Investigating relationships within the Index of Learning Styles: a data driven approach
Bibliographic record
Abstract
Learning styles are incorporated more and more in e‐education, mostly in order to provide adaptivity with respect to the learning styles of students. For identifying learning styles, at the present time questionnaires are widely used. While such questionnaires exist for most learning style models, their validity and reliability is an important issue and has to be investigated to guarantee that the questionnaire really assesses what the learning style theory aims at. In this paper, we focus on the Index of Learning Styles (ILS), a 44‐item questionnaire to identify learning styles based on Felder‐ Silverman learning style model. The aim of this paper is to analyse data gathered from ILS by a data‐driven approach in order to investigate relationships within the learning styles. Results, obtained by Multiple Correspondence Analysis and cross‐validated by correlation analysis, show the consistent dependencies between some learning styles and lead then to conclude for scarce validity of the ILS questionnaire. Some latent dimensions present in data, that are unexpected, are discussed. Results are then compared with the ones given by literature concerning validity and reliability of the ILS questionnaire. Both the results and the comparisons show the effectiveness of data‐driven methods for patterns extraction even when unexpected dependencies are found and the importance of coherence and consistency of mathematical representation of data with respect to the methods selected for effective, precise and accurate modelling.
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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 | 0.022 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".