Designing inclusive learning for twice exceptional students in Minecraft
Bibliographic record
Abstract
Twice exceptional learners are intellectually or creatively gifted yet also experience one or more learning difficulties. These students face a unique set of challenges in educational settings. Recommended strategies for accommodating twice exceptional learners focus on – among other things – (1) providing freedom and variety, so that students can engage with learning in a way that interests them, plays to their strengths, and compensates for their learning difficulties; (2) allowing students to engage with simulated and real-world problems; and (3) providing an adaptable environment that is pleasing to students, and sensitive to any specific needs they may have as a result of learning difficulties. In this article, we show how the video game Minecraft can facilitate learning environments that embody these recommendations. We describe in detail a variety of specific techniques for implementing such environments, including contextualised learning artefacts and puzzle rooms. We then demonstrate examples of learning environments that we have previously implemented using these techniques. These environments are currently being used in an empirical evaluation, as part of a larger project investigating the effectiveness of Minecraft as an educational resource for twice exceptional students. Resume Bien qu’ils eprouvent une ou plusieurs difficultes d’apprentissage, les etudiants doublement exceptionnels (c’est-dire ayant des besoins educatifs speciaux) sont doues intellectuellement ou au niveau de la creativite. Ces etudiants font face a un ensemble unique de defis en contexte educatif. Les strategies recommandees pour accommoder les apprenants doublement exceptionnels sont, en autres, centres sur (1) proposer de la liberte et de la variete afin que les etudiants puissent s’engager dans leur apprentissage de la maniere qui les interesse, qui fonctionne avec leurs forces et compense leurs difficultes ; (2) permettre aux etudiants de s’engager dans des problemes reels ou simules; et (3) offrir un environnement adaptable qui plait aux etudiants et qui est sensible a tout besoin particulier qu’ils peuvent avoir en fonction de leurs difficultes d’apprentissage. Dans cet article, nous montrons comment le jeu video Minecraft peut faciliter les environnements d’apprentissage qui integrent ces recommandations. Nous decrivons en detail une variete de techniques specifiques pour implementer de tels environnements, y compris des artefacts d’apprentissage contextualises et de casse-tete. Nous montrons ensuite des exemples d’environnements d’apprentissage que nous avons precedemment implementes en utilisant ces techniques. Ces environnements font en ce moment l’objet d’une evaluation empirique, en tant que partie d’un plus grand projet d’enquete sur l’efficacite de Minecraft comme ressource educative pour les etudiants doublement exceptionnels. Mots-cles: apprenants doublement exceptionnels, Minecraft, douance, conception inclusive, mondes virtuels, theorie du calcul
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".