Self-regulated learning strategies and computer software training
Bibliographic record
Abstract
User learning is important to the effective use of information technology within organizations, particularly given the changing nature of IT. Research indicates that self-directed training is the most common means by which users learn. In addition, the use of Web-based training within organizations in these self-directed learning situations is increasing. The purpose of this research is to investigate the increasingly popular self-training phenomenon within organizations by examining the self-regulated learning strategies that individuals use in Web-based training situations, several key factors that influence the use of these strategies, and how they influence learning outcomes in this context. To do this, a two-phase study was designed. Phase I was comprised of twenty-seven interviews with knowledge-workers from a variety of organizations to understand the strategies they used during their learning experiences and the learning difficulties they encountered. Findings from Phase I supported the notion that self-directed learning remains the most popular form of computer software training in the field, and provided initial support for the key themes comprising the research model. Revisions to the model were incorporated based on these findings. Phase II was comprised of a field test of the research model where employees conducted a self-led web-based software training session, completed a study questionnaire and two knowledge tests. Results from the study provided support for the research model. Those who devoted effort toward the use of self-regulated learning strategies benefited in terms of higher learning outcomes. Organizations invest a great deal of resources toward training end users. This research assists organizations in gaining a return from this sizable investment in training end users, and in managing the organizations' most important resource—knowledge.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".