Using Self-Regulatory Learning to Enhance E-Learning-Based Information Technology Training
Bibliographic record
Abstract
Technology-mediated learning methods are widely used by organizations and educational institutions to deliver information technology training. One form of technology-mediated learning, e-learning, in which the platform is the tutor, is quickly becoming the cost-effective solution of choice for many corporations. Unfortunately, the learning outcomes have been very disappointing. E-learning training makes an implicit assumption that learners can apply a high level of self-directed learning to assimilate the training content. In contrast, based on perspectives from social cognitive theory, we propose that instructional strategies need to persuade learners to follow self-regulated learning strategies. We test our ideas with participants who were trained through e-learning to design a website. Our findings indicate that participants who were induced to follow self-regulated learning strategies scored significantly higher on learning outcomes than those who were not persuaded to do so. We discuss our findings, and suggest that the interaction among information technology features, instructional strategies, and psychological learning processes offers a fruitful avenue for future information systems training research.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".