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      Quantifying the Influence of Achievement Emotions for Student Learning in MOOCs

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          Abstract

          Massive Open Online Courses (MOOCs) have become a popular tool for worldwide learners. However, a lack of emotional interaction and support is an important reason for learners to abandon their learning and eventually results in poor learning performance. This study applied an integrative framework of achievement emotions to uncover their holistic influence on students’ learning by analyzing more than 400,000 forum posts from 13 MOOCs. Six machine-learning models were first built to automatically identify achievement emotions, including K-Nearest Neighbor, Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machines. Results showed that Random Forest performed the best with a kappa of 0.83 and an ROC_AUC of 0.97. Then, multilevel modeling with the “Stepwise Build-up” strategy was used to quantify the effect of achievement emotions on students’ academic performance. Results showed that different achievement emotions influenced students’ learning differently. These findings allow MOOC platforms and instructors to provide relevant emotional feedback to students automatically or manually, thereby improving their learning in MOOCs.

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          One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.
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            A new look at the statistical model identification

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              Estimating the Dimension of a Model

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                Author and article information

                Contributors
                Journal
                Journal of Educational Computing Research
                Journal of Educational Computing Research
                SAGE Publications
                0735-6331
                1541-4140
                June 2021
                October 20 2020
                June 2021
                : 59
                : 3
                : 429-452
                Affiliations
                [1 ]Faculty of Education, East China Normal University, Shanghai, China
                [2 ]School of Teaching and Learning, University of Florida, Gainesville, Florida, United States
                [3 ]Department of Educational Psychology and Leadership, Texas Tech University, Lubbock, Texas, United States
                Article
                10.1177/0735633120967318
                a6b7e826-60fb-4c52-9fb2-b0f38bd5a7ac
                © 2021

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


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