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Zimmermann, Judith; Brodersen, Kay H.; Heinimann, Hans R.; Buhmann, Joachim M. – Journal of Educational Data Mining, 2015
The graduate admissions process is crucial for controlling the quality of higher education, yet, rules-of-thumb and domain-specific experiences often dominate evidence-based approaches. The goal of the present study is to dissect the predictive power of undergraduate performance indicators and their aggregates. We analyze 81 variables in 171…
Descriptors: Undergraduate Students, Graduate Students, Academic Achievement, Prediction
Saarela, Mirka; Karkkainen, Tommi – Journal of Educational Data Mining, 2015
Curricula for Computer Science (CS) degrees are characterized by the strong occupational orientation of the discipline. In the BSc degree structure, with clearly separate CS core studies, the learning skills for these and other required courses may vary a lot, which is shown in students' overall performance. To analyze this situation, we apply…
Descriptors: Data Analysis, Academic Achievement, Undergraduate Students, Core Curriculum
Mallavarapu, Aditi; Lyons, Leilah; Shelley, Tia; Minor, Emily; Slattery, Brian; Zellner, Moria – Journal of Educational Data Mining, 2015
Interactive learning environments can provide learners with opportunities to explore rich, real-world problem spaces, but the nature of these problem spaces can make assessing learner progress difficult. Such assessment can be useful for providing formative and summative feedback to the learners, to educators, and to the designers of the…
Descriptors: Spatial Ability, Urban Areas, Neighborhoods, Conservation (Environment)