Motivation to transfer learning to multiple contexts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
 
 
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it