The Development of Approaches to Learning and Perceptions of the Teaching-Learning Environment During Bachelor Level Studies and Their Relation to Study Success
Bibliographic record
Abstract
The aim of the present study is to explore changes both in approaches to learning as well as in students’ experiences of the teaching-learning environment and how these changes are related to each other during their Bachelor studies by using a longitudinal data set. The aim is further to explore how students’ approaches to learning and their perceptions of the teaching-learning environment relate to study success. Participants comprised of 103 students at the Faculty of Biological and Environmental Sciences who participated in the present study from the first to third year of their Bachelor degree studies. We used a modified version of the ETLQ questionnaire and conducted Confirmatory factor analyses on scales representing approaches to learning and the teaching-learning environment at both measurements. We investigated changes at the group level using a paired sample t-test and conducted the final analysis with regression analyses. We then used Structural Equation Modelling to analyse the relationship between approaches to learning, perceptions of the teaching-learning environment, and study success. The results showed an increase in the deep approach to learning and a decrease in students’ perceptions of teaching for understanding. Organised studying and interest in studies were related to study success. The results reveal the existence of complex relations between changes in approaches to learning and perceptions of the teaching-learning environment.
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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 | 0.021 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".