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Direct linkConijn, Rianne; Snijders, Chris; Kleingeld, Ad; Matzat, Uwe – IEEE Transactions on Learning Technologies, 2017
With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…
Descriptors: Blended Learning, Predictor Variables, Predictive Validity, Predictive Measurement
Tempelaar, Dirk T.; Rienties, Bart; Nguyen, Quan – IEEE Transactions on Learning Technologies, 2017
Studies in the field of learning analytics (LA) have shown students' demographics and learning management system (LMS) data to be effective identifiers of "at risk" performance. However, insights generated by these predictive models may not be suitable for pedagogically informed interventions due to the inability to explain why students…
Descriptors: Student Behavior, Integrated Learning Systems, Personality, Educational Research
de Freitas, Sara; Mayer, Igor; Arnab, Sylvester; Marshall, Ian – Journal of Higher Education Policy and Management, 2014
This paper explores how, in the light of global economic downturn and rising student populations, new academic-industrial models for research collaboration based upon specific technological expertise and knowledge can be developed as potential mechanisms for preserving and extending central university research infrastructure. The paper explores…
Descriptors: School Business Relationship, Institutional Cooperation, Blended Learning, Models
