Teachers’ perception of the use of mobile technologies with smart applications to enhance students’ thinking skills: a study among primary school teachers in Thailand

References

• Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215. https://doi.org/10.1287/isre.9.2.204
   Web of Science ®Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Amornkitpinyo, T., & Piriyasurawong, P. (2017). The concept framework of structural equation model of mobile cloud learning acceptance for higher education students in the 21st century. TEM Journal, 6(3), 464–468. https://doi.org/10.18421/TEM63-05
   Google Scholar
• Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., Raths, J., & Wittrock, M. C. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives (complete edition). Longman.
   Google Scholar
• Ansari, A., & Sanayei, A. (2012). Determine the effects of mobile technology, mobile learning on customer satisfaction and loyalty (case study: Mellat bank). International Journal of Information Science and Management, 10(SPL.ISSUE), 137–152. https://ijism.ricest.ac.ir/index.php/ijism/article/view/159/144
   Google Scholar
• Arpaci, I., & Basol, G. (2020). The impact of preservice teachers’ cognitive and technological perceptions on their continuous intention to use flipped classroom. Education and Information Technologies, 25(5), 3503–3514.https://doi.org/10.1007/s10639-020-10104-8
   Web of Science ®Google Scholar
• Asiri, Y., Millard, D., & Weal, M. (2018). Digital mobile-based behaviour change interventions to assess and promote critical thinking and research skills among undergraduate students. In M. Auer & T. Tsiatsos (Eds.), Interactive mobile communication technologies and learning. IMCL 2017. Advances in intelligent systems and computing (Vol. 725). Springer. https://doi.org/10.1007/978-3-319-75175-7_17
   Google Scholar
• Baldock, B. L., Fernandez, A. L., Franco, J., Provencher, B. A., & McCoy, M. R. (2021). Overcoming the challenges of remote instruction: Using mobile technology to promote active learning. Journal of Chemical Education, 98(3), 833–842. https://doi.org/10.1021/acs.jchemed.0c00992
   Web of Science ®Google Scholar
• Baydas, O., & Yilmaz, R. M. (2018). Pre-service teachers’ intention to adopt mobile learning: A motivational model. British Journal of Educational Technology, 49(1), 137–152. https://doi.org/10.1111/bjet.12521
   Web of Science ®Google Scholar
• Birch, A., & Irvine, V. (2009). Preservice teachers’ acceptance of ICT integration in the classroom: Applying the UTAUT model. Educational Media International, 46(4), 295–315. https://doi.org/10.1080/09523980903387506
   Google Scholar
• Bloom, B., Englehart, M., Furst, E., Hill, W., & Krathwohl, D. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. Longmans, Green.
   Google Scholar
• Bornstein, M. H., Jager, J., & Putnick, D. L. (2013). Sampling in developmental science: Situations, shortcomings, solutions, and standards. Developmental Review, 33(4), 357–370. https://doi.org/10.1016/j.dr.2013.08.003
   PubMed Web of Science ®Google Scholar
• Bray, A., & Tangney, B. (2016). Enhancing student engagement through the affordances of mobile technology: A 21st century learning perspective on realistic mathematics education. Mathematics Education Research Journal, 28(1), 173–197. 10.1007/s13394-015-0158-7
   Web of Science ®Google Scholar
• Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J. A., & García-Peñalvo, F. J. (2017). Learning with mobile technologies – Students’ behavior. Computers in Human Behavior, 72, 612–620. https://doi.org/10.1016/j.chb.2016.05.027
   Web of Science ®Google Scholar
• Car, T., Pilepic, L., & Šimunic, M. (2014). Mobile technologies and supply chain management – Lessons for the hospitality industry. Tourism and Hospitality Management, 20(2), 207–219. https://doi.org/10.20867/thm.20.2.5
   Google Scholar
• Chang, C., Yan, C., & Tseng, J. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5), 809–826. https://doi.org/10.14742/ajet.818
   Web of Science ®Google Scholar
• Cheong, C., Bruno, V., & Cheong, F. (2012). Designing a mobile-app-based collaborative learning system. Journal of Information Technology Education: Innovations in Practice, 11(1), 94–119.
   Google Scholar
• Chew, S. L., & Cerbin, W. J. (2021). The cognitive challenges of effective teaching. The Journal of Economic Education, 52(1), 17–40. https://doi.org/10.1080/00220485.2020.1845266
   Web of Science ®Google Scholar
• Chuang, Y. (2015). SSCLS: A smartphone-supported collaborative learning system. Telematics and Informatics, 32(3), 463–474. https://doi.org/10.1016/j.tele.2014.10.004
   Web of Science ®Google Scholar
• Chuang, Y. (2017). MEMIS: A mobile-supported English-medium instruction system. Telematics and Informatics, 34(2), 640–656. https://doi.org/10.1016/j.tele.2016.10.007
   Web of Science ®Google Scholar
• Ciampa, K. (2014). Learning in a mobile age: An investigation of student motivation. Journal of Computer Assisted Learning, 30(1), 82–96. https://doi.org/10.1111/jcal.12036
   Web of Science ®Google Scholar
• Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–210. https://doi.org/10.2307/249688
   Web of Science ®Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
   Web of Science ®Google Scholar
• Economides, A. A. (2009). Conative feedback in computer-based assessment. Computers in the Schools, 26(3), 207–223. https://doi.org/10.1080/07380560903095188
   Google Scholar
• Ertmer, P. A. (1999). Addressing first- and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47–61. https://doi.org/10.1007/BF02299597
   Web of Science ®Google Scholar
• Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
   Google Scholar
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
   Google Scholar
• Fornell, C., & Laker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
   Web of Science ®Google Scholar
• Francis, H., Taylor, M., Sudirman, S., Trongtortam, S., & Symons, A. (2017). The attitude towards the use of mobile learning technology enhanced teaching. In Developments in eSystems engineering (DeSE), 2017 10th international conference on (pp. 135–138). IEEE.
   Google Scholar
• Fu, Q., & Hwang, G. (2018). Trends in mobile technology-supported collaborative learning: A systematic review of journal publications from 2007 to 2016. Computers and Education, 119, 129–143. 10.1016/j.compedu.2018.01.004
   Web of Science ®Google Scholar
• Gefen, D., Straub, D. W., & Boudreau, M. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1–78. http://cits.tamiu.edu/kock/NedWebArticles/Gefenetal2000.pdf
   Google Scholar
• Hair, J. F. J., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis. Pearson Prentice Hall, Pearson Education.
   Google Scholar
• Halaweh, M. (2017). Using mobile technology in the classroom: A reflection based on teaching experience in UAE. TechTrends, 61(3), 218–222. https://doi.org/10.1007/s11528-017-0184-2
   Web of Science ®Google Scholar
• Heflin, H., Shewmaker, J., & Nguyen, J. (2017). Impact of mobile technology on student attitudes, engagement, and learning. Computers and Education, 107, 91–99. https://doi.org/10.1016/j.compedu.2017.01.006
   Web of Science ®Google Scholar
• Hofstede, G. (2008). Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. Shanghai Foreign Language Education Press.
   Google Scholar
• Huang, F., Teo, T., & Guo, J. Y. (2021). Understanding English teachers’ non-volitional use of online teaching: A Chinese study. System, 101, 102574. https://doi.org/10.1016/j.system.2021.102574
   Web of Science ®Google Scholar
• Huang, F., Teo, T., & Zhou, M. (2019). Factors affecting Chinese English as a foreign language teachers’ technology acceptance: A qualitative study. Journal of Educational Computing Research, 57(1), 83–105. https://doi.org/10.1177/0735633117746168
   Web of Science ®Google Scholar
• Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587–605. https://doi.org/10.1016/0305-0483(95)00035-6
   Web of Science ®Google Scholar
• Izuagbe, R., & Popoola, S. O. (2017). Social influence and cognitive instrumental factors as facilitators of perceived usefulness of electronic resources among library personnel in private universities in south-west Nigeria. Library Review, 66(8-9), 679–694. https://doi.org/10.1108/LR-09-2016-0086
   Google Scholar
• Jackson, J. D., Yi, M. Y., & Park, J. S. (2013). An empirical test of three mediation models for the relationship between personal innovativeness and user acceptance of technology. Information and Management, 50(4), 154–161. https://doi.org/10.1016/j.im.2013.02.006
   Web of Science ®Google Scholar
• Jankra, S., Kajornsin, B., & Chuntra, C. (2018). Development of analytical thinking skills by using GPAS process and assessment for learning on mathematics Grade 6 Students, Wat Donmuang (Thaharn-Akart-Uthid) School, under Bangkok Metropolitan Administration. 20(1).
   Google Scholar
• Kantiya, P., Chomphucome, W., & Khawsiri, S. (2016). The development of analytical thinking skills for science through five steps learning management of secondary school. Journal of Graduate Research, 7(2), 137–152. https://www.tcithaijo.org/index.php/banditvijai/article/view/96255
   Google Scholar
• Kaplan, D. E. (2018). Piagetian theory in online teacher education. Creative Education, 9(06), 831–837. https://doi.org/10.4236/ce.2018.96061
   Google Scholar
• Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183–213. https://doi.org/10.2307/249751
   Web of Science ®Google Scholar
• Khlaif, Z. (2018). Teachers’ perceptions of factors affecting their adoption and acceptance of mobile technology in K-12 settings. Computers in the Schools, 35(1), 49–67. https://doi.org/10.1080/07380569.2018.1428001
   Web of Science ®Google Scholar
• Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). Guilford Press.
   Google Scholar
• Kongsen, J., & Intho, P. (2010). Adoption of m-Learning system and guide the development of teacher use m-learning in Walailak University. [ebook] Retrieved January 16, 2018, from http://www.snc.lib.su.ac.th/libmedia/pulinet/document/
   Google Scholar
• Krathwohl, D. R. (2002). A revision of bloom's taxonomy: An overview. Theory into Practice, 41(4), 212–218. https://doi.org/10.1207/s15430421tip4104_2
   Web of Science ®Google Scholar
• Lai, C. (2020). Trends of mobile learning: A review of the top 100 highly cited papers. British Journal of Educational Technology, 51(3), 721–742. https://doi.org/10.1111/bjet.12884
   Web of Science ®Google Scholar
• Lai, H., & Chen, C. (2011). Factors influencing secondary school teachers’ adoption of teaching blogs. Computers and Education, 56(4), 948–960. https://doi.org/10.1016/j.compedu.2010.11.010
   Web of Science ®Google Scholar
• Landry, J. P., Pardue, J. H., Doran, M. V., & Daigle, R. J. (2002). Encouraging students to adopt software engineering methodologies: The influence of structured group labs on beliefs and attitudes. Journal of Engineering Education, 91(1), 103–108. https://doi.org/10.1002/j.2168-9830.2002.tb00678.x
   Google Scholar
• Latifah, S., Koderi, F. R., Khoeriyah, E. T., Hidayah, N., & Ahmad, M. N. F. (2021). The influence of mobile instant messaging with scientific approach on students’ critical-thinking skills in physics learning during covid-19 pandemic. Paper presented at the IOP Conference Series: Earth and Environmental Science, 1796(1). https://doi.org/10.1088/1742-6596/1796/1/012057
   Google Scholar
• Lee, D. Y., & Ryu, H. (2013). Learner acceptance of a multimedia-based learning system. International Journal of Human-Computer Interaction, 29(6), 419–437. https://doi.org/10.1080/10447318.2012.715278
   Web of Science ®Google Scholar
• Lee, H., Parsons, D., Kwon, G., Kim, J., Petrova, K., Jeong, E., & Ryu, H. (2016). Cooperation begins: Encouraging critical thinking skills through cooperative reciprocity using a mobile learning game. Computers and Education, 97, 97–115. https://doi.org/10.1016/j.compedu.2016.03.006
   Web of Science ®Google Scholar
• Leem, J., & Sung, E. (2019). Teachers’ beliefs and technology acceptance concerning smart mobile devices for SMART education in South Korea. British Journal of Educational Technology, 50(2), 601–613. https://doi.org/10.1111/bjet.12612
   Web of Science ®Google Scholar
• Lin, H. (2013). The effect of absorptive capacity perceptions on the context-aware ubiquitous learning acceptance. Campus-Wide Information Systems, 30(4), 249–265. https://doi.org/10.1108/CWIS-09-2012-0031
   Google Scholar
• Lindsay, L. (2016). Transformation of teacher practice using mobile technology with one-to-one classes: M-learning pedagogical approaches. British Journal of Educational Technology, 47(5), 883–892. https://doi.org/10.1111/bjet.12265
   Web of Science ®Google Scholar
• Ling, C., Harnish, D., & Shehab, R. (2014). Educational apps: Using mobile applications to enhance student learning of statistical concepts. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 532–543. https://doi.org/10.1002/hfm.20550
   Web of Science ®Google Scholar
• Margallo, D. N., Billner-Garcia, R., & Bradley, K. (2021). The show must Go On: Using technology for rapid onboarding and orientation during COVID-19 and beyond. The Journal of Continuing Education in Nursing, 52(3), 115–117. https://doi.org/10.3928/00220124-20210216-04
   PubMed Web of Science ®Google Scholar
• Martínez-Torres, M. R., Díaz-Fernández, M. C., Toral, S. L., & Barrero, F. (2015). The moderating role of prior experience in technological acceptance models for ubiquitous computing services in urban environments. Technological Forecasting and Social Change, 91, 146–160. https://doi.org/10.1016/j.techfore.2014.02.004
   Web of Science ®Google Scholar
• Marty, P. F., Alemanne, N. D., Mendenhall, A., Maurya, M., Southerland, S. A., Sampson, V., & Schellinger, J. (2013). Scientific inquiry, digital literacy, and mobile computing in informal learning environments. Learning, Media and Technology, 38(4), 407–428. https://doi.org/10.1080/17439884.2013.783596
   Web of Science ®Google Scholar
• McCoy, S., Everard, A., & Jones, B. M. (2005). An examination of the technology acceptance model in Uruguay and the US: A focus on culture. Journal of Global Information Technology Management, 8(2), 27–45. https://doi.org/10.1080/1097198X.2005.10856395
   Google Scholar
• Mellado, L., Parte, L., Sánchez-Herrera, S., & Bermejo, M. L. (2021). Evolution of prospective secondary education economics teachers’ personal and emotional metaphors. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.606153
   PubMed Web of Science ®Google Scholar
• Mingmuang, C. (2018). The study on mobile learning using spaced repetition technique. Journal of Information Technology Management and Innovation, 4(2), 167–176. https://tci-thaijo.org/index.php/itm-journal/article/view/115363
   Google Scholar
• Ministry of Education. (2015). PISA 2015 Report by Institute for the Promotion of Teaching Science and Technology and Organisation for Economic Co-operation and Development. Retrieved from http://www.oic.go.th/FILEWEB/CABINFOCENTER6/DRAWER056/GENERAL/DATA0000/00000070.PDF.
   Google Scholar
• Ministry of Education. (2021). Digital for learning. https://moe360.blog/2021/04/12/digital-for-learning/
   Google Scholar
• Ministry of Information and Communication Technology. (2011). Information and communication technology policy framework, 2011-2020 of Thailand
   Google Scholar
• Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers and Education, 109, 56–73. https://doi.org/10.1016/j.compedu.2017.02.005
   Web of Science ®Google Scholar
• OECD. (2019). OECD future of education and skills 2030. https://www.oecd.org/education/2030-project/teaching-and-learning/learning/transformative-competencies/Transformative%20Competencies%20for%202030.pdf
   Google Scholar
• Office of the Permanent Secretary, Ministry of Education. (2016). 12th educational development plan of the Ministry of Education (2017-2021).
   Google Scholar
• Orru, G., Gobbo, F., O’Sullivan, D., & Longo, L. (2018). An investigation of the impact of a social constructivist teaching approach, based on trigger questions, through measures of mental workload and efficiency.
   Google Scholar
• Piaget, J. (1970). Piaget’s theory. (G. Gellerier & J. Langer, Trans.). In P. H. Mussen (Ed.), Carmichael’s manual of child psychology (3rd ed., Vol. 1). Wiley.
   Google Scholar
• Prensky, M. (2001). Digital natives, digital immigrants part 1. On the Horizon, 9(5), 1–6. https://doi.org/10.1108/10748120110424816
   Google Scholar
• Pruet, P., Ang, C. S., & Farzin, D. (2016). Understanding tablet computer usage among primary school students in underdeveloped areas: Students’ technology experience, learning styles and attitudes. Computers in Human Behavior, 55, 1131–1144. https://doi.org/10.1016/j.chb.2014.09.063
   Web of Science ®Google Scholar
• Qushem, U. B., Christopoulos, A., Oyelere, S. S., Ogata, H., & Laakso, M. (2021). Multimodal technologies in precision education: Providing new opportunities or adding more challenges? Education Sciences, 11(7), 338. https://doi.org/10.3390/educsci11070338
   Web of Science ®Google Scholar
• Raykov, T., & Marcoulides, G. A. (2012). An introduction to applied multivariate analysis. Routledge.
   Google Scholar
• Reychav, I., Najami, I., Raban, D. R., McHaney, R., & Azuri, J. (2018). The impact of media type on shared decision processes in third-age populations. International Journal of Medical Informatics, 112, 45–58. https://doi.org/10.1016/j.ijmedinf.2018.01.004
   PubMed Web of Science ®Google Scholar
• Rogers, E. M. (1962). The diffusion of innovation (1st ed.). The Fress Press.
   Google Scholar
• Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519–528. https://doi.org/10.1016/j.chb.2015.07.002
   Web of Science ®Google Scholar
• Srite, M. (2006). Culture as an explanation of technology acceptance differences: An empirical investigation of Chinese and US users. Australasian Journal of Information Systems, 14(1), 5–26. https://doi.org/10.3127/ajis.v14i1.4
   Google Scholar
• Srivastava, S., Arya, M., & Lamba, S. (2019). Swift-IoT: A framework for small scale IoT system development for students. Paper presented at the 2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT. ICCCNT45670.2019.8944805
   Google Scholar
• Teo, T. (2009). The impact of subjective norm and facilitating conditions on pre-service teachers’ attitude toward computer use: A structural equation modeling of an extended technology acceptance model. Journal of Educational Computing Research, 40(1), 89–109. https://doi.org/10.2190/EC.40.1.d
   Web of Science ®Google Scholar
• Teo, T., Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475. https://doi.org/10.1080/10494820.2017.1341940
   Web of Science ®Google Scholar
• Teo, T., Khlaisang, J., Thammetar, T., Ruangrit, N., Satiman, A., & Sunphakitjumnong, K. (2014). A survey of pre-service teachers’ acceptance of technology in Thailand. Asia Pacific Education Review, 15(4), 609–616. https://doi.org/10.1007/s12564-014-9348-3
   Web of Science ®Google Scholar
• Terzis, V., Moridis, C. N., & Economides, A. A. (2012). The effect of emotional feedback on behavioral intention to use computer based assessment. Computers and Education, 59(2), 710–721. https://doi.org/10.1016/j.compedu.2012.03.003
   Web of Science ®Google Scholar
• Thangsoomboon, D. (2008). A study of Analytical Thinking Development of Prathomsuksa 3 Students by using Investigation and Discovery activity Together with Authentic Assessment. Srinakharinwirot University.
   Google Scholar
• Toral, S. L., Barrero, F., & Martínez-Torres, M. R. (2007). Analysis of utility and use of a web-based tool for digital signal processing teaching by means of a technological acceptance model. Computers & Education, 49(4), 957–975. https://doi.org/10.1016/j.compedu.2005.12.003
   Web of Science ®Google Scholar
• Tsai, P., Tsai, C., & Hwang, G. (2010). Elementary school students’ attitudes and self-efficacy of using PDAs in a ubiquitous learning context. Australasian Journal of Educational Technology, 26(3), 297–308. https://doi.org/10.14742/ajet.1076
   Web of Science ®Google Scholar
• Tsai, Y. R. (2014). Applying the technology acceptance model (TAM) to explore the effects of a course management system (CMS)-assisted EFL writing instruction. CALICO Journal, 32(1), 153–171. https://doi.org/10.1558/calico.v32i1.25961
   Web of Science ®Google Scholar
• Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872
   Web of Science ®Google Scholar
• Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
   Web of Science ®Google Scholar
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
   Web of Science ®Google Scholar
• von Glasersfeld, E. (1995). A constructivist approach to teaching. In L. P. Steffe, & J. Gale (Eds.), Constructivism in education (pp. 3–15). Erlbaum, Hillsdale. http://www.vonglasersfeld.com/172
   Google Scholar
• Wang, J., Yang, Y., Li, H., & van Aalst, J. (2021). Continuing to teach in a time of crisis: The Chinese rural educational system’s response and student satisfaction and social and cognitive presence. British Journal of Educational Technology, 52(4), 1494–1512. https://doi.org/10.1111/bjet.13129
   Web of Science ®Google Scholar
• World Economic Forum. (2020). The COVID-19 pandemic has changed education forever. This is how. https://www.weforum.org/agenda/2020/04/coronavirus-education-global-covid19-online-digital-learning/
   Google Scholar
• Xiangming, L., & Song, S. (2018). Mobile technology affordance and its social implications: A case of “rain classroom”. British Journal of Educational Technology, 49(2), 276–291. https://doi.org/10.1111/bjet.12586
   Web of Science ®Google Scholar
• Yi, M. Y., Fiedler, K. D., & Park, J. S. (2006). Understanding the role of individual innovativeness in the acceptance of IT-based innovations: Comparative analyses of models and measures. Decision Sciences, 37(3), 393–426. https://doi.org/10.1111/j.1540-5414.2006.00132.x
   Web of Science ®Google Scholar
• Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: Self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431–449. https://doi.org/10.1016/S1071-5819(03)00114-9
   Web of Science ®Google Scholar