Gamified Mobile Collaborative Location-Based Language Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it