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Guleria, Pratiyush; Sood, Manu – Education and Information Technologies, 2023
Machine Learning concept learns from experiences, inferences and conceives complex queries. Machine learning techniques can be used to develop the educational framework which understands the inputs from students, parents and with intelligence generates the result. The framework integrates the features of Machine Learning (ML), Explainable AI (XAI)…
Descriptors: Artificial Intelligence, Career Counseling, Data Analysis, Employment Potential
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Smithson, Conor J. R.; Eichbaum, Quentin G.; Gauthier, Isabel – Cognitive Research: Principles and Implications, 2023
We investigated the relationship between category learning and domain-general object recognition ability (o). We assessed this relationship in a radiological context, using a category learning test in which participants judged whether white blood cells were cancerous. In study 1, Bayesian evidence negated a relationship between o and category…
Descriptors: Recognition (Psychology), Classification, Learning Processes, Medicine
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Shu, Tian; Luo, Guanzhong; Luo, Zhaosheng; Yu, Xiaofeng; Guo, Xiaojun; Li, Yujun – Journal of Educational and Behavioral Statistics, 2023
Cognitive diagnosis models (CDMs) are the statistical framework for cognitive diagnostic assessment in education and psychology. They generally assume that subjects' latent attributes are dichotomous--mastery or nonmastery, which seems quite deterministic. As an alternative to dichotomous attribute mastery, attention is drawn to the use of a…
Descriptors: Cognitive Measurement, Models, Diagnostic Tests, Accuracy
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Zhu, Hongyue; Jiao, Hong; Gao, Wei; Meng, Xiangbin – Journal of Educational and Behavioral Statistics, 2023
Change-point analysis (CPA) is a method for detecting abrupt changes in parameter(s) underlying a sequence of random variables. It has been applied to detect examinees' aberrant test-taking behavior by identifying abrupt test performance change. Previous studies utilized maximum likelihood estimations of ability parameters, focusing on detecting…
Descriptors: Bayesian Statistics, Test Wiseness, Behavior Problems, Reaction Time
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Nayak, Padmalaya; Vaheed, Sk.; Gupta, Surbhi; Mohan, Neeraj – Education and Information Technologies, 2023
Students' academic performance prediction is one of the most important applications of Educational Data Mining (EDM) that helps to improve the quality of the education process. The attainment of student outcomes in an Outcome-based Education (OBE) system adds invaluable rewards to facilitate corrective measures to the learning processes.…
Descriptors: Predictor Variables, Academic Achievement, Data Collection, Information Retrieval
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Yamaguchi, Kazuhiro; Okada, Kensuke – Journal of Educational and Behavioral Statistics, 2020
In this article, we propose a variational Bayes (VB) inference method for the deterministic input noisy AND gate model of cognitive diagnostic assessment. The proposed method, which applies the iterative algorithm for optimization, is derived based on the optimal variational posteriors of the model parameters. The proposed VB inference enables…
Descriptors: Bayesian Statistics, Statistical Inference, Cognitive Measurement, Mathematics
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Madsen, Jens Koed; Hahn, Ulrike; Pilditch, Toby D. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2020
In this article, we explore how people revise their belief in a hypothesis and the reliability of sources in circumstances where those sources are either independent or are partially dependent because of their shared, common background. Specifically, we examine people's revision of perceived source reliability by comparison with a formal model of…
Descriptors: Beliefs, Reliability, Information Sources, Foreign Countries
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Du, Han; Bradbury, Thomas N.; Lavner, Justin A.; Meltzer, Andrea L.; McNulty, James K.; Neff, Lisa A.; Karney, Benjamin R. – Research Synthesis Methods, 2020
Researchers often seek to synthesize results of multiple studies on the same topic to draw statistical or substantive conclusions and to estimate effect sizes that will inform power analyses for future research. The most popular synthesis approach is meta-analysis. There have been few discussions and applications of other synthesis approaches.…
Descriptors: Bayesian Statistics, Meta Analysis, Statistical Inference, Synthesis
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Regional Educational Laboratory Mid-Atlantic, 2024
These are the appendixes for the report, "Stabilizing School Performance Indicators in New Jersey to Reduce the Effect of Random Error." This study applied a stabilization model called Bayesian hierarchical modeling to group-­level data (with groups assigned according to demographic designations) within schools in New Jersey with the aim…
Descriptors: Institutional Evaluation, Elementary Secondary Education, Bayesian Statistics, Test Reliability
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SeungHoon Han; Jordan M. Hyatt; Geoffrey C. Barnes; Lawrence W. Sherman – Evaluation Review, 2024
This analysis employs a Bayesian framework to estimate the impact of a Cognitive-Behavioral Therapy (CBT) intervention on the recidivism of high-risk people under community supervision. The study relies on the reanalysis of experimental datal using a Bayesian logistic regression model. In doing so, new estimates of programmatic impact were…
Descriptors: Behavior Modification, Cognitive Restructuring, Criminals, Recidivism
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Luyu Zhu; Jia Hao; Jianhou Gan – Interactive Learning Environments, 2024
Nowadays, Massive Open Online Courses (MOOC) has been gradually accepted by the public as a new type of education and teaching method. However, due to the lack of timely intervention and guidance from educators, learners' performance is not as effective as it could be. To address this problem, predicting MOOC learners' performance and providing…
Descriptors: MOOCs, Academic Achievement, Prediction, Bayesian Statistics
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Jabar, Syaheed B.; Fougnie, Daryl – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2022
Expectations about the environment play a large role in shaping behavior, but how does this occur? Do expectations change the way we perceive the world, or just our decisions based on unbiased perceptions? We investigated the relative contributions of priors to these 2 stages by manipulating "when" information about expected color was…
Descriptors: Expectation, Behavior Change, Visual Perception, Decision Making
Kaplan, David; Chen, Jianschen; Yavuz, Sinan; Lyu, Weicong – Grantee Submission, 2022
The purpose of this paper is to demonstrate and evaluate the use of "Bayesian dynamic borrowing"(Viele et al, in Pharm Stat 13:41-54, 2014) as a means of systematically utilizing historical information with specific applications to large-scale educational assessments. Dynamic borrowing via Bayesian hierarchical models is a special case…
Descriptors: Bayesian Statistics, Models, Prediction, Accuracy
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Yao, Ching-Bang; Wu, Yu-Ling – International Journal of Information and Communication Technology Education, 2022
With the impacts of COVID-19 epidemic, e-learning has become a popular research issue. Therefore, how to upgrade the interactivity of e-learning, and allow learners to quickly access personalized and popular learning information from huge digital materials, is very important. However, chatbots are mostly used in automation, as well as simple…
Descriptors: Electronic Learning, Artificial Intelligence, Individualized Instruction, Bayesian Statistics
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John Deke; Mariel Finucane; Dan Thal – Society for Research on Educational Effectiveness, 2022
Background/Context: Methodological background: Meta-analysis typically depends on the assumption that true effects follow the normal distribution. While assuming normality of effect "estimates" is often supported by a central limit theorem, normality for the distribution of interventions' "true" effects is a computational…
Descriptors: Bayesian Statistics, Meta Analysis, Regression (Statistics), Research Design
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