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In 2016, Michigan State University developed a new model of classroom education and assessment in their Mechanics of Materials course. This model used a modified mastery approach that stresses formative assessment, guidance in the problem-solving process, and structured student reflection. We now refer to this new approach as SMART Assessment - short for Supported Mastery Assessment using Repeated Testing. The effects of this model have been very positive, and results on overall student success in Mechanics of Materials have been presented in full at prior ASEE conferences. In this paper, we focus on the effects of this new assessment model on the performance of students who may be at greater risk due to their first-generation status or economic disadvantage, while accounting for other measures such as incoming GPA and performance in the prerequisite course, Statics. The evaluation was conducted across 3.5 academic years and involved 1275 students divided among 9 experimental sections and 6 control sections. Statistical analysis indicated that there were no significant differences between the performance indices for students in the SMART sections based on their parents’ history of university education or their eligibility to receive a Pell Grant. While students in the Traditional section tended to have higher grades in ME222, this cannot be compared directly to the grades in the SMART section due to the difference in grading framework. Previous work, however, has indicated that students who complete the SMART framed sections have a deeper understanding of the course material, as demonstrated by their improved performance on common final exam problems that were evaluated with a mastery-focused rubric.
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A Demographic Analysis on Prerequisite Preparation in an Advanced Data Structures Course
Previous work in computing has shown that Black, Latinx, Native American and Pacific islander (BLNPI), women, first-generation, and transfer students tend to have worse outcomes during their time in university compared to their majority counterparts. Previous work has also found that students' incoming prerequisite course proficiency is positively correlated with their outcomes in a course. In this work, we investigate the role that prerequisite course proficiency has on outcomes between these groups of students. Specifically, we examine incoming prerequisite course proficiency in an Advanced Data Structures course. When comparing incoming prerequisite course proficiency between demographic pairs, we only see small differences for gender or by first-generation status. There is a sizeable difference by BLNPI status, although this difference is not statistically significant, possibly due to the small number of BLNPI students. In addition, we find that transfer students have sizeable and statistically significantly lower prerequisite course proficiency when compared to non-transfer students. For BLNPI and transfer students, we find that they also have lower grades in the prerequisite courses, which may partially explain their lower prerequisite course proficiency. These findings suggest that institutions need to find ways to better serve BLNPI and transfer students.
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- PAR ID:
- 10338955
- Date Published:
- Journal Name:
- Proceedings of the 53rd ACM Technical Symposium on Computer Science Education
- Volume:
- 1
- Page Range / eLocation ID:
- 661 to 667
- 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/10.1145/3478431.3499337
