Skip to main content
Springer Nature Link
Log in

Acceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Mobile-learning (M-learning) apps have grown in popularity and demand in recent years and have become a typical occurrence in modern educational systems, particularly with the deployment of M-learning initiatives. The key objective of this study was to reveal the key factors that impact university students’ behavioural intention and actual use of mobile learning in their education. The technology acceptance model (TAM) is used in this study to investigate the impacts of several factors found in the literature on students' adoption of M-learning systems in higher education. The data was gathered from 176 university students who completed a paper questionnaire. The data was analyzed using the SEM technique. The findings revealed that perceived mobile value (PMV), academic relevance (AR), and self-management of M-learning (SML) are the primary drivers of students' acceptance of M-learning and, as a result, the success of M-learning projects’ implementation. The findings of this study give crucial information on how higher education institutions may improve students' acceptance of M-learning in order to promote students' attitudes toward M-learning (ATT) it and their behavioural intentions (BIM) to use it in the teaching and learning process. These findings have significant implications for the acceptance and use of M-learning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abramson, J., Dawson, M., & Stevens, J. (2015). An examination of the prior use of e-learning within an extended technology acceptance model and the factors that influence the behavioral intention of users to use m-learning. SAGE Open, 5(4), 2158244015621114.

    Article  Google Scholar 

  • Aburub, F., & Alnawas, I. (2019). A new integrated model to explore factors that influence adoption of mobile learning in higher education: An empirical investigation. Education and Information Technologies, 24(3), 2145–2158.

    Article  Google Scholar 

  • Al-Adwan, A. S., Al-Adwan, A., & Berger, H. (2018a). Solving the mystery of mobile learning adoption in higher education. International Journal of Mobile Communications, 16(1), 24–49.

    Article  Google Scholar 

  • Al-Adwan, A. S., Albelbisi, N. A., Hujran, O., Al-Rahmi, W. M., & Alkhalifah, A. (2021). Developing a holistic success model for sustainable e-learning: A structural equation modeling approach. Sustainability, 13(16), 9453.

    Article  Google Scholar 

  • Al-Adwan, A.S., Al-Madadha, A., Zvirzdinaite, Z., 2018b. Modeling students’ readiness to adopt mobile learning in higher education: An empirical study. International Review of Research in Open and Distributed Learning, 19(1).

  • Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100–110.

    Article  Google Scholar 

  • Alalwan, N., Al-Rahmi, W. M., Alfarraj, O., Alzahrani, A., Yahaya, N., & Al-Rahmi, A. M. (2019). Integrated three theories to develop a model of factors affecting students’ academic performance in higher education. Ieee Access, 7, 98725–98742.

    Article  Google Scholar 

  • Alamri, M. M., Almaiah, M. A., & Al-Rahmi, W. M. (2020a). Social media applications affecting Students’ academic performance: A model developed for sustainability in higher education. Sustainability, 12(16), 6471.

    Article  Google Scholar 

  • Alamri, M. M., Almaiah, M. A., & Al-Rahmi, W. M. (2020b). The role of compatibility and task-technology fit (TTF): On social networking applications (SNAs) usage as sustainability in higher education. IEEE Access, 8, 161668–161681.

    Article  Google Scholar 

  • Alamri, M.M., Al-Rahmi, W.M., Yahaya, N., Al-Rahmi, A.M., Abualrejal, H., Zeki, A.M., Al-Maatouk, Q., 2019. Towards adaptive E-Learning among university students: by applying Technology Acceptance Model (TAM). e-learning, 7, 10.

  • Alasmari, T., & Zhang, K. (2019). Mobile learning technology acceptance in Saudi Arabian higher education: An extended framework and A mixed-method study. Education and Information Technologies, 24(3), 2127–2144.

    Article  Google Scholar 

  • Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology Acceptance Model in M-learning context: A systematic review. Computers & Education, 125, 389–412.

    Article  Google Scholar 

  • Alenazy, W. M., Al-Rahmi, W. M., & Khan, M. S. (2019). Validation of TAM model on social media use for collaborative learning to enhance collaborative authoring. Ieee Access, 7, 71550–71562.

    Article  Google Scholar 

  • Alhussain, T., Al-Rahmi, W.M., Othman, M.S., 2020. Students’ Perceptions of Social Networks Platforms use in Higher Education: A Qualitative Research. International Journal of Advanced Trends in Computer Science and Engineering, 9(3).

  • Al-Maatouk, Q., Othman, M.S., Alsayed, A.O., Al-Rahmi, A.M., Abuhassna, H., Al-Rahmi, W.M., 2020. Applying Communication Theory to Structure and Evaluate the Social Media Platforms in Academia. International Journal, 9(2).

  • Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019a). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673–174686.

    Article  Google Scholar 

  • Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. M. (2019b). Analysis the effect of different factors on the development of Mobile learning applications at different stages of usage. IEEE Access, 8, 16139–16154.

    Article  Google Scholar 

  • Almaiah, M. A., Jalil, M. A., & Man, M. (2016). Extending the TAM to examine the effects of quality features on mobile learning acceptance. Journal of Computers in Education, 3(4), 453–485.

    Article  Google Scholar 

  • Al-Rahmi, A. M., Ramin, A. K., Alamri, M. M., Al-Rahmi, W. M., Yahaya, N., Abualrejal, H., & Al-Maatouk, Q. (2019a). Evaluating the intended use of Decision Support System (DSS) via academic staff: An applying Technology Acceptance Model (TAM). Int. J. Recent Technol. Eng. (IJRTE), 8, 268–275.

    Google Scholar 

  • Al-Rahmi, W. M., Alzahrani, A. I., Yahaya, N., Alalwan, N., & Kamin, Y. B. (2020). Digital communication: Information and communication technology (ICT) usage for education sustainability. Sustainability, 12(12), 5052.

    Article  Google Scholar 

  • Al-Rahmi, W. M., Yahaya, N., Alamri, M. M., Aljarboa, N. A., Kamin, Y. B., & Moafa, F. A. (2018). A model of factors affecting cyber bullying behaviors among university students. IEEE Access, 7, 2978–2985.

    Article  Google Scholar 

  • Al-Rahmi, W.M., Yahaya, N., Alamri, M.M., Alyoussef, I.Y., Al-Rahmi, A.M., Kamin, Y.B., 2019b. Integrating innovation diffusion theory with technology acceptance model: Supporting students’ attitude towards using a massive open online courses (MOOCs) systems. Interactive Learning Environments,1–13.

  • Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alturki, U., Alamri, M. M., Saud, M. S. B., Kamin, Y. B., Aljeraiwi, A. A., & Alhamed, O. A. (2019c). Big data adoption and knowledge management sharing: An empirical investigation on their adoption and sustainability as a purpose of education. IEEE Access, 7, 47245–47258.

    Article  Google Scholar 

  • Althunibat, A. (2015). Determining the factors influencing students’ intention to use m-learning in Jordan higher education. Computers in Human Behavior, 52, 65–71.

    Article  Google Scholar 

  • Ameri, A., Khajouei, R., Ameri, A., & Jahani, Y. (2020). Acceptance of a mobile-based educational application (LabSafety) by pharmacy students: An application of the UTAUT2 model. Education and Information Technologies, 25(1), 419–435.

    Article  Google Scholar 

  • Badwelan, A., Drew, S., & Bahaddad, A. A. (2016). Towards acceptance m-learning approach in higher education in Saudi Arabia. International Journal of Business and Management, 11(8), 12.

    Article  Google Scholar 

  • Bakon, K., Hassan, Z., 2013. Perceived value of smartphone and its impact on deviant behaviour: An investigation on higher education students in Malaysia. International Journal of Information System and Engineering (IJISE) Volume, 1.

  • Bano, M., Zowghi, D., Kearney, M., Schuck, S., & Aubusson, P. (2018). Mobile learning for science and mathematics school education: A systematic review of empirical evidence. Computers & Education, 121, 30–58.

    Article  Google Scholar 

  • Barreh, K. A., & Abas, Z. W. (2015). A Framework for Mobile Learning for Enhancing Learning in Higher Education. Malaysian Online Journal of Educational Technology, 3(3), 1–9.

    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.

    Article  Google Scholar 

  • Byrne, B.M., 2013. Structural equation modeling with Mplus: Basic concepts, applications, and programming. routledge.

  • Camilleri, M.A., Camilleri, A., 2017, April. The technology acceptance of mobile applications in education. In 13th International Conference on Mobile Learning (Budapest, April 10th). Proceedings, pp., International Association for Development of the Information Society.

  • Chavoshi, A., & Hamidi, H. (2019). Social, individual, technological and pedagogical factors influencing mobile learning acceptance in higher education: A case from Iran. Telematics and Informatics, 38, 133–165.

    Article  Google Scholar 

  • Cheng, G. (2019). Exploring factors influencing the acceptance of visual programming environment among boys and girls in primary schools. Computers in Human Behavior, 92, 361–372.

    Article  Google Scholar 

  • Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064.

    Article  Google Scholar 

  • Dachyar, M., Zagloel, T.Y.M., Saragih, L.R., 2019. Knowledge growth and development: internet of things (IoT) research, 2006–2018. Heliyon, 5(8), p.e02264.

  • Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319–340.

  • Demir, K., & Akpinar, E. (2018). The Effect of Mobile Learning Applications on Students’ Academic Achievement and Attitudes toward Mobile Learning. Malaysian Online Journal of Educational Technology, 6(2), 48–59.

    Article  Google Scholar 

  • Domingo, M. G., & Garganté, A. B. (2016). Exploring the use of educational technology in primary education: Teachers’ perception of mobile technology learning impacts and applications’ use in the classroom. Computers in Human Behavior, 56, 21–28.

    Article  Google Scholar 

  • Eames, C., Aguayo, C., 2020. Designing mobile learning with education outside the classroom to enhance marine ecological literacy.

  • Ekanayake, S. Y., & Wishart, J. (2015). Integrating mobile phones into teaching and learning: A case study of teacher training through professional development workshops. British Journal of Educational Technology, 46(1), 173–189.

    Article  Google Scholar 

  • Fagan, M. H. (2019). Factors influencing student acceptance of mobile learning in higher education. Computers in the Schools, 36(2), 105–121.

    Article  Google Scholar 

  • Fenton, W., 2018. The best (LMS) learning management systems for 2018. Retrieved May, 10, 2019.

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Gan, C., Li, H., Liu, Y., 2017. Understanding mobile learning adoption in higher education: An empirical investigation in the context of the mobile library. The Electronic Library.

  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433.

    Article  Google Scholar 

  • Hamidi, H., & Chavoshi, A. (2018). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics, 35(4), 1053–1070.

    Article  Google Scholar 

  • Hamidi, H., & Jahanshaheefard, M. (2019). Essential factors for the application of education information system using mobile learning: A case study of students of the university of technology. Telematics and Informatics, 38, 207–224.

    Article  Google Scholar 

  • Heflin, H., Shewmaker, J., & Nguyen, J. (2017). Impact of mobile technology on student attitudes, engagement, and learning. Computers & Education, 107, 91–99.

    Article  Google Scholar 

  • Hsu, C. L., & Lin, J. C. C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45(1), 65–74.

    Article  MathSciNet  Google Scholar 

  • Hu, X., Ng, J., Tsang, K. K., & Chu, S. K. (2020). Integrating mobile learning to learning management system in community college. Community College Journal of Research and Practice, 44(10–12), 722–737.

    Article  Google Scholar 

  • Huang, J.H., Lin, Y.R., Chuang, S.T., 2007. Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. The electronic library.

  • Huang, R. T., Jang, S. J., Machtmes, K., & Deggs, D. (2012). Investigating the roles of perceived playfulness, resistance to change and self-management of learning in mobile English learning outcome. British Journal of Educational Technology, 43(6), 1004–1015.

    Article  Google Scholar 

  • Huang, R.T., Yu, C.L., 2019. Exploring the impact of self-management of learning and personal learning initiative on mobile language learning: A moderated mediation model. Australasian Journal of Educational Technology, 35(3).

  • Huang, Y., 2014. Empirical analysis on factors impacting mobile learning acceptance in higher engineering education.

  • Hwang, G. J., Lai, C. L., & Wang, S. Y. (2015). Seamless flipped learning: A mobile technology-enhanced flipped classroom with effective learning strategies. Journal of Computers in Education, 2(4), 449–473.

    Article  Google Scholar 

  • Irby, T. L., & Strong, R. (2015). A synthesis of mobile learning research implications: Agricultural faculty and student acceptance of mobile learning in academia. NACTA Journal, 59(1), 10–17.

    Google Scholar 

  • Jeno, L. M., Grytnes, J. A., & Vandvik, V. (2017). The effect of a mobile-application tool on biology students’ motivation and achievement in species identification: A Self-Determination Theory perspective. Computers & Education, 107, 1–12.

    Article  Google Scholar 

  • Kang, M., Liew, B.Y.T., Lim, H., Jang, J., Lee, S., 2015. Investigating the determinants of mobile learning acceptance in Korea using UTAUT2. In Emerging issues in smart learning , 209–216.

  • Kanwal, F., & Rehman, M. (2017). Factors affecting e-learning adoption in developing countries–empirical evidence from Pakistan’s higher education sector. IEEE Access, 5, 10968–10978.

    Article  Google Scholar 

  • Khan, A. I., Al-Shihi, H., Al-Khanjari, Z. A., & Sarrab, M. (2015). Mobile Learning (M-Learning) adoption in the Middle East: Lessons learned from the educationally advanced countries. Telematics and Informatics, 32(4), 909–920.

    Article  Google Scholar 

  • Kline, R. B. (2018). Response to leslie hayduk’s review of principles and practice of structural equation modeling. Canadian Studies in Population [ARCHIVES], 45(3–4), 188–195.

    Article  Google Scholar 

  • Kuhnel, M., Seiler, L., Honal, A., Ifenthaler, D., 2018. Mobile learning analytics in higher education: Usability testing and evaluation of an app prototype. Interactive Technology and Smart Education.

  • Lin, H.H., Lin, S., Yeh, C.H., Wang, Y.S., 2016. Measuring mobile learning readiness: scale development and validation. Internet Research.

  • Lin, S.H., Lee, H.C., Chang, C.T., Fu, C.J., 2020. Behavioral intention towards mobile learning in Taiwan, China, Indonesia, and Vietnam. Technology in Society, 63, 101387.

  • Maksaev, A. A., Vasbieva, D. G., Sherbakova, O. Y., Mirzoeva, F. R., & Kralik, R. (2021). Education at a Cooperative University in the Digital Economy. In Frontier Information Technology and Systems Research in Cooperative Economics (pp. 33–42). Springer, Cham.

  • Mohammadi, H. (2015). Social and individual antecedents of m-learning adoption in Iran. Computers in Human Behavior, 49, 191–207.

    Article  Google Scholar 

  • Mutambara, D., & Bayaga, A. (2020). Understanding rural parents’ behavioral intention to allow their children to use mobile learning. Responsible Design, Implementation and Use of Information and Communication Technology, 12066, 520.

    Google Scholar 

  • Naveed, Q. N., Alam, M. M., & Tairan, N. (2020). Structural equation modeling for mobile learning acceptance by university students: An empirical study. Sustainability, 12(20), 8618.

    Article  Google Scholar 

  • Onojah, A. O., Onojah, A. A., & Nweke-Richards, N. E. (2021). Stimulus of Specialization on Postgraduate Students’ Application of Mobile Technologies for Learning. Journal of Educational Technology Development and Exchange (JETDE), 13(2), 5.

    Google Scholar 

  • Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605.

    Article  Google Scholar 

  • Pavlíková, M., Sirotkin, A., Králik, R., Petrikovičová, L., & Martin, J. G. (2021). How to Keep University Active during COVID-19 Pandemic: Experience from Slovakia. Sustainability, 13(18), 10350.

    Article  Google Scholar 

  • Prajapati. M., and Jayesh. M. P. (2014). The Factors Influencing in Mobile Learning Adoption. International Journal of Application or Innovation in Engineering & Management (IJAIEM) Volume 3, Issue 9, September 2014, SSN 2319 – 4847.

  • Rafique, H., Almagrabi, A.O., Shamim, A., Anwar, F., Bashir, A.K., 2020. Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732.

  • Rassameethes, B. (2012). Analysis and integration of Thailand ICT master plan. International Journal of Synergy and Research, 1(2), 77–90.

    Google Scholar 

  • Raza, S. A., Umer, A., Qazi, W., & Makhdoom, M. (2018). The effects of attitudinal, normative, and control beliefs on m-learning adoption among the students of higher education in Pakistan. Journal of Educational Computing Research, 56(4), 563–588.

    Article  Google Scholar 

  • Sabah, N. M. (2016). Exploring students’ awareness and perceptions: Influencing factors and individual differences driving m-learning adoption. Computers in Human Behavior, 65, 522–533.

    Article  Google Scholar 

  • Sampson, D.G., Isaias, P., Ifenthaler, D., Spector, J.M. eds., 2012. Ubiquitous and mobile learning in the digital age. Springer Science & Business Media.

  • Saroia, A. I., & Gao, S. (2019). Investigating university students’ intention to use mobile learning management systems in Sweden. Innovations in Education and Teaching International, 56(5), 569–580.

    Article  Google Scholar 

  • Seppälä, P., & Alamäki, H. (2003). Mobile learning in teacher training. Journal of Computer Assisted Learning, 19(3), 330–335.

    Article  Google Scholar 

  • Sharma, S. K., Sarrab, M., & Al-Shihi, H. (2017). Development and validation of mobile learning acceptance measure. Interactive Learning Environments, 25(7), 847–858.

    Article  Google Scholar 

  • Smith, P. J., Murphy, K. L., & Mahoney, S. E. (2003). Towards identifying factors underlying readiness for online learning: An exploratory study. Distance Education, 24(1), 57–67.

    Article  Google Scholar 

  • Soleimani, E., Ismail, K., & Mustaffa, R. (2014). The acceptance of mobile assisted language learning (MALL) among post graduate ESL students in UKM. Procedia-Social and Behavioral Sciences, 118, 457–462.

    Article  Google Scholar 

  • Sönmez, A., Göçmez, L., Uygun, D., & Ataizi, M. (2018). A review of current studies of mobile learning. Journal of Educational Technology and Online Learning, 1(1), 12–27.

    Article  Google Scholar 

  • Sophonhiranrak, S. (2021). Features, barriers, and influencing factors of mobile learning in higher education: A systematic review. Heliyon, 7(4), 06696.

    Article  Google Scholar 

  • Suartama, I.K., Setyosari, P., Ulfa, S., 2019. Development of an instructional design model for mobile blended learning in higher education. International Journal of Emerging Technologies in Learning, 14(16).

  • Tkáčová, H., Pavlíková, M., Jenisová, Z., Maturkanič, P., & Králik, R. (2021). Social Media and Students’ Wellbeing: An Empirical Analysis during the Covid-19 Pandemic. Sustainability, 13(18), 10442.

    Article  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.

    Article  Google Scholar 

  • Venter, P., van Rensburg, M.J., Davis, A., 2012. Drivers of learning management system use in a South African open and distance learning institution. Australasian Journal of Educational Technology, 28(2).

  • Wang, W. T., & Li, M. (2012). Factors influencing mobile services adoption: A brand equity perspective. Internet Research, 22(2), 142.

    Article  Google Scholar 

  • Wong, K., Wang, F.L., Ng, K.K., Kwan, R., 2015. Investigating acceptance towards mobile learning in higher education students. In Technology in education. Transforming educational practices with technology, 9–19.

  • Zhu, Q., Wang, M., Zou, P., Marquez, A., 2019, August. Team-Based Mobile Learning: A Framework for Supporting Interactive Learning. In Intelligent Environments, 239–247.

Download references

Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group No (RGP-1435-033).

Author information

Authors and Affiliations

  1. Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia

    Ali Mugahed Al-Rahmi

  2. Faculty of Social Sciences & Humanities, School of Education, Universiti Teknologi Malaysia, UTM, 81310, Skudai, Johor, Malaysia

    Waleed Mugahed Al-Rahmi

  3. Educational Technology Department, College of Education, King Saud University, Riyadh, 11451, Saudi Arabia

    Uthman Alturki, Ahmed Aldraiweesh & Sultan Almutairy

  4. Electronic Business and Commerce Department, Business School, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Ahmad Samed Al-Adwan

Authors
  1. Ali Mugahed Al-Rahmi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Waleed Mugahed Al-Rahmi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Uthman Alturki
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ahmed Aldraiweesh
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Sultan Almutairy
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Ahmad Samed Al-Adwan
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Waleed Mugahed Al-Rahmi.

Ethics declarations

Conflict of interest statement

None.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al-Rahmi, A.M., Al-Rahmi, W.M., Alturki, U. et al. Acceptance of mobile technologies and M-learning by university students: An empirical investigation in higher education. Educ Inf Technol 27, 7805–7826 (2022). https://doi.org/10.1007/s10639-022-10934-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-022-10934-8

Keywords