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Bilal Hamamra; Asala Mayaleh; Zuheir N. Khlaif – Cogent Education, 2024
This article, drawing on essays written by students with the assistance of ChatGPT and interviews with some students who used this learning machine, highlights a shift in the educational landscape brought about by this technology. In broader terms, Palestinian universities follow the traditional methods of teaching based on memorization and rote…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, College Students
Aydogdu, Seyhmus – Education and Information Technologies, 2020
Prediction of student performance is one of the most important subjects of educational data mining. Artificial neural networks are seen to be an effective tool in predicting student performance in e-learning environments. In the studies carried out with artificial neural networks, performance predictions based on student scores are generally made,…
Descriptors: Prediction, Academic Achievement, Electronic Learning, Artificial Intelligence
Pytlarz, Ian; Pu, Shi; Patel, Monal; Prabhu, Rajini – International Educational Data Mining Society, 2018
Identifying at-risk students at an early stage is a challenging task for colleges and universities. In this paper, we use students' oncampus network traffic volume to construct several useful features in predicting their first semester GPA. In particular, we build proxies for their attendance, class engagement, and out-of-class study hours based…
Descriptors: College Freshmen, Grade Point Average, At Risk Students, Academic Achievement