DOES DIGITAL GAME-BASED LEARNING IMPROVE STUDENT TIME-ON-TASK BEHAVIOR AND ENGAGEMENT IN COMPARISON TO ALTERNATIVE INSTRUCTIONAL STRATEGIES?
Bibliographic record
Abstract
Digital Game-Based Learning (DGBL) activities were examined in comparison with effective, research-based learning strategies to observe any difference in student engagement and time-on task behavior. Experimental and control groups were randomly selected amongst the intermediate elementary school students ages 8 to 10 years old. Student observations and attitudinal surveys were completed after eight lesson cycles to determine which student group had a higher level of engagement and time-on-task behavior. Six of the 8 trials showed a higher student survey average in the level of student enjoyment while experiencing DGBL. Six of the 8 trials produced equal or higher class average scores for focus and attentiveness during DGBL versus alternative strategies. Seven out of 8 trials produced higher student table observation averages for DGBL. In conclusion, the data suggests DGBL can be as effective in the classroom as other research-proven instructional strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".