Supporting Mastery Learning Through a Multiple-Submission Policy for Assignments in a Purely Online Programming Class

The Learning Edge Momentum (LEM) theory suggests that once students fall behind, it gets more difficult to catch up with the course material. It then becomes increasingly more difficult to connect new, higher-level concepts to those solid edges of knowledge with mastery of basic concepts. Learning for Mastery (LFM) acknowledges that students learn at different paces by allowing students unable to master tests the first time to catch up eventually. This paper describes how an online introductory Python programming course offered to business students followed a multiple-submission policy for assignments to support LFM. The multiple submission policy contributed to the students’ mastery by encouraging individual practice and experimentation while also increasing the students’ comfort level and confidence. The research attempts to find relationships between taking advantage of the multiple-submit policy and results of summative assessments. Qualitative data on students’ self-reported progress per week is cross-referenced with quantitative data from the results of a regression analysis performed on LMS logs related to students’ engagement with course material. Performance on summative assessments is used as the regression’s dependent variable, and engagement with formative assessments in terms of the number of attempts and performance per attempt is used as the explanatory variable.