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Saleem Malik; K. Jothimani – Education and Information Technologies, 2024
Monitoring students' academic progress is vital for ensuring timely completion of their studies and supporting at-risk students. Educational Data Mining (EDM) utilizes machine learning and feature selection to gain insights into student performance. However, many feature selection algorithms lack performance forecasting systems, limiting their…
Descriptors: Algorithms, Decision Making, At Risk Students, Learning Management Systems
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Quintana, Rafael – Sociological Methods & Research, 2023
Causal search algorithms have been effectively applied in different fields including biology, genetics, climate science, medicine, and neuroscience. However, there have been scant applications of these methods in social and behavioral sciences. This article provides an illustrative example of how causal search algorithms can shed light on…
Descriptors: Academic Achievement, Causal Models, Algorithms, Social Problems
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Smyth, G. K.; And Others – Australian Journal of Education, 1990
A method for predicting freshman performance based on high school grades allows calculation of any student's likely grades in a similar university course. The method is contrasted with several more traditional predictive methods and examined in a study of 3,734 University of Western Australia students. (MSE)
Descriptors: Academic Achievement, Algorithms, College Freshmen, Comparative Analysis
Birenbaum, Menucha; Tatsuoka, Kikumi K. – 1980
Much valuable information can be gained by analyzing the students' wrong responses. When a student answers a free response item she/he gives the response which she/he considers to be the correct one. Therefore, diagnosing the algorithm that led the student to his/her answer provides an important source of information for assessing his/her…
Descriptors: Academic Achievement, Achievement Tests, Adaptive Testing, Algorithms