PDF

Published

2023-12-22

Issue

Vol. 9 No. 2 (2024)

Section

Research Articles

License

The journal adopts the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), which means that anyone can reuse and redistribute the materials for non-commercial purposes as long as you follow the license terms and the original source is properly cited.

Author(s) shall retain the copyright of their work and grant the Journal/Publisher rights for the first publication with the work concurrently licensed since 2023 Vol.8 No.2.

Under this license, author(s) will allow third parties to download, reuse, reprint, modify, distribute and/or copy the content under the condition that the authors are given credit. No permission is required from the authors or the publisher.

This broad license intends to facilitate free access, as well as the unrestricted use of original works of all types. This ensures that the published work is freely and openly available in perpetuity.

By providing open access, the following benefits are brought about:

  • Higher Visibility, Availability and Citations-free and unlimited accessibility of the publication over the internet without any restrictions increases citation of the article.
  • Ease of search-publications are easily searchable in search engines and indexing databases.
  • Rapid Publication – accepted papers are immediately published online.
  • Available for free download immediately after publication at https://esp.as-pub.com/index.php/ESP

 

Copyright Statement

1.The authors certify that the submitted manuscripts are original works, do not infringe the rights of others, are free from academic misconduct and confidentiality issues, and that there are no disputes over the authorship scheme of the collaborative articles. In case of infringement, academic misconduct and confidentiality issues, as well as disputes over the authorship scheme, all responsibilities will be borne by the authors.

2. The author agrees to grant the Editorial Office of Environment and Social Psychology a licence to use the reproduction right, distribution right, information network dissemination right, performance right, translation right, and compilation right of the submitted manuscript, including the work as a whole, as well as the diagrams, tables, abstracts, and any other parts that can be extracted from the work and used in accordance with the characteristics of the journal. The Editorial Board of Environment and Social Psychology has the right to use and sub-licence the above mentioned works for wide dissemination in print, electronic and online versions, and, in accordance with the characteristics of the periodical, for the period of legal protection of the property right of the copyright in the work, and for the territorial scope of the work throughout the world.

3. The authors are entitled to the copyright of their works under the relevant laws of Singapore, provided that they do not exercise their rights in a manner prejudicial to the interests of the Journal.

About Licence

Environment and Social Psychology is an open access journal and all published work is available under the Creative Commons Licence, Authors shall retain copyright of their work and grant the journal/publisher the right of first publication, and their work shall be licensed under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

Under this licence, the author grants permission to third parties to download, reuse, reprint, modify, distribute and/or copy the content with attribution to the author. No permission from the author or publisher is required.

This broad licence is intended to facilitate free access to and unrestricted use of original works of all kinds. This ensures that published works remain free and accessible in perpetuity. Submitted manuscripts, once accepted, are immediately available to the public and permanently accessible free of charge on the journal’s official website (https://esp.as-pub.com/index.php/ESP). Allowing users to read, download, copy, print, search for or link to the full text of the article, or use it for other legal purposes. However, the use of the work must retain the author's signature, be limited to non-commercial purposes, and not be interpretative.

Click to download <Agreement on the Licence for the Use of Copyright on Environmental and Social Psychology>.

How to Cite

Chen, L., Aris, S. R. S., & Rahmat, M. K. (2023). Exploring in-service preschool teachers’ acceptance of mobile learning in science teaching practice. Environment and Social Psychology, 9(2). https://doi.org/10.54517/esp.v9i2.2010

Exploring in-service preschool teachers’ acceptance of mobile learning in science teaching practice

Lin Chen

Faculty of Education, Universiti Teknologi Mara; School of Humanities and Teacher Education, Wuyi University

Sharipah Ruzaina Syed Aris

Faculty of Education, Universiti Teknologi Mara

Mohd Khairezan Rahmat

Faculty of Education, Universiti Teknologi Mara


DOI: https://doi.org/10.54517/esp.v9i2.2010


Keywords: in-service preschool teachers, behavioral intention, mobile learning, UTAUT2

Abstract

In the sphere of preschool and elementary education, new interactive technologies built on intelligent mobile devices and auxiliary applications have drawn increasing attention. Based on the UTAUT2 (The expanding of the unified theory of acceptance and use of technology) theoretical model, the purpose of this study is to understand the situation of pre-school preschool teachers’ willingness to use mobile learning. This study conducted a survey on 329 in-service preschool teachers in 9 cities in Fujian Province, China, and conducted data analysis through statistical analysis software SPSS (Statistical Product and Service Solutions) 22.0 and AMOS (Analyze of Moment Structures) 22.0, verifying the UTAUT2 model in Effectiveness in understanding in-service early childhood teachers’ intention to move to learn. The results of structural equation modeling show that the proposed model has acceptable fitting data. The results of the study show that in-service preschool teachers have the willingness to actively accept mobile learning. Among many influencing factors, performance expectancy, effort expectancy, social influence, facilitating conditions, learning value, habit have significantly impact on behavioral intention to accept mobile learning. In addition, hedonic motivation did not support to affect behavioral intention and habit to affect use behavior. The study has important implications for researchers, educators, policy makers and mobile learning app designers.

References

[1]. Kalogiannakis M, Papadakis S. Hybrid learning for women and socially sensitive groups for the promotion of digital literacy. In: Proceedings of the 5th WSEAS/IASME International Conference on Engineering Education; 22–24 July 2008; Heraklion, Greece. pp. 305–311.

[2]. Kalogiannakis M, Papadakis S. Combining mobile technologies in environmental education: A Greek case study. International Journal of Mobile Learning and Organisation 2017; 11(2): 108. doi: 10.1504/ijmlo.2017.084272

[3]. Dorouka P, Papadakis S, Kalogiannakis M. Tablets and apps for promoting robotics, mathematics, STEM education and literacy in early childhood education. International Journal of Mobile Learning and Organisation 2020; 14(2): 255. doi: 10.1504/ijmlo.2020.106179

[4]. Al-Rahmi AM, Al-Rahmi WM, Alturki U, et al. Exploring the factors affecting mobile learning for sustainability in higher education. Sustainability 2021; 13(14): 7893. doi: 10.3390/su13147893

[5]. Quan Z, Grant L, Hocking D, Connor A. Distinctive mobile learning: Where it is different and how it can make a difference. Interactive Learning Environments 2022; 1–16. doi: 10.1080/10494820.2022.2086267

[6]. García-Martínez I, Fernández-Batanero JM, Cobos Sanchiz D, Luque de la Rosa A. Using mobile devices for improving learning outcomes and teachers’ professionalization. Sustainability 2019; 11(24): 6917. doi: 10.3390/su11246917

[7]. Vallejo-Correa P, Monsalve-Pulido J, Tabares-Betancur M. A systematic mapping review of context-aware analysis and its approach to mobile learning and ubiquitous learning processes. Computer Science Review 2021; 39: 100335. doi: 10.1016/j.cosrev.2020.100335

[8]. Criollo-C S, Guerrero-Arias A, Jaramillo-Alcázar Á, Luján-Mora S. Mobile learning technologies for education: Benefits and pending issues. Applied Sciences 2021; 11(9): 4111. doi: 10.3390/app11094111

[9]. Dimock M. Defining generations: Where Millennials end and Generation Z begins. Pew Research Center; 2019.

[10]. Amadu L, Syed Muhammad S, Mohammed AS, et al. Using technology acceptance model to measure the ese of social media for collaborative learning in Ghana. Journal of Technology and Science Education 2018; 8(4): 321. doi: 10.3926/jotse.383

[11]. Papadakis S, Kalogiannakis M, Zaranis N. Educational apps from the Android Google Play for Greek preschoolers: A systematic review. Computers & Education 2018; 116: 139–160. doi: 10.1016/j.compedu.2017.09.007

[12]. Betancourt-Odio MA, Sartor-Harada A, Ulloa-Guerra O, Azevedo-Gomes J. Self-perceptions on digital competences for m-learning and education sustainability: A study with teachers from different countries. Sustainability 2021; 13(1): 343. doi: 10.3390/su13010343

[13]. Iqbal S, Bhatti ZA. A qualitative exploration of teachers’ perspective on smartphones usage in higher education in developing countries. International Journal of Educational Technology in Higher Education 2020; 17(1): 29. doi: 10.1186/s41239-020-00203-4

[14]. Nikolopoulou K. Preschool teachers’ practices of ICT-supported early language and mathematics. Creative Education 2020; 11(10): 2038–2052. doi: 10.4236/ce.2020.1110149

[15]. Zhou X. Mechanism and structural parameter optimization method of linear energy-gathering cutting device for towering steel structures based on computational intelligence. Wireless Communications and Mobile Computing 2022; 2022: 9092062. doi: 10.1155/2022/9092062

[16]. Kalogiannakis M, Papadakis S. Evaluating pre-service kindergarten teachers’ intention to adopt and use tablets into teaching practice for natural sciences. International Journal of Mobile Learning and Organisation 2019; 13(1): 113–127. doi: 10.1504/ijmlo.2019.096479

[17]. Papadakis S, Kalogiannakis M. eTwinning in the early childhood as starting line of innovative practices for the didactic of natural sciences. In: Proceedings of the HSci2010 7th International Conference Hands-on Science Bridging the Science and Society gap; 25–31 July 2010; Rethymno, Greece. pp. 235–240.

[18]. Luo W, Berson IR, Berson MJ, Li H. Are early childhood teachers ready for digital transformation of instruction in Mainland China? A systematic literature review. Children and Youth Services Review 2021; 120: 105718. doi: 10.1016/j.childyouth.2020.105718

[19]. Nan J. Research of application of artificial intelligence in preschool education. Journal of Physics: Conference Series 2020; 1607(1): 012119. doi: 10.1088/1742-6596/1607/1/012119

[20]. Chai SZ. Research on the cultivation of children’s core literacy in kindergarten science education [Master’s thesis]. Shandong Normal University; 2018. p. 71.

[21]. Stockless A. Acceptance of learning management system: The case of secondary school teachers. Education and Information Technologies 2017; 23(3): 1101–1121. doi: 10.1007/s10639-017-9654-6

[22]. Nikolopoulou K, Gialamas V, Lavidas K. Acceptance of mobile phone by university students for their studies: An investigation applying UTAUT2 model. Education and Information Technologies 2020; 25(5): 4139–4155. doi: 10.1007/s10639-020-10157-9

[23]. Nikolopoulou K, Gialamas V, Lavidas K. Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers and Education Open 2021; 2: 100041. doi: 10.1016/j.caeo.2021.100041

[24]. Momani AM, Jamous MM, Hilles SMS. Technology acceptance theories. International Journal of Cyber Behavior, Psychology and Learning 2017; 7(2): 1–14. doi: 10.4018/ijcbpl.2017040101

[25]. Chao CM. Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in psychology 2019; 10: 1652. doi: 10.3389/fpsyg.2019.01652

[26]. Goksu I. Bibliometric mapping of mobile learning. Telematics and Informatics 2021; 56: 101491. doi: 10.1016/j.tele.2020.101491

[27]. Al-Hunaiyyan A, Al-Sharhan S, Alhajri R. A new mobile learning model in the context of the smart classrooms environment: A holistic approach. International Journal of Interactive Mobile Technologies (iJIM) 2017; 11(3): 39–56. doi: 10.3991/ijim.v11i3.6186

[28]. Samad MRA, Ihsan ZH, Khalid F. The use of mobile learning in teaching and learning session during the Covid-19 pandemic in Malaysia. Journal of Contemporary Social Science and Educational Studies 2021; 1(2): 46–65.

[29]. Alsswey A, Al-Samarraie H. M-learning adoption in the Arab gulf countries: A systematic review of factors and challenges. Education and Information Technologies 2019; 24(5): 3163–3176. doi: 10.1007/s10639-019-09923-1

[30]. Kumar BA, Chand SS. Mobile learning adoption: A systematic review. Education and Information Technologies 2018; 24(1): 471–487. doi: 10.1007/s10639-018-9783-6

[31]. Tabiin A. Implementation of STEAM method (science, technology, engineering, arts and mathematics) for early childhood developing in Kindergarten Mutiara Paradise Pekalongan. Early Childhood Research Journal (ECRJ) 2020; 2(2): 36–49. doi: 10.23917/ecrj.v2i2.9903

[32]. Ravanis K. Early childhood science education: State of the art and perspectives. Journal of Baltic Science Education 2017; 16(3): 284–288. doi: 10.33225/jbse/17.16.284

[33]. Chatti H, Hadoussa S. Factors affecting the adoption of e-learning technology by students during the COVID-19 quarantine period: The application of the UTAUT model. Engineering, Technology & Applied Science Research 2021; 11(2): 6993–7000. doi: 10.48084/etasr.3985

[34]. Idris WIS, Razak RA, Rahman SSBA. The design and development of mobile learning for preschool education. JuKu: Jurnal Kurikulum & Pengajaran Asia Pasifik 2021; 9(1): 37–48.

[35]. Haiyan L, Lin Z. An empirical study on the current situation and effect of mobile learning for kindergarten teachers in the internet plus context. Journal of Guizhou Education University 2021; 37(1): 70–77.

[36]. Kara N, Cagiltay K. In-service preschool teachers’ thoughts about technology and technology use in early educational settings. Contemporary Educational Technology 2017; 8(2): 119–141. doi: 10.30935/cedtech/6191

[37]. Li HX, Zhao CL, Jiang ZH, Liang YZ. A study on the influencing factors about preschool teachers’ acceptance of informational teaching based on the UTAUT model. Studies in Early Childhood Education 2017; 14: 25.

[38]. Doğan Y, Simsar A. Preschool teachers’ views on science education, the methods they use, science activities, and the problems they face. International Journal of Progressive Education 2018; 14(5): 57–76. doi: 10.29329/ijpe.2018.157.6

[39]. Islamoglu H, Kabakci Yurdakul I, Ursavas OF. Pre-service teachers’ acceptance of mobile-technology-supported learning activities. Educational Technology Research and Development 2021; 69(2): 1025–1054. doi: 10.1007/s11423-021-09973-8

[40]. Kumar JA, Bervell B. Google classroom for mobile learning in higher education: Modelling the initial perceptions of students. Education and Information Technologies 2019; 24(2): 1793–1817. doi: 10.1007/s10639-018-09858-z

[41]. Venkataraman JB, Ramasamy S. Factors influencing mobile learning: A literature review of selected journal papers. International Journal of Mobile Learning and Organisation 2018; 12(2): 99–112. doi: 10.1504/ijmlo.2018.090836

[42]. Alkhwaldi A, Kamala M. Why do users accept innovative technologies? A critical review of models and theories of technology acceptance in the information system literature. Journal of Multidisciplinary Engineering Science and Technology (JMEST) 2017; 4(8): 7962–7971.

[43]. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Quarterly 2003; 27(3): 425–478. doi: 10.2307/30036540

[44]. Venkatesh V, Thong JYL, Xu X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly 2012; 36(1): 157–178. doi: 10.2307/41410412

[45]. Tamilmani K, Rana NP, Wamba SF, Dwivedi R. The extended unified theory of acceptance and use of technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management 2021; 57: 102269. doi: 10.1016/j.ijinfomgt.2020.102269

[46]. Xie A. Research on influence factors of university student’s willingness of mobile phonelearning base on UTAUT [Master’s thesis]. Zhejiang Normol University; 2012.

[47]. Yakubu MN, Dasuki SI. Factors affecting the adoption of e-learning technologies among higher education students in Nigeria. Information Development 2018; 35(3): 492–502. doi: 10.1177/0266666918765907

[48]. Sitar-Taut DA, Mican D. Mobile learning acceptance and use in higher education during social distancing circumstances: An expansion and customization of UTAUT2. Online Information Review 2021; 45(5): 1000–1019. doi: 10.1108/oir-01-2021-0017

[49]. Mtebe JS, Mbwilo B, Kissaka MM. Factors influencing teachers’ use of multimedia enhanced content in secondary schools in Tanzania. The International Review of Research in Open and Distributed Learning 2016; 17(2). doi: 10.19173/irrodl.v17i2.2280

[50]. Ain N, Kaur K, Waheed M. The influence of learning value on learning management system use. Information Development 2016; 32(5): 1306–1321. doi: 10.1177/0266666915597546

[51]. Zwain AAA. Technological innovativeness and information quality as neoteric predictors of users’ acceptance of learning management system. Interactive Technology and Smart Education 2019; 16(3): 239–254. doi: 10.1108/itse-09-2018-0065

[52]. Alghazi SS, Kamsin A, Almaiah MA, et al. For sustainable application of mobile learning: An extended UTAUT model to examine the effect of technical factors on the usage of mobile devices as a learning tool. Sustainability 2021; 13(4): 1856. doi: 10.3390/su13041856

[53]. Hair JF Jr, Black WC, Babin BJ, Anderson RE. Multivariate Data Analysis: A Global Perspective. Pearson; 2010.

[54]. Hair JF Jr, Babin BJ, Anderson RE, Black WC. Multivariate Data Analysis, 8th ed. Cengage India; 2018.

[55]. Tabachnick BG, Fidell LS. Using Multivariate Statistics, 7th ed. Pearson; 2018.

[56]. Hu L, Bentler PM. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods 1998; 3(4): 424–453. doi: 10.1037/1082-989x.3.4.424

[57]. Alghatrifi I, Khalid H. A systematic review of UTAUT and UTAUT2 as a baseline framework of information system research in adopting new technology: A case study of IPV6 adoption. In: Proceedings of 2019 6th International Conference on Research and Innovation in Information Systems (ICRIIS); 2–3 December 2019; Johor Bahru, Malaysia.

[58]. Schumacker RE, Lomax RG. A Beginner’s Guide to Structural Equation Modeling, 3rd ed. Routledge; 2010.

[59]. Kline RB. Principles and Practice of Structural Equation Modeling, 2nd ed. Guilford Press; 2005.

[60]. Hooper D, Coughlan J, Mullen M. Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods 2008; 6(1): 53–60.

[61]. Abbad MMM. Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies 2021; 26(6): 7205–7224. doi: 10.1007/s10639-021-10573-5

[62]. Dahri NA, Al-Rahmi WM, Almogren AS, et al. Acceptance of mobile learning technology by teachers: Influencing mobile self-efficacy and 21st-century skills-based training. Sustainability 2023; 15(11): 8514. doi: 10.3390/su15118514

[63]. Fidani A, Idrizi F. Investigating students’ acceptance of a learning management system in university education: A structural equation modeling approach. ICT Innovations 2012 Web Proceedings 2012; 2(23): 311–320.

[64]. Owusu Kwateng K, Osei Atiemo KA, Appiah C. Acceptance and use of mobile banking: An application of UTAUT2. Journal of Enterprise Information Management 2019; 32(1): 118–151. doi: 10.1108/jeim-03-2018-0055

[65]. Raman A, Don Y. Preservice teachers’ acceptance of learning management software: An application of the UTAUT2 model. International Education Studies 2013; 6(7): 157–164. doi: 10.5539/ies.v6n7p157

[66]. Šumak B, Polancic G, Hericko M. An empirical study of virtual learning environment adoption using UTAUT. In: Proceedings of 2010 Second International Conference on Mobile, Hybrid, and On-Line Learning; 10–16 February 2010; Saint Maarten, Netherlands Antilles. doi: 10.1109/elml.2010.11



21 Woodlands Close #02-10 Primz Bizhub Singapore 737854

Email:editorial_office@as-pub.com