The relationship between students' self‐regulated learning behaviours and problem‐solving efficiency in technology‐rich learning environments
Bibliographic record
Abstract
Abstract Background Scholars have confirmed the vital roles of self‐regulated learning (SRL) behaviours in predicting task performance, especially within non‐linear technology‐rich learning environments (TREs). However, few studies focused on the learning costs (e.g., study effort and time‐on‐task) related to SRL and the efficiency outcome of SRL (i.e., the relative relationship between learning costs and performance). Objectives This study examined the relationship between students' SRL behaviours and problem‐solving efficiency in the context of TREs. Methods Eighty‐two medical students accomplished a diagnostic task in a computer‐simulated environment, and they were classified into the efficient or less efficient group according to diagnostic performance and time‐on‐task. Then we coded students' SRL behaviours from trace data and counted the frequency of each SRL behaviour. The recurrence quantification and lag sequential analyses were performed to extract the dynamic characteristics of SRL behaviours, including recurrent patterns and sequential transitions. Results and Conclusions Efficient students conducted more frequent Self‐reflection behaviours than the less efficient. For the recurrent patterns, efficient students tended to exhibit longer SRL behaviour sequences comprising a variety of different SRL behaviours (e.g., Task Analysis > Add Test > Add Hypotheses > Categorise Evidence) as well as longer sequences of repeated SRL behaviours (e.g., Add Test > Add Test > Add Test > Add Test). Moreover, efficient students exhibited more sequential transitions between different SRL behaviours than less efficient. Takeaways Overall, this study revealed the effects of SRL on problem‐solving efficiency, which inspired researchers to incorporate problem‐solving efficiency as an evaluation criterion of SRL processes.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".