Evidence map›Paper›PMID 41276572›Full record

ArticleScientific reports2025

Student acceptance and evolving teacher roles in mixed reality dance education.

Yang Meng, Ng Khar Thoe, Abd Razak Nurul Nadiah

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Yang MengShaanxi University of International Trade & Commerce, Xianyang, China.
Ng Khar ThoeINTI International University, Nilai, Malaysia.
Abd Razak Nurul NadiahUniversity of Malaya, Kuala Lumpur, Malaysia. nurulnadiah.abdrazak@um.edu.my.

Funding

Xi'an Academy of Social Sciences 25TY04
6 · The paper itself

Abstract

With the ongoing digitalization of dance education, there remains a lack of instruments to evaluate students’ needs concerning the integration of Mixed Reality (MR). This study aimed to develop the MR Dance Attitude Scale (MR-DAS) based on Gardner’s Multiple Intelligence (MI) Theory as the conception framework and to investigate how MR acceptance may reshape future teaching roles. A mixed-methods approach was employed, comprising a validated questionnaire administered to 110 dance majors and semi-structured interviews (n = 7). Validity and reliability were assessed using SPSS 26.0. Quantitative results demonstrated strong suitability for factor analysis (KMO = 0.926, p < 0.001). Exploratory Factor Analysis extracted 27 items grouped into three dimensions: Learning Impact, MR Teaching Intention, and Equipment Requirements. Qualitative findings highlighted multiple barriers influencing technology acceptance. Findings indicated that students are highly receptive to MR due to its potential to enhance learning efficiency and interactivity; however, they also emphasized the irreplaceable role of human teachers in fostering creativity, interpreting culture, and ensuring safety. These insights support a collaborative model combining teachers and MR technologies. The validated instrument as a reliable assessment instrument, is capable of identifying the varied learning needs of students, and offers a practical resource for educators aiming to further investigate the application and advancement of MR technology in educational contexts. This contributes to the realization of sustainable and quality education aligned with Sustainable Development Goal 4.

Indexed as

Emerging technologiesHigher educationMixed reality (MR)Personalized learningSustainable development education

Identifiers

PMID41276572
PMCPMC12706031

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.