Insights into undergraduate pathways using course load analytics

Borchers, Conrad; Pardos, Zachary A.

doi:10.1145/3576050.3576081

Cited by 4 publications

(10 citation statements)

References 40 publications

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“…Presently, more than 1,500 faculty members and 45,000 students are affiliated with the university. 2…”

Section: Methodology 31 Study Contextmentioning

confidence: 99%

“…The present study presents initial evidence that mining course transaction data of enrollments and drops can also create insights into student academic outcomes and help explain their underlying decision processes. Past work gathered evidence that the courses students take and their attributes (e.g., course workload) relate to academic outcomes such as GPA and dropout [2]. This work, and other prior work (e.g., [21]), has led to the hypothesis that if students take on a workload that is too high and mismanage their resources, their academic outcomes suffer.…”

Section: Introductionmentioning

confidence: 92%

“…Therefore, a better understanding of late course dropping through data mining could inform student academic advising and course recommender systems, which is part of nascent research in the field [18,9]. The present study investigates the workload taken on by procrastinators through the emerging lens of course workload analytics [2], which offer more accurate predictions of the workload students experience in a course than the credit hour units reported in course catalogs [13]. When contrasted to traditional, time-based credit hour units, course load analytics can speak to mismatched expectations of students signing up for courses.…”

Section: Late Course Dropping As Mismanagementmentioning

confidence: 99%

“…Course load analytics can represent a more accurate prediction of students' workload experiences than credit hours alone by providing course-level estimates of time load, mental effort, and psychological stress based on learning management system and enrollment data [13,2]. We generate course workload predictions for each course and semester via models trained and validated in prior work [2].…”

Section: Course Workload Analyticsmentioning

confidence: 99%

See 3 more Smart Citations

Are You an Early Dropper or Late Shopper? Mining Enrollment Transaction Data to Study Procrastination in Higher Education

Borchers,

Xu,

Pardos

2024

Preprint

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Educational data mining increasingly leverages enrollment data for higher education applications. However, these data describe final end-of-semester course selections, not the often complex enrollment activities leading up to a finalized schedule. Fine-grain transaction data of student waitlist, add, and drop actions during academic semester planning can explain the decision-making processes that lead to mismanaged course selection (e.g., dropping a course late with an institutional penalty). The present study investigates student late drops through the lens of procrastination. Results suggest that students who procrastinate on enrolling in courses are likelier to drop courses late. Further, students who enrolled late had a larger predicted workload than their nominal enrollment based on credit hours would suggest, pointing to potential mismanagement in the workload these students take on. These findings motivate the hypothesis that academic advising interventions that support students in academic planning could mitigate the adverse effects of procrastination on academic outcomes.

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“…Presently, more than 1,500 faculty members and 45,000 students are affiliated with the university. 2…”

Section: Methodology 31 Study Contextmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

Section: Late Course Dropping As Mismanagementmentioning

confidence: 99%

Section: Course Workload Analyticsmentioning

confidence: 99%

See 2 more Smart Citations

Are You an Early Dropper or Late Shopper? Mining Enrollment Transaction Data to Study Procrastination in Higher Education

Borchers,

Xu,

Pardos

2024

Preprint

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“…The workload requirements are often defined based on the students' in-class contact hours as well as out-of-class preparation time-a metric often referred to as course credit hours information (Pardos et al, 2022). Recent research, however, has suggested that course load analytics (CLA), derived from learning management system (LMS) usage and enrollment data, may provide a more precise measure of actual course workload than the traditional credit hour metric (Borchers & Pardos, 2023). This task (of choosing a course) is further complicated by the depth and breadth of courses that are offered within a department and across other departments.…”

Section: Introductionmentioning

confidence: 99%

Curriculum Analytics of Course Choices:

Srivastava,

Nawaz,

Tsai

et al. 2024

Learning Analytics

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In a higher education context, students are expected to take charge of their learning by deciding “what” to learn and “how” to learn. While the learning analytics (LA) community has seen increasing research on the “how” to learn part (i.e., researching methods for supporting students in their learning journey), the “what” to learn part is still underinvestigated. We present a case study of curriculum analytics and its application to a dataset of 243 students of the bachelor’s program in the broad discipline of health sciences to explore the effects of course choices on students’ academic performance. Using curriculum metrics such as grading stringency, course temporal position, and duration, we investigated how course choices differed between high- and low-performing students using both temporal and sequential analysis methods. We found that high-performing students were likely to pick an elective course of low difficulty. It appeared that these students were more strategic in terms of their course choices than their low-performing peers. Generally, low-performing students seemed to have made suboptimal choices when selecting elective courses; e.g., when they picked an elective course of high difficulty, they were less likely to pick a following course of low difficulty. The findings of this study have design implications for researchers, program directors, and coordinators, because they can use the results to (i) update the course sequencing, (ii) guide students about course choices based on their current GPA (such as through course recommendation dashboards), (iii) identify bottleneck courses, and (iv) assist higher education institutions in planning a more balanced course roadmap to help students manage their workload effectively.

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