Evidence map›Paper›PMID 35377333›Full record

ArticleJMIR serious games2022

Identifying Player Types to Tailor Game-Based Learning Design to Learners: Cross-sectional Survey using Q Methodology.

A E J Van Gaalen, J Schönrock-Adema, R J Renken, A D C Jaarsma, J R Georgiadis

Abstract read
In one paragraph

Article in JMIR serious games, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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

5 authors.

A E J Van GaalenAnatomy & Medical Physiology Section, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University Groningen, Groningen, Netherlands.ORCID https://orcid.org/0000-0002-8134-9583
J Schönrock-AdemaWenckebach Institute for Education and Training, University Medical Center Groningen, Groningen, Netherlands.ORCID https://orcid.org/0000-0002-9844-339X
R J RenkenCognitive Neuroscience Center, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University Groningen, Groningen, Netherlands.ORCID https://orcid.org/0000-0002-7777-9484
A D C JaarsmaWenckebach Institute for Education and Training, University Medical Center Groningen, Groningen, Netherlands.ORCID https://orcid.org/0000-0003-1668-2002
J R GeorgiadisAnatomy & Medical Physiology Section, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University Groningen, Groningen, Netherlands.ORCID https://orcid.org/0000-0002-9699-9466

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGame-based learning appears to be a promising instructional method because of its engaging properties and positive effects on motivation and learning. There are numerous options to design game-based learning; however, there is little data-informed knowledge to guide the choice of the most effective game-based learning design for a given educational context. The effectiveness of game-based learning appears to be dependent on the degree to which players like the game. Hence, individual differences in game preferences should be taken into account when selecting a specific game-based learning design.

objectiveWe aimed to identify patterns in students' perceptions of play and games-player types and their most important characteristics.

methodsWe used Q methodology to identify patterns in opinions on game preferences. We recruited undergraduate medical and dental students to participate in our study and asked participants to sort and rank 49 statements on game preferences. These statements were derived from a prior focus group study and literature on game preferences. We used by-person factor analysis and varimax rotation to identify common viewpoints. Both factors and participants' comments were used to interpret and describe patterns in game preferences.

resultsFrom participants' (n=102) responses, we identified 5 distinct patterns in game preferences: the social achiever, the explorer, the socializer, the competitor, and the troll. These patterns revolved around 2 salient themes: sociability and achievement. The 5 patterns differed regarding cheating, playing alone, story-telling, and the complexity of winning.

conclusionsThe patterns were clearly interpretable, distinct, and showed that medical and dental students ranged widely in how they perceive play. Such patterns may suggest that it is important to take students' game preferences into account when designing game-based learning and demonstrate that not every game-based learning-strategy fits all students. To the best of our knowledge, this study is the first to use a scientifically sound approach to identify player types. This can help future researchers and educators select effective game-based learning game elements purposefully and in a student-centered way.

Indexed as

computersfocus groupframeworkgame-based learninggamificationmedical educationnew technologyplayplayer typesqualitativeserious gamestaxonomytheory

Identifiers

PMID35377333
PMCPMC9016507

What OpenQuestion holds

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LicenceCC BY
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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.