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Record W4391617569 · doi:10.32920/25164632.v1

Encouraging Student Agency Using Gamification & Game-Based Learning to Support Student Mental Health

2024· preprint· en· W4391617569 on OpenAlexaff
Tabitha Grant

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAgency (philosophy)Flexibility (engineering)Mental healthGame based learningProcess (computing)Subject (documents)PsychologyEducational gameMathematics educationMedical educationComputer scienceSociologyMedicineManagementSocial science

Abstract

fetched live from OpenAlex

<p>Teaching methods and practices used in North American Secondary Schools lack the flexibility and support structures needed to aid students who identify as marginalized and living with a mental health disorder. This study seeks to identify means by which gamification, in combination with game-based learning, can develop student agency and desire for academic success. In exploring literature pertaining to the subject, this paper will present new grounds in personal development, suggesting supplementary practices and digital tools that may help these students achieve academic fulfillment through the development of their agency and behavioural change. This MRP will present a game that utilizes practices found in gamification and game-based learning to demonstrate how this process can be applied within society.</p>

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.086
GPT teacher head0.457
Teacher spread0.371 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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