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      How Do We Model Learning at Scale? A Systematic Review of Research on MOOCs

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          Abstract

          Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning-related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics), and learning outcomes (i.e., academic, social, and affective). The proposed model affords further interstudy comparisons as well as comparative studies with more traditional education models.

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          Most cited references55

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          A Coefficient of Agreement for Nominal Scales

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            School Engagement: Potential of the Concept, State of the Evidence

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              The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Review of Educational Research
                Review of Educational Research
                American Educational Research Association (AERA)
                0034-6543
                1935-1046
                February 2018
                November 14 2017
                February 2018
                : 88
                : 1
                : 43-86
                Affiliations
                [1 ]University of South Australia
                [2 ]University of Michigan
                [3 ]University of Notre Dame
                [4 ]University of Edinburgh
                [5 ]University of Memphis
                Article
                10.3102/0034654317740335
                7be339fc-e506-4b6f-9a12-57f9d93dac64
                © 2018

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

                History

                Quantitative & Systems biology,Biophysics
                Quantitative & Systems biology, Biophysics

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