Research Trends in Mobile Learning: A Systematic Literature Review From 2011-2021
Bibliographic record
Abstract
As of late, coordinating innovation into schooling keeps on standing out alongside the fast development of information and communication technology. In the writing survey, mobile learning is a learning idea that underscores the learning system with cell phones without relying upon the actual area of learning. This review means to give an exhaustive perspective on the past writing and some potential headings for scientists and instructors for additional mobile learning research. A sum of 45 papers was chosen from the ERIC database. Utilization of the term mobile learning in the title, research strategies, number of authors, major contributing nations, most useful diaries, and cell phones utilized in portable learning are investigated. The outcomes show that exploration of mobile learning has kept on getting consideration from specialists somewhat recently. Among the distributions explored, every one of the 40 articles contained the term mobile learning in the title and dynamic. As of recently, quantitative techniques are more regularly taken on in mobile learning research than quantitative strategies, blended techniques, and research and development (RnD) strategies. When arranged by country, Turkey has the most elevated commitment contrasted with different nations in this field, followed by Indonesia, South Africa, Malaysia, Thailand, China, and Spain. The greater part of the papers distributed in mobile learning research has four authors. In light of the number of articles distributed in mobile learning, Canadian Center of Science and Education, South African Journal of Education, and International Journal of Education and Development utilizing information and communication turned into the most useful diaries in this exploration. The most generally involved cellular phones in this review are cellular phones and tablets
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.040 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".