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Peter, Johannes; Leichner, Nikolas; Mayer, Anne-Kathrin; Krampen, Günter – Studies in Higher Education, 2017
This paper presents an approach to information literacy instruction in colleges and universities that combines online and classroom learning (Blended Learning). The concept includes only one classroom seminar, so the approach presented here can replace existing one-shot sessions at colleges and universities without changes to the current workflow.…
Descriptors: Information Literacy, Blended Learning, Instructional Design, Foreign Countries
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Gaševic, Dragan; Jovanovic, Jelena; Pardo, Abelardo; Dawson, Shane – Journal of Learning Analytics, 2017
The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper…
Descriptors: Foreign Countries, Undergraduate Students, Engineering Education, Educational Research
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Meade, Adam W.; Craig, S. Bartholomew – Psychological Methods, 2012
When data are collected via anonymous Internet surveys, particularly under conditions of obligatory participation (such as with student samples), data quality can be a concern. However, little guidance exists in the published literature regarding techniques for detecting careless responses. Previously several potential approaches have been…
Descriptors: Online Surveys, Data Collection, Research Problems, Identification
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Amershi, Saleema; Conati, Cristina – Journal of Educational Data Mining, 2009
In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…
Descriptors: Supervision, Classification, Models, Educational Environment