The Effects of Design-Based Learning on Novice Teachers' Active Learning Management Abilities
Bibliographic record
Abstract
This study investigated the effects of design-based learning (DBL) activities on novice teachers' active learning management abilities. The Participants included 11 novice teachers and 18 experienced teachers in a master's program. The intervention comprised four sequential DBL phases: problem identification, exploration, design and development, and implementation and revision. Novice teachers' abilities were assessed through lesson plan design and classroom teaching performance using validated assessment rubrics. Results demonstrated that the majority of novice teachers achieved high or very high-performance levels in active learning management. However, assessment-related components consistently scored lower than other instructional areas, revealing three distinct challenges: misalignment between assessment methods and learning activity goals, failures to translate planned assessments into classroom practice, and insufficient orchestration of assessment for both individual achievement and collaborative processes. This study provides evidence that DBL, enhanced through collaborative mentorship with experienced teachers, effectively develops novice teachers' active learning competencies, yet assessment implementation remains a critical area requiring explicit attention. Implications for teacher educators include prioritizing mentor preparation that develops procedural skill in assessment orchestration, and moving beyond conceptual knowledge alone. The short-term design tends to limit conclusions about sustained practice changes; longitudinal research is recommended.
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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 | 0.015 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".