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Record W4238218121 · doi:10.6007/ijarbss/v9-i7/6790

Development of Gamyflip-Pro Module and Determination of Its Content Validity

2019· article· en· W4238218121 on OpenAlexfundno aff
Azia Sulong, Abu Bakar Ibrahim, Ashardi Abas

Bibliographic record

VenueInternational Journal of Academic Research in Business and Social Sciences · 2019
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
FundersRotman School of Management, University of TorontoUniversity of Toronto
KeywordsFlipped classroomComputer scienceContent validityProcess (computing)Subject matterSubject (documents)Flexibility (engineering)Mathematics educationMultimediaPsychologyPedagogyMathematicsWorld Wide WebProgramming languageCurriculum

Abstract

fetched live from OpenAlex

In recent years, the integration of Flipped Classroom and gamification in teaching and learning process became popular in order to provide flexibility in teaching and learning process. The purpose of this study was to develop and validate the content of GamyFlip-Pro module in teaching and learning a topic Programming for pre-university students. This module was developed based on Sidek and Jamaludin's Model, Flipped Classroom Approach, and The Five Steps of Applying Gamification in Education by Huang and Soman. The development process involves two main process namely; development of module draft, experts' reviews and implementation of content validity for the modules. Since the development of module draft complete, five subject matter experts will review the module draft. Then, module modification was done based on suggestion and recommendations by subject matter experts followed by the process of calculation content validity of the module. The percentage of experts' agreement obtained for this module ranged from 73% to 90%. This finding shows that GamyFlip-Pro module has a good content validity. This finding reinforces the use of flipped classroom and gamification approach in the design of teaching and learning modules and establish the pedagogy of flipped learning and gamification in teaching programming.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.143

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.462
GPT teacher head0.517
Teacher spread0.054 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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