The Tensions Between Student Dropout and Flexibility in Learning Design: The Voices of Professors in Open Online Higher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Flexibility is typical of open universities and their e-learning designs. While this constitutes their main attraction, promising learners will be able to study “anytime, anyplace,” this also demands more self-regulation and engagement, a cause for student dropout. This case study explores professors’ experiences of flexibility in e-learning design and continuous assessment and their perception of the risks and opportunities that more flexibility implies for student persistence and dropout. In-depth interviews with 18 full professors, who are the e-learning designers of undergraduate courses at the Open University of Catalonia (UOC), were analyzed, employing qualitative content analysis. According to the professors, the main causes for dropout are student-centered, yet they are connected to learning design: workload and time availability, as well as students’ expectations, profiles, and time management skills. In the professors’ view, flexibility has both positive and negative effects. Some are conducive to engagement and persistence: improvement of personalized feedback, formative assessment, and module workload. Others generate resistance: more flexibility may increase workload, procrastination, dropout, and risk of losing professorial control, and may threaten educational standards and quality. Untangling the tensions between dropout and flexibility may enhance learning design and educational practices that help prevent student dropout. Stakeholders should focus on measures perceived as positive, such as assessment extension, personalized feedback and monitoring, and course workload calibration. As higher education is globally turning to online delivery due to the COVID-19 viral pandemic, such findings may be useful in both hybrid and fully online educational contexts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it