Factors Contributing to Collaborative Game-Based Learning (CGBL) Effectiveness
Bibliographic record
Abstract
In facing the Covid-19 pandemic, the teaching and learning landscape in Malaysian schools has also changed accordingly. The Ministry of Education has introduced Teaching and Learning at Home to take over the previous methods. Conventional teaching methods are unfitting during the ‘new norm’. Therefore, teachers need to diversify their instructional strategies and search for various resources in the digital environment - learning in this mode should create a fun digital learning environment. Digital Game-based Learning (DGBL) is a teaching aid that is capable of promoting enjoyment in learning. This article focused on DGBL as a learning method in a collaborative environment called Collaborative Game-based Learning (CGBL). There is a shortage of insight on the factors that support DGBL’s efficiency in the digital environment, specifically in CGBL in educational settings. This article employed a systematic Thematic Review (TR) approach to synthesise the literature published from 2016 until 2021 on CGBL in the digital environment. A keyword search was conducted, followed by a filtering process using inclusion criteria from the Scopus, Lens, and Mendeley databases. The author identified 65 peer-reviewed journal papers. Only 34 articles were used to be reviewed after the inclusion and exclusion processes. A TR of these articles identified 95 initial codes, later grouped into 32 codes, and created ten categories from three themes. From the TR results, it is found that the factors contributing to CGBL effectiveness are learning environment, learning motivation and learning strategies. This work provides insight on various parties in considering the implementation of CGBL in Teaching and Learning at Home as one of the appropriate alternative resources and methods.
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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 | 0.017 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".