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Moderating effects of gender differences on the relationships between perceived learning support, intention to use, and learning performance in a personalized e-learning

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Abstract

Current endeavors to integrate students’ personal characteristics with e-learning environments designed for the delivery of content to individual students are growing. However, few studies have been conducted to investigate how gender differences moderate the relationships between students’ perceived personalized learning support and learning performance, and between the intention to use a system and the users’ learning performance. Drawing together perspectives from the literature on developing effective e-learning systems, technology acceptance, and gender differences, this research proposes a conceptual model to examine the influences of the relationships among students’ attitudes, acceptance, gender differences, and learning performance. Moreover, a personalized learning system was developed by taking learners’ to-be-enhanced concepts and learning preferences into account. An experiment was conducted with four classes of Thai high-school students studying the same topic of simple electricity to examining the proposed conceptual model as well as evaluate the performance of the personalized learning system. The Partial Least Square technique was employed to analyze data collected from school settings in Thailand. The path coefficient results showed that the perceived usefulness of the mastery learning support and intention to use had direct effects on the students’ learning performance in the personalized e-learning environment, and that gender moderated the relationship between perceived usefulness of conceptual learning suggestions and learning performance, and between intention to use and learning performance. These findings suggest that there are direct attitudinal and gender moderating factors affecting learning performance in personalized e-learning environments.

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References

  • Achufusi, N. N., & Mgbemena, C. O. (2012). The effect of using mastery learning approach on academic achievement of senior secondary school II physics students. Educational Technology,51, 10735–10737.

    Google Scholar 

  • Adnan Khan, F. M., & Masood, M. (2013). The development and testing of Multimedia-assisted Mastery Learning Courseware with regard to the learning of cellular respiration. Procedia - Social and Behavioral Sciences,103(2013), 999–1005.

    Google Scholar 

  • Ajzen I, Fishbein M (1980) Understanding attitudes and predicting social behavior (Vol. 278). Prentice Hall, Englewood Cliffs, NY.

  • Al-Azawei, A. (2019). The Moderating effect of gender differences on learning management systems acceptance: A multi-group analysis. Italian Journal of Educational Technology,27(3), 257–278.

    Google Scholar 

  • Ali, Z., Gongbing, B., & Mehreen, A. (2018). Understanding and predicting academic performance through cloud computing adoption: A perspective of technology acceptance model. Journal of Computers in Education,5(3), 297–327.

    Google Scholar 

  • Amiruddin, M. H., Samad, N. A., & Othman, N. (2015). An investigation effects of mastery learning strategy on entrepreneurship knowledge acquisition among aboriginal students. Procedia - Social and Behavioral Sciences,204, 183–190.

    Google Scholar 

  • Aypay, A., Çelik, H. C., Aypay, A., & Sever, M. (2012). Technology acceptance in education: A study of pre-service teachers in Turkey. The Turkish Online Journal of Educational Technology,11(4), 264–272.

    Google Scholar 

  • Bachari, E. E., Abelwahed, E. H., & El Adnani, M. (2011). E-Learning personalization based on Dynamic learners’ preference. International Journal of Computer Science and Information Technology,3(3), 200–216.

    Google Scholar 

  • Bajraktarevic, N., Hall, W., & Fullick, P. (2003). Incorporating learning styles in hypermedia environment : Empirical evaluation. In Proceedings of the Workshop on Adaptive Web-Based System (pp. 41–52).

  • Borun, M., Schaller, D. T., Chambers, M. B., & Allison-Bunnell, S. (2010). Implications of learning style, age group, and gender for developing online learning activities. Visitor Studies,13(2), 145–159.

    Google Scholar 

  • Bossers, A., Phelan, S., Kinsella, E. A., Jenkins, K., Ferguson, K., Moosa, T., et al. (2014). Participants’ self-identified learning outcomes in an online preceptor education program for health professionals and students. The Journal of Practice Teaching and Learning,1(1), 79–97.

    Google Scholar 

  • Brady, C. L. (2013). Understanding learning styles: Providing the optimal learning experience. International Journal of Childbirth Education,28(2), 16–20.

    Google Scholar 

  • Brock KL, Cameron BJ (1999) Enlivening political science courses with Kolb's learning preference model. PS: Political Science & Politics, 32(2), 251–256.

  • Casamayor, A., Amandi, A., & Campo, M. (2009). Intelligent assistance for teachers in collaborative e-learning environments. Computers and Education,53(4), 1147–1154.

    Google Scholar 

  • Chen, C.-M. (2009). Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance. Expert Systems with Applications,36(5), 8816–8829.

    Google Scholar 

  • Chen, L.-H. (2010). Web-based learning programs: Use by learners with various cognitive styles. Computers & Education,54(4), 1028–1035.

    Google Scholar 

  • Chen, L. H. (2011). Enhancement of student learning performance using personalized diagnosis and remedial learning system. Computers and Education,56(1), 289–299.

    Google Scholar 

  • Chen, S. Y., & Huang, P.-R. (2013). The comparisons of the influences of prior knowledge on two game-based learning systems. Computers & Education,68, 177–186.

    Google Scholar 

  • Chen, C.-H., Hwang, G.-J., & Tsai, C.-H. (2014). A progressive prompting approach to conducting context-aware learning activities for natural science courses. Interacting with Computers,26(4), 348–359.

    Google Scholar 

  • Cheng, B., Wang, M., Yang, S. J. H., Kinshuk, A., & Peng, J. (2011). Acceptance of competency-based workplace e-learning systems: Effects of individual and peer learning support. Computers & Education,57(1), 1317–1333.

    Google Scholar 

  • Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education,63, 160–175.

    Google Scholar 

  • Chiang, T. H. C., Yang, S. J. H., & Hwang, G.-J. (2014). An Augmented Reality-based Mobile Learning System to Improve Students’ Learning Achievements and Motivations in Natural Science Inquiry Activities. Educational Technology & Society,17(4), 352–365.

    Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Modern Methods for Business Research (pp. 295–336).

  • Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research,14(2), 189–217.

    Google Scholar 

  • Cho, V., Cheng, T. C. E., & Lai, W. M. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers and Education,53(2), 216–227.

    Google Scholar 

  • Chookaew, S., Panjaburee, P., Wanichsan, D., & Laosinchai, P. (2014). A personalized e-learning environment to promote student’s conceptual learning on basic computer programming. Procedia - Social and Behavioral Sciences,116, 815–819.

    Google Scholar 

  • Chookaew, S., Wanichsan, D., Hwang, G. J., & Panjaburee, P. (2015). Effects of a personalized ubiquitous learning support system on university students’ learning performance and attitudes in computer-programming courses. International Journal of Mobile Learning and Organisation,9(3), 240–257.

    Google Scholar 

  • Chyung, S. Y. (2007). Age and gender differences in online behavior, self-efficacy, and academic performance. The Quarterly Review of Distance Education,8(208), 213–222.

    Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Routledge.

  • Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology,78(1), 98–104.

    Google Scholar 

  • Cuadrado-García, M., Ruiz-Molina, M.-E., & Montoro-Pons, J. D. (2010). Are there gender differences in e-learning use and assessment? Evidence from an interuniversity online project in Europe. Procedia - Social and Behavioral Sciences,2(2), 367–371.

    Google Scholar 

  • DeBate, R. D., Severson, H. H., Cragun, D., Bleck, J., Gau, J., Merrell, L., et al. (2014). Randomized trial of two e-learning programs for oral health students on secondary prevention of eating disorders. Journal of Dental Education,78(1), 5–15.

    Google Scholar 

  • Doran, R. L. (1980). Basic measurement and evaluation of science instruction. ERIC.

  • Elgamal, A. F., Abas, H. A., & Baladoh, E.-S. M. (2011). An interactive e-learning system for improving web programming skills. Education and Information Technologies,18(1), 29–46.

    Google Scholar 

  • Faqih, K. M. S., & Jaradat, M.-I. R. M. (2015). Assessing the moderating effect of gender differences and individualism-collectivism at individual-level on the adoption of mobile commerce technology: TAM3 perspective. Journal of Retailing and Consumer Services, 22, 37–52.

  • Furo, P. T. (2014). Effect of mastery learning approach on secondary school students achievement in chemistry in rivers state Nigeria. Chemistry and Materials Research,6(9), 104–110.

    Google Scholar 

  • Garrison, D. R. (2011). E-learning in the 21st century: A framework for research and practice. Taylor & Francis.

  • Giannakos, M. N. (2013). Enjoy and learn with educational games: Examining factors affecting learning performance. Computers and Education,68(246016), 429–439.

    Google Scholar 

  • Gikandi, J. W., Morrow, D., & Davis, N. E. (2011). Online formative assessment in higher education: A review of the literature. Computers and Education,57(4), 2333–2351.

    Google Scholar 

  • Goffee, R., & Jones, G. (1996). What holds the modern company together? Harvard Business Review,74(6), 133–148.

    Google Scholar 

  • Graf, S. (2007). Adaptivity in learning management systems focussing on learning styles. Vienna: Vienna University of Technology.

  • Grasha, A. F. (2002). Teaching with style: A practical guide to enhancing learning by understanding teaching and learning styles. Alliance Publ.

  • Guskey, T. R. (2007). Closing Achievement Gaps: Revisiting Benjamin S. Bloom’s “Learning for Mastery.” Journal of Advanced Academics, 19(1), 8–31.

  • Guskey, T. R. (2010). Lessons of Mastery Learning. Educational Leadership,68(2), 52–57.

    Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6).

  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate Data Analysis. Prentice Hall.

  • Halbert, C., Kriebel, R., Cuzzolino, R., Coughlin, P., & Fresa-Dillon, K. (2011). Self-assessed learning style correlates to use of supplemental learning materials in an online course management system. Medical Teacher,33(4), 331–333.

    Google Scholar 

  • Horton, W. K. (2000). Designing web-based training: How to teach anyone anything anywhere anytime (Vol. 1). Wiley New York, NY.

  • Huang, S.-L., & Yang, C.-W. (2009). Designing a semantic bliki system to support different types of knowledge and adaptive learning. Computers & Education,53(3), 701–712.

    Google Scholar 

  • Huang, C. H., Chin, S. L., Hsin, L. H., Hung, J. C., & Yu, Y. P. (2011). A Web-based E-learning Platform for physical education. Journal of Networks,6(5), 721–727.

    Google Scholar 

  • Huang, Y. M., Liang, T. H., & Chiu, C. H. (2013). Gender differences in the reading of e-books: Investigating children’s attitudes, reading behaviors and outcomes. Educational Technology and Society,16(4), 97–110.

    Google Scholar 

  • Hung, Y. H., Chang, R. I., & Lin, C. F. (2015). Hybrid learning style identification and developing adaptive problem-solving learning activities. Computers in Human Behavior,55, 552–561.

    Google Scholar 

  • Hwang, G.-J., & Chang, H.-F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers & Education,56(4), 1023–1031.

    Google Scholar 

  • Hwang, G.-J., Tseng, J., & Hwang, G.-H. (2008). Diagnosing student learning problems based on historical assessment records. Innovations in Education and Teaching International,45(1), 77–89.

    Google Scholar 

  • Hwang, G. J., Shi, Y. R., & Chu, H. C. (2011). A concept map approach to developing collaborative Mindtools for context-aware ubiquitous learning. British Journal of Educational Technology,42(5), 778–789.

    Google Scholar 

  • Hwang, G.-J., Sung, H.-Y., Hung, C.-M., Huang, I., & Tsai, C.-C. (2012). Development of a personalized educational computer game based on students’ learning styles. Educational Technology Research and Development,60(4), 623–638.

    Google Scholar 

  • Hwang, G.-J., Panjaburee, P., Triampo, W., & Shih, B.-Y. (2013a). A group decision approach to developing concept-effect models for diagnosing student learning problems in mathematics. British Journal of Educational Technology,44(3), 453–468.

    Google Scholar 

  • Hwang, G., Sung, H., Hung, C., & Huang, I. (2013b). A learning style perspective to investigate the necessity of developing adaptive learning systems. Educational Technology and Society,16(2), 188–197.

    Google Scholar 

  • Joo, Y. J., Lim, K. Y., & Lim, E. (2014). Investigating the structural relationship among perceived innovation attributes, intention to use and actual use of mobile learning in an online university in South Korea. Australasian Journal of Educational Technology, 30(4).

  • Kim, D. G., & Lee, J. (2013). Development of an intelligent instruction system for mathematical computation. Informatics in Education,12(1), 93–106.

    Google Scholar 

  • Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education,56(3), 885–899.

    Google Scholar 

  • Klett, F. (2010). The design of a sustainable competency-based human resources management: A holistic approach. Knowledge Management & E-Learning: An International Journal (KM&EL),2(3), 278–292.

    Google Scholar 

  • Komalawardhana, N., & Panjaburee, P. (2018). Proposal of personalised mobile game from inquiry-based learning activities perspective: relationships among genders, learning styles, perceptions, and learning interest. International Journal of Mobile Learning and Organisation,12(1), 55–76.

    Google Scholar 

  • Koohang, A., & Paliszkiewicz, J. (2013). Knowledge construction in e-learning: an empirical validation of an active learning model. Journal of Computer Information Systems,53(3), 109–114.

    Google Scholar 

  • Kularbphettong, K., Kedsiribut, P., & Roonrakwit, P. (2015). Developing an Adaptive Web-based Intelligent Tutoring System Using Mastery Learning Technique. Procedia - Social and Behavioral Sciences,191, 686–691.

    Google Scholar 

  • Larmuseau, C., Evens, M., Elen, J., Van Den Noortgate, W., Desmet, P., & Depaepe, F. (2018). The Relationship Between Acceptance, Actual Use of a Virtual Learning Environment and Performance: An Ecological Approach. Journal of Computers in Education,5(1), 95–111.

    Google Scholar 

  • Latham, A., Crockett, K., & McLean, D. (2014). An adaptation algorithm for an intelligent natural language tutoring system. Computers and Education,71, 97–110.

    Google Scholar 

  • Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education,54(2), 506–516.

    Google Scholar 

  • Lee, Y. J., & Lee, D. (2015). Factors Influencing Learning Satisfaction of Migrant Workers in Korea with E-learning-Based Occupational Safety and Health Education. Safety and Health at Work,6(3), 211–217.

    Google Scholar 

  • Lewis, K. O., Cidon, M. J., Seto, T. L., Chen, H., & Mahan, J. D. (2014). Leveraging e-learning in medical education. Current Problems in Pediatric and Adolescent Health Care,44(6), 150–163.

    Google Scholar 

  • Liaw, S.-S., & Huang, H.-M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education,60(1), 14–24.

    Google Scholar 

  • Lin, H.-T., Liu, E. Z.-F., & Yuan, S.-M. (2008). An Implementation of Web-based Mastery Learning System. International Journal of Instructional Media,35(2), 209–320.

    Google Scholar 

  • Lin, C. H., Liu, E. Z. F., Chen, Y. L., Liou, P. Y., Chang, M., Wu, C. H., et al. (2013). Game-based remedial instruction in mastery learning for upper-primary school students. Educational Technology and Society,16(2), 271–281.

    Google Scholar 

  • Liu, C.-L., Wu, S., Chang, M., & Heh, J.-S. (2008). Guiding students to do remedial learning in school campus with learning objects’ spatial relations. In Proceedings - ICCE 2008: 16th International Conference on Computers in Education (pp. 249–256).

  • Liu, I.-F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C.-H. (2010). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers and Education,54(2), 600–610.

    Google Scholar 

  • Martinez, J. G. R., & Martinez, N. C. (1999). Teacher effectiveness and learning for mastery. The Journal of Educational Research,92(5), 279–285.

    Google Scholar 

  • Mayer, R. E., & Massa, L. J. (2003). Three facets of visual and verbal learners: Cognitive ability, cognitive style, and learning preference. Journal of educational psychology,95(4), 833.

    Google Scholar 

  • Merhi, M. I. (2015). Factors influencing higher education students to adopt podcast: An empirical study. Computers & Education,83, 32–43.

    Google Scholar 

  • Moghavvemi, S., Paramanathan, T., Rahin, N. M., & Sharabati, M. (2017). Student’s perceptions towards using e-learning via Facebook. Behaviour & Information Technology,36(10), 1081–1100.

    Google Scholar 

  • Nguyen, D. (2011). College Students Attitudes Toward Learning Process And Outcome Of Online Instruction and Distance Learning Across Learning Styles,8(12), 35–43.

    Google Scholar 

  • Oyelade, O. J., Oladipupo, O. O., & Obagbuwa, I. C. (2010). Application of k means clustering algorithm for prediction of students academic performance. International Journal of Computer Science and Information Security,7(1), 292–295.

    Google Scholar 

  • Ozden, M. (2008). Improving Science and Technology Education Achievement Using Mastery Learning Model. World Applied Sciences Journal,5(1), 62–67.

    Google Scholar 

  • Panjaburee, P., & Srisawasdi, N. (2016). An integrated learning styles and scientific investigation-based personalized web approach: a result on conceptual learning achievements and perceptions of high school students. Journal of Computers in Education,3(3), 253–272.

    Google Scholar 

  • Panjaburee, P., Hwang, G. J., Triampo, W., & Shih, B. Y. (2010). A multi-expert approach for developing testing and diagnostic systems based on the concept effect model. Computers & Education,55(2), 510–540.

    Google Scholar 

  • Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning. Educational Technology & Society,12(3), 150–162.

    Google Scholar 

  • Park, C., Kim, D. G., Cho, S., & Han, H. J. (2019). Adoption of multimedia technology for learning and gender difference. Computers in Human Behavior,92, 288–296.

    Google Scholar 

  • Pavlou, P. A., & El Sawy, O. A. (2006). From IT leveraging competence to competitive advantage in turbulent environments: The case of new product development. Information Systems Research,17(3), 198–227.

    Google Scholar 

  • Popescu, E. (2010). Adaptation provisioning with respect to learning styles in a Web-based educational system: An experimental study. Journal of Computer Assisted Learning,26(4), 243–257.

    Google Scholar 

  • Rodrigues, F., & Oliveira, P. (2014). A system for formative assessment and monitoring of students’ progress. Computers and Education,76, 30–41.

    Google Scholar 

  • Rodríguez-Ardura, I., & Meseguer-Artola, A. (2019). Flow experiences in personalised e-learning environments and the role of gender and academic performance. Interactive Learning Environments, 1–24.

  • Rovai, A. P., & Baker, J. D. (2005). Gender differences in online learning: Sense of community, perceived learning, and interpersonal interactions. The Quarterly Review of Distance Education,6(1), 31–44.

    Google Scholar 

  • Santo, S. A. (2006). Relationships between Learning Styles and Online Learning, 19(3).

  • Schiefele, U., & Csikszentmihalyi, M. (1995). Motivation and Ability As Factors in Mathematics Experience and. Journal for Research in Mathematics Education,26(2), 163–181.

    Google Scholar 

  • Shafie, N., Shahdan, T. N. T., & Liew, M. S. (2010). Mastery Learning Assessment Model (MLAM) in Teaching and Learning Mathematics. Procedia - Social and Behavioral Sciences,8, 294–298.

    Google Scholar 

  • Shih, C.-C., & Gamon, J. (2001). Web-Based Learning: Relationships Among Student Motivation, Attitude, Learning Styles, And Achievement. Journal of Agricultural Education,42(4), 12–20.

    Google Scholar 

  • Soflano, M., Connolly, T. M., & Hainey, T. (2015). An Application of Adaptive Games-Based Learning based on Learning Style to Teach SQL. Computers & Education,86, 192–211.

    Google Scholar 

  • Srisawasdi, N., & Panjaburee, P. (2015). Exploring effectiveness of simulation-based inquiry learning in science with integration of formative assessment. Journal of Computers in Education,2(3), 323–352.

    Google Scholar 

  • Srisawasdi, N., Srikasee, S., & Panjaburee, P. (2012). Development of a Constructivist Web-based Learning System with Student Personalized Conceptual Profile. In Proceedings of the 20th International Conference on Computers in Education (pp. 44–50).

  • Staiger, J. (1997). Hybrid or Inbred: The Purity Hypothesis and Hollywood Genre History. Film Criticism,22(2), 5–20.

    Google Scholar 

  • Stiggins, R. (2006). Assessment For Learning: A Key to Motivate and Achievement (Vol. 2). Phi Delta Kappa International.

  • Sung, H.-Y., Hwang, G.-J., & Hwang, G.-J. (2013). A collaborative game-based learning approach to improving students’ learning performance in science courses. Computers & Education,63, 43–51.

    Google Scholar 

  • Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research,51(2), 163–184.

    Google Scholar 

  • Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the Technology Acceptance Model (TAM). Computers & Education,53(3), 1000–1009.

    Google Scholar 

  • Thanyaphongphat, J., & Panjaburee, P. (2019). Effects of a personalised ubiquitous learning support system based on learning style-preferred technology type decision model on university students’ SQL learning performance. International Journal of Mobile Learning and Organisation,13(3), 233–254.

    Google Scholar 

  • Tosuntaş, Ş. B., Karadağ, E., & Orhan, S. (2015). The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the Unified Theory of acceptance and use of technology. Computers & Education,81, 169–178.

    Google Scholar 

  • Toven-Lindsey, B., Rhoads, R. A., & Lozano, J. B. (2015). Virtually unlimited classrooms: Pedagogical practices in massive open online courses. Internet and Higher Education,24, 1–12.

    Google Scholar 

  • Trukhacheva, N., Tchernysheva, S., & Krjaklina, T. (2011). The impact of E-learning on medical education in Russia. E-Learning and Digital Media,8(1), 31–35.

    Google Scholar 

  • Tsai, Y.-R., Ouyang, C.-S., & Chang, Y. (2015). Identifying engineering students’ english sentence reading comprehension errors applying a data mining technique. Journal of Educational Computing Research,54(1), 62–84.

    Google Scholar 

  • Tseng, J. C. R., Chu, H.-C., Hwang, G.-J., & Tsai, C.-C. (2008). Development of an adaptive learning system with two sources of personalization information. Computers and Education,51(2), 776–786.

    Google Scholar 

  • Vaessen, B. E., Prins, F. J., & Jeuring, J. (2014). University students’ achievement goals and help-seeking strategies in an intelligent tutoring system. Computers & Education,72, 196–208.

    Google Scholar 

  • Walonoski, J. a., & Heffernan, N. T. (2006). Detection and analysis of off-task gaming behavior in Intelligent Tutoring Systems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4053 LNCS, 382–391.

  • Wambugu, P. W., & Changeiywo, J. M. (2008). Effects of mastery learning approach on secondary school students’ physics achievement. Eurasia Journal of Mathematics, Science and Technology Education,4(3), 293–302.

    Google Scholar 

  • Wang, H.-C., & Huang, T.-H. (2011). Personalized e-learning environment for bioinformatics. Interactive Learning Environments,21(1), 1–21.

    Google Scholar 

  • Wang, S.-L., & Wu, C.-Y. (2011). Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Systems with Applications,38(9), 10831–10838.

    Google Scholar 

  • Wang, K.-H., Wang, T. H., Wang, W.-L., & Huang, S. C. (2006). Learning styles and formative assessment strategy: Enhancing student achievement in Web-based learning. Journal of Computer Assisted Learning,22(3), 207–217.

    Google Scholar 

  • Wongwatkit, C., Tekaew, S.-A., Kanjana, S., & Khrutthaka, C. (2015). A Systematic Vocabulary Learning-based Mobile Game Application to Improving English Vocabulary Learning Achievement for University Admission Examination in Thailand. In Proceedings of the 23rd International Conference on Computers in Education (pp. 549–558). Hangzhou, China: Asia-Pacific Society for Computers in Education.

  • Wongwatkit, C., Srisawasdi, N., Hwang, G., & Panjaburee, P. (2017). Influence of an integrated learning diagnosis and formative assessment-based personalized web learning approach on students learning performances and perceptions. Interactive Learning Environments,25(7), 889–903.

    Google Scholar 

  • Wu, T. T., Yang, T. C., Hwang, G. J., & Chu, H. N. (2008). Conducting situated learning in a context-aware ubiquitous learning environment. Proceedings - 5th IEEE International Conference on Wireless, Mobile, and Ubiquitous Technologies in Education, WMUTE 2008, 82–86.

  • Wu, H. M., Kuo, B. C., & Yang, J. M. (2012a). Evaluating knowledge structure-based adaptive testing algorithms and system development. Educational Technology and Society,15(2), 73–88.

    Google Scholar 

  • Wu, P.-H., Hwang, G.-J., Milrad, M., Ke, H.-R., & Huang, Y.-M. (2012b). An innovative concept map approach for improving students’ learning performance with an instant feedback mechanism. British Journal of Educational Technology,43(2), 217–232.

    Google Scholar 

  • Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education,140, 103599.

    Google Scholar 

  • Yang, J. C., & Chen, S. Y. (2010). Effects of gender differences and spatial abilities within a digital pentominoes game. Computers & Education,55(3), 1220–1233.

    Google Scholar 

  • Yang, T.-C., Hwang, G.-J., Chiang, T. C., & Yang, S. H. (2013). A Multi-dimensional Personalization Approach to Developing Adaptive Learning Systems. In A. Holzinger & G. Pasi (Eds.) (Vol. 7947, pp. 326–333). Berlin: Springer

  • Yang, K.-H., Lu, B.-C., Chu, H.-C., & Chen, J.-Y. (2015). Developing a Game-Based Learning System with Two-Tier Diagnostic Tool for Math Courses. In Advanced Applied Informatics (IIAI-AAI), 2015 IIAI 4th International Congress on (pp. 363–366). IEEE.

  • Yang, T.-C., Hwang, G.-J., Yang, S. J. H., & Hwang, G.-H. (2015). A two-tier test-based approach to improving students’ computer-programming skills in a web-based learning environment. Journal of Educational Technology & Society,18(1), 198–210.

    Google Scholar 

  • Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: Self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies,59(4), 431–449.

    Google Scholar 

  • Yukselturk, E., & Bulut, S. (2009). Gender differences in self-regulated online learning environment. Educatiuonal Technology Society,12(3), 12–22.

    Google Scholar 

  • Zhang, Z., Ran, P., Peng, Y., Hu, R., & Yan, W. (2015). Effectiveness of e-learning in public health education: A pilot study. International Journal of Information and Education Technology,5(8), 577.

    Google Scholar 

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Acknowledgements

The authors would like to acknowledge the support of Wannee Sangduanchay and her colleagues who helped us with the experimental facilitation. Furthermore, the authors also gratefully appreciate the efforts of all participants who took part in this study.

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Authors and Affiliations

  1. School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand

    Charoenchai Wongwatkit & Pongpon Seprum

  2. Institute for Innovative Learning, Mahidol University, Nakhon Pathom, Thailand

    Patcharin Panjaburee

  3. Faculty of Education, Khon Kaen University, Khon Kaen, Thailand

    Niwat Srisawasdi

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  1. Charoenchai Wongwatkit
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  2. Patcharin Panjaburee
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  3. Niwat Srisawasdi
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  4. Pongpon Seprum
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Correspondence to Patcharin Panjaburee.

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Appendix

Appendix

See Table 6.

Table 6 Questionnaire items
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Wongwatkit, C., Panjaburee, P., Srisawasdi, N. et al. Moderating effects of gender differences on the relationships between perceived learning support, intention to use, and learning performance in a personalized e-learning. J. Comput. Educ. 7, 229–255 (2020). https://doi.org/10.1007/s40692-020-00154-9

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