Motivation to transfer learning to multiple contexts
Bibliographic record
Abstract
To stay up-to-date in contemporary information intensive societies it is important to be able to effectively and efficiently find, evaluate, process and present required information. In educational contexts training in these so-called information literacy competences is mainly the domain of institutional libraries. Essential to education is the long-term transfer of learning, that is the application of newly acquired competencies also outside the training environment. Research learns that this often takes place sparsely, leading to what is called a Transfer Paradox. The aim of this study is to develop a practical instrument for instructional designers to measure the influence of a set of key variables on the learner's motivation to transfer learning to the wider educational and the work context. Two hundred and thirty-four students of the Open University of the Netherlands doing an information literacy course filled out a questionnaire before entering the course. Data was analyzed using factor analyses and hierarchical multiple regression analysis. Results show that the opportunity to apply new learning and sanctions from supervisors are two important factors that influence the learner's motivation to transfer learning in both the study and the work context already before the course has started.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".