Does Successful Use of Digital Learning Materials Predict Teachers’ Intention to Use Them Again in the Future?
Bibliographic record
Abstract
Do perceptions of success in using digital learning materials (DLMs) regularly (i.e., several times a week) strengthen (or weaken) teachers’ behavioural intentions to use DLMs again? And which psychological factors have a relationship with the intention to use DLMs again? These questions are important in light of stimulating teacher’s use of DLMs. To answer this question, teacher “flows” were analysed using crosstabs and multinomial logistic regression. These flows visualize how teachers go from a certain degree of perceived success to a certain strength of behavioural intention. Second, Hayes (2013) process method and structured equation modeling (SEM) techniques were applied to determine the mediating role of attitude, perceived norm, and perceived behavioural control of perceived success on the behavioural intention. The results suggested that the teachers’ strength in behavioural intention was in accordance with their perceived success for the case that their self-prediction was positive but that this strength became weaker when teachers’ self-prediction was negative. Attitude and perceived norm mediated the effects only when self-prediction was positive whereas perceived behaviour control did this in both cases. Also, there was a direct effect between perceived success and behavioural intention. It is important that teachers get a chance to experience success with the use of DLMs, enabled either by school leaders (regarding in-service teachers) or by teacher training institutions (regarding pre-service teachers). Only then we will see teachers’ willingness to use DLMs on a regular basis to grow.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".