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The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

, , , , , , , , , and . International Journal of Educational Technology in Higher Education, (April 2020)
DOI: 10.1186/s41239-020-00187-1

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Cost-Sensitive Boosting: Fitting an Additive Asymmetric Logistic Regression Model., , , and . ACML, volume 5828 of Lecture Notes in Computer Science, page 234-247. Springer, (2009)Using clickstream data to measure, understand, and support self-regulated learning in online courses., , and . Internet High. Educ., (2020)Can student-facing analytics improve online students' effort and success by affecting how they explain the cause of past performance?, , , , and . Comput. Educ., (2022)Online instruction for a humanized learning experience: Techniques used by college instructors., , , and . Comput. Educ., (2022)Does blended instruction enhance English language learning in developing countries? Evidence from Mexico., , , , , and . Br. J. Educ. Technol., 51 (1): 211-227 (2020)A review of boosting methods for imbalanced data classification., and . Pattern Anal. Appl., 17 (4): 679-693 (2014)The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes, , , , , , , , , and . International Journal of Educational Technology in Higher Education, (April 2020)An efficient data-scalable algorithm for image orientation detection., , and . ICIP, page 2665-2668. IEEE, (2010)Understanding Engagement in MOOCs., and . EDM, page 605-606. International Educational Data Mining Society (IEDMS), (2016)Total leaf area estimation based on the total grid area measured using mobile laser scanning., and . Comput. Electron. Agric., (2023)