An official website of the United States government
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.
more »
« less
This content will become publicly available on November 10, 2026
Prerequisites and Performance in a Machine Learning Course: A Quantitative Analysis
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
- PAR ID:
- 10652655
- Publisher / Repository:
- ACM
- Date Published:
- Page Range / eLocation ID:
- 1 to 11
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract BackgroundEngineering requires new solutions to improve undergraduate performance outcomes, including course grades and continued enrollment in engineering pathways. Belonging and engineering role identity have long been associated with successful outcomes in engineering, including academic success, retention, and well‐being. PurposeWe measure the relationships between belonging and role identity at the beginning of a first‐year engineering course with course grade and continued enrollment in engineering courses. We test the effect of an ecological belonging intervention on student belonging, course grade, and persistence. MethodStudents (n = 834) reported their sense of belonging in engineering, cross‐racial experiences, engineering performance/competence, interest in engineering, and engineering recognition before and after an in‐class intervention to improve classroom belonging ecology. Through a series of longitudinal multigroup path analyses, a form of structural equation modeling, we tested the predictive relationships of the measured constructs with engineering identity and investigated differences in these relationships by student gender and race/ethnicity. FindingsThe proposed model predicts course grades and continued enrollment, providing insight into the potential for interventions to support first‐year engineering students. Group analysis results demonstrate the difference in the function of these psychosocial measures for women and Black, Latino/a/x, and Indigenous (BLI) students, providing insights into the potential importance of sociocultural interventions within engineering classrooms to improve the engineering climate, engagement, and retention of students. ImplicationsThe results highlight the need for more specific, nuanced theoretical investigations of how marginalized students experience the engineering environment and develop social belonging and engineering role identity.more » « less
-
Abstract Research suggests that interacting with more peers about physics course material is correlated with higher student performance. Some studies, however, have demonstrated that different topics of peer interactions may correlate with their performance in different ways, or possibly not at all. In this study, we probe both the peers with whom students interact about their physics course and the particular aspects of the course material about which they interacted in six different introductory physics courses: four lecture courses and two lab courses. Drawing on social network analysis methods, we replicate prior work demonstrating that, on average, students who interact with more peers in their physics courses have higher final course grades. Expanding on this result, we find that students discuss a wide range of aspects of course material with their peers: concepts, small-group work, assessments, lecture, and homework. We observe that in the lecture courses, interacting with peers about concepts is most strongly correlated with final course grade, with smaller correlations also arising for small-group work and homework. In the lab courses, on the other hand, small-group work is the only interaction topic that significantly correlates with final course grade. We use these findings to discuss how course structures (e.g. grading schemes and weekly course schedules) may shape student interactions and add nuance to prior work by identifying how specific types of student interactions are associated (or not) with performance.more » « less
-
null (Ed.)This Complete Research paper will describe the implementation of an introductory course (ENGR194) for first semester engineering students. The course is meant to improve retention and academic success of engineering first-year students in the College of Engineering at the University of Illinois at Chicago. The implementation of this course is part of an ongoing National Science Foundation (NSF) Scholarships in Science, Technology, Engineering, and Math (S-STEM) project. This paper reports on the impact of combinatorial enrollment in ENGR194 and a previously described two-week Summer Bridge Program (SBP) offered only for entering S-STEM scholars before their first semester. To measure the impact of this course on student retention and academic success, various evaluation metrics are compared for three separate Comparison Groups (C-Groups) of students. The results show that the ENGR194 course had a significant positive impact on the first-year retention rate. The results also revealed that students who participated in both ENGR194 and SBP (C-Group 1) made changes to their declared majors earlier than students who had only taken ENGR 123 or neither of the courses (C-Groups 2 and 3 respectively). Furthermore, students in C-Group 1 received better grades in math and science than their peers, and students in C-Groups 1 and 2 had significantly higher GPAs than their peers in C-Group 3.more » « less
-
Academic bridge courses are implemented to impact students’ academic success by revising fundamental concepts and skills necessary to successfully complete discipline-specific courses. The bridge courses are often short (one to three weeks) and highly dense in content (commonly mathematics or math-related applications). With the support of the NSF-funded (DUE - Division of Undergraduate Education) STEM Center at Sam Houston State University (SHSU), we designed a course for upcoming engineering majors (i.e., first-year students and transfer students) that consists of a two-week-long pre-semester course organized into two main sessions. The first sessions (delivered in the mornings) were synchronous activities focused on strengthening student academic preparedness and socio-academic integration and fostering networking leading to a strong STEM learning community. The second sessions (delivered in the afternoons) were asynchronous activities focused on discipline-specific content knowledge in engineering. The engineering concepts were organized via eight learning modules covering basic math operations, applied trigonometry, functions in engineering, applied physics, introduction to statics and Microsoft Excel, and engineering economics and its applied decision. All materials in the course were designed by engineering faculty (from the chair of the department to assistant professors and lecturers in engineering) and one educational research faculty (from the department of chemistry). The course design process started with a literature review on engineering bridge courses to understand prior work, followed by surveying current engineering faculty to propose goals for the course. The designed team met weekly after setting the course goals over two semesters. The design process was initiated with backward design principles (i.e., start with the course goals, then the assessments, end with the learning activities) and continued with ongoing revision. The work herein presents this new engineering bridge course’s goals, strategy, and design process. Preliminary student outcomes will be discussed based on the course’s first implementation during summer 2021.more » « less
