Latin American undergraduates and learning patterns in the transition to higher education: an exploratory study in Colombia
Bibliographic record
Abstract
Introduction. The aim of this study was to analyse the relationship between learning patterns, associated factors, and academic performance in 115 Colombian first-year university students. We posed the need to discuss the Vermunt model in other contexts, with an aim to supply evidence toward a more robust, inclusive model in analyzing learning processes. Method. Data were collected using a Spanish version of the Inventory of Learning Style (Martínez-Fernández et al., 2009; Vermunt, 1998). Additionally, we collected data about the students’ age, gender, dedication to study, perception of teaching, effort, and academic performance. The data were processed by means of descriptive analysis, correlation, MANOVA, and path analysis. Results. The results show a structure of four learning patterns consisting of different factor combinations according to Vermunt: 1) Meaning-directed with external regulation (MD/er); 2) Passive-Idealistic (PI); 3) Passive-Motivated (PM); and 4) Reproduction-directed with lack of regulation (RD/lr). The relationship between learning patterns and the different factors was not sustained. However, we found an interesting explanation of academic performance from the perspective of self- and external regulation.Discussion and Conclusion. Based on these results, we defend the need to make the cultural dimension of learning patterns a key topic in the research agenda on learning processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".