Research priorities in mobile learning: An international Delphi study / Les priorités de recherche en matière d'apprentissage mobile: Une étude de Delphes internationale
Bibliographic record
Abstract
Along with advancing mobile technologies and proliferating mobile devices and applications, mobile learning research has gained great momentum in recent years. While there have been review articles summarizing past research, studies identifying mobile learning research priorities based on experts’ latest insights have been lacking. This study employed the Delphi method to obtain a consensus from experts about areas that are most in need of research in mobile learning. An international expert panel participated in a three-round Delphi process involving two cycles of online questionnaires and feedback reports. Participants responded to the question, “What should be the research priorities for the field of mobile learning over the next 5 years?” Ten research categories were identified and ranked in order of priority: 1) teaching and learning strategies; 2) affordances; 3) theory; 4) settings of learning; 5) evaluation/assessment; 6) learners; 7) mobile technologies and interface design; 8) context awareness and augmented reality; 9) infrastructure and management; and 10) country and digital divide. This study also reported expert-generated research statements for each research category and the importance of these research statements rated by the experts. Selected research papers were summarized to help contextualize the discussions of research categories and statements. Avec l'avancement des technologies mobiles et la prolifération des appareils mobiles et des applications, la recherche consacrée à l'apprentissage mobile a récemment pris de l’ampleur. Si des articles ont résumé les recherches antérieures, les études s’appuyant sur les dernières connaissances d'experts pour identifier les priorités de recherche sur l'apprentissage mobile font défaut. La présente étude a utilisé la méthode de Delphes pour obtenir un consensus des experts sur les domaines nécessitant le plus des recherches sur l'apprentissage mobile. Un groupe international d'experts a participé à un processus de Delphes structuré en trois rondes impliquant deux séries de questionnaires en ligne et des rapports de rétroaction. Les participants ont répondu à la question : "Quelles devraient être les priorités de recherche dans le domaine de l'apprentissage mobile pour les cinq prochaines années ?" Dix catégories de recherche ont été identifiées et classées par ordre de priorité : 1 ) stratégies d'enseignement et d'apprentissage ; 2 ) affordances ; 3 ) théorie ; 4 ) paramètres d’apprentissage ; 5 ) évaluation ; 6 ) apprenants ; 7 ) technologies mobiles et conception de l'interface ; 8 ) perception du contexte et réalité augmentée ; 9 ) infrastructure et gestion ; et 10 ) pays et fossé numérique. Cette étude a également repris les déclarations de recherche énoncées pour chaque catégorie par les experts ainsi que le classement par ordre d’importance des déclarations de recherche selon l’avis de ces experts. Quelques articles choisis ont été résumés pour faciliter la contextualisation des discussions portant sur les catégories de recherche et sur les déclarations.
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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 | 0.125 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".