Perceived usefulness of mobile devices in assessment: a comparative study of three technology acceptance models using PLS-SEM

Referencias bibliográficas

• 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