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Morsomme, Raphaël; Alferez, Sofia Vazquez – International Educational Data Mining Society, 2019
Liberal Arts programs are often characterized by their open curriculum. Yet, the abundance of courses available and the highly personalized curriculum are often overwhelming for students who must select courses relevant to their academic interests and suitable to their academic background. This paper presents the course recommender system that we…
Descriptors: Liberal Arts, Course Selection (Students), Courses, College Students
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Bao, Yingying; Chen, Guanliang; Hauff, Claudia – International Educational Data Mining Society, 2017
Massive Open Online Courses (MOOCs) are a promising form of online education. However, the occurrence of academic dishonesty has been threatening MOOC certificates' effectiveness as a serious tool for recruiters and employers. Recently, a large-scale study on the log traces from more than one hundred MOOCs created by Harvard and MIT has identified…
Descriptors: Large Group Instruction, Online Courses, Cheating, Incidence
Dekker, Gerben W.; Pechenizkiy, Mykola; Vleeshouwers, Jan M. – International Working Group on Educational Data Mining, 2009
The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program…
Descriptors: Information Retrieval, Engineering Education, College Freshmen, Case Studies
Bosker, Roel; And Others – 1996
This article explores the following questions: first, "Do colleges of higher vocational education vary in the mean success of their graduates on the labor market?" and, second, "Are the differences between these colleges stable over time?". The analysis drew upon school effectiveness, quality assurance and labor market…
Descriptors: Accountability, College Outcomes Assessment, College Students, Data Collection