Making sense of learning analytics with a configurational approach
MetadataShow full item record
This paper is an attempt to provide the basic guidelines on how to implement configurational analysis in the context of learning analytics. In detail, we offer a step by step approach on the fuzzy set qualitative comparative analysis (fsQCA). Learning analytics gain increased popularity, however studies use traditional symmetric statistical methods to analyze them. Building on the theory of complexity and configuration theory we suggest on using fsQCA in order to gain a deeper understanding of the data, which may lead to understanding different learning phenomena as well as to the creation of new theories. We further describe the steps on how to perform a contrarian case analysis, which will help in identifying asymmetric relations among the data. Finally, testing for predictive validity with fsQCA is explained. Many of the steps described here may be implemented in various contexts, however we tried to provide examples and instructions for learning analytics oriented research.
Showing items related by title, author, creator and subject.
Student attitudes toward learning analytics in higher education: "The fitbit version of the learning world"Roberts, Lynne; Howell, J.; Seaman, K.; Gibson, D. (2016)Increasingly, higher education institutions are exploring the potential of learning analytics to predict student retention, understand learning behaviors, and improve student learning through providing personalized feedback ...
Student Attitudes toward Learning Analytics in Higher Education: "The Fitbit Version of the Learning World"Roberts, Lynne; Howell, Joel; Seaman, K.; Gibson, D. (2016)Increasingly, higher education institutions are exploring the potential of learning analytics to predict student retention, understand learning behaviors, and improve student learning through providing personalized feedback ...
Giannakos, M.; Sampson, Demetrios; Kidzinski, L. (2016)Smart learning has become a new term to describe technological and social developments (e.g., Big and Open Data, Internet of Things, RFID, and NFC) enable effective, efficient, engaging and personalized learning. Collecting ...