Differences Between High Achieving and Low Achieving Students in the Initial Exploration Phase of Discovery-Based Learning
Bibliographic record
Abstract
Exploration and self-direction are key components of discovery-based learning (DBL), but research has shown that without suitable guidance many students are unable to regulate knowledge discovery in a productive and strategic fashion. To investigate learners’ behaviors and strategies in the exploration phase of DBL, we conducted qualitative research in which undergraduate participants ( N =10) explored an electric circuit simulation and were encouraged to seek help if needed. We categorized participants into high and low achieving based on their gain scores, and then coded and analyzed their behaviors. By identifying sequential patterns of codes, we were able to determine the relative effectiveness of participants' exploration behaviours and researchers' prompts. Our analysis indicated that, compared with low achievers, high achievers tended to have more consistent goal-directed exploratory actions, greater attention to details, stronger determination to understand part-whole relationships, and be more willing to seek help. Additionally, we found adaptive scaffolding such as just-in-time prompts were essential elements of effective learning for both low and high achievers in a DBL simulation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".