Academic Performance in Blended-Learning and Face-to-Face University Teaching
Bibliographic record
Abstract
<p>The benefits promoted by the use of the blended-learning model in higher education have been well studied from a general point of view, but no conclusive results have been achieved so far. However, within the field of engineering, these researches are quite scarce and become even rarer in the case of researches trying to demonstrate whether the benefits of blended learning could be compared to those achieved by classroom education. Learning platforms allow us to incorporate rich learning resources, interactive tools that foster collaborative learning, student to student, student to professor and student-professor-student interactions. Learning platforms also give us the opportunity of incorporating tasks that allow students to check the progress of their own learning processes. This paper presents the results of a research carried out at theSchoolofTechnical Architectureof theUniversityofSevillewith students enrolled in the Materials Science course. The aim of this investigation was to compare students’ results when trained by means of traditional teaching and blended learning. In order to achieve our goal we followed a quasi-experimental, descriptive and correlational design applied to two non-equivalent groups. The results indicated that in the blended-learning model, the students had more academic success as compared to traditional teaching.</p>
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".