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Evaluation Of Machine Learning Techniques For Early Identification Of At-Risk Students, Mansour Hamoud Awaji
Evaluation Of Machine Learning Techniques For Early Identification Of At-Risk Students, Mansour Hamoud Awaji
CCE Theses and Dissertations
Student attrition is one of the long-standing problems facing higher education institutions despite the extensive research that has been undertaken to address it. To increase students’ success and retention rates, there is a need for early alert systems that facilitate the identification of at-risk students so that remedial measures may be taken in time to reduce the risk. However, incorporating ML predictive models into early warning systems face two main challenges: improving the accuracy of timely predictions and the generalizability of predictive models across on-campus and online courses. The goal of this study was to develop and evaluate predictive models …