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Gontzis, Andreas F.; Kotsiantis, Sotiris; Panagiotakopoulos, Christos T.; Verykios, Vassilios S. – Interactive Learning Environments, 2022
Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze and predict students' failure,…
Descriptors: Student Attrition, Distance Education, At Risk Students, Achievement
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Gkontzis, Andreas F.; Kotsiantis, Sotiris; Panagiotakopoulos, Christos T.; Verykios, Vassilios S. – Interactive Learning Environments, 2022
Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze, and predict students' failure,…
Descriptors: Student Attrition, Distance Education, At Risk Students, Achievement
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Xu, Tonghui – Journal of Educators Online, 2023
The early detection of students' academic performance or final grades helps instructors prepare their online courses. In the Open University Learning Analytics Dataset, I found many online students clicked the course materials before the first day of class. This study aims to investigate how data mining models can use this student interaction data…
Descriptors: College Students, Online Courses, Academic Achievement, Data Analysis
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Yang, Juan; Huang, Zhi Xing; Gao, Yue Xiang; Liu, Hong Tao – IEEE Transactions on Learning Technologies, 2014
During the past decade, personalized e-learning systems and adaptive educational hypermedia systems have attracted much attention from researchers in the fields of computer science Aand education. The integration of learning styles into an intelligent system is a possible solution to the problems of "learning deviation" and…
Descriptors: Cognitive Style, Pattern Recognition, Intelligent Tutoring Systems, Prediction
Michalski, Greg V. – Association for Institutional Research (NJ1), 2011
Excessive college course withdrawals are costly to the student and the institution in terms of time to degree completion, available classroom space, and other resources. Although generally well quantified, detailed analysis of the reasons given by students for course withdrawal is less common. To address this, a text mining analysis was performed…
Descriptors: College Instruction, Courses, Withdrawal (Education), College Students
Kosslyn, Stephen M.; And Others – 1985
This paper investigates the processes by which visual mental images--the precept-like short-term memory representations--are created from information stored in long-term memory. It also presents a new method for studying image generation. Three experiments were conducted using college students as subjects. In the first experiment, a Podgorny and…
Descriptors: Analysis of Variance, College Students, Computer Oriented Programs, Conceptual Tempo
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers