Required Collaborative Work in Online Courses: A Predictive Modeling Approach |
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| Authors: | Marlene A Smith Deborah L Kellogg |
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| Affiliation: | University of Colorado Denver, Denver, CO |
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| Abstract: | This article describes a predictive model that assesses whether a student will have greater perceived learning in group assignments or in individual work. The model produces correct classifications 87.5% of the time. The research is notable in that it is the first in the education literature to adopt a predictive modeling methodology using data collected via a designed experiment. All subjects experienced both a collaborative and an individual assignment, thus mitigating uncontrolled external factors in the measurement of differences in perceived learning. The exploratory nature of the work prompted the use of Partial Least Squares Regression for estimation. The work serves as an illustration of how predictive modeling might enlighten those in educational and academic settings. |
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| Keywords: | Collaborative teaching Curriculum design Online education and Pedagogy
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