Application of The Learning Strategies and Motivation Questionnaire (LEMO) at the University: Reliability and Relation to First-year GPA
Bibliographic record
Abstract
As part of efforts to enhance academic achievement in higher education, incoming first-year students are becoming more and more subjected to surveys and assessments, e.g., regarding motivation and learning strategies. The Learning Strategies and Motivation Questionnaire (LEMO; Donche, Van Petegem, Van de Mosselaer, & Vermunt, 2010) is one of these surveys, applied mostly in professional bachelor programmes. The current study examines the reliability and predictive validity of the LEMO questionnaire in a sample of 416 first-year university students. All 13 scales were included in the study, i.e. Concrete Processing, Analysing, Memorising, Critical Processing, Relating-Structuring, External Regulation, Self-Regulation, Lack of Regulation, Amotivation, Controlled Motivation, Autonomous Motivation, Self-Efficacy, and Learning Together. In line with its reliability in previous studies, Cronbach’s alfa of most LEMO scales was below .70, which is the minimum threshold for scientific research, as was the Composite Reliability of eight of the 13 LEMO-scales. A confirmatory factor analysis showed that several factor loadings were below .70, resulting in an average variance extracted (AVE) below .50 for 11 of the 13 scales. Most scales had no or only a limited correlation to first-year GPA (FYGPA). Only Self-Efficacy and Analysing correlated ≥ .20 with FYGPA. These two scales explained 10.4% of the variance in study success. Hereby, Self-Efficacy is the most important predictor. The other 11 scales had no significant contribution to the prediction of academic performance in addition to Self-Efficacy and Analysing (ΔR2 = 3.4%, n.s.). Additional analyses showed that the correlation between the LEMO scales and FYGPA varied according to Bachelor programme.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".