Credit hours is not enough: Explaining undergraduate perceptions of course workload using LMS records

Pardos, Zachary A.; Borchers, Conrad; Yu, Run

doi:10.1016/j.iheduc.2022.100882

Cited by 10 publications

(6 citation statements)

References 60 publications

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“…This aligns with prior work linking excessive workload to adverse academic outcomes [2]. As an alternative interpretation to these high workloads representing mismanaged academic planning, it could be that high-workload courses tend to be the only courses that have leftover seats for students at the end of the enrollment period, hence forcing students to enroll in them, which could have potential ramifications for institutional policies on course seat allocation For example, it could be that these courses generally have more prerequisites than others (which correlates with effective course workload [13]), such that late enrollers tend to be more underprepared, which is undesirable from an institutional standpoint if they end up dropping these courses late more often. Future work could test these causal theories of why enrollment procrastinators overenroll and its relationship to other academic outcomes (i.e., GPA).…”

Section: Discussionmentioning

confidence: 99%

“…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 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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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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Section: Discussionmentioning

confidence: 99%

Section: Late Course Dropping As Mismanagementmentioning

confidence: 99%

Section: Course Workload Analyticsmentioning

confidence: 99%

See 1 more Smart Citation

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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“…This load may lead to dropping out over time [1][2][3][4]. Credit readings as an information source for course selection are important for students in their planning [25]. Those with better grades tend to choose a higher workload per semester than their peers [26].…”

Section: Study Duration and Student Workloadmentioning

confidence: 99%

Multiple Enrollment Policy: Clustering Dropout and Graduation Constellations in Psychology and Sociology Bachelor’s Programs

Loder

2024

Trends in Higher Education

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In Austria, it is possible to enroll in an unlimited number of programs simultaneously and consecutively. Study duration and student performance are important performance indicators of the university system. The disciplines of psychology and sociology share similarities in their curriculum structures but are different in selectiveness due to their admission rules. They show differences in their motivations to enroll and their trajectories through higher education, leading to different constellations of “dropout” and “graduation.” The aim was to identify and compare groups of students’ outcomes in bachelor’s programs of psychology and sociology along the dimensions of workload and duration, incorporating the possibility of enrolling in multiple programs. The sample consisted of 6498 students between the academic years of 2010/11 and 2022/23 who either graduated or dropped out of one or more programs. Cluster analyses using four algorithms per individual outcome constellation, categorization, and aggregation of the results suggest a longer overall study duration for students with more than one program. In graduation–graduation and graduation–dropout clusters, groups with less overall workload could be identified. The transferability of passed exams may be an important component for students in multiple programs. Implications for policy and practice are discussed.

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“…For instance, for students to take multiple courses, they must choose between courses that can be taken concurrently so that the workload requirements can be met and students do not feel overwhelmed. 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).…”

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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Credit hours is not enough: Explaining undergraduate perceptions of course workload using LMS records

Cited by 10 publications

References 60 publications

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

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

Multiple Enrollment Policy: Clustering Dropout and Graduation Constellations in Psychology and Sociology Bachelor’s Programs

Curriculum Analytics of Course Choices:

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