Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/2901
Title: Qualitative, Quantitative, and Data Mining Methods for Analyzing Log Data to Characterize Students' Learning Strategies and Behaviors
Authors: Baker, Ryan
Gobert, Janice
Azevedo, Roger
Roll, Ido
van Joolingen, Wouter
Issue Date: Jun-2010
Publisher: International Society of the Learning Sciences (ISLS)
Citation: Baker, R., Gobert, J., Azevedo, R., Roll, I., & van Joolingen, W. (2010). Qualitative, Quantitative, and Data Mining Methods for Analyzing Log Data to Characterize Students' Learning Strategies and Behaviors. In Gomez, K., Lyons, L., & Radinsky, J. (Eds.), Learning in the Disciplines: Proceedings of the 9th International Conference of the Learning Sciences (ICLS 2010) - Volume 2, Short Papers, Symposia, and Selected Abstracts (pp. 45-52). Chicago IL: International Society of the Learning Sciences.
Abstract: This symposium addresses how different classes of research methods, all based upon the use of log data from educational software, can facilitate the analysis of students' learning strategies and behaviors. To this end, four multi-method programs of research are discussed, including the use of qualitative, quantitative-statistical, quantitative-modeling, and educational data mining methods. The symposium presents evidence regarding the applicability of each type of method to research questions of different grain sizes, and provides several examples of how these methods can be used in concert to facilitate our understanding of learning processes, learning strategies, and behaviors related to motivation, meta-cognition, and engagement.
URI: https://doi.dx.org/10.22318/icls2010.2.45
https://repository.isls.org//handle/1/2901
Appears in Collections:ICLS 2010

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