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Shi, Yang; Chi, Min; Barnes, Tiffany; Price, Thomas W. – International Educational Data Mining Society, 2022
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) mainly leverage each student's response either as correct or incorrect, ignoring its content. In…
Descriptors: Programming, Knowledge Level, Prediction, Instructional Innovation
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Ayesha Sohail; Huma Akram – Pedagogical Research, 2025
The ability to properly evaluate one's own academic progress has long been considered a predictor of academic success. However, its distinctive role in the context of computational mathematics remains underexplored. Grounded in social cognitive theory, this study investigates the critical role of self-regulated learning (SRL) strategies in…
Descriptors: Undergraduate Students, Mathematics Education, Mathematics Achievement, Self Evaluation (Individuals)
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Albert, Jim; Hu, Jingchen – Journal of Statistics Education, 2020
Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniques much more accessible to applied statisticians and, in turn, have potentially transformed Bayesian education at the undergraduate level. This article provides an…
Descriptors: Bayesian Statistics, Computation, Statistics Education, Undergraduate Students
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Johnson, Marina E.; Misra, Ram; Berenson, Mark – Decision Sciences Journal of Innovative Education, 2022
In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods--Bayesian analysis and Markov chains--can be combined to enhance student learning using the Analytics…
Descriptors: Bayesian Statistics, Programming Languages, Artificial Intelligence, Data Analysis
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Hu, Jingchen – Journal of Statistics Education, 2020
We propose a semester-long Bayesian statistics course for undergraduate students with calculus and probability background. We cultivate students' Bayesian thinking with Bayesian methods applied to real data problems. We leverage modern Bayesian computing techniques not only for implementing Bayesian methods, but also to deepen students'…
Descriptors: Bayesian Statistics, Statistics Education, Undergraduate Students, Computation
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Sarafoglou, Alexandra; van der Heijden, Anna; Draws, Tim; Cornelisse, Joran; Wagenmakers, Eric-Jan; Marsman, Maarten – Psychology Learning and Teaching, 2022
Current developments in the statistics community suggest that modern statistics education should be structured holistically, that is, by allowing students to work with real data and to answer concrete statistical questions, but also by educating them about alternative frameworks, such as Bayesian inference. In this article, we describe how we…
Descriptors: Bayesian Statistics, Thinking Skills, Undergraduate Students, Psychology
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Hooshyar, Danial; Binti Ahmad, Rodina; Wang, Minhong; Yousefi, Moslem; Fathi, Moein; Lim, Heuiseok – Journal of Educational Computing Research, 2018
Games with educational purposes usually follow a computer-assisted instruction concept that is predefined and rigid, offering no adaptability to each student. To overcome such problem, some ideas from Intelligent Tutoring Systems have been used in educational games such as teaching introductory programming. The objective of this study was to…
Descriptors: Intelligent Tutoring Systems, Teaching Methods, Introductory Courses, Programming