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Zhenchang Xia; Nan Dong; Jia Wu; Chuanguo Ma – IEEE Transactions on Learning Technologies, 2024
As an excellent means of improving students' effective learning, knowledge tracking can assess the level of knowledge mastery and discover latent learning patterns based on students' historical learning evaluation of related questions. The advantage of knowledge tracking is that it can better organize and adjust students' learning plans, provide…
Descriptors: Graphs, Artificial Intelligence, Multivariate Analysis, Prediction
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Parian Haghighat; Denisa Gandara; Lulu Kang; Hadis Anahideh – Grantee Submission, 2024
Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque…
Descriptors: Prediction, Learning Analytics, Multivariate Analysis, Regression (Statistics)
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Denisa Gandara; Hadis Anahideh – Society for Research on Educational Effectiveness, 2024
Background/Context: Predictive analytics has emerged as an indispensable tool in the education sector, offering insights that can improve student outcomes and inform more equitable policies (Friedler et al., 2019; Kleinberg et al., 2018). However, the widespread adoption of predictive models is hindered by several challenges, including the lack of…
Descriptors: Prediction, Learning Analytics, Ethics, Statistical Bias
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Balqis Albreiki; Tetiana Habuza; Nishi Palakkal; Nazar Zaki – Education and Information Technologies, 2024
The nature of education has been transformed by technological advances and online learning platforms, providing educational institutions with more options than ever to thrive in a complex and competitive environment. However, they still face challenges such as academic underachievement, graduation delays, and student dropouts. Fortunately, by…
Descriptors: Multivariate Analysis, Graphs, Identification, At Risk Students
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Siu-Cheung Kong; Wei Shen – Interactive Learning Environments, 2024
Logistic regression models have traditionally been used to identify the factors contributing to students' conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected…
Descriptors: Student Characteristics, Predictor Variables, Comprehension, Computation
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David Bamat – Measurement: Interdisciplinary Research and Perspectives, 2024
The National Assessment of Educational Progress (NAEP) program only reports state-level subgroup results if it samples at least 62 students identifying with the subgroup. Since some subgroups constitute small proportions of many states' general student populations, these minority subgroups are seldom sufficiently sampled to meet this sample size…
Descriptors: Reading Achievement, Achievement Gap, Prediction, National Competency Tests