Perceived usefulness of mobile devices in assessment: a comparative study of three technology acceptance models using PLS-SEM
-
1
Universidad de Salamanca
info
ISSN: 2254-7339
Argitalpen urtea: 2024
Alea: 13
Zenbakia: 1
Orrialdeak: 1-23
Mota: Artikulua
Beste argitalpen batzuk: Journal of New Approaches in Educational Research
Laburpena
The use of digital media in education has already been addressed in numerous technology acceptance models, but there is very little research on establishing a link between acceptance and assessment using mobile devices, a reality in educational institutions. This work aims to extend research by developing the TAM model and studying teachers’ perceived usefulness of mobile devices in terms of how they understand assessment: generically, as a summative and a formative assessment, or as the complementarity of these. This study proposes a comparison between three models using the partial least squares structural equation modeling (PLS‑SEM) on a sample of 262 master’s degree students (pre‑service teachers). The results show the validity of the three proposals and confirm the advantages to specifically consider assessment in acceptance models, as well as the importance of addressing its modalities differently after obtaining better results in the two models that do so. The study also confirms the importance of self‑efficacy in the use of mobile devices as a predictor of usefulness and intention to use in the three models. The use of a comparative approach and the development of the perceived usefulness construct in assessment represents a new contribution to the field of acceptance studies.
Erreferentzia bibliografikoak
- Abd-Karim, R., Abu, A., Adnan, A., & Suhandoko, A. (2018). The Use of Mobile Technology in Promoting Education 4.0 for Higher Education. 2, 34–39. https://doi.org/10.26666/rmp.ajtve.2018.3.6
- Ahmad, B., & Bhat, G. J. (2019). Formative and summative evaluation techniques for improvement of learning process. European Journal of Business & Social Sciences, 7(5), 776–785.
- Ajms, E. (2015). Structure Equation Modeling Basic Assumptions and Concepts: A Novices Guide. Asian Journal of Management Sciences, 3(1), Article 1. https://www.ajmsjournal.com/index.php/ajms/article/view/70
- Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020). An empirical examination of continuous intention to use m-learning: an integrated model. Education and Information Technologies, 25(4), 2899–2918. https://doi.org/10.1007/s10639-019-10094-2
- Al-Gasawneh, J. A., Al Khoja, B., Al-Qeed, M. A., Nusairat, N. M., Hammouri, Q., & Anuar, M. M. (2022). Mobile-customer relationship management and its effect on post-purchase behavior: The moderating of perceived ease of use and perceived usefulness. https://digitallibrary.aau.ac.ae/handle/123456789/672
- Aljawarneh, S. A. (2020). Reviewing and exploring innovative ubiquitous learning tools in higher education. Journal of Computing in Higher Education, 32(1), 57–73. https://doi.org/10.1007/s12528-019-09207-0
- Ally, M., & Prieto-Blázquez, J. (2014). What is the future of mobile learning in education? RUSC. Universities and Knowledge Society Journal, 11(1), Article 1. https://doi.org/10.7238/rusc.v11i1.2033
- Alrfooh, A., & Lakulu, M. (2020). A systematic review of mobile-based assessment acceptance studies from 2009 to 2019. Journal of Theoretical and Applied Information Technology, 97(20), 1–25.
- Alshurideh, M., Al Kurdi, B., Salloum, S. A., Arpaci, I., & Al-Emran, M. (2020). Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms. Interactive Learning Environments, 1–15. https://doi.org/10.1080/10494820.2020.1826982
- Anderson, A. A. (1996). Predictors of computer anxiety and performance in information systems. Computers in Human Behavior, 12(1), 61–77. https://doi.org/10.1016/0747-5632(95)00019-4
- Anisimova, T. I., Sabirova, F. M., & Shatunova, O. V. (2020). Formation of Design and Research Competencies in Future Teachers in the Framework of STEAM Education. International Journal of Emerging Technologies in Learning (IJET), 15(02), Article 02. https://doi.org/10.3991/ijet.v15i02.11537
- Baier, F., & Kunter, M. (2020). Construction and validation of a test to assess (pre-service) teachers’ technological pedagogical knowledge (TPK). Studies in Educational Evaluation, 67, 100936. https://doi.org/10.1016/j.stueduc.2020.100936
- Bayaga, A., & kyobe, M. (2021). PLS-SEM technique and phases of analysis – implications for information systems’ exploratory design researchers. Conference on Information Communications Technology and Society (ICTAS), 2021, 46–51. https://doi.org/10.1109/ICTAS50802.2021.9395029
- Bernacki, M. L., Greene, J. A., & Crompton, H. (2020). Mobile technology, learning, and achievement: advances in understanding and measuring the role of mobile technology in education. Contemporary Educational Psychology, 60, 101827. https://doi.org/10.1016/j.cedpsych.2019.101827
- Bizzo, E. (2021). Aceptación y a la adopción del e-learning en los países en desarrollo: Una revisión de la literatura. Ensaio: Avaliação e Políticas Públicas em Educação, 30, 458–483.
- Black, P. J. (1993). Formative and summative assessment by teachers. Studies in Science Education, 21(1), 49–97. https://doi.org/10.1080/03057269308560014
- Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102
- Black, P., Harrison, C., Lee, C., Marshall, B., & Wiliam, D. (2003). Formative and summative assessment: Can they serve learning together? Annual meeting of the American Educational Research Association, Chicago.
- Bollen, K. A. (2011). Evaluating effect, composite, and causal indicators in structural equation models. MIS Quarterly, 35(2), 359–372. https://doi.org/10.2307/23044047
- Bozdogan, H. (1987). Model selection and Akaike’s Information Criterion (AIC): the general theory and its analytical extensions. Psychometrika, 52(3), 345–370. https://doi.org/10.1007/BF02294361
- Buchholtz, N. F., Krosanke, N., Orschulik, A. B., & Vorhölter, K. (2018). Combining and integrating formative and summative assessment in mathematics teacher education. ZDM Mathematics Education, 50(4), 715–728. https://doi.org/10.1007/s11858-018-0948-y
- Burbules, N. C., Fan, G., & Repp, P. (2020). Five trends of education and technology in a sustainable future. Geography and Sustainability, 1(2), 93–97. https://doi.org/10.1016/j.geosus.2020.05.001
- Canales-García, A., Fernández-Valverde, M., & Ulate-Solís, G. (2020). Aprender y enseñar con recursos TIC: experiencias innovadoras en la formación docente universitaria. Ensayos Pedagógicos, 15(1), 235–248.
- Castañeda-Vázquez, C., Espejo-Garcés, T., Zurita-Ortega, F., & Fernández-Revelles, A. (2019). La formación de los futuros docentes a través de la gamificación, tic y evaluación continua. SPORT TK-Revista EuroAmericana de Ciencias del Deporte, 8(2), Article 2. https://doi.org/10.6018/sportk.391751
- Ciobanu, R.-C. (2022). M-learning and E-learning Educational Solutions Impact in the COVID-19 Pandemic. Informatica Economica, 26(3), 64–73.
- Clark, R. M., Kaw, A. K., & Braga Gomes, R. (2022). Adaptive learning: helpful to the flipped classroom in the online environment of COVID? Computer Applications in Engineering Education, 30(2), 517–531. https://doi.org/10.1002/cae.22470
- Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2.a ed.). Routledge. https://doi.org/10.4324/9780203771587
- Cosi, A., Voltas, N., Lázaro-Cantabrana, J. L., Morales, P., Calvo, M., Molina, S., & Quiroga, M. Á. (2020). Formative assessment at university through digital technology tools. Profesorado, Revista de Currículum y Formación Del Profesorado, 24(1), 164–83. https://doi.org/10.30827/profesorado.v24i1.9314. Article 1.
- Criollo, S., Guerrero-Arias, A., Jaramillo-Alcázar, Á., & Luján-Mora, S. (2021). Mobile Learning Technologies for Education: Benefits and Pending Issues. Applied Sciences, 11(9), Article 9. https://doi.org/10.3390/app11094111
- Cruz-Benito, J., Sánchez-Prieto, J. C., Therón, R., & García-Peñalvo, F. J. (2019). Measuring Students’ Acceptance to AI-Driven Assessment in eLearning: Proposing a First TAM-Based Research Model. In P. Zaphiris & A. Ioannou (Eds.), Learning and Collaboration Technologies. Designing Learning Experiences (pp. 15–25). Springer International Publishing. https://doi.org/10.1007/978-3-030-21814-0_2
- 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
- Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge University Press. https://doi.org/10.1017/CBO9780511802843
- Dixson, D. D., & Worrell, F. C. (2016). Formative and summative assessment in the classroom. Theory into Practice, 55(2), 153–159. https://doi.org/10.1080/00405841.2016.1148989
- Dolin, J., Black, P., Harlen, W., & Tiberghien, A. (2018). Exploring Relations Between Formative and Summative Assessment. In J. Dolin and R. Evans (Eds.), Transforming Assessment: Through an Interplay Between Practice, Research and Policy (pp. 53–80). Springer International Publishing. https://doi.org/10.1007/978-3-319-63248-3_3
- Domingo-Coscollola, M., Bosco-Paniagua, A., Carrasco-Segovia, S., & Sánchez-Valero, J.-A. (2020). Fomentando la competencia digital docente en la universidad: Percepción de estudiantes y docentes. Revista de Investigación Educativa, 38(1), Article 1. https://doi.org/10.6018/rie.340551
- Engard, N. C. (2009). LimeSurvey. Public Services Quarterly, 5(4), 272–273. https://doi.org/10.1080/15228950903288728
- Evans, C., & Robertson, W. (2020). The four phases of the digital natives debate. Human Behavior and Emerging Technologies, 2(3), 269–277. https://doi.org/10.1002/hbe2.196
- Fishbein, M., & Ajzen, I. (1975). Belief. Attitude, Intention And Behavior: An introduction to theory and research. Addison-Wesley.
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
- García-Aretio, L. (2019). Necesidad de una educación digital en un mundo digital. RIED. Revista iberoamericana de educación a distancia. https://doi.org/10.5944/ried.22.2.23911
- Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107. https://doi.org/10.2307/2334290
- Gómez-Galán, J. (2020). Media Education in the ICT Era: Theoretical Structure for Innovative Teaching Styles. Information, 11(5), Article 5. https://doi.org/10.3390/info11050276
- Gómez-Ruiz, M. Á., Vázquez-Recio, R., López-Gil, M., & Ruiz-Romero, A. (2022). La pesadilla de la evaluación: Análisis de los sueños de estudiantes universitarios. Revista Iberoamericana de Evaluación Educativa, 15(1), 139–60. https://doi.org/10.15366/riee2022.15.1.008. Article 1.
- Guardia, J. J., Del Olmo, J. L., Roa, I., & Berlanga, V. (2019). Innovation in the teaching-learning process: The case of Kahoot! On the Horizon, 27(1), 35–45. https://doi.org/10.1108/OTH-11-2018-0035
- Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate Data Analysis. Prentice-Hall.
- Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
- Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46(1), 1–2.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Evaluation of Formative Measurement Models. En J. F. Hair Jr., G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, & S. Ray (Eds.), Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook (pp. 91–113). Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7_5
- Hair, J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). https://doi.org/10.1007/978-3-030-80519-7
- Hao, S., Dennen, V. P., & Mei, L. (2017). Influential factors for mobile learning acceptance among Chinese users. Educational Technology Research and Development, 65(1), 101–123. https://doi.org/10.1007/s11423-016-9465-2
- Harchay, A., Berguiga, A., Cheniti-Belcadhi, L., & Braham, R. (2019). Student Perception of Mobile Self-assessment: An Evaluation of the Technology Acceptance Model. Interaction Design and Architecture(s), 2019, 109–124. https://doi.org/10.55612/s-5002-041-008
- Harlen, W., & James, M. (1997). Assessment and Learning: differences and relationships between formative and summative assessment. Assessment in Education: Principles, Policy & Practice, 4(3), 365–379. https://doi.org/10.1080/0969594970040304
- Hébert, C., Jenson, J., & Terzopoulos, T. (2021). “Access to technology is the major challenge”: Teacher perspectives on barriers to DGBL in K-12 classrooms. E-Learning and Digital Media, 18, 204275302199531. https://doi.org/10.1177/2042753021995315
- Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
- Horvat, L., Balen, J., & Martinović, G. (2012). Proposal of mLearning system for written exams. Proceedings ELMAR, 2012, 345–348.
- Hossain, S. F. A., Shan, X., Nurunnabi, M., Tushar, H., Mohsin, A. K. M., & Ahsan, F. T. (2021). Opportunities and Challenges of M-Learning During the COVID-19 Pandemic: A Mixed Methodology Approach. In E-Collaboration Technologies and Strategies for Competitive Advantage Amid Challenging Times (pp. 210–227). IGI Global. https://doi.org/10.4018/978-1-7998-7764-6.ch007
- Hu, P.J.-H., Clark, T. H. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: a longitudinal study. Information & Management, 41(2), 227–241. https://doi.org/10.1016/S0378-7206(03)00050-8
- Jin, Y., Lin, C.-L., Zhao, Q., Yu, S.-W., & Su, Y.-S. (2021). A study on traditional teaching method transferring to e-learning under the covid-19 pandemic: from chinese students’ perspectives. Frontiers in Psychology, 12, 632787. https://doi.org/10.3389/fpsyg.2021.632787
- Kimmons, R., Clark, B., & Lim, M. (2017). Understanding web activity patterns among teachers, students and teacher candidates. Journal of Computer Assisted Learning, 33(6), 588–596. https://doi.org/10.1111/jcal.12202
- Knight, P. T. (2002). Summative Assessment in Higher Education: Practices in disarray. Studies in Higher Education, 27(3), 275–286. https://doi.org/10.1080/03075070220000662
- Lau, A. M. S. (2016). ‘Formative good, summative bad?’ – a review of the dichotomy in assessment literature. Journal of Further and Higher Education, 40(4), 509–525. https://doi.org/10.1080/0309877X.2014.984600
- MacLellan, E. (2001). Assessment for learning: the differing perceptions of tutors and students. Assessment & Evaluation in Higher Education, 26(4), 307–318. https://doi.org/10.1080/02602930120063466
- Marín-Díaz, V., Sampedro, B. E., Aznar, I., & Trujillo, J. M. (2022). Perceptions on the use of mixed reality in mobile environments in secondary education. Education + Training,65(2), 312–323. https://doi.org/10.1108/ET-06-2022-0248
- Matas, A. (2018). Diseño del formato de escalas tipo Likert: Un estado de la cuestión. Revista Electrónica De Investigación Educativa, 20(1), 38–47.
- Mejía-Pérez, O. (2012). De la evaluación tradicional a una nueva evaluación basada en competencias. Revista Electrónica Educare, 16(1), Article 1. https://doi.org/10.15359/ree.16-1.3
- Moccozet, L., Benkacem, O., Berisha, E., Trindade, R. T., & Bürgi, P.-Y. (2019). A versatile and flexible e-assessment framework towards more authentic summative examinations in higher-education. International Journal of Continuing Engineering Education and Life Long Learning, 29(3), 211–229. https://doi.org/10.1504/IJCEELL.2019.101032
- Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.
- Moreno-Guerrero, A.-J., Rodríguez-Jiménez, C., Gómez-García, G., & Ramos Navas-Parejo, M. (2020). Educational Innovation in Higher Education: Use of Role Playing and Educational Video in Future Teachers’ Training. Sustainability, 12(6), Article 6. https://doi.org/10.3390/su12062558
- Morris, R., Perry, T., & Wardle, L. (2021). Formative assessment and feedback for learning in higher education: a systematic review. Review of Education, 9(3), e3292. https://doi.org/10.1002/rev3.3292
- Mutambara, D., & Bayaga, A. (2021). Learners’ and teachers’ acceptance of mobile learning: an exploratory study in a developing country. International Journal of Learning Technology, 16(2), 90–108. https://doi.org/10.1504/IJLT.2021.117763
- Nikou, S., & Economides, A. (2017b). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, Query date: 2022–03–22 16:32:53. https://www.sciencedirect.com/science/article/pii/S0360131517300283
- Nikou, S. A., & Economides, A. A. (2021). A Framework for Mobile-Assisted Formative Assessment to Promote Students’ Self-Determination. Future Internet, 13(5), 116. https://doi.org/10.3390/fi13050116
- Nikou, S., & Economides, A. (2016). An outdoor mobile-based assessment activity: measuring students’ motivation and acceptance. International Journal of Interactive Mobile Technologies (iJIM), 10, 11–17. https://doi.org/10.3991/ijim.v10i4.5541
- Nikou, S., & Economides, A. (2017a). Mobile-based assessment: integrating acceptance and motivational factors into a combined model of self-determination theory and technology acceptance. Computers in Human Behavior, 68, 83–95. https://doi.org/10.1016/j.chb.2016.11.020
- Olimov, S. S. (2021). The innovation process is a priority in the development of pedagogical sciences. European Journal of Research Development and Sustainability, 2(3), 86–8. Article 3.
- Patton, M. Q. (1996). A world larger than formative and summative. Evaluation Practice, 17(2), 131–144. https://doi.org/10.1177/109821409601700205
- Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Research, 14(3), 224–235. https://doi.org/10.1108/10662240410542652
- Rahmawati, R. N. (2019). Self-efficacy and use of e-learning: a theoretical review Technology Acceptance Model (TAM). American Journal of Humanities and Social Sciences Research (AJHSSR), 3(5), 41–55.
- Ramayah, T., Hwa, C., Chuah, F., Ting, H., & Memon, M. (2017). PLS-SEM using SmartPLS 3.0: Chapter 8: Assessment of Formative Measurement Models. En Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0: An Updated and Practical Guide to Statistical Analysis. Pearson.
- Reisoğlu, İ, & Çebi, A. (2020). How can the digital competences of pre-service teachers be developed? Examining a case study through the lens of DigComp and DigCompEdu. Computers & Education, 156, 103940. https://doi.org/10.1016/j.compedu.2020.103940
- Rothmann, S. (2015). A structural model of technology acceptance. South African Journal of Industrial Psychology, 41(1), 1–2.
- Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017a). MLearning and pre-service teachers: an assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644–654. https://doi.org/10.1016/J.CHB.2016.09.061
- Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017b). ¿Utilizarán los futuros docentes las tecnologías móviles? Validación de una propuesta de modelo TAM extendido. Revista de Educación a Distancia (RED), 52, Article 52. https://revistas.um.es/red/article/view/282191
- Sánchez-Prieto, J., Hernández-García, Á., García-Peñalvo, F., Chaparro-Peláez, J., & Olmos, S. (2019). Break the Walls! second-order barriers and the acceptance of mlearning by first-year pre-service teachers. Computers in Human Behavior, 95, 158–67. https://doi.org/10.1016/j.chb.2019.01.019
- Sar, A., & Misra, S. N. (2020). A study on policies and implementation of information and communication technology (ICT) in educational systems. Materials Today, 8. https://doi.org/10.1016/j.matpr.2020.10.507
- Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998–4010. https://doi.org/10.1016/j.jbusres.2016.06.007
- Scriven, M. (1967). The Methodology of Evaluation. In R. W. Tyler, R. M. Gagne, & M. Scriven (Eds.), Perspectives of Curriculum Evaluation (pp. 39–83). Rand McNally.
- Scriven, M. (1991). Beyond formative and summative evaluation. Teachers College Record, 92(6), 19–64. https://doi.org/10.1177/016146819109200603
- Sharma, P., Liengaard, B., Jr., & H., Sarstedt, M., Ringle, C. (2022). Predictive model assessment and selection in composite-based modeling using PLS-SEM: Extensions and guidelines for using CVPAT. European Journal of Marketing. https://doi.org/10.1108/EJM-08-2020-0636
- Sharma, P., & Kim, K. (2012). Model Selection in Information Systems Research Using Partial Least Squares Based Structural Equation Modeling. International Conference on Interaction Sciences. https://www.semanticscholar.org/paper/Model-Selection-in-Information-Systems-Research-Sharma-Kim/cfde34aa3bd19983b07dc16fc2801cdd377b05d7
- Shepard, L. (2006). La evaluación en el aula. In R. Brennan (Ed.). En Educational Measurement (4 Edition, pp. 623–646). Praeger Westport.
- Simonetto, A. (2012). Formative and reflective models: State of the art. Electronic Journal of Applied Statistical Analysis, 5(3), Article 3-7. https://doi.org/10.1285/i20705948v5n3p452
- Skulmowski, A., & Rey, G. D. (2020). COVID-19 as an accelerator for digitalization at a German university: establishing hybrid campuses in times of crisis. Human Behavior and Emerging Technologies, 2(3), 212–216. https://doi.org/10.1002/hbe2.201
- Smith, C. A. (2021). Development and Integration of Freely Available Technology into Online STEM Courses to Create a Proctored Environment During Exams. Journal of Higher Education Theory and Practice, 4. https://papers.iafor.org/submission59360/
- Souabi, S., Retbi, A., Idrissi, M. K., & Bennani, S. (2021). Towards an Evolution of E-Learning Recommendation Systems: From 2000 to Nowadays. International Journal of Emerging Technologies in Learning (IJET), 16(06), Article 06. https://doi.org/10.3991/ijet.v16i06.18159
- Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
- Sun, Y., Li, N., Hao, J. L., Di Sarno, L., & Wang, L. (2022). Post-COVID-19 Development of Transnational Education in China: Challenges and Opportunities. Education Sciences, 12(6), Article 6. https://doi.org/10.3390/educsci12060416
- Terán-Guerrero, F. N. (2019). Acceptance of university students in the use of Moodle e-learning systems from the perspective of the TAM model. UNEMI, 12(29), 63–76.
- Thorsteinsson, G., & Niculescu, A. (2013). Examining teachers’ mindset and responsibilities in using ICT. Studies in Informatics and Control, 22(2), 315–322. https://doi.org/10.24846/v22i3y201308
- Tyler, R. (1950). Basic principle of curriculum and instruction. Chicago University.
- Valverde-Berrocoso, J., Fernández-Sánchez, M. R., Dominguez, F. I. R., & Sosa-Díaz, M. J. (2021). The educational integration of digital technologies preCovid-19: Lessons for teacher education. PLoS ONE, 16(8), e0256283. https://doi.org/10.1371/journal.pone.0256283
- Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39, 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
- Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 184–204. https://doi.org/10.1287/mnsc.46.2.186.11926
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
- Vieira, H., & Ribeiro, C. P. (2018). Implementing Flipped Classroom in History: The reactions of eighth grade students in a Portuguese school. Yesterday and Today, 19, 35–49. https://doi.org/10.17159/2223-0386/2018/n18a3
- Vilches, A., & Gil, D. (2010). Máster de formación inicial del profesorado de enseñanza secundaria. Algunos análisis y propuestas. Revista Eureka sobre Enseñanza y Divulgación de las Ciencias, 661–666.
- Wang, R., Chen, L., & Solheim, I. (2020). Modeling dyslexic students’ motivation for enhanced learning in E-learning systems. The ACM Transactions on Interactive Intelligent Systems, 2. https://doi.org/10.1145/3341197
- Wan-Sulaiman, W. N. A., & Mustafa, S. E. (2020). Usability elements in digital textbook development: a systematic review. Publishing Research Quarterly, 36(1), 74–101. https://doi.org/10.1007/s12109-019-09675-3