Hybrid Learning: An Effective Resource in University Education?
Bibliographic record
Abstract
The organisation of university education in Europe is undergoing profound changes as a consequence of the establishment of the European Higher Education Area (EHEA). This transformation entails methodological changes that are focused on student work. The student is now considered to be an autonomous individual who is able to choose a path of study and capable of self-regulation. These objectives are believed to be achievable with hybrid learning models. The economic cost of including these methods makes it necessary to demonstrate whether the investment can be profitable in terms of improved academic results and increased acceptability among students. We analyse whether the use of two tools by students (assessments and forums) influences their grades and whether there are correlations between performance and the evaluation of the tool by students and between the evaluation and the degree of use. The sample consists of 176 students. We follow an ex post facto methodological design, with descriptive and correlational techniques. We found significant differences in the grades received according to the degree of use of the tools studied. Additionally, we found a correlation between grades and student evaluation.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".