Serious Games as a Complementary Tool for Social Skill Development in Young People: A Systematic Review of the Literature
Bibliographic record
Abstract
Background The use of games for social skill development in the classroom is accelerating at a tremendous rate. At the same time, the research surrounding games designed for teaching social skills remains fragmented. This systematic review summarizes the current existing literature on social skill serious games for young people ages 5 to 19 and is the first review of serious games to note the demographic and geographic component of these studies. Method This review included papers that: evaluated a game designed to teach social skills; included measurable, quantitative outcomes; have a translation or be published in English; were peer-reviewed; date from January 2010 to May 2020; and have a nonclinical study population between ages of 5 to 19. Keywords were obtained from the CASEL 5 framework. Results Our findings are mixed but suggest that serious games may improve social skills when used alongside in-person discussion. We also found potential effects of the length of time of gameplay, intervention, and follow-up on social skill serious game effectiveness. Although this review found promising research conducted in East Asian countries and with minority samples in the United States, the majority of social skill serious game research takes place in the United States and Australia, with unreported demographic information and white-majority samples. Conclusions Due to the limited number of published studies in this area and studies lacking methodological rigor, the effectiveness of using games to teach social skills and the impact of background on social skill learning require further discussion.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".