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Student intervention system using machine learning.

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dc.contributor.author Boyajian, Missak Yessai,
dc.date.accessioned 2020-03-28T17:18:23Z
dc.date.available 2022-05
dc.date.available 2020-03-28T17:18:23Z
dc.date.issued 2019
dc.date.submitted 2019
dc.identifier.other b23508735
dc.identifier.uri http://hdl.handle.net/10938/21843
dc.description Thesis. M.S. American University of Beirut. Department of Computer Science, 2019. T:6979.
dc.description Advisor : Dr. Fatima Abu Salem, Asssociate Professor, Computer Science ; Members of Committee : Dr. Mohammad Jaber, Assistant Professor, Computer Science ; Dr. Shady Elbassuoni, Assistant Professor, Computer Science ; Dr. Haidar Safa, Professor, Computer Science.
dc.description Includes bibliographical references (leaves 107-109)
dc.description.abstract In most universities, advisors guide students in their course selection, and warn those who might be at risk of being dismissed or placed on probation. The large number of students makes it difficult for universities to identify those at risk, as it would get very time consuming and inaccurate. Hence, there is a need for a system that can recognize these students at the end of each semester. In this work, we build a semester-based automated intervention system using machine learning that identifies students who are at risk and suggests upcoming courses, providing them with a more personalized approach. We applied supervised learning such as logistic regression, neural networks, and AdaBoost to predict three outcomes: 1) risk of dismissal, 2) risk of probation, and 3) time needed to graduate. Then we applied reinforcement learning (RL) using value iteration technique to create an optimal policy that will recommend courses to students with the goal of keeping them safe. We applied different evaluation methods, such as ROC curve and F-measure, to compare the performance of the supervised learning algorithms, and proposed a new approach to measure the effectiveness of the recommendation system. The dataset was provided by the American University of Beirut and contained a sample of 30,000 student records.
dc.format.extent 1 online resource (viii, 109 leaves) : illustrations (some color)
dc.language.iso eng
dc.subject.classification T:006979
dc.subject.lcsh American University of Beirut.
dc.subject.lcsh Machine learning.
dc.subject.lcsh Recommender systems (Information filtering)
dc.subject.lcsh Reinforcement learning.
dc.subject.lcsh Learning classifier systems.
dc.title Student intervention system using machine learning.
dc.type z
dc.contributor.department Faculty of Arts and Sciences.
dc.contributor.department Department of Computer Science.
dc.subject.classificationsource AUBNO
dc.contributor.institution American University of Beirut.


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