank positions of familiar items in the hierarchy, has a marked effect on transitivity performance and the development of relational knowledge of the hierarchy that cannot be accounted for by more general changes in task strategy. Further, we show that participants are capable of deploying prior knowledge to successful effect under surprising conditions (i.e., when corrective feedback is totally absent), but only when the associative schema is robust. Finally, our results provide insights into the cognitive mechanisms underlying such schema-driven effects, and suggest that new hierarchy learning in the transitive inference task can occur through a contextual transfer mechanism that exploits the structure of associative experiences.

Making the Most of What We Already Know: A Three-Stage Approach to Systematic Reviewing.

PubMed

Rebelo Da Silva, Natalie; Zaranyika, Hazel; Langer, Laurenz; Randall, Nicola; Muchiri, Evans; Stewart, Ruth

2016-09-06

Conducting a systematic review in social policy is a resource-intensive process in terms of time and funds. It is thus important to understand the scope of the evidence base of a topic area prior to conducting a synthesis of primary research in order to maximize these resources. One approach to conserving resources is to map out the available evidence prior to undertaking a traditional synthesis. A few examples of this approach exist in the form of gap maps, overviews of reviews, and systematic maps supported by social policy and systematic review agencies alike. Despite this growing call for alternative approaches to systematic reviews, it is still common for systematic review teams to embark on a traditional in-depth review only. This article describes a three-stage approach to systematic reviewing that was applied to a systematic review focusing in interventions for smallholder farmers in Africa. We argue that this approach proved useful in helping us to understand the evidence base. By applying preliminary steps as part of a three-stage approach, we were able to maximize the resources needed to conduct a traditional systematic review on a more focused research question. This enabled us to identify and fill real knowledge gaps, build on work that had already been done, and avoid wasting resources on areas of work that would have no useful outcome. It also facilitated meaningful engagement between the review team and our key policy stakeholders. © The Author(s) 2016.

Positive relationship between odor identification and affective responses of negatively valenced odors

PubMed Central

Martinec Nováková, Lenka; Plotěná, Dagmar; Roberts, S. Craig; Havlíček, Jan

2015-01-01

Hedonic ratings of odors and olfactory preferences are influenced by a number of modulating factors, such as prior experience and knowledge about an odor’s identity. The present study addresses the relationship between knowledge about an odor’s identity due to prior experience, assessed by means of a test of cued odor identification, and odor pleasantness ratings in children who exhibit ongoing olfactory learning. Ninety-one children aged 8–11 years rated the pleasantness of odors in the Sniffin’ Sticks test and, subsequently, took the odor identification test. A positive association between odor identification and pleasantness was found for two unpleasant food odors (garlic and fish): higher pleasantness ratings were exhibited by those participants who correctly identified these odors compared to those who failed to correctly identify them. However, we did not find a similar effect for any of the more pleasant odors. The results of this study suggest that pleasantness ratings of some odors may be modulated by the knowledge of their identity due to prior experience and that this relationship might be more evident in unpleasant odors. PMID:26029143

A technology training protocol for meeting QSEN goals: Focusing on meaningful learning.

PubMed

Luo, Shuhong; Kalman, Melanie

2018-01-01

The purpose of this paper is to describe and discuss how we designed and developed a 12-step technology training protocol. The protocol is meant to improve meaningful learning in technology education so that nursing students are able to meet the informatics requirements of Quality and Safety Education in Nursing competencies. When designing and developing the training protocol, we used a simplified experiential learning model that addressed the core features of meaningful learning: to connect new knowledge with students' prior knowledge and real-world workflow. Before training, we identified students' prior knowledge and workflow tasks. During training, students learned by doing, reflected on their prior computer skills and workflow, designed individualized procedures for integration into their workflow, and practiced the self-designed procedures in real-world settings. The trainer was a facilitator who provided a meaningful learning environment, asked the right questions to guide reflective conversation, and offered scaffoldings at critical moments. This training protocol could significantly improve nurses' competencies in using technologies and increase their desire to adopt new technologies. © 2017 Wiley Periodicals, Inc.

Topics in inference and decision-making with partial knowledge

NASA Technical Reports Server (NTRS)

Safavian, S. Rasoul; Landgrebe, David

1990-01-01

Two essential elements needed in the process of inference and decision-making are prior probabilities and likelihood functions. When both of these components are known accurately and precisely, the Bayesian approach provides a consistent and coherent solution to the problems of inference and decision-making. In many situations, however, either one or both of the above components may not be known, or at least may not be known precisely. This problem of partial knowledge about prior probabilities and likelihood functions is addressed. There are at least two ways to cope with this lack of precise knowledge: robust methods, and interval-valued methods. First, ways of modeling imprecision and indeterminacies in prior probabilities and likelihood functions are examined; then how imprecision in the above components carries over to the posterior probabilities is examined. Finally, the problem of decision making with imprecise posterior probabilities and the consequences of such actions are addressed. Application areas where the above problems may occur are in statistical pattern recognition problems, for example, the problem of classification of high-dimensional multispectral remote sensing image data.

Bayesian network prior: network analysis of biological data using external knowledge

PubMed Central

Isci, Senol; Dogan, Haluk; Ozturk, Cengizhan; Otu, Hasan H.

2014-01-01

Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event ‘gene interaction’ and is used to calculate the probability of a candidate graph (G) in the structure learning process. Results: Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods. Availability: Accompanying BNP software package is freely available for academic use at http://bioe.bilgi.edu.tr/BNP. Contact: hasan.otu@bilgi.edu.tr Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:24215027

Overview of refinement procedures within REFMAC5: utilizing data from different sources.

PubMed

Kovalevskiy, Oleg; Nicholls, Robert A; Long, Fei; Carlon, Azzurra; Murshudov, Garib N

2018-03-01

Refinement is a process that involves bringing into agreement the structural model, available prior knowledge and experimental data. To achieve this, the refinement procedure optimizes a posterior conditional probability distribution of model parameters, including atomic coordinates, atomic displacement parameters (B factors), scale factors, parameters of the solvent model and twin fractions in the case of twinned crystals, given observed data such as observed amplitudes or intensities of structure factors. A library of chemical restraints is typically used to ensure consistency between the model and the prior knowledge of stereochemistry. If the observation-to-parameter ratio is small, for example when diffraction data only extend to low resolution, the Bayesian framework implemented in REFMAC5 uses external restraints to inject additional information extracted from structures of homologous proteins, prior knowledge about secondary-structure formation and even data obtained using different experimental methods, for example NMR. The refinement procedure also generates the `best' weighted electron-density maps, which are useful for further model (re)building. Here, the refinement of macromolecular structures using REFMAC5 and related tools distributed as part of the CCP4 suite is discussed.

The effects of prior knowledge on study-time allocation and free recall: investigating the discrepancy reduction model.

PubMed

Verkoeijen, Peter P J L; Rikers, Remy M J P; Schmidt, Henk G

2005-01-01

In this study, the authors examined the influence of prior knowledge activation on information processing by means of a prior knowledge activation procedure adopted from the read-generate paradigm. On the basis of cue-target pairs, participants in the experimental groups generated two different sets of items before studying a relevant list. Subsequently, participants were informed that they had to study the items in the list and that they should try to remember as many items as possible. The authors assessed the processing time allocated to the items in the list and free recall of those items. The results revealed that the experimental groups spent less time on items that had already been activated. In addition, the experimental groups outperformed the control group in overall free recall and in free recall of the activated items. Between-group comparisons did not demonstrate significant effects with respect to the processing time and free recall of nonactivated items. The authors interpreted these results in terms of the discrepancy reduction model of regulating the amount of processing time allocated to different parts of the list.

The Influence of the Knowledge Base on the Development of Mnemonic Strategies.

ERIC Educational Resources Information Center

Ornstein, Peter A.; Naus, Mary J.

A dominant theme in cognitive psychology is that prior knowledge in long-term memory has a strong influence on an individual's cognitive processing. Citing numerous memory studies with children, knowledge base effects are presented as part of a broader picture of memory development. Using the sort/recall procedure (asking subjects to group sets of…

The Roles of Ability, Personality, and Interests in Acquiring Current Events Knowledge: A Longitudinal Study

ERIC Educational Resources Information Center

Hambrick, David Z.; Pink, Jeffrey E.; Meinz, Elizabeth J.; Pettibone, Jonathan C.; Oswald, Frederick L.

2008-01-01

The purpose of this study was to investigate sources of inter-individual differences in current events knowledge. The study occurred in two sessions. In the initial session, 579 participants completed tests to ability, personality, and interest factors, as well as prior knowledge of current events. Approximately 10 weeks later, participants…

Cognitive Demand Differences in Causal Inferences: Characters' Plans Are More Difficult to Comprehend than Physical Causation

ERIC Educational Resources Information Center

Shears, Connie; Miller, Vanessa; Ball, Megan; Hawkins, Amanda; Griggs, Janna; Varner, Andria

2007-01-01

Readers may draw knowledge-based inferences to connect sentences in text differently depending on the knowledge domain being accessed. Most prior research has focused on the direction of the causal explanation (predictive vs. backward) without regard to the knowledge domain drawn on to support comprehension. We suggest that less cognitive effort…

A Connective Ethnography of Peer Knowledge Sharing and Diffusion in a Tween Virtual World

ERIC Educational Resources Information Center

Fields, Deborah A.; Kafai, Yasmin B.

2009-01-01

Prior studies have shown how knowledge diffusion occurs in classrooms and structured small groups around assigned tasks yet have not begun to account for widespread knowledge sharing in more native, unstructured group settings found in online games and virtual worlds. In this paper, we describe and analyze how an insider gaming practice spread…

Examining the Acquisition of Vocabulary Knowledge Depth among Preschool Students

ERIC Educational Resources Information Center

Hadley, Elizabeth B.; Dickinson, David K.; Hirsh-Pasek, Kathy; Golinkoff, Roberta Michnick; Nesbitt, Kimberly T.

2016-01-01

Well-developed lexical representations are important for reading comprehension, but there have been no prior attempts to track growth in the depth of knowledge of particular words. This article examines increases in depth of vocabulary knowledge in 4-5-year-old preschool students (n = 240) who participated in a vocabulary intervention that taught…

High School Students' Meta-Modeling Knowledge

ERIC Educational Resources Information Center

Fortus, David; Shwartz, Yael; Rosenfeld, Sherman

2016-01-01

Modeling is a core scientific practice. This study probed the meta-modeling knowledge (MMK) of high school students who study science but had not had any explicit prior exposure to modeling as part of their formal schooling. Our goals were to (A) evaluate the degree to which MMK is dependent on content knowledge and (B) assess whether the upper…

How to Write "How-to" Books with High School Ecology & Horticulture Students

ERIC Educational Resources Information Center

Merritt, Maya; Shajira, Natasya; Daisey, Peggy

2003-01-01

It is essential for students to think clearly about fundamental biological concepts. One of the benefits of writing is that it promotes and enhances thinking. If students can write clearly, they are thinking clearly. Writing helps to connect new knowledge with prior knowledge and promotes the construction of knowledge. Writing-to-learn activities…

5 CFR 4701.102 - Prior approval for certain outside employment.

Code of Federal Regulations, 2012 CFR

2012-01-01

... STANDARDS OF ETHICAL CONDUCT FOR EMPLOYEES OF THE FEDERAL ELECTION COMMISSION § 4701.102 Prior approval for... or consultation, which requires advanced knowledge in a field of science or learning customarily... obtain written approval from the Designated Agency Ethics Official before engaging in outside employment...

5 CFR 4701.102 - Prior approval for certain outside employment.

Code of Federal Regulations, 2013 CFR

2013-01-01

... STANDARDS OF ETHICAL CONDUCT FOR EMPLOYEES OF THE FEDERAL ELECTION COMMISSION § 4701.102 Prior approval for... or consultation, which requires advanced knowledge in a field of science or learning customarily... obtain written approval from the Designated Agency Ethics Official before engaging in outside employment...

5 CFR 4701.102 - Prior approval for certain outside employment.

Code of Federal Regulations, 2014 CFR

2014-01-01

... STANDARDS OF ETHICAL CONDUCT FOR EMPLOYEES OF THE FEDERAL ELECTION COMMISSION § 4701.102 Prior approval for... or consultation, which requires advanced knowledge in a field of science or learning customarily... obtain written approval from the Designated Agency Ethics Official before engaging in outside employment...

Conceptualising GP teachers' knowledge: a pedagogical content knowledge perspective.

PubMed

Cantillon, Peter; de Grave, Willem

2012-05-01

Most teacher development initiatives focus on enhancing knowledge of teaching (pedagogy), whilst largely ignoring other important features of teacher knowledge such as subject matter knowledge and awareness of the learning context. Furthermore, teachers' ability to learn from faculty development interventions is limited by their existing (often implicit) pedagogical knowledge and beliefs. Pedagogical content knowledge (PCK) represents a model of teacher knowledge incorporating what they know about subject matter, pedagogy and context. PCK can be used to explore teachers' prior knowledge and to structure faculty development programmes so that they take account of a broader range of teachers' knowledge. We set out to examine the application of a PCK model in a general practice education setting. This study is part of a larger study that employed a mixed method approach (concept mapping, phenomenological interviews and video-stimulated recall) to explore features of GP teachers' subject matter knowledge, pedagogical knowledge and knowledge of the learning environment in the context of a general practice tutorial. This paper presents data on GP teachers' pedagogical and context knowledge. There was considerable overlap between different GP teachers' knowledge and beliefs about learners and the clinical learning environment (i.e. knowledge of context). The teachers' beliefs about learners were largely based on assumptions derived from their own student experiences. There were stark differences, however, between teachers in terms of pedagogical knowledge, particularly in terms of their teaching orientations (i.e. transmission or facilitation orientation) and this was manifest in their teaching behaviours. PCK represents a useful model for conceptualising clinical teacher prior knowledge in three domains, namely subject matter, learning context and pedagogy. It can and should be used as a simple guiding framework by faculty developers to inform the design and delivery of their faculty development programmes.

Discrete Sparse Coding.

PubMed

Exarchakis, Georgios; Lücke, Jörg

2017-11-01

Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions. We consider the general case in which the latents (while being sparse) can take on any value of a finite set of possible values and in which we learn the prior probability of any value from data. This approach can be applied to any data generated by discrete causes, and it can be applied as an approximation of continuous causes. As the prior probabilities are learned, the approach then allows for estimating the prior shape without assuming specific functional forms. To efficiently train the parameters of our probabilistic generative model, we apply a truncated expectation-maximization approach (expectation truncation) that we modify to work with a general discrete prior. We evaluate the performance of the algorithm by applying it to a variety of tasks: (1) we use artificial data to verify that the algorithm can recover the generating parameters from a random initialization, (2) use image patches of natural images and discuss the role of the prior for the extraction of image components, (3) use extracellular recordings of neurons to present a novel method of analysis for spiking neurons that includes an intuitive discretization strategy, and (4) apply the algorithm on the task of encoding audio waveforms of human speech. The diverse set of numerical experiments presented in this letter suggests that discrete sparse coding algorithms can scale efficiently to work with realistic data sets and provide novel statistical quantities to describe the structure of the data.

A chromatographic objective function to characterise chromatograms with unknown compounds or without standards available.

PubMed

Alvarez-Segura, T; Gómez-Díaz, A; Ortiz-Bolsico, C; Torres-Lapasió, J R; García-Alvarez-Coque, M C

2015-08-28

Getting useful chemical information from samples containing many compounds is still a challenge to analysts in liquid chromatography. The highest complexity corresponds to samples for which there is no prior knowledge about their chemical composition. Computer-based methodologies are currently considered as the most efficient tools to optimise the chromatographic resolution, and further finding the optimal separation conditions. However, most chromatographic objective functions (COFs) described in the literature to measure the resolution are based on mathematical models fitted with the information obtained from standards, and cannot be applied to samples with unknown compounds. In this work, a new COF based on the automatic measurement of the protruding part of the chromatographic peaks (or peak prominences) that indicates the number of perceptible peaks and global resolution, without the need of standards, is developed. The proposed COF was found satisfactory with regard to the peak purity criterion when applied to artificial peaks and simulated chromatograms of mixtures built using the information of standards. The approach was applied to mixtures of drugs containing unknown impurities and degradation products and to extracts of medicinal herbs, eluted with acetonitrile-water mixtures using isocratic and gradient elution. Copyright © 2015 Elsevier B.V. All rights reserved.

Bias in the physical examination of patients with lumbar radiculopathy

PubMed Central

2010-01-01

Background No prior studies have examined systematic bias in the musculoskeletal physical examination. The objective of this study was to assess the effects of bias due to prior knowledge of lumbar spine magnetic resonance imaging findings (MRI) on perceived diagnostic accuracy of the physical examination for lumbar radiculopathy. Methods This was a cross-sectional comparison of the performance characteristics of the physical examination with blinding to MRI results (the 'independent group') with performance in the situation where the physical examination was not blinded to MRI results (the 'non-independent group'). The reference standard was the final diagnostic impression of nerve root impingement by the examining physician. Subjects were recruited from a hospital-based outpatient specialty spine clinic. All adults age 18 and older presenting with lower extremity radiating pain of duration ≤ 12 weeks were evaluated for participation. 154 consecutively recruited subjects with lumbar disk herniation confirmed by lumbar spine MRI were included in this study. Sensitivities and specificities with 95% confidence intervals were calculated in the independent and non-independent groups for the four components of the radiculopathy examination: 1) provocative testing, 2) motor strength testing, 3) pinprick sensory testing, and 4) deep tendon reflex testing. Results The perceived sensitivity of sensory testing was higher with prior knowledge of MRI results (20% vs. 36%; p = 0.05). Sensitivities and specificities for exam components otherwise showed no statistically significant differences between groups. Conclusions Prior knowledge of lumbar MRI results may introduce bias into the pinprick sensory testing component of the physical examination for lumbar radiculopathy. No statistically significant effect of bias was seen for other components of the physical examination. The effect of bias due to prior knowledge of lumbar MRI results should be considered when an isolated sensory deficit on examination is used in medical decision-making. Further studies of bias should include surgical clinic populations and other common diagnoses including shoulder, knee and hip pathology. PMID:21118558

Bias in the physical examination of patients with lumbar radiculopathy.

PubMed

Suri, Pradeep; Hunter, David J; Katz, Jeffrey N; Li, Ling; Rainville, James

2010-11-30

No prior studies have examined systematic bias in the musculoskeletal physical examination. The objective of this study was to assess the effects of bias due to prior knowledge of lumbar spine magnetic resonance imaging findings (MRI) on perceived diagnostic accuracy of the physical examination for lumbar radiculopathy. This was a cross-sectional comparison of the performance characteristics of the physical examination with blinding to MRI results (the 'independent group') with performance in the situation where the physical examination was not blinded to MRI results (the 'non-independent group'). The reference standard was the final diagnostic impression of nerve root impingement by the examining physician. Subjects were recruited from a hospital-based outpatient specialty spine clinic. All adults age 18 and older presenting with lower extremity radiating pain of duration ≤ 12 weeks were evaluated for participation. 154 consecutively recruited subjects with lumbar disk herniation confirmed by lumbar spine MRI were included in this study. Sensitivities and specificities with 95% confidence intervals were calculated in the independent and non-independent groups for the four components of the radiculopathy examination: 1) provocative testing, 2) motor strength testing, 3) pinprick sensory testing, and 4) deep tendon reflex testing. The perceived sensitivity of sensory testing was higher with prior knowledge of MRI results (20% vs. 36%; p = 0.05). Sensitivities and specificities for exam components otherwise showed no statistically significant differences between groups. Prior knowledge of lumbar MRI results may introduce bias into the pinprick sensory testing component of the physical examination for lumbar radiculopathy. No statistically significant effect of bias was seen for other components of the physical examination. The effect of bias due to prior knowledge of lumbar MRI results should be considered when an isolated sensory deficit on examination is used in medical decision-making. Further studies of bias should include surgical clinic populations and other common diagnoses including shoulder, knee and hip pathology.

Does the success of a school-based HPV vaccine programme depend on teachers' knowledge and religion? -- A survey in a multicultural society.

PubMed

Ling, Woo Yin; Razali, Sharina Mohd; Ren, Chong Kuoh; Omar, Siti Zawiah

2012-01-01

Organized introduction of prophylactic human papillomavirus (HPV) vaccination can reduce the burden of cervical cancer in developing countries. One of the most effective ways is through a national school-based program. Information on teachers is therefore important since this group may have a disproportionate influence in the success of any implementation. To assess teachers' knowledge and perception of HPV, cervical cancer and HPV vaccine prior to commencing a school-based HPV vaccination program in a multiethnic, predominantly Muslim country. Factors associated with acceptability of the vaccine were identified. A bilingual questionnaire was applied to 1,500 secondary school teachers from 20 urban schools in Malaysia. Data collected were analyzed using SPSS version 17. 1,166 questionnaires were returned. From this group, 46.1% had never heard of HPV while 50.9% had never had a pap smear. However, 73.8% have heard of the HPV vaccine with 75% agreeing to have it. 96% considered themselves religious with 79.8% agreeing to have the vaccine. A national school-based HPV immunization program can be implemented effectively in a multiethnic, cultural and religious country despite limited knowledge of HPV-related pathology among teachers. In addition, the perception that religion has a negative influence on such a program is unwarranted.

Living Animals in the Classroom: A Meta-Analysis on Learning Outcome and a Treatment-Control Study Focusing on Knowledge and Motivation

NASA Astrophysics Data System (ADS)

Hummel, Eberhard; Randler, Christoph

2012-02-01

Prior research states that the use of living animals in the classroom leads to a higher knowledge but those previous studies have methodological and statistical problems. We applied a meta-analysis and developed a treatment-control study in a middle school classroom. The treatments (film vs. living animal) differed only by the presence of the living animal. Both treatments were based on the self-determination theory. More than 400 pupils filled in pre-test, post-test and two follow-up-tests (with a delay of 6-8 weeks and 7-8 months). After each lesson, pupils rated the lesson on a short intrinsic motivation scale. In the meta-analysis, we found that the living animal treatments significantly scored better than a control group, but not when comparing living animals with alternative treatments. In the treatment-control study, both treatments led to a significant increase in knowledge but there were no differences between film and living animal treatment. Pre-test and previous grading had a significant influence on post- and both follow-up tests. In the mouse lesson, pupils of the living animal group showed higher values in interest and competence and lower values in pressure. Interest and competence correlated positively with achievement, while pressure correlated negatively.

[Effects of an educational program for the reduction of physical restraint use by caregivers in geriatric hospitals].

PubMed

Choi, Keumbong; Kim, Jinsun

2009-12-01

The purposes of this study were to develop an educational program to reduce the use of physical restraints for caregivers in geriatric hospitals and to evaluate the effects of the program on caregivers' knowledge, attitude and nursing practice related to the use of physical restraints. A quasi experimental study with a non-equivalent control group pretest-posttest design was used. Participants were recruited from two geriatric hospitals. Eighteen caregivers were assigned to the experimental group and 20 to the control group. The data were collected prior to the intervention and at 6 weeks after the intervention through the use of self-administered questionnaires. Descriptive statistics, X(2) test, Fisher's exact probability test, and Mann-Whitney U test were used to analyze the data. After the intervention, knowledge about physical restraints increased significantly in experimental group compared to the control group. However, there were no statistically significant differences between the groups for attitude and nursing practice involving physical restraints. Findings indicate that it is necessary to apply knowledge acquired through educational programs to nursing practice to reduce the use of physical restraints. User friendly guidelines for physical restraints, administrative support of institutions, and multidisciplinary approaches are required to achieve this goal.

Opening our eyes to a critical approach to medicine: The humanities in medical education.

PubMed

Liao, Lester

2017-02-01

This paper examines a recent medical graduate's perspective on how undergraduate education tends to focus on imparting medical knowledge with little reference to the human aspects in clinical medicine. This is problematic because medicine is both about people and practiced by people. Students often have minimal exposure to the humanities prior to and in medical school and are frequently unaware of the societal trends that impact their view of medical practice. Familiarity with the humanities is a crucial means to understanding human nature, recognizing personal sociocultural biases, and practicing patient-centered medicine. This gap in knowledge may be due to the increase in medical information and optimistic ideologies related to medical progress. Philosophical paradigms and historical examples are considered to demonstrate the relevance of both fields in the humanities in understanding the role of moral human agents in applying medical knowledge. Educational changes in the humanities are proposed as a potential solution to our current deficits. Informal changes include mentorship relationships and shifting the general underpinning attitude in medical culture. Formal changes include specific courses teaching a critical approach to medicine. Changes in competency-based education and admissions are also suggested. These amendments are proposed to practice a fuller, truly human medicine.

Nontargeted diagnostic ion network analysis (NINA): A software to streamline the analytical workflow for untargeted characterization of natural medicines.

PubMed

Ye, Hui; Zhu, Lin; Sun, Di; Luo, Xiaozhuo; Lu, Gaoyuan; Wang, Hong; Wang, Jing; Cao, Guoxiu; Xiao, Wei; Wang, Zhenzhong; Wang, Guangji; Hao, Haiping

2016-11-30

The characterization of herbal prescriptions serves as a foundation for quality control and regulation of herbal medicines. Previously, the characterization of herbal chemicals from natural medicines often relied on the analysis of signature fragment ions from the acquired tandem mass spectrometry (MS/MS) spectra with prior knowledge of the herbal species present in the herbal prescriptions of interest. Nevertheless, such an approach is often limited to target components, and it risks missing the critical components that we have no prior knowledge of. We previously reported a "diagnostic ion-guided network bridging" strategy. It is a generally applicable and robust approach to analyze unknown substances from complex mixtures in an untargeted manner. In this study, we have developed a standalone software named "Nontargeted Diagnostic Ion Network Analysis (NINA)" with a graphical user interface based on a strategy for post-acquisition data analysis. NINA allows one to rapidly determine the nontargeted diagnostic ions (NIs) by summarizing all of the fragment ions shared by the precursors from the acquired MS/MS spectra. A NI-guided network using bridging components that possess two or more NIs can then be established via NINA. With such a network, we could sequentially identify the structures of all the NIs once a single compound has been identified de novo. The structures of NIs can then be used as "priori" knowledge to narrow the candidates containing the sub-structure of the corresponding NI from the database hits. Subsequently, we applied the NINA software to the characterization of a model herbal prescription, Re-Du-Ning injection, and rapidly identified 56 herbal chemicals from the prescription using an ultra-performance liquid chromatography quadrupole time-of-flight system in the negative mode with no knowledge of the herbal species or herbal chemicals in the mixture. Therefore, we believe the applications of NINA will greatly facilitate the characterization of complex mixtures, such as natural medicines, especially when no advance information is available. In addition to herbal medicines, the NINA-based workflow will also benefit many other fields, such as environmental analysis, nutritional science, and forensic analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization.

PubMed

Niu, Zhenxing; Hua, Gang; Wang, Le; Gao, Xinbo

Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.

28 CFR 12.3 - Prior registration with the Foreign Agents Registration Unit.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 28 Judicial Administration 1 2013-07-01 2013-07-01 false Prior registration with the Foreign Agents Registration Unit. 12.3 Section 12.3 Judicial Administration DEPARTMENT OF JUSTICE REGISTRATION OF CERTAIN PERSONS HAVING KNOWLEDGE OF FOREIGN ESPIONAGE, COUNTERESPIONAGE, OR SABOTAGE MATTERS UNDER THE ACT...

28 CFR 12.3 - Prior registration with the Foreign Agents Registration Unit.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 28 Judicial Administration 1 2014-07-01 2014-07-01 false Prior registration with the Foreign Agents Registration Unit. 12.3 Section 12.3 Judicial Administration DEPARTMENT OF JUSTICE REGISTRATION OF CERTAIN PERSONS HAVING KNOWLEDGE OF FOREIGN ESPIONAGE, COUNTERESPIONAGE, OR SABOTAGE MATTERS UNDER THE ACT...

28 CFR 12.3 - Prior registration with the Foreign Agents Registration Unit.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Prior registration with the Foreign Agents Registration Unit. 12.3 Section 12.3 Judicial Administration DEPARTMENT OF JUSTICE REGISTRATION OF CERTAIN PERSONS HAVING KNOWLEDGE OF FOREIGN ESPIONAGE, COUNTERESPIONAGE, OR SABOTAGE MATTERS UNDER THE ACT...

28 CFR 12.3 - Prior registration with the Foreign Agents Registration Unit.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 28 Judicial Administration 1 2011-07-01 2011-07-01 false Prior registration with the Foreign Agents Registration Unit. 12.3 Section 12.3 Judicial Administration DEPARTMENT OF JUSTICE REGISTRATION OF CERTAIN PERSONS HAVING KNOWLEDGE OF FOREIGN ESPIONAGE, COUNTERESPIONAGE, OR SABOTAGE MATTERS UNDER THE ACT...

28 CFR 12.3 - Prior registration with the Foreign Agents Registration Unit.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 28 Judicial Administration 1 2012-07-01 2012-07-01 false Prior registration with the Foreign Agents Registration Unit. 12.3 Section 12.3 Judicial Administration DEPARTMENT OF JUSTICE REGISTRATION OF CERTAIN PERSONS HAVING KNOWLEDGE OF FOREIGN ESPIONAGE, COUNTERESPIONAGE, OR SABOTAGE MATTERS UNDER THE ACT...

Findings from TIMSS 2007: What Drives Utilization of Inquiry-Based Science Instruction?

ERIC Educational Resources Information Center

Kuzhabekova, Aliya

2015-01-01

Prior research has shown that greatest student achievement in sciences is attributed to "inquiry-based instructional approach", in which the goal of science teaching is nurturing attitudes and skills necessary for independent quest for scientific knowledge. While prior research has clearly demonstrated positive instructional effects of…

Using Students' Prior Knowledge to Teach Social Penetration Theory

ERIC Educational Resources Information Center

Chornet-Roses, Daniel

2010-01-01

Bransford, Brown, and Cocking argue that acknowledging students' prior ideas and beliefs about a subject and incorporating them into the classroom enhances student learning. This article presents an activity which serves to hone three student learning outcomes: analysis of communication, inductive reasoning, and self-reflection. The goal of this…

Genetic Classification of Populations Using Supervised Learning

PubMed Central

Bridges, Michael; Heron, Elizabeth A.; O'Dushlaine, Colm; Segurado, Ricardo; Morris, Derek; Corvin, Aiden; Gill, Michael; Pinto, Carlos

2011-01-01

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case–control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies. PMID:21589856

Informed walks: whispering hints to gene hunters inside networks' jungle.

PubMed

Bourdakou, Marilena M; Spyrou, George M

2017-10-11

Systemic approaches offer a different point of view on the analysis of several types of molecular associations as well as on the identification of specific gene communities in several cancer types. However, due to lack of sufficient data needed to construct networks based on experimental evidence, statistical gene co-expression networks are widely used instead. Many efforts have been made to exploit the information hidden in these networks. However, these approaches still need to capitalize comprehensively the prior knowledge encrypted into molecular pathway associations and improve their efficiency regarding the discovery of both exclusive subnetworks as candidate biomarkers and conserved subnetworks that may uncover common origins of several cancer types. In this study we present the development of the Informed Walks model based on random walks that incorporate information from molecular pathways to mine candidate genes and gene-gene links. The proposed model has been applied to TCGA (The Cancer Genome Atlas) datasets from seven different cancer types, exploring the reconstructed co-expression networks of the whole set of genes and driving to highlighted sub-networks for each cancer type. In the sequel, we elucidated the impact of each subnetwork on the indication of underlying exclusive and common molecular mechanisms as well as on the short-listing of drugs that have the potential to suppress the corresponding cancer type through a drug-repurposing pipeline. We have developed a method of gene subnetwork highlighting based on prior knowledge, capable to give fruitful insights regarding the underlying molecular mechanisms and valuable input to drug-repurposing pipelines for a variety of cancer types.

Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET

PubMed Central

Androsova, Ganna; del Sol, Antonio

2015-01-01

High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states. PMID:26058016

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

PubMed Central

2010-01-01

Background Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals. To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network. Results We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (Simple Expression Ranking). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the Heat Kernel Diffusion Ranking leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%. Conclusion In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype. PMID:20840752

Correction of odds ratios in case-control studies for exposure misclassification with partial knowledge of the degree of agreement among experts who assessed exposures.

PubMed

Burstyn, Igor; Gustafson, Paul; Pintos, Javier; Lavoué, Jérôme; Siemiatycki, Jack

2018-02-01

Estimates of association between exposures and diseases are often distorted by error in exposure classification. When the validity of exposure assessment is known, this can be used to adjust these estimates. When exposure is assessed by experts, even if validity is not known, we sometimes have information about interrater reliability. We present a Bayesian method for translating the knowledge of interrater reliability, which is often available, into knowledge about validity, which is often needed but not directly available, and applying this to correct odds ratios (OR). The method allows for inclusion of observed potential confounders in the analysis, as is common in regression-based control for confounding. Our method uses a novel type of prior on sensitivity and specificity. The approach is illustrated with data from a case-control study of lung cancer risk and occupational exposure to diesel engine emissions, in which exposure assessment was made by detailed job history interviews with study subjects followed by expert judgement. Using interrater agreement measured by kappas (κ), we estimate sensitivity and specificity of exposure assessment and derive misclassification-corrected confounder-adjusted OR. Misclassification-corrected and confounder-adjusted OR obtained with the most defensible prior had a posterior distribution centre of 1.6 with 95% credible interval (Crl) 1.1 to 2.6. This was on average greater in magnitude than frequentist point estimate of 1.3 (95% Crl 1.0 to 1.7). The method yields insights into the degree of exposure misclassification and appears to reduce attenuation bias due to misclassification of exposure while the estimated uncertainty increased. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Quantifying structural uncertainty on fault networks using a marked point process within a Bayesian framework

NASA Astrophysics Data System (ADS)

Aydin, Orhun; Caers, Jef Karel

2017-08-01

Faults are one of the building-blocks for subsurface modeling studies. Incomplete observations of subsurface fault networks lead to uncertainty pertaining to location, geometry and existence of faults. In practice, gaps in incomplete fault network observations are filled based on tectonic knowledge and interpreter's intuition pertaining to fault relationships. Modeling fault network uncertainty with realistic models that represent tectonic knowledge is still a challenge. Although methods that address specific sources of fault network uncertainty and complexities of fault modeling exists, a unifying framework is still lacking. In this paper, we propose a rigorous approach to quantify fault network uncertainty. Fault pattern and intensity information are expressed by means of a marked point process, marked Strauss point process. Fault network information is constrained to fault surface observations (complete or partial) within a Bayesian framework. A structural prior model is defined to quantitatively express fault patterns, geometries and relationships within the Bayesian framework. Structural relationships between faults, in particular fault abutting relations, are represented with a level-set based approach. A Markov Chain Monte Carlo sampler is used to sample posterior fault network realizations that reflect tectonic knowledge and honor fault observations. We apply the methodology to a field study from Nankai Trough & Kumano Basin. The target for uncertainty quantification is a deep site with attenuated seismic data with only partially visible faults and many faults missing from the survey or interpretation. A structural prior model is built from shallow analog sites that are believed to have undergone similar tectonics compared to the site of study. Fault network uncertainty for the field is quantified with fault network realizations that are conditioned to structural rules, tectonic information and partially observed fault surfaces. We show the proposed methodology generates realistic fault network models conditioned to data and a conceptual model of the underlying tectonics.

Reverse engineering highlights potential principles of large gene regulatory network design and learning.

PubMed

Carré, Clément; Mas, André; Krouk, Gabriel

2017-01-01

Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10 4 genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data ( Escherichia coli K14 network reconstruction using network and transcriptomic data), we show that the formalism used to build FRANK can to some extent be a reasonable model for gene regulatory networks in real cells.

A knowledge-based system for patient image pre-fetching in heterogeneous database environments--modeling, design, and evaluation.

PubMed

Wei, C P; Hu, P J; Sheng, O R

2001-03-01

When performing primary reading on a newly taken radiological examination, a radiologist often needs to reference relevant prior images of the same patient for confirmation or comparison purposes. Support of such image references is of clinical importance and may have significant effects on radiologists' examination reading efficiency, service quality, and work satisfaction. To effectively support such image reference needs, we proposed and developed a knowledge-based patient image pre-fetching system, addressing several challenging requirements of the application that include representation and learning of image reference heuristics and management of data-intensive knowledge inferencing. Moreover, the system demands an extensible and maintainable architecture design capable of effectively adapting to a dynamic environment characterized by heterogeneous and autonomous data source systems. In this paper, we developed a synthesized object-oriented entity- relationship model, a conceptual model appropriate for representing radiologists' prior image reference heuristics that are heuristic oriented and data intensive. We detailed the system architecture and design of the knowledge-based patient image pre-fetching system. Our architecture design is based on a client-mediator-server framework, capable of coping with a dynamic environment characterized by distributed, heterogeneous, and highly autonomous data source systems. To adapt to changes in radiologists' patient prior image reference heuristics, ID3-based multidecision-tree induction and CN2-based multidecision induction learning techniques were developed and evaluated. Experimentally, we examined effects of the pre-fetching system we created on radiologists' examination readings. Preliminary results show that the knowledge-based patient image pre-fetching system more accurately supports radiologists' patient prior image reference needs than the current practice adopted at the study site and that radiologists may become more efficient, consultatively effective, and better satisfied when supported by the pre-fetching system than when relying on the study site's pre-fetching practice.

Cooperative Education, Experiential Learning, and Personal Knowledge.

ERIC Educational Resources Information Center

Abrahamsson, Kenneth, Ed.

Cooperative education, experiential learning, and personal knowledge are addressed in nine conference papers. Kenneth Abrahamsson considers the nature of experiential learning, the recognition of prior learning, educational design and the assessment of quality, and policy and practice for integrating learning and experience. Harry Hienemann…

"Bricolage" and Teachers' Theorizing.

ERIC Educational Resources Information Center

Wagner, Jon

1990-01-01

Discusses issues stimulated by Elizabeth Hatton's examination of teachers' work as "bricolage" in a prior article. Explores the connection between creative potential and pedagogic knowledge and the science of the abstract and the concrete. Presents potential reforms that could enhance teachers' capacity to gain pedagogic knowledge. (JS)

26 CFR 1.691(e)-1 - Installment obligations transmitted at death when prior law applied.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 26 Internal Revenue 8 2010-04-01 2010-04-01 false Installment obligations transmitted at death....691(e)-1 Installment obligations transmitted at death when prior law applied. (a) In general—(1... death of a holder of such obligations were required to be reported in the return of the decedent for the...

Improving transmission efficiency of large sequence alignment/map (SAM) files.

PubMed

Sakib, Muhammad Nazmus; Tang, Jijun; Zheng, W Jim; Huang, Chin-Tser

2011-01-01

Research in bioinformatics primarily involves collection and analysis of a large volume of genomic data. Naturally, it demands efficient storage and transfer of this huge amount of data. In recent years, some research has been done to find efficient compression algorithms to reduce the size of various sequencing data. One way to improve the transmission time of large files is to apply a maximum lossless compression on them. In this paper, we present SAMZIP, a specialized encoding scheme, for sequence alignment data in SAM (Sequence Alignment/Map) format, which improves the compression ratio of existing compression tools available. In order to achieve this, we exploit the prior knowledge of the file format and specifications. Our experimental results show that our encoding scheme improves compression ratio, thereby reducing overall transmission time significantly.

Spatial-time-state fusion algorithm for defect detection through eddy current pulsed thermography

NASA Astrophysics Data System (ADS)

Xiao, Xiang; Gao, Bin; Woo, Wai Lok; Tian, Gui Yun; Xiao, Xiao Ting

2018-05-01

Eddy Current Pulsed Thermography (ECPT) has received extensive attention due to its high sensitive of detectability on surface and subsurface cracks. However, it remains as a difficult challenge in unsupervised detection as to identify defects without knowing any prior knowledge. This paper presents a spatial-time-state features fusion algorithm to obtain fully profile of the defects by directional scanning. The proposed method is intended to conduct features extraction by using independent component analysis (ICA) and automatic features selection embedding genetic algorithm. Finally, the optimal feature of each step is fused to obtain defects reconstruction by applying common orthogonal basis extraction (COBE) method. Experiments have been conducted to validate the study and verify the efficacy of the proposed method on blind defect detection.

A method to improve the range resolution in stepped frequency continuous wave radar

NASA Astrophysics Data System (ADS)

Kaczmarek, Paweł

2018-04-01

In the paper one of high range resolution methods - Aperture Sampling - was analysed. Unlike MUSIC based techniques it proved to be very efficient in terms of achieving unambiguous synthetic range profile for ultra-wideband stepped frequency continuous wave radar. Assuming that minimal distance required to separate two targets in depth (distance) corresponds to -3 dB width of received echo, AS provided a 30,8 % improvement in range resolution in analysed scenario, when compared to results of applying IFFT. Output data is far superior in terms of both improved range resolution and reduced side lobe level than used typically in this area Inverse Fourier Transform. Furthermore it does not require prior knowledge or an estimate of number of targets to be detected in a given scan.

Learning builds on learning: Infants' use of native language sound patterns to learn words

PubMed Central

Graf Estes, Katharine

2014-01-01

The present research investigated how infants apply prior knowledge of environmental regularities to support new learning. The experiments tested whether infants could exploit experience with native language (English) phonotactic patterns to facilitate associating sounds with meanings during word learning. Fourteen-month-olds heard fluent speech that contained cues for detecting target words; they were embedded in sequences that occur across word boundaries. A separate group heard the target words embedded without word boundary cues. Infants then participated in an object label-learning task. With the opportunity to use native language patterns to segment the target words, infants subsequently learned the labels. Without this experience, infants failed. Novice word learners can take advantage of early learning about sounds scaffold lexical development. PMID:24980741

Primary-Grade Students' Knowledge and Thinking about the Supply of Utilities (Water, Heat, and Light) to Modern Homes.

ERIC Educational Resources Information Center

Brophy, Jere; Alleman, Janet

2003-01-01

This interview study gathered information about the prior knowledge and thinking of kindergarten to third- graders regarding the supply of water, heat, and light to modern homes. Findings indicated that students possessed only limited and spotty knowledge about utilities in modern homes. Within general trends, there was evidence of growth in…

Psychosocial Aspects of ART Counseling: A Comparison of HIV Beliefs and Knowledge in PMTCT and ART-Naïve Women.

PubMed

Gouse, Hetta; Henry, Michelle; Robbins, Reuben N; Lopez-Rios, Javier; Mellins, Claude A; Remien, Robert H; Joska, John A

Antiretroviral therapy (ART)-readiness counseling has been deemed critical to adherence, instilling knowledge, and promoting positive beliefs and attitudes. In the landscape of changing policy in South Africa, some ART initiators have had prior ART-readiness counseling (e.g., for prevention-of-mother-to-child-transmission [PMTCT] programs). The extent to which previous counseling resulted in retained knowledge and belief is unknown, which may be important to the promotion of women's ART adherence. We compared 320 women living with HIV and initiating ART, with and without prior PMTCT on HIV knowledge, treatment, beliefs, and attitudes. The PMTCT group held more accurate beliefs and more positive attitudes about ART. Both groups lacked understanding of basic HIV biology. Nondisclosure of HIV status was high. Thus, in individuals re-initiating therapy, some knowledge about HIV and its treatment was not well retained. Tailored education and counseling may be critical to adherence, with a focus on biological concepts that impact ART resistance. Copyright © 2017 Association of Nurses in AIDS Care. Published by Elsevier Inc. All rights reserved.

Schematic knowledge changes what judgments of learning predict in a source memory task.

PubMed

Konopka, Agnieszka E; Benjamin, Aaron S

2009-01-01

Source monitoring can be influenced by information that is external to the study context, such as beliefs and general knowledge (Johnson, Hashtroudi, & Lindsay, 1993). We investigated the extent to which metamnemonic judgments predict memory for items and sources when schematic information about the sources is or is not provided at encoding. Participants made judgments of learning (JOLs) to statements presented by two speakers and were informed of the occupation of each speaker either before or after the encoding session. Replicating earlier work, prior knowledge decreased participants' tendency to erroneously attribute statements to schematically consistent but episodically incorrect speakers. The origin of this effect can be understood by examining the relationship between JOLs and performance: JOLs were equally predictive of item and source memory in the absence of prior knowledge, but were exclusively predictive of source memory when participants knew of the relationship between speakers and statements during study. Background knowledge determines the information that people solicit in service of metamnemonic judgments, suggesting that these judgments reflect control processes during encoding that reduce schematic errors.

Early testimonial learning: monitoring speech acts and speakers.

PubMed

Stephens, Elizabeth; Suarez, Sarah; Koenig, Melissa

2015-01-01

Testimony provides children with a rich source of knowledge about the world and the people in it. However, testimony is not guaranteed to be veridical, and speakers vary greatly in both knowledge and intent. In this chapter, we argue that children encounter two primary types of conflicts when learning from speakers: conflicts of knowledge and conflicts of interest. We review recent research on children's selective trust in testimony and propose two distinct mechanisms supporting early epistemic vigilance in response to the conflicts associated with speakers. The first section of the chapter focuses on the mechanism of coherence checking, which occurs during the process of message comprehension and facilitates children's comparison of information communicated through testimony to their prior knowledge, alerting them to inaccurate, inconsistent, irrational, and implausible messages. The second section focuses on source-monitoring processes. When children lack relevant prior knowledge with which to evaluate testimonial messages, they monitor speakers themselves for evidence of competence and morality, attending to cues such as confidence, consensus, access to information, prosocial and antisocial behavior, and group membership. © 2015 Elsevier Inc. All rights reserved.

Optimal sampling with prior information of the image geometry in microfluidic MRI.

PubMed

Han, S H; Cho, H; Paulsen, J L

2015-03-01

Recent advances in MRI acquisition for microscopic flows enable unprecedented sensitivity and speed in a portable NMR/MRI microfluidic analysis platform. However, the application of MRI to microfluidics usually suffers from prolonged acquisition times owing to the combination of the required high resolution and wide field of view necessary to resolve details within microfluidic channels. When prior knowledge of the image geometry is available as a binarized image, such as for microfluidic MRI, it is possible to reduce sampling requirements by incorporating this information into the reconstruction algorithm. The current approach to the design of the partial weighted random sampling schemes is to bias toward the high signal energy portions of the binarized image geometry after Fourier transformation (i.e. in its k-space representation). Although this sampling prescription is frequently effective, it can be far from optimal in certain limiting cases, such as for a 1D channel, or more generally yield inefficient sampling schemes at low degrees of sub-sampling. This work explores the tradeoff between signal acquisition and incoherent sampling on image reconstruction quality given prior knowledge of the image geometry for weighted random sampling schemes, finding that optimal distribution is not robustly determined by maximizing the acquired signal but from interpreting its marginal change with respect to the sub-sampling rate. We develop a corresponding sampling design methodology that deterministically yields a near optimal sampling distribution for image reconstructions incorporating knowledge of the image geometry. The technique robustly identifies optimal weighted random sampling schemes and provides improved reconstruction fidelity for multiple 1D and 2D images, when compared to prior techniques for sampling optimization given knowledge of the image geometry. Copyright © 2015 Elsevier Inc. All rights reserved.

Legal Professionals' Knowledge of Eyewitness Testimony in China: A Cross-Sectional Survey

PubMed Central

Jiang, Lina; Luo, Dahua

2016-01-01

Purpose To examine legal professionals’ knowledge of a wide range of factors that affect eyewitness accuracy in China. Methods A total of 812 participants, including 210 judges, 244 prosecutors, 202 police officers, and 156 defense attorneys, were asked to respond to 12 statements about eyewitness testimony and 3 basic demographic questions (i.e., gender, age, and prior experience). Results Although the judges and the defense attorneys had a somewhat higher number of correct responses than the other two groups, all groups showed limited knowledge of eyewitness testimony. In addition, the participants’ responses to only four items (i.e., weapon focus, attitude and expectations, child suggestibility, and the impact of stress) were roughly unanimous within the four legal professional groups. Legal professionals’ gender showed no significant correlations with their knowledge of eyewitness testimony. Prior experiences were significantly and negatively correlated with the item on the knowledge of forgetting curve among judges but positively correlated with two items (i.e., attitudes and exposure time) among defense attorneys and with 4 statements (i.e., the knowledge of attitudes and expectations, impact of stress, child witness accuracy, and exposure time) among prosecutors. Conclusions The findings suggest that knowledge of the factors that influence eyewitness accuracy must be more effectively communicated to legal professionals in the future. PMID:26828933

Pre-service teachers' knowledge of phonemic awareness: relationship to perceived knowledge, self-efficacy beliefs, and exposure to a multimedia-enhanced lecture.

PubMed

Martinussen, Rhonda; Ferrari, Julia; Aitken, Madison; Willows, Dale

2015-10-01

This study examined the relations among perceived and actual knowledge of phonemic awareness (PA), exposure to PA instruction during practicum, and self-efficacy for teaching PA in a sample of 54 teacher candidates (TCs) enrolled in a 1-year Bachelor of Education program in a Canadian university. It also assessed the effects of a brief multimedia-enhanced lecture on TCs' actual knowledge of PA and efficacy ratings. Prior to the lecture, teacher candidates' scores on the PA assessment were relatively low with a mean percentage correct of 56.3%. Actual knowledge was not significantly correlated with perceived knowledge or self-efficacy ratings. Perceived knowledge was significantly and positively correlated with efficacy ratings and students' rating of their exposure to PA instruction during their practicum experience. A path analysis revealed that the relationship between exposure to PA instruction and self-efficacy beliefs was mediated by perceived knowledge controlling for actual knowledge and general prior experience working with young children. Analyses also revealed that TCs made significant gains in self-efficacy as well as actual knowledge when re-assessed after the lecture with a mean post-lecture score of 71.4%. Written feedback from the TCs indicated that the digital video clips included in the lecture provided clarity regarding the type of instructional practices that teachers could use to support phonemic awareness development in children. Implications for practice and future research on teacher preparation are discussed.

An introduction to Bayesian statistics in health psychology.

PubMed

Depaoli, Sarah; Rus, Holly M; Clifton, James P; van de Schoot, Rens; Tiemensma, Jitske

2017-09-01

The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.

Impact of a United Kingdom-wide campaign to tackle antimicrobial resistance on self-reported knowledge and behaviour change.

PubMed

Chaintarli, Katerina; Ingle, Suzanne M; Bhattacharya, Alex; Ashiru-Oredope, Diane; Oliver, Isabel; Gobin, Maya

2016-05-12

As part of the 2014 European Antibiotic Awareness Day plans, a new campaign called Antibiotic Guardian (AG) was launched in the United Kingdom, including an online pledge system to increase commitment from healthcare professionals and members of the public to reduce antimicrobial resistance (AMR). The aim of this evaluation was to determine the impact of the campaign on self-reported knowledge and behaviour around AMR. An online survey was sent to 9016 Antibiotic Guardians (AGs) to assess changes in self-reported knowledge and behaviour (outcomes) following the campaign. Logistic regression models, adjusted for variables including age, sex and pledge group (pledging as member of public or as healthcare professional), were used to estimate associations between outcomes and AG characteristics. 2478 AGs responded to the survey (27.5 % response rate) of whom 1696 (68.4 %) pledged as healthcare professionals and 782 (31.6 %) as members of public (similar proportions to the total number of AGs). 96.3 % of all AGs who responded had prior knowledge of AMR. 73.5 % of participants were female and participants were most commonly between 45 and 54 years old. Two thirds (63.4 %) of participants reported always acting according to their pledge. Members of the public were more likely to act in line with their pledge than professionals (Odds Ratio (OR) =3.60, 95 % Confidence Interval (CI): 2.88-4.51). Approximately half of participants (44.5 %) (both healthcare professionals and members of public) reported that they acquired more knowledge about AMR post-campaign. People that were confused about AMR prior to the campaign acquired more knowledge after the campaign (OR = 3.10, 95 % CI: 1.36-7.09). More participants reported a sense of personal responsibility towards tackling AMR post-campaign, increasing from 58.3 % of participants pre-campaign to 70.5 % post-campaign. This study demonstrated that the campaign increased commitment to tackling AMR in both healthcare professional and member of the public, increased self-reported knowledge and changed self-reported behaviour particularly among people with prior AMR awareness. Online pledge schemes can be an effective and inexpensive way to engage people with the problem of AMR especially among those with prior awareness of the topic.