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Kerstin Wagner; Agathe Merceron; Petra Sauer; Niels Pinkwart – Journal of Educational Data Mining, 2024
In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design, is based on the explainable k-nearest neighbor algorithm and recommends a…
Descriptors: At Risk Students, Algorithms, Foreign Countries, Course Selection (Students)
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Freeze, Ronald D.; Alshare, Khaled A.; Lane, Peggy L.; Wen, H. Joseph – Journal of Information Systems Education, 2010
This study utilized the Information Systems Success (ISS) model in examining e-learning systems success. The study was built on the premise that system quality (SQ) and information quality (IQ) influence system use and user satisfaction, which in turn impact system success. A structural equation model (SEM), using LISREL, was used to test the…
Descriptors: Student Attitudes, User Satisfaction (Information), Structural Equation Models, Information Systems
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Wang, Pei-Yu; Vaughn, Brandon K.; Liu, Min – Computers & Education, 2011
This study examined the impact of animation interactivity on novices' learning of introductory statistics. The interactive animation program used in this study was created with Adobe Flash following Mayer's multimedia design principles as well as Kristof and Satran's interactivity theory. This study was guided by three main questions: 1) Is there…
Descriptors: Feedback (Response), Control Groups, Animation, Computer Assisted Instruction
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George-Palilonis, Jennifer; Filak, Vincent – International Journal on E-Learning, 2010
As graphically driven, animated, interactive applications offer educators new opportunities for shaping course content, new avenues for research arise as well. Along with these developments comes a need to study the effectiveness of the individual tools at our disposal as well as various methods for integrating those tools in a classroom setting.…
Descriptors: Course Content, Learner Engagement, Science Instruction, Electronic Learning
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Macfadyen, Leah P.; Dawson, Shane – Computers & Education, 2010
Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international…
Descriptors: Network Analysis, Academic Achievement, At Risk Students, Prediction