A Treasure Hunt Model for Inquiry-Based Learning in the Development of a Web-based Learning Support System
Bibliographic record
Abstract
Abstract: One of the main problems of web-based learning is staying motivated at a sufficient level. Learning games offering challenges and entertainment may stimulate student motivation for learning and mitigate this problem. Web-based learning support systems combined with learning games may efficiently promote learning by encouraging student participation in learning. This study introduces a treasure hunt model, which represents the idea of inquiry-based learning using set theory. We demonstrate this via a prototype of a web-based learning support system called OTHI, which employs an online treasure hunt game as the learning game. We integrate the sound learning strategies of inquiry-based learning with the Web and online game technologies in this system. We expect that our learning support system will motivate students, and furnish an interactive student-centered learning environment. Key Words: inquiry-based learning, web-based learning support systems, game-based learning, treasure hunt
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".