Using Self-Regulatory Learning to Enhance E-Learning-Based Information Technology Training
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
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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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it