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Orji, Fidelia A.; Vassileva, Julita – Journal of Educational Technology Systems, 2023
This research presents a proposed approach that could be applied in modeling students' study strategies and performance in higher education. The research used key learning attributes, including intrinsic motivation, extrinsic motivation, autonomy, relatedness, competence, and self-esteem in the modeling. Five machine learning models were…
Descriptors: Student Motivation, Learner Engagement, Undergraduate Students, Learning Strategies
Barramuño, Mauricio; Meza-Narváez, Claudia; Gálvez-García, Germán – Journal of Applied Research in Higher Education, 2022
Purpose: The prediction of student attrition is critical to facilitate retention mechanisms. This study aims to focus on implementing a method to predict student attrition in the upper years of a physiotherapy program. Design/methodology/approach: Machine learning is a computer tool that can recognize patterns and generate predictive models. Using…
Descriptors: Student Attrition, School Holding Power, Foreign Countries, Physical Therapy