Integrating a Mobile-Based Gaming Application into a Postsecondary Forest Ecology Course
Bibliographic record
Abstract
Increased disengagement of the current generation of postsecondary students (sometimes referred as “net generation”) from traditional instruction coupled with on-going popularity of games and mobile technologies have prompted interest in game-based learning in education. The objective of this study was to develop and evaluate the viability of a mobile game-based learning quest, based on the Questogo platform (website and mobile app) in an undergraduate Forest Ecology course offered at the University of British Columbia (UBC), Vancouver. The disturbance ecology (DE) quest was designed as a self-study activity that supports field-based laboratory sections of the course. The quest included instructional, location-based, and question- and answer-type of tasks that tested students’ knowledge of forest and disturbance ecology in an outdoor setting. After completing the DE quest, students provided feedback via an online survey. The majority of students found the DE quest to be a useful self-study tool, with 81% of respondents indicating that they were able to successfully engage with the mobile game-based learning technology. Sixty-six percent of the students would like to also see quests incorporated into other courses and 28% would like to have additional quests in the Forest Ecology course. This study provides a framework for incorporating mobile game-based learning into outdoor learning activities that offer students an engaging self-study educational experience.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".