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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.
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The Role of Prior Knowledge in the Performance of Engineering Students
In engineering, students’ completion of prerequisites indicates an understanding of fundamental knowledge. Recent studies have shown a significant relationship between student performance and prior knowledge. Weak knowledge retention from prerequisite coursework can present challenges in progressive learning. This study investigates the relationship between prior knowledge and students’ performance over a few courses of Statics. Statistics has been considered as the subject of interest since it is the introductory engineering course upon which many subsequent engineering courses rely, including many engineering analysis and design courses. The prior knowledge was determined based on the quantitative and qualitative preparedness. A quiz set was designed to assess quantitative preparedness. The qualitative preparedness was assessed using a survey asking students’ subjective opinions about their preparedness at the beginning of the semester. Student performance was later quantified through final course grades. Each set of data were assigned three categories for grouping purposes to reflect preparedness: 1) high preparedness: 85% or higher score, 2) medium preparedness: between 60% and 85%, and 3) weak preparedness: 60% or lower. Pearson correlation coefficient and T-test was conducted on 129 students for linear regression and differences in means. The analysis revealed a non-significant correlation between the qualitative preparedness and final scores (p-value = 0.29). The data revealed that students underestimated their understanding of the prerequisites for the class, since the quantitative preparedness scores were relatively higher than the qualitative preparedness scores. This can be partially understood by the time gap between when prerequisites were taken and when the course under investigation was taken. Students may have felt less confident at first but were able to pick up the required knowledge quickly. A moderately significant correlation between students’ quantitative preparedness and course performance was observed (p -value < 0.05). Students with high preparedness showed >80% final scores, with a few exceptions; students with weak preparedness also showed relatively high final scores. However, most of the less prepared students made significant efforts to overcome their weaknesses through continuous communication and follow-up with the instructor. Despite these efforts, these students could not obtain higher than 90% as final scores, which indicates that level of preparedness reflects academic excellence. Overall, this study highlights the role of prior knowledge in achieving academic excellence for engineering. The study is useful to Civil Engineering instructors to understand the role of students’ previous knowledge in their understanding of difficult engineering concepts.
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- Award ID(s):
- 1928409
- PAR ID:
- 10294949
- Publisher / Repository:
- ASEE Conferences
- Date Published:
- Format(s):
- Medium: X
- Location:
- Virtual Conference
- 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/10.18260/1-2--37892
