What college students say, and what they do: Aligning self-regulated learning theory with behavioral logs. Quick, J., Motz, B., Israel, J., & Kaetzel, J. In International Conference on Learning Analytics & Knowledge, pages 534-543, 3, 2020. Association for Computing Machinery. ![What college students say, and what they do: Aligning self-regulated learning theory with behavioral logs [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
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Website doi abstract bibtex A central concern in learning analytics specifically and educational research more generally is the alignment of robust, coherent measures to well-developed conceptual and theoretical frameworks. Capturing and representing processes of learning remains an ongoing challenge in all areas of educational inquiry and presents substantive considerations on the nature of learning, knowledge, and assessment & measurement that have been continuously refined in various areas of education and pedagogical practice. Learning analytics as a still developing method of inquiry has yet to substantively navigate the alignment of measurement, capture, and representation of learning to theoretical frameworks despite being used to identify various practical concerns such as at risk students. This study seeks to address these concerns by comparing behavioral measurements from learning management systems to established measurements of components of learning as understood through self-regulated learning frameworks. Using several prominent and robustly supported self-reported survey measures designed to identify dimensions of self-regulated learning, as well as typical behavioral features extracted from a learning management system, we conducted descriptive and exploratory analyses on the relational structures of these data. With the exception of learners' selfreported time management strategies and level of motivation, the current results indicate that behavioral measures were not well correlated with survey measurements. Possibilities and recommendations for learning analytics as measurements for selfregulated learning are discussed. © 2020 Association for Computing Machinery.
@inproceedings{
title = {What college students say, and what they do: Aligning self-regulated learning theory with behavioral logs},
type = {inproceedings},
year = {2020},
keywords = {LMS,Self-regulated learning,Self-reports,Trace data},
pages = {534-543},
websites = {https://dl.acm.org/doi/10.1145/3375462.3375516},
month = {3},
publisher = {Association for Computing Machinery},
day = {23},
city = {New York, NY, USA},
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created = {2020-04-22T21:44:57.119Z},
accessed = {2020-04-21},
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last_modified = {2020-05-11T14:43:33.770Z},
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abstract = {A central concern in learning analytics specifically and educational research more generally is the alignment of robust, coherent measures to well-developed conceptual and theoretical frameworks. Capturing and representing processes of learning remains an ongoing challenge in all areas of educational inquiry and presents substantive considerations on the nature of learning, knowledge, and assessment & measurement that have been continuously refined in various areas of education and pedagogical practice. Learning analytics as a still developing method of inquiry has yet to substantively navigate the alignment of measurement, capture, and representation of learning to theoretical frameworks despite being used to identify various practical concerns such as at risk students. This study seeks to address these concerns by comparing behavioral measurements from learning management systems to established measurements of components of learning as understood through self-regulated learning frameworks. Using several prominent and robustly supported self-reported survey measures designed to identify dimensions of self-regulated learning, as well as typical behavioral features extracted from a learning management system, we conducted descriptive and exploratory analyses on the relational structures of these data. With the exception of learners' selfreported time management strategies and level of motivation, the current results indicate that behavioral measures were not well correlated with survey measurements. Possibilities and recommendations for learning analytics as measurements for selfregulated learning are discussed. © 2020 Association for Computing Machinery.},
bibtype = {inproceedings},
author = {Quick, Joshua and Motz, Benjamin and Israel, Jamie and Kaetzel, Jason},
doi = {10.1145/3375462.3375516},
booktitle = {International Conference on Learning Analytics & Knowledge}
}
Downloads: 0
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