Influence of Pre-service and In-service Teachers’ Gender and Experience on the Acceptance of AR Technology

Ning, Fangjing; Yang, Yang; Zhu, Tong; Bayarmaa, Tseden-Ish; Ma, Ning

doi:10.1007/978-981-13-6908-7_18

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…Fifthly, and most importantly, there is no acceptance model for MAR that can be used both with pre-service teachers and in-service teachers. The study by Ning et al (2019) used the UTAUT on both samples of educators, however, the second and fourth limitations mentioned above were observed in this study.…”

Section: Introductionmentioning

confidence: 94%

“…For example, Nizar et al (2019) used the UTAUT to determine the factors that influence pre-service teachers to use a specific MAR app named MARLCardio. The UTAUT was also used by Ning et al (2019) to investigate pre-service and in-service teachers' intention to use AR in their teaching. More recently, Ateş and Garzón (2023) combined the TPB and the UTAUT2 to investigate science teachers' intention to use AR in their classrooms.…”

Section: Chinese K-12 Teachersmentioning

confidence: 99%

“…Fourthly, the acceptance models are tested on a sample who used a MAR that had either been developed by the researchers (e.g., Cabero-Almenara et al, 2019;Ibili et al, 2019;Nizar et al, 2019;Rahmat and Mohamad, 2021) or institutions like the Ministry of Education (Jang et al, 2021). Some studies failed to mention whether the sample interacted with the MAR (e.g., Asiri and El-Aasar, 2022), while in others, the sample merely attended a brief seminar or lecture about AR (Ning et al, 2019). Studies in which the sample had the opportunity to create their own AR educational applications, are limited.…”

Section: Introductionmentioning

confidence: 99%

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The mobile augmented reality acceptance model for teachers and future teachers

Koutromanos,

Mikropoulos,

Mavridis

et al. 2023

Educ Inf Technol

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This study examines the factors that affect pre-service and in-service teachers’ intention to use Mobile Augmented Reality (MAR) in their teaching through the proposed Mobile Augmented Reality Acceptance Model (MARAM). The MARAM builds on the existing Technology Acceptance Model (TAM) to incorporate four other components namely perceived relative advantage, perceived enjoyment, facilitating conditions, and mobile self-efficacy. In addition, this study investigates the validity of the MARAM. Data were collected from 137 pre-service and 169 in-service teachers who completed a questionnaire after having developed their own MAR applications during an undergraduate university course and a training seminar, respectively. Structural Equation Model (SEM) analysis was conducted separately for each group, as well as for both samples together. We also conducted a multi-group analysis to examine differences across the two samples. The results for both samples together (N = 306), showed that intention was affected by attitude, perceived usefulness and facilitating conditions. In turn, attitude was affected by perceived enjoyment and perceived usefulness. Perceived usefulness was affected by perceived enjoyment and perceived relative advantage. Perceived ease of use was affected by mobile self-efficacy and facilitating conditions. However, perceived ease of use did not affect perceived usefulness or attitude. A multi-group analysis conducted on the sample of pre-service and in-service teachers produced similar results, with minor differentiations. These results have implications for the use of MAR in research and schools, as well as technology acceptance models in education.

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Section: Introductionmentioning

confidence: 94%

Section: Chinese K-12 Teachersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The mobile augmented reality acceptance model for teachers and future teachers

Koutromanos,

Mikropoulos,

Mavridis

et al. 2023

Educ Inf Technol

View full text Add to dashboard Cite

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“…Notably, Fathi and Ebadi [52] verified that technical support was the most influential factor affecting pre-service teachers' adoption of technology. Studies conducted by Kim and Lee [53], Ning, Yang, Zhu, Bayarmaa, and Ma [39], and Wong [54] have also consistently proven the significant impact of these facilitating conditions on teachers' intentions to use technology in their teaching practices and also emphasized the necessity of establishing a sound supportive infrastructure to ensure effective technology integration in education.…”

Section: The Utaut Modelmentioning

confidence: 93%

“…The Unified Theory of Acceptance and Use of Technology (UTAUT) model is a wellestablished framework for investigating how individuals' perceptions about the eternal factors impact their intention to use technology [37]. Originally developed by Venkatesh et al [38], it combines elements from eight established theories, including the Theory of Reasoned Action, the Motivational Model, and the Model of PC Utilization [39]. This comprehensive model specifically emphasizes four key elements-performance expectancy, effort expectancy, social influence, and facilitating conditions-to explain the factors influencing users' acceptance and utilization behavior towards technologies [38,40].…”

Section: The Utaut Modelmentioning

confidence: 99%

Pre-Service Teachers’ GenAI Anxiety, Technology Self-Efficacy, and TPACK: Their Structural Relations with Behavioral Intention to Design GenAI-Assisted Teaching

Wang,

Ruan,

Zhang

et al. 2024

Behavioral Sciences

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Generative artificial intelligence (GenAI) has taken educational settings by storm in the past year due to its transformative ability to impact school education. It is crucial to investigate pre-service teachers’ viewpoints to effectively incorporate GenAI tools into their instructional practices. Data gathered from 606 pre-service teachers were analyzed to explore the predictors of behavioral intention to design Gen AI-assisted teaching. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this research integrates multiple variables such as Technological Pedagogical Content Knowledge (TPACK), GenAI anxiety, and technology self-efficacy. Our findings revealed that GenAI anxiety, social influence, and performance expectancy significantly predicted pre-service teachers’ behavioral intention to design GenAI-assisted teaching. However, effort expectancy and facilitating conditions were not statistically associated with pre-service teachers’ behavioral intentions. These findings offer significant insights into the intricate relationships between predictors that influence pre-service teachers’ perspectives and intentions regarding GenAI technology.

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Teachers’ Perceptions Towards the Use of Augmented Reality Smart Glasses in Their Teaching

Kazakou,

Koutromanos

2023

Communications in Computer and Information Science

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Influence of Pre-service and In-service Teachers’ Gender and Experience on the Acceptance of AR Technology

Cited by 6 publications

References 20 publications

The mobile augmented reality acceptance model for teachers and future teachers

The mobile augmented reality acceptance model for teachers and future teachers

Pre-Service Teachers’ GenAI Anxiety, Technology Self-Efficacy, and TPACK: Their Structural Relations with Behavioral Intention to Design GenAI-Assisted Teaching

Teachers’ Perceptions Towards the Use of Augmented Reality Smart Glasses in Their Teaching

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