Exploring the potential of blended learning to promote retention and achievement in higher education professional study programs
Bibliographic record
Abstract
In this paper, we present a blended learning model designed for a university professional study program attended by full-time professional workers, i.e. in-service teachers studying in the field of School Administration. The model integrates four main instructional strategies at the program level: mentoring; participation in an online community of professional learning and practice; collaborative concept-mapping with an object-typed knowledge modeling software, and face-to-face seminars in a work setting. Based on interview and observation data collected during two successive small-scale experimentations of the model, we explored potential factors that could have had an impact on students' academic retention and achievement. Four types of factors were identified: personal, professional, institutional and pedagogical. We found that pedagogical and professional factors, which are insufficiently considered in theoretical models of student retention, are of primary concern for students who work full-time as professionals. A blended learning model designed at the program level and strongly “situated” in the professional practice of the students is a promising avenue to adjust to their career constraints and aspirations and, thus, promoting their academic retention and achievement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".