Tool Use of Experienced Learners in Computer-Based Learning Environments: Can Tools Be Beneficial?
Bibliographic record
Abstract
Research has documented the use of tools in computer-based learning environments as problematic, that is, learners do not use the tools and when they do, they tend to do it suboptimally. This study attempts to disentangle cause and effect of this suboptimal tool use for experienced learners. More specifically, learner variables (metacognitive and motivational) were related to the tool presentation (non-/embedded), interventions, type of tool use (quantitatively and qualitative) and learners’ performance. One hundred and seventeen graduate students were assigned to one of five conditions (embedded and non-embedded with explained tool functionality, embedded and non-embedded with non-explained tool functionality and one control condition) to study a hypertext using semi-structured concept maps as the tools. Findings are discussed with respect to experienced learners’ role on tool use and performance. Although no differences among conditions and performance were found, results reveal that the self-regulation skill of organization and the explained tool functionality affected time on tool negatively, while the self-regulation skill of elaboration and perceived tool usability showed a positive effect. Time on tool influenced performance positively. Quality influenced performance negatively. It is argued that some tools and interventions are unnecessary for experienced learners.
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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 | 0.034 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".