%0 Journal Article %1 BARTHAKUR2021106674 %A Barthakur, Abhinava %A Kovanovic, Vitomir %A Joksimovic, Srecko %A Siemens, George %A Richey, Michael %A Dawson, Shane %D 2021 %J Computers in Human Behavior %K MOOC assessment latentclassanalysis learningactivities learninganalytics learningstrategies sequenceanalysis %P 106674 %R https://doi.org/10.1016/j.chb.2020.106674 %T Assessing program-level learning strategies in MOOCs %U http://www.sciencedirect.com/science/article/pii/S0747563220304210 %V 117 %X Massive Open Online Courses have provided researchers with considerable opportunities to assess student learning within technically mediated online environments. However, for most reported studies, analyses are conducted from a single MOOC or over multiple MOOCs with different learner enrolments. Thus, this limits the opportunities to assess the changing behavior of learners over time. In this paper, the behavioral engagement of 175 students was examined, who were enrolled in a professional development study program consisting of four different MOOCs, comprising a complete program of study. A comprehensive analysis was conducted to understand the changing behavior of learners within each course and across the MOOC program. To do this, we used latent class analysis, a clustering technique widely used in other fields, but mostly unexplored within educational technology research. Our results revealed six weekly learning strategies based on student course engagement and three different program-level learning strategies, which not only differed in their learning behavior but also their final academic outcomes. Our study also showed substantial effects of MOOC course design on the level of student engagement in the courses. The results and implications are further discussed.

@article{BARTHAKUR2021106674, abstract = {Massive Open Online Courses have provided researchers with considerable opportunities to assess student learning within technically mediated online environments. However, for most reported studies, analyses are conducted from a single MOOC or over multiple MOOCs with different learner enrolments. Thus, this limits the opportunities to assess the changing behavior of learners over time. In this paper, the behavioral engagement of 175 students was examined, who were enrolled in a professional development study program consisting of four different MOOCs, comprising a complete program of study. A comprehensive analysis was conducted to understand the changing behavior of learners within each course and across the MOOC program. To do this, we used latent class analysis, a clustering technique widely used in other fields, but mostly unexplored within educational technology research. Our results revealed six weekly learning strategies based on student course engagement and three different program-level learning strategies, which not only differed in their learning behavior but also their final academic outcomes. Our study also showed substantial effects of MOOC course design on the level of student engagement in the courses. The results and implications are further discussed.}, added-at = {2021-01-03T16:35:12.000+0100}, author = {Barthakur, Abhinava and Kovanovic, Vitomir and Joksimovic, Srecko and Siemens, George and Richey, Michael and Dawson, Shane}, biburl = {https://www.bibsonomy.org/bibtex/2f10aae532a301fb189f6cfd4253e4c91/ereidt}, doi = {https://doi.org/10.1016/j.chb.2020.106674}, interhash = {b498a5ae1df97ddc6e17ad6fca9f7199}, intrahash = {f10aae532a301fb189f6cfd4253e4c91}, issn = {0747-5632}, journal = {Computers in Human Behavior}, keywords = {MOOC assessment latentclassanalysis learningactivities learninganalytics learningstrategies sequenceanalysis}, pages = 106674, timestamp = {2021-01-03T16:35:12.000+0100}, title = {Assessing program-level learning strategies in MOOCs}, url = {http://www.sciencedirect.com/science/article/pii/S0747563220304210}, volume = 117, year = 2021 }