Educational Platforms and Learning Approaches in University Education
Bibliographic record
Abstract
This study was conducted at the Superior Technical School of Construction Engineering of Seville (Spain) with students registered in the Materials I course who received academic training using blended learning methodology. Technical degree programs are characterized by a high dropout rate and academic underachievement. For that reason, the study aimed to assess the dominant learning approaches among these students, the relationship between leaning approaches and academic achievement, the relationship between learning approaches and the extent of WebCT platform use, and the influence of learning approaches on the students´ assessment of the platform. To identify students´ learning approaches, the R-SPQ-2F questionnaire developed by Biggs, Kember, and Leung (2001) was used. The students assessed the WebCT platform using an ad hoc PSEW questionnaire. The study was descriptive and used a correlational design. The study was conducted retroactively and measured variables that were not experimentally manipulated. The results indicate that the majority of the students have a low-intensity deep approach. The deep approach was more common among the female students than the male students, and the female students obtained higher scores on the deep motivation and deep strategy subscales. The dominant learning approach had no influence on academic achievement, but the dominant learning approach did influence the extent of WebCT platform use, as well as the students’ assessments of the platform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".