Bibliographic record
Abstract
As design-based research, this study describes the development and analysis of two location-based augmented reality (AR) serious learning games (SLG) for French second language (FL2) learning. Explorez and VdeUVic are collaborative quest-based SLGs. At different locations on campus, players interact with characters that give them quests including clues or options to further the storyline. These interactions take place in the form of either written text, or audio and video recordings, encouraging students to develop language skills both written and oral. Students choose their own learning path and advance at their own pace. Three cohorts of FL2 university students play-tested the games, with 58 of the 77 students choosing to participate in the study. The design-based research framework for the development of the game iterations and subsequent testing was an iterative process with each stage producing output that became input for the next stage. The evaluation of the AR language tools was implemented by means of a mixed-method case study, collecting data of both a qualitative and quantitative nature, through pre and post-play questionnaires, interviews, and video recordings of student gameplay interactions for analysis. Informed by situated cognition, one of the goals was to provide a contextual and immersive learning experience. Additionally, this research drew on sociocultural theory and the social nature of language learning, emphasizing learner interactions as a principal learning force. This research examined the learners’ perceptions of their learning experience, as well as the ways in which students collaborated to complete the tasks. Employing a situative approach framework informed by social regulation and content processing, student learning patterns were examined. Distinct types of learner interactions amongst teams during gameplay were shown. Patterns in the emergence of learners’ high-level co-regulation during collaborative learning are indicated in the findings. Key elements for the development and implementation of location-based serious games to foster collaborative learning are highlighted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".