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In an earlier study we showed that small amounts of extra credit offered for early progress on online homework assignments can reduce cramming behavior in introductory physics students. This work expands on the prior study by implementing a planning prompt intervention inspired by Yeomans and Reich's similar treatment. In the prompt we asked students to what degree they intended to earn extra credit offered for early work on the module sequence, and what their plan was to realize their intentions. The survey was assigned for ordinary course credit and due several days before the first extra credit deadline. We found that students who completed the prompt earned on average 0.6 more extra credit points and completed the modules an average of 1.1 days earlier compared to a previous semester. We detect the impact of the survey by creating a multilinear model based on data from students exposed to the intervention as well as students in a previous semester. Data from five homework sequences are included in the model to account for differences between the two semesters that cannot be attributed to the planning prompt intervention.
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A Simple, Language-Independent Approach to Identifying Potentially At-Risk Introductory Programming Students
For decades computing educators have been trying to identify and predict at-risk students, particularly early in the first programming course. These efforts range from the analyzing demographic data that pre-exists undergraduate entrance to using instruments such as concept inventories, to the analysis of data arising during education. Such efforts have had varying degrees of success, have not seen widespread adoption, and have left room for improvement. We analyse results from a two-year study with several hundred students in the first year of programming, comprising majors and non-majors. We find evidence supporting a hypothesis that engagement with extra credit assessment provides an effective method of differentiating students who are not at risk from those who may be. Further, this method can be used to predict risk early in the semester, as any engagement – not necessarily completion – is enough to make this differentiation. Additionally, we show that this approach is not dependent on any one programming language. In fact, the extra credit opportunities need not even involve programming. Our results may be of interest to educators, as well as researchers who may want to replicate these results in other settings.
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- Award ID(s):
- 1432190
- PAR ID:
- 10301928
- Date Published:
- Journal Name:
- Australasian Computing Education Conference
- Page Range / eLocation ID:
- 168 to 175
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3441636.3442318
