Evidence map›Paper›PMID 35370439›Full record

ArticleEducation and information technologies2022

Serious game design model for language learning in the cultural context.

Kashif Ishaq, Fadhilah Rosdi, Nor Azan Mat Zin, Adnan Abid

Abstract read
In one paragraph

Article in Education and information technologies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kashif IshaqFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia.
Fadhilah RosdiFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia.
Nor Azan Mat ZinFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia.
Adnan AbidSchool of Systems and Technology (SST), University of Management and Technology, Lahore, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital learning environments have been gaining prominence during the last few years. In particular, the rising usage of mobile devices, including smartphones and tabs, has invited researchers to design and develop learning applications and games for such platforms. Mobile applications and games have been developed for learning languages like many other domains. However, most of these games are fun-based and lack a holistic design and development approach. Therefore, as a principal contribution, this research presents a theoretical model for designing language learning games in a cultural context. The proposed model combines the elements of sociocultural theory with the concepts and elements of gamification, keeping in view the requirements and educational settings, including level and mode of education, etc., to ensure the effectiveness and usability of the developed game. Subsequently, based on the proposed model, a Language Learning Game (LLG) has been designed and developed through a systematic process that involves game design, low-fidelity, and high-fidelity prototyping and its validation. The LLG has been evaluated comprehensively at different stages by incorporating standard methods. Whereby this research augments the existing set of heuristics by proposing a number of specialized heuristics for the evaluation of serious games to gauge their conformance to the cultural context. The evaluation results show that the game has overall usability scores of 90%. While the quasi-experiment-based pre-test and post-test have been conducted, the results reveal that the results obtained by LLG are statistically significantly better than adopted mobile application and traditional group.

Indexed as

Digital gamesEffectivenessGamificationHeuristic evaluationPrototypeSerious gameThink-aloud

Identifiers

PMID35370439
PMCPMC8964384

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.