MétaCan
Menu
Back to cohort
Record W3048708821 · doi:10.2196/18644

The Effects of Gamification on Computerized Cognitive Training: Systematic Review and Meta-Analysis

2020· article· en· W3048708821 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Serious Games · 2020
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsPsycINFOScopusMeta-analysisApplied psychologyContext (archaeology)Systematic reviewCognitionPsychologyTimelineMEDLINEMedical educationMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: There has been a growing interest in the application of gamification (ie, the use of game elements) to computerized cognitive training. The introduction of targeted gamification features to such tasks may increase motivation and engagement as well as improve intervention effects. However, it is possible that game elements can also have adverse effects on cognitive training (eg, be a distraction), which can outweigh their potential motivational benefits. So far, little is known about the effectiveness of such applications. OBJECTIVE: This study aims to conduct a systematic review and meta-analysis to investigate the effect of gamification on process outcomes (eg, motivation) and on changes in the training domain (eg, cognition), as well as to explore the role of potential moderators. METHODS: We searched PsycINFO, Cumulative Index to Nursing and Allied Health Literature, ProQuest Psychology, Web of Science, Scopus, PubMed, Science Direct, Excerpta Medica dataBASE, Institute of Electrical and Electronics Engineers Xplore, Association for Computing Machinery, and a range of gray-area literature databases. The searches included papers published between 2008 and 2018. Meta-analyses were performed using a random-effects model. RESULTS: The systematic review identified 49 studies, of which 9 randomized controlled trials were included in the meta-analysis. The results of the review indicated that research in this context is still developing and lacks well-controlled empirical studies. Gamification in cognitive training is applied to a large range of age groups and audiences and is mostly delivered at a research site through computers. Rewards and feedback continue to dominate the gamification landscape, whereas social-oriented features (eg, competition) are underused. The meta-analyses showed that gamified training tasks were more motivating/engaging (Hedges g=0.72) and more demanding/difficult (Hedges g=-0.52) than non- or less-gamified tasks, whereas no effects on the training domain were found. Furthermore, no variables moderated the impact of gamified training tasks. However, meta-analytic findings were limited due to a small number of studies. CONCLUSIONS: Overall, this review provides an overview of the existing research in the domain and provides evidence for the effectiveness of gamification in improving motivation/engagement in the context of cognitive training. We discuss the shortcomings in the current literature and provide recommendations for future research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.813
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.353
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it