An official website of the United States government
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.
more »
« less
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.
more »
« less
- 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
More Like this
-
-
Demand for Machine Learning (ML) courses remains high, and educators face open questions about which prerequisites are important for student success in upper-year ML courses. Prior work has shown that instructors and students in ML courses believe that the math prerequisites and their relative recency are barriers to success, but this relationship has not been demonstrated quantitatively. In this paper, we study the link between prerequisite grades and performance in an upper-year ML course at two sites. We use linear models to study the extent to which student grades in prerequisite courses in calculus, linear algebra, statistics, and software design are predictive of student performance in the ML course. We consider the effect of additional factors like gender, first-in-family status, prior experience, comfort with mathematics, and comfort with academic English. Like prior work in many domains, and consistent with ML instructor and student perspectives, we find that prerequisite grades are predictive of ML performance. However, different combinations of prerequisites are important at different sites. Also, we find that cumulative grade point average (cGPA) in past technical and non-technical courses are as predictive of ML grade, if not more. Moreover, recency in prerequisite courses is not predictive of ML course grades in our setting. These findings suggest that general academic preparation may be as robust a predictor of ML course performance as specific math prerequisites, challenging assumptions about the role of mathematical recency and preparedness—at least as measured by grades.more » « less
-
Abstract Course‐based Undergraduate Research Experiences (CUREs) have beneficial impacts on students and the capacity to provide authentic research experiences that are accessible and beneficial to all students, especially those from Minoritized Groups. CUREs can be presented in a full semester format (cCURE) and shorter modules incorporated into laboratory courses (mCURE). In this study, protein‐centric CUREs were implemented at two minority‐serving Community Colleges (CCs) in introductory biology and chemistry courses. Using validated assessment tools, student self‐reported gains, and institutional data, we examined student outcomes in three conditions: control, mCURE, and cCURE courses. We also examined whether there was a differential impact on student outcomes by Minoritized Group status. Our findings show that students from Minoritized Groups have improved scientific literacy compared to their White/Asian peers in the cCUREs, whereas students from Minoritized Groups in the control course had lower relative scientific literacy. There was no significant difference in STEM Career Interest between the three conditions. Most significantly, the one‐year retention rate of students from the mCURE condition was 24% higher than that seen among control students. Furthermore, retention of students from Minoritized Groups in mCUREs was significantly higher than in control courses, whereas no significant difference was observed in White/Asian students. Taken together, these data suggest that CUREs can be an impactful practice in introductory courses at CCs, especially for students from Minoritized Groups.more » « less
-
Transfer students, who are disproportionately URM and first-generation, are a target population for boosting engineering representation. Transfer students in mechanical, aerospace and civil engineering at [the institution] take thermodynamics, a required gateway course, in their first or second term. This paper outlines the results from an observational study to determine how students interact in a peer-led learning environment. The PEERSIST (Peer-led, Student Instructed, Study group) model promotes academic competence through peer dialogue, in which disciplinary knowledge is socially co-constructed and refined over successive sessions. In order to help demonstrate that student interactions are the main source of learning in Peer-Led Study Groups (PLSGs), interactions between students were recorded and compared to those in traditional TA-led recitations using the observation protocol. Results show that students in PLSGs interact with their peers significantly more than students in the TA-led control group. The study also compares peer interactions by incoming course preparedness and finds a non-significant relationship between incoming GPA and peer-to-peer interactions. In contrast, the study finds a negative relationship between the rate at which students ask for and receive help and incoming GPA.more » « less
-
Parks, Samantha T. (Ed.)ABSTRACT Diversifying the STEM workforce is a national priority, yet white males continue to dominate the ranks of professional scientists and engineers in the United States. This is partly due to disparities in academic success for women and minoritized students in prerequisite introductory STEM courses, leading to higher attrition from B.S. degree programs. Past research has demonstrated that when social-psychological interventions targeting “stereotype threat” or “fixed” mindsets are implemented in STEM courses, equity gaps may be significantly reduced. We incorporated two such interventions into introductory biology courses for life science B.S. majors and Associate’s degree allied health students taught at a regional research university and a community college. We observed no significant effects of the values-affirmation interventions on grade outcomes for students in any of the courses, regardless of students' gender identity, race/ethnicity, or first-generation status, suggesting that students, on average, were not experiencing stereotype threat on either campus. We found a significant positive association between completing more weekly reflective journal entries and higher mean content-based grades for students in the university majors course overall, especially first-generation students, although the association was significantly negative for women. Our results confirm that context matters when implementing interventions aimed at reducing achievement gaps, and we propose that educators assess their students’ social-psychological characteristics and then select interventions accordingly.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3478431.3499337
