Research article Special Issues

Predicting secondary school mathematics teachers' digital teaching behavior using partial least squares structural equation modeling

  • Received: 17 August 2023 Revised: 11 September 2023 Accepted: 12 September 2023 Published: 21 September 2023
  • Digital technologies play a key role in the digital transformation of education. In order to improve teaching effectiveness and efficiency, teachers should use digital technologies appropriately. However, some secondary school mathematics teachers have little confidence in their digital teaching behavior. This study aimed to explore the predictors of secondary school mathematics teachers' digital teaching behavior. An extended unified theory of acceptance and use of technology (UTAUT) model was adopted to predict secondary school mathematics teachers' digital teaching behavior. A questionnaire survey was conducted with all junior high school mathematics teachers in a state-level new area, which is located in a central province of China. Three hundred and eighty-five valid data were collected. The partial least squares structural equation modeling (PLS-SEM) method was used to analyze the data. It was found that technological pedagogical content knowledge (TPACK) was the biggest predictor of secondary school mathematics teachers' digital teaching behavior. Attitude, behavioral intention, performance expectancy, effort expectancy and social influence can also affect teachers' digital teaching behavior in direct and/or indirect ways. The findings have noteworthy realistic implications for enhancing digital teaching behavior of secondary school mathematics teachers and promoting digital transformation of secondary school mathematics education.

    Citation: Xin Tang, Zhiqiang Yuan, Xi Deng, Liping Xiang. Predicting secondary school mathematics teachers' digital teaching behavior using partial least squares structural equation modeling[J]. Electronic Research Archive, 2023, 31(10): 6274-6302. doi: 10.3934/era.2023318

    Related Papers:

  • Digital technologies play a key role in the digital transformation of education. In order to improve teaching effectiveness and efficiency, teachers should use digital technologies appropriately. However, some secondary school mathematics teachers have little confidence in their digital teaching behavior. This study aimed to explore the predictors of secondary school mathematics teachers' digital teaching behavior. An extended unified theory of acceptance and use of technology (UTAUT) model was adopted to predict secondary school mathematics teachers' digital teaching behavior. A questionnaire survey was conducted with all junior high school mathematics teachers in a state-level new area, which is located in a central province of China. Three hundred and eighty-five valid data were collected. The partial least squares structural equation modeling (PLS-SEM) method was used to analyze the data. It was found that technological pedagogical content knowledge (TPACK) was the biggest predictor of secondary school mathematics teachers' digital teaching behavior. Attitude, behavioral intention, performance expectancy, effort expectancy and social influence can also affect teachers' digital teaching behavior in direct and/or indirect ways. The findings have noteworthy realistic implications for enhancing digital teaching behavior of secondary school mathematics teachers and promoting digital transformation of secondary school mathematics education.



    加载中


    [1] MOE, Standards For Teachers' Digital Literacy, Beijing, (2022).
    [2] V. Tomé, A. M. Kılıç, A. Bargaoanu, A. Varanauskas, C. Hague, C. Sádaba, Guidelines for Teachers and Educators on Tackling Disinformation and Promoting Digital Literacy Through Education and Training, Publications Office of the European Union, 2022.
    [3] M. Beardsley, L. Albo, P. Aragon, D. Hernandez-Leo, Emergency education effects on teacher abilities and motivation to use digital technologies, Bri. J. Educ. Technol., 52 (2021), 1455–1477. https://doi.org/10.1111/bjet.13101 doi: 10.1111/bjet.13101
    [4] C. Audrin, B. Audrin, Key factors in digital literacy in learning and education: A systematic literature review using text mining, Educ. Inf. Technol., 27 (2022), 7395–7419. https://doi.org/10.1007/s10639-021-10832-5 doi: 10.1007/s10639-021-10832-5
    [5] A. Clark-Wilson, O. Robutti, M. Thomas, Teaching with digital technology, ZDM-Math. Educ., 52 (2020), 1223–1242. https://doi.org/10.1007/s11858-020-01196-0 doi: 10.1007/s11858-020-01196-0
    [6] NCTM, Principles and Standards for School Mathematics, NCTM, Reston, VA, 2000.
    [7] UNESCO, Reimagining Our Futures Together: In A New Social Contract For Education, UNESCO, Paris, 2022.
    [8] S. Chatterjee, K. K. Bhattacharjee, Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling, Educ. Inf. Technol., 25 (2020), 3443–3463. https://doi.org/10.1007/s10639-020-10159-7 doi: 10.1007/s10639-020-10159-7
    [9] T. Wijaya, Y. Cao, R. Weinhandl, E. Yusron, Z. Lavicza, Applying the UTAUT model to understand factors affecting micro-lecture usage by mathematics teachers in China, Mathematics, 10 (2022), 1008. https://doi.org/10.3390/math10071008 doi: 10.3390/math10071008
    [10] L. L. Wah, H. Hashim, Determining pre-service teachers' intention of using technology for teaching English as a second language (ESL), Sustainability, 13 (2021), 7568. https://doi.org/10.3390/su13147568 doi: 10.3390/su13147568
    [11] J. Sultana, Determining the factors that affect the uses of mobile cloud learning (MCL) platform Blackboard-a modification of the UTAUT model, Educ. Inf. Technol., 25 (2020), 223–238. https://doi.org/10.1007/s10639-019-09969-1 doi: 10.1007/s10639-019-09969-1
    [12] S. Hu, K. Laxman, K. Lee, Exploring factors affecting academics' adoption of emerging mobile technologies-An extended UTAUT perspective, Educ. Inf. Technol., 25 (2020), 4615–4635.https://doi.org/10.1007/s10639-020-10171-x doi: 10.1007/s10639-020-10171-x
    [13] Z. Yuan, J. Liu, X. Deng, T. Ding, T. Wijaya, Facilitating conditions as the biggest factor influencing elementary school teachers' usage behavior of dynamic mathematics software in China, Mathematics, 11 (2023), 1536. https://doi.org/10.3390/math11061536 doi: 10.3390/math11061536
    [14] T. T. Wijaya, Y. Zhou, T. Houghton, R. Weinhandl, Z. Lavicza, F. D. Yusop, Factors affecting the use of digital mathematics textbooks in Indonesia, Mathematics, 10 (2022), 1808. https://doi.org/10.3390/math10111808 doi: 10.3390/math10111808
    [15] A. Baki, T. Kösa, B. Guven, A comparative study of the effects of using dynamic geometry software and physical manipulatives on the spatial visualisation skills of pre‐service mathematics teachers, Bri. J. Educ. Technol., 42 (2009), 291–310. https://doi.org/10.1111/j.1467-8535.2009.01012.x doi: 10.1111/j.1467-8535.2009.01012.x
    [16] O. Birgin, H. Acar, The effect of computer-supported collaborative learning using GeoGebra software on 11th grade students' mathematics achievement in exponential and logarithmic functions, Int. J. Math. Educ. Sci. Technol., 53 (2020), 1–18. https://doi.org/10.1080/0020739X.2020.1788186 doi: 10.1080/0020739X.2020.1788186
    [17] Y. Zengin, H. Furkan, T. Kutluca, The effect of dynamic mathematics software geogebra on student achievement in teaching of trigonometry, Proc. Soc. Behav. Sci., 31 (2012), 183–187. https://doi.org/10.1016/j.sbspro.2011.12.038 doi: 10.1016/j.sbspro.2011.12.038
    [18] M. Demir, Y. Zengin, The effect of a technology-enhanced collaborative learning environment on secondary school students' mathematical reasoning: A mixed method design, Educ. Inf. Technol., 28 (2023), 9855–9883. https://doi.org/10.1007/s10639-023-11587-x doi: 10.1007/s10639-023-11587-x
    [19] Y. Zengin, Effectiveness of a professional development course based on information and communication technologies on mathematics teachers' skills in designing technology-enhanced task, Educ. Inf. Technol., (2023). https://doi.org/10.1007/s10639-023-11728-2
    [20] Z. Yuan, X. Deng, T. Ding, L. Jing, Q. Tan, Factors influencing secondary school teachers' usage behavior of dynamic mathematics software: A partial least squares structural equation modeling (PLS-SEM) method, Electron. Res. Arch., 31 (2023), 5649–5684. https://doi.org/10.3934/era.2023287 doi: 10.3934/era.2023287
    [21] A. W. McCulloch, K. Hollebrands, H. Lee, T. Harrison, A. Mutlu, Factors that influence secondary mathematics teachers' integration of technology in mathematics lessons, Comput. Educ., 123 (2018), 26–40. https://doi.org/10.1016/j.compedu.2018.04.008 doi: 10.1016/j.compedu.2018.04.008
    [22] T. Assude, C. Buteau, H. Forgasz, Factors influencing implementation of technology-rich mathematics curriculum and practices, in Mathematics Education and Technology-Rethinking the Terrain: The 17th ICMI Study, (Eds. C. Hoyles and J. B. Lagrange), Springer, (2010), 405–419. https://doi.org/10.1007/978-1-4419-0146-0_19
    [23] K. J. Topping, W. Douglas, D. Robertson, N. Ferguson, Effectiveness of online and blended learning from schools: A systematic review, Rev. Educ., 10 (2022). https://doi.org/10.1002/rev3.3353
    [24] S. Timotheou, O. Miliou, Y. Dimitriadis, S. V. Sobrino, N. Giannoutsou, R. Cachia, et al., Impacts of digital technologies on education and factors influencing schools digital capacity and transformation: A literature review, Educ. Inf. Technol., 28 (2023), 6695–6726. https://doi.org/10.1007/s10639-022-11431-8 doi: 10.1007/s10639-022-11431-8
    [25] J. Vrugte, T. Jong, S. Vandercruysse, P. Wouters, H. Oostendorp, J. Elen, How competition and heterogeneous collaboration interact in prevocational game-based mathematics education, Comput. Educ., 89 (2015), 42–52. https://doi.org/10.1016/j.compedu.2015.08.010 doi: 10.1016/j.compedu.2015.08.010
    [26] M. S. Alabdulaziz, COVID-19 and the use of digital technology in mathematics education, Educ. Inf. Technol., 26 (2021), 7609–7633. https://doi.org/10.1007/s10639-021-10602-3 doi: 10.1007/s10639-021-10602-3
    [27] G. Bozkurt, K. Ruthven, Classroom-based professional expertise: A mathematics teacher's practice with technology, Educ. Stud. Math., 94 (2017), 309–328. https://doi.org/10.1007/s10649-016-9732-5 doi: 10.1007/s10649-016-9732-5
    [28] M. H. Hussein, S. H. Ow, M. M. Elaish, E. O. Jensen, Digital game-based learning in K-12 mathematics education: A systematic literature review, Educ. Inf. Technol., 27 (2022), 2859–2891. https://doi.org/10.1007/s10639-021-10721-x doi: 10.1007/s10639-021-10721-x
    [29] N. Nkopodi, M. Mosimege, Incorporating the indigenous game of morabaraba in the learning of mathematics, South African J. Educ., 29 (2009). https://doi.org/10.15700/saje.v29n3a273
    [30] E. Ortiz-Martínez, J. M. Santos-Jaén, M. Palacios-Manzano, Games in the classroom? Analysis of their effects on financial accounting marks in higher education, Int. J. Manage. Educ., 20 (2022), 100584. https://doi.org/10.1016/j.ijme.2021.100584 doi: 10.1016/j.ijme.2021.100584
    [31] E. Ortiz-Martínez, J. M. Santos-Jaén, S. Marín-Hernández, Kahoot! and its effect on financial accounting marks at the university, Educ. Inf. Technol., (2023). https://doi.org/10.1007/s10639-023-11612-z
    [32] C. K. Lo, K. F. Hew, A comparison of flipped learning with gamification, traditional learning, and online independent study: The effects on students' mathematics achievement and cognitive engagement, Interacitve Learn. Environ., 28 (2020), 464–481. https://doi.org/10.1080/10494820.2018.1541910
    [33] D. Thurm, E. Vandervieren, F. Moons, P. Drijvers, B. Barzel, M. Klinger, et al., Distance mathematics education in Flanders, Germany, and the Netherlands during the COVID 19 lockdown: The student perspective, ZDM Math. Educ., 55 (2023), 79–93. https://doi.org/10.1007/s11858-022-01409-8 doi: 10.1007/s11858-022-01409-8
    [34] M. Wijers, V. Jonker, P. Drijvers, MobileMath: Exploring mathematics outside the classroom, ZDM Math. Educ., 42 (2010), 789–799. https://doi.org/10.1007/s11858-010-0276-3 doi: 10.1007/s11858-010-0276-3
    [35] G. Greefrath, C. Hertleif, H. S. Siller, Mathematical modelling with digital tools: A quantitative study on mathematising with dynamic geometry software, ZDM Math. Educ., 50 (2018), 233–244. https://doi.org/10.1007/s11858-018-0924-6 doi: 10.1007/s11858-018-0924-6
    [36] F. Reinhold, S. Hoch, B. Werner, J. Richter-Gebert, K. Reiss, Learning fractions with and without educational technology: What matters for high-achieving and low-achieving students?, Learn. Instr., 65 (2020), 101264. https://doi.org/10.1016/j.learninstruc.2019.101264 doi: 10.1016/j.learninstruc.2019.101264
    [37] F. Z. Barrane, G. E. Karuranga, D. Poulin, Technology adoption and diffusion: A new application of the UTAUT model, Int. J. Innovation Technol. Manage., 15 (2018). https://doi.org/10.1142/S0219877019500044
    [38] E. Fianu, C. Blewett, G. O. A. Ampong, K. S. Ofori, Factors affecting MOOC usage by students in selected Ghanaian universities, Educ. Sci., 8 (2018), 70. https://doi.org/10.3390/educsci8020070 doi: 10.3390/educsci8020070
    [39] V. Venkatesh, M. Morris, G. Davis, F. Davis, User acceptance of information technology: Toward a unified view, MIS Q., 27 (2003), 425–478. https://doi.org/10.2307/30036540 doi: 10.2307/30036540
    [40] S. S. Alghazi, S. Y. Wong, A. Kamsin, E. Yadegaridehkordi, L. Shuib, Towards sustainable mobile learning: A brief review of the factors influencing acceptance of the use of mobile phones as learning tools, Sustainability, 12 (2020), 10527. https://doi.org/10.3390/su122410527 doi: 10.3390/su122410527
    [41] A. Aytekin, H. Ozkose, A. Ayaz, Unified theory of acceptance and use of technology (UTAUT) in mobile learning adoption: Systematic literature review and bibliometric analysis, Collnet J. Sci. Inf. Manage., 16 (2022), 75–116. https://doi.org/10.1080/09737766.2021.2007037 doi: 10.1080/09737766.2021.2007037
    [42] A. Shaqrah, A. Almrs, Examining the internet of educational things adoption using an extended unified theory of acceptance and use of technology, Int. Things, 19 (2022), 100558. https://doi.org/10.1016/j.iot.2022.100558 doi: 10.1016/j.iot.2022.100558
    [43] M. N. Al-Nuaimi, M. Al-Emran, Learning management systems and technology acceptance models: A systematic review, Educ. Inf. Technol., 26 (2021), 5499–5533. https://doi.org/10.1007/s10639-021-10513-3 doi: 10.1007/s10639-021-10513-3
    [44] A. S. Almogren, Art education lecturers' intention to continue using the blackboard during and after the COVID-19 pandemic: An empirical investigation into the UTAUT and TAM model, Front. Psychol., 13 (2022), 944335. https://doi.org/10.3389/fpsyg.2022.944335 doi: 10.3389/fpsyg.2022.944335
    [45] A. Granic, Educational technology adoption: A systematic review, Educ. Inf. Technol., 27 (2022), 9725–9744. https://doi.org/10.1007/s10639-022-10951-7 doi: 10.1007/s10639-022-10951-7
    [46] S. Orhan-Ozen, M. Sumer, Factors affecting undergraduate students' acceptance and use of live instructions for learning, Interacitve Learn. Environ., (2023). https://doi.org/10.1080/10494820.2023.2190355
    [47] T. Teo, P. Moses, P. K. Cheah, F. Huang, T. C. Y. Tey, Influence of achievement goal on technology use among undergraduates in Malaysia, Interacitve Learn. Environ., (2023). https://doi.org/10.1080/10494820.2023.2197957
    [48] T. Teo, J. Noyes, Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the unified theory of acceptance and use of technology, Interacitve Learn. Environ., 22 (2012), 1–16. https://doi.org/10.1080/10494820.2011.641674 doi: 10.1080/10494820.2011.641674
    [49] H. V. Osei, K. O. Kwateng, K. A. Boateng, Integration of personality trait, motivation and UTAUT 2 to understand e-learning adoption in the era of COVID-19 pandemic, Educ. Inf. Technol., 27 (2022), 10705–10730. https://doi.org/10.1007/s10639-022-11047-y doi: 10.1007/s10639-022-11047-y
    [50] M. Tuncer, Investigation of effects of computer anxiety and internet attitudes on computer self-efficacy, J. Acad. Social Sci. Stud., 5 (2012), 205–222. https://doi.org/10.9761/jasss_156 doi: 10.9761/jasss_156
    [51] T. Roh, B. I. Park, S. S. Xiao, Adoption of AI-enabled Robo-advisors in fintech: Simultaneous employment of UTAUT and the theory of reasoned action, J. Electron. Commerce Res., 24 (2023), 29–47.
    [52] A.T. Lumpe, E. A. Chambers, Assessing teachers' context beliefs about technology use, J. Res. Technol. Educ., 34 (2001), 107–193. https://doi.org/10.1080/15391523.2001.10782337 doi: 10.1080/15391523.2001.10782337
    [53] A. Albirini, Teachers' attitudes toward information and communication technologies: The case of Syrian EFL teachers, Comput. Educ., 47 (2006), 373–398. https://doi.org/10.1016/j.compedu.2004.10.013 doi: 10.1016/j.compedu.2004.10.013
    [54] G. N. Wambiri, M. N. Ndani, Kenya primary school teachers' preparation in ICT teaching: Teacher beliefs, attitudes, self-efficacy, computer competence, and age, African J. Teacher Educ., 5 (2017). https://doi.org/10.21083/ajote.v5i1.3515
    [55] M. Moca, A. Badulescu, Determinants of economical high school students' attitudes toward mobile devices use, Sustainability, 15 (2023), 9331. https://doi.org/10.3390/su15129331 doi: 10.3390/su15129331
    [56] A. Sawyerr, D. D. Agyei, Mathematics teachers' use of ICT in classroom instruction: Exploring the will-skill-tool-pedagogy model in the Ghanaian context, Educ. Inf. Technol., 28 (2022), 9397–9416. https://doi.org/10.1007/s10639-022-11234-x doi: 10.1007/s10639-022-11234-x
    [57] G. García-Murillo, P. Novoa-Hernández, R. Serrano Rodríguez, On the technological acceptance of moodle by higher education faculty: A nationwide study based on UTAUT2, Behav. Sci., 13 (2023), 419. https://doi.org/10.3390/bs13050419 doi: 10.3390/bs13050419
    [58] L. S. Shulman, Those who understand: Knowledge growth in teaching, Educ. Res., 15 (1986), 4–14. https://doi.org/10.3102/0013189X015002004 doi: 10.3102/0013189X015002004
    [59] P. Mishra, M. J. Koehler, Technological pedagogical content knowledge: A framework for teacher knowledge, Teachers College Record, 108 (2006), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x doi: 10.1111/j.1467-9620.2006.00684.x
    [60] E. Brianza, M. Schmid, J. Tondeur, D. Petko, The digital silver lining of the pandemic: The impact on preservice teachers' technological knowledge and beliefs, Educ. Inf. Technol., 28 (2023), 1–26. https://doi.org/10.1007/s10639-023-11801-w doi: 10.1007/s10639-023-11801-w
    [61] S. Q. Luo, D. Zou, K-12 teacher readiness for flipped foreign language teaching: Scale development and validation, J. Res. Technol. Educ., (2023). https://doi.org/10.1080/15391523.2023.2196459
    [62] A. L. Max, S. Lukas, H. Weitzel, The pedagogical makerspace: Learning opportunity and challenge for prospective teachers' growth of TPACK, Bri. J. Educ. Technol., (2023). https://doi.org/10.1111/bjet.13324
    [63] Y. Sidi, T. Shamir-Inbal, Y. Eshet-Alkalai, From face-to-face to online: Teachers' perceived experiences in online distance teaching during the COVID-19 pandemic, Comput. Educ., 201 (2023). https://doi.org/10.1016/j.compedu.2023.104831
    [64] Q. K. L. Ong, N. Annamalai, Technological pedagogical content knowledge for twenty-first century learning skills: The game changer for teachers of industrial revolution 5.0, Educ. Inf. Technol., (2023). https://doi.org/10.1007/s10639-023-11852-z
    [65] A. Cebi, T. B. Ozdemir, I. Reisoglu, C. Colak, From digital competences to technology integration: Re-formation of pre-service teachers' knowledge and understanding, Int. J. Educ. Res., 113 (2022). https://doi.org/10.1016/j.ijer.2022.101965
    [66] N. Demeshkant, S. Trusz, K. Potyrala, Interrelationship between levels of digital competences and technological, pedagogical and content knowledge (TPACK): A preliminary study with Polish academic teachers, Technol. Pedagogy Educ., 31 (2022), 579–595. https://doi.org/10.1080/1475939X.2022.2092547
    [67] C. Chai, J. Koh, C. C. Tsai, A review of technological pedagogical content knowledge, Educ. Technol. Soc., 16 (2013), 31–51.
    [68] M. L. Niess, Preparing teachers to teach science and mathematics with technology: Developing a technology pedagogical content knowledge, Teach. Teacher Educ., 21 (2005), 509–523. https://doi.org/10.1016/j.tate.2005.03.006 doi: 10.1016/j.tate.2005.03.006
    [69] Z. Yuan, S. Li, Developing prospective mathematics teachers' technological pedagogical content knowledge (TPACK): A case of normal distribution, in The 12th International Congress on Mathematical Education, COEX, Seoul, Korea, (2012), 5804–5813.
    [70] X. An, C. S. Chai, Y. Li, Y. Zhou, X. Shen, C. Zheng, et al., Modeling English teachers' behavioral intention to use artificial intelligence in middle schools, Educ. Inf. Technol., 28 (2023), 5187–5208. https://doi.org/10.1007/s10639-022-11286-z doi: 10.1007/s10639-022-11286-z
    [71] T. T. Wijaya, Y. Cao, M. Bernard, I. F. Rahmadi, Z. Lavicza, H. D. Surjono, Factors influencing microgame adoption among secondary school mathematics teachers supported by structural equation modelling-based research, Front. Psychol., 13 (2022), 952549. https://doi.org/10.3389/fpsyg.2022.952549 doi: 10.3389/fpsyg.2022.952549
    [72] V. Venkatesh, J. Thong, X. Xu, Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of Technology, MIS Q., 36 (2012), 157–178. https://doi.org/10.2307/41410412 doi: 10.2307/41410412
    [73] J. Wu, H. Du, Toward a better understanding of behavioral intention and system usage constructs, Eur. J. Inf. Syst., 21 (2012), 680–698. https://doi.org/10.1057/ejis.2012.15 doi: 10.1057/ejis.2012.15
    [74] R. J. Fisher, Social desirability bias and the validity of indirect questioning, J. Consumer Res., 20 (1993), 303–315. https://doi.org/10.1086/209351 doi: 10.1086/209351
    [75] Z. Drezner, O. Turel, D. Zerom, A modified Kolmogorov–Smirnov test for normality, Commun. Stat. Simul. Comput., 39 (2010), 693–704. https://doi.org/10.1080/03610911003615816 doi: 10.1080/03610911003615816
    [76] P. M. Podsakoff, S. B. MacKenzie, J. Y. Lee, N. P. Podsakoff, Common method biases in behavioral research: A critical review of the literature and recommended remedies, J. Appl. Psychol., 88 (2003), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 doi: 10.1037/0021-9010.88.5.879
    [77] J. F. Hair, G. T. M. Hult, C. M. Ringle, M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd edition, Sage Publications, Thousand oaks, CA, USA, 2022.
    [78] C. Cao, C. Chu, J. Yang, "If you don't buy it, it's gone! ": The effect of perceived scarcity on panic buying, Electron. Res. Arch., 31 (2023), 5485–5508. https://doi.org/10.3934/era.2023279 doi: 10.3934/era.2023279
    [79] J. M. Santos-Jaén, A. Madrid-Guijarro, D. García-Pérez-de-Lema, The impact of corporate social responsibility on innovation in small and medium-sized enterprises: The mediating role of debt terms and human capital, Corporate Soc. Responsib. Environ. Manage., 28 (2021), 1200–1215. https://doi.org/10.1002/csr.2125 doi: 10.1002/csr.2125
    [80] M. A. Moteri, M. Alojail, Factors influencing the supply chain management in e-Health using UTAUT model, 31 (2023), 2855–2877. https://doi.org/10.3934/era.2023144
    [81] J. F. Hair, J. J. Risher, M. Sarstedt, C. M. Ringle, When to use and how to report the results of PLS-SEM, Europ. Business Rev., 31 (2019), 2–24. https://doi.org/10.1108/EBR-11-2018-0203 doi: 10.1108/EBR-11-2018-0203
    [82] C. Fornell, D. F. Larcker, Evaluating structural equation models with unobservable variables and measurement error, J. Mark. Res., 18 (1981), 39–50. https://doi.org/10.1177/002224378101800104 doi: 10.1177/002224378101800104
    [83] J. Hair, W. Black, B. Babin, R. Anderson, Multivariate Data Analysis, 8th edition, Cengate, 2018.
    [84] J. Henseler, G. Hubona, P. Ray, Using PLS path modeling in new technology research: Updated guidelines, Industr. Manage. Data Syst., 116 (2016), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382 doi: 10.1108/IMDS-09-2015-0382
    [85] C. H. Huang, Using PLS-SEM model to explore the influencing factors of learning satisfaction in blended learning, Educ. Sci., 11 (2021), 249. https://doi.org/10.3390/educsci11050249 doi: 10.3390/educsci11050249
    [86] F. Schuberth, M. Rademaker, J. Henseler, Assessing the overall fit of composite models estimated by partial least squares path modeling, Eur. J. Mark., 57 (2022), 1678–1702. https://doi.org/10.1108/EJM-08-2020-0586 doi: 10.1108/EJM-08-2020-0586
    [87] G. Shmueli, M. Sarstedt, J. F. Hair, J. H. Cheah, H. Ting, S. Vaithilingam, et al., Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict, Eur. J. Mark., 53 (2019), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189 doi: 10.1108/EJM-02-2019-0189
    [88] M. Ndlovu, V. Ramdhany, E. Spangenberg, R. Govender, Preservice teachers' beliefs and intentions about integrating mathematics teaching and learning ICTs in their classrooms, ZDM Math. Educ., 52 (2020), 1365–1380. https://doi.org/10.1007/s11858-020-01186-2 doi: 10.1007/s11858-020-01186-2
    [89] I. Celik, Towards Intelligent-TPACK: An empirical study on teachers' professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education, Comput. Human Behav., 138 (2023), 107468. https://doi.org/10.1016/j.chb.2022.107468 doi: 10.1016/j.chb.2022.107468
    [90] J. Guggemos, S. Seufert, Teaching with and teaching about technology: Evidence for professional development of in-service teachers, Comput. Human Behav., 115 (2021), 106613. https://doi.org/10.1016/j.chb.2020.106613 doi: 10.1016/j.chb.2020.106613
    [91] R. Hamalainen, K. Nissinen, J. Mannonen, J. Lamsa, K. Leino, M. Taajamo, Understanding teaching professionals' digital competence: What do PIAAC and TALIS reveal about technology-related skills, attitudes, and knowledge?, Comput. Human Behav., 117 (2021), 106672. https://doi.org/10.1016/j.chb.2020.106672
    [92] M. Schmid, E. Brianza, D. Petko, Self-reported technological pedagogical content knowledge (TPACK) of pre-service teachers in relation to digital technology use in lesson plans, Comput. Human Behav., 115 (2021), 106586. https://doi.org/10.1016/j.chb.2020.106586 doi: 10.1016/j.chb.2020.106586
    [93] S. Seufert, J. Guggemos, M. Sailer, Technology-related knowledge, skills, and attitudes of pre- and in-service teachers: The current situation and emerging trends, Comput. Human Behav., 115 (2021), 106552. https://doi.org/10.1016/j.chb.2020.106552
    [94] B. Anthony, A. Kamaludin, A. Romli, Predicting academic staffs behaviour intention and actual use of blended learning in higher education: Model development and validation, Technol. Knowl. Learn., 28 (2021), 1223–1269. https://doi.org/10.1007/s10758-021-09579-2 doi: 10.1007/s10758-021-09579-2
    [95] S. Bardakci, M. F. Alkan, Investigation of Turkish preservice teachers' intentions to use IWB in terms of technological and pedagogical aspects, Educ. Inf. Technol., 24 (2019), 2887–2907. https://doi.org/10.1007/s10639-019-09904-4 doi: 10.1007/s10639-019-09904-4
    [96] Y. S. Wang, Y. W. Shih, Why do people use information kiosks? A validation of the unified theory of acceptance and use of technology, Govern. Inf. Q., 26 (2009), 158–165. https://doi.org/10.1016/j.giq.2008.07.001
    [97] K. T. Wong, T. Teo, P. Goh, Understanding the intention to use interactive whiteboards: Model development and testing, Int. Learn. Environ., 23 (2013), 1–17. https://doi.org/10.1080/10494820.2013.806932 doi: 10.1080/10494820.2013.806932
    [98] P. K. Chopdar, N. Korfiatis, V. J. Sivakumar, M. D. Lytras, Mobile shopping apps adoption and perceived risks: A cross-country perspective utilizing the unified theory of acceptance and use of technology, Comput. Human Behav., 86 (2018), 109–128. https://doi.org/10.1016/j.chb.2018.04.017 doi: 10.1016/j.chb.2018.04.017
    [99] MOE, An opinion on the implementing a project named improvement of information technology application abilities of elementary and secondary school teachers of China, Inservice Educ. Training School Teachers, (2013), 3–4.
    [100] F. Okocha, Determinants of electronic book adoption in Nigeria, DESIDOC J. Library Inf. Technol., 39 (2019), 175–179. https://doi.org/10.14429/djlit.39.4.14384 doi: 10.14429/djlit.39.4.14384
    [101] E. Kurilovas, On data-driven decision-making for quality education, Comput. Human Behav., 107 (2020), 105774. https://doi.org/10.1016/j.chb.2018.11.003 doi: 10.1016/j.chb.2018.11.003
    [102] A. Ameri, R. Khajouei, A. Ameri, Y. Jahani, Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model, Educ. Inf. Technol., 25 (2020), 419–435. https://doi.org/10.1007/s10639-019-09965-5 doi: 10.1007/s10639-019-09965-5
    [103] M. A. N. Elmaadaway, Y. A. M. Abouelenein, In-service teachers' TPACK development through an adaptive e-learning environment (ALE), Educ. Inf. Technol., 28 (2023), 8273–8298. https://doi.org/10.1007/s10639-022-11477-8 doi: 10.1007/s10639-022-11477-8
    [104] I. Aktas, H. ozmen, Assessing the performance of Turkish science pre-service teachers in a TPACK-practical course, Educ. Inf. Technol., 27 (2022), 3495–3528. https://doi.org/10.1007/s10639-021-10757-z doi: 10.1007/s10639-021-10757-z
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1731) PDF downloads(202) Cited by(5)

Article outline

Figures and Tables

Figures(3)  /  Tables(12)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog