Understanding factors influencing EFL students' technology-based self-directed learning

Year 2020, , 450 - 459, 01.10.2020

Xiaoquan Pan , Huijuan Shao

https://doi.org/10.24289/ijsser.781472

Abstract

Based on the theories of self-directed learning, technology acceptance model (TAM) and the theory of planned behavior (TPB), this study constructed a model of predictors of English as a Foreign Language (EFL) students’ technology-based self-directed learning. Through a questionnaire survey of 386 EFL students from a Chinese university and structural equation model analysis, this study intended to identify the factors that influence the students’ use of technology for language learning and the relationship between these factors. The results revealed that attitude towards technology use, perceived usefulness and technological facilitating conditions are the dominant predictors of technology-based self-directed learning, whereas technological complexity had no direct impact on technology-based self-directed learning, but played an intermediary role through attitude towards technology use. Subjective norms had less predicative power on technology-based self-directed learning. The findings suggest improving students’ perception of the usefulness of technology in learning, enhancing students’ attitude towards technology use and constructing convenient technological facilitating conditions

Keywords

self-directed learning, technology acceptance, influencing factor, empirical research

Supporting Institution

This work was funded by Humanities and Social Sciences Research Project of the Ministry of Education of the People's Republic of China

Project Number

20YJC740047

References

• Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665-683.
• Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
• Ayan, E. (2015). Moodle as builder of motivation and autonomy in English courses. Open Journal of Modern Linguistics, 5(1), 6-20.
• Benson, P., & Reinders, H. (2011). Beyond the language classroom. New York, NY: Palgrave Macmillan.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user-acceptance of information technology. MIS Quarterly, 13(3), 319-339.
• Dickinson, L. (1995). Autonomy and motivation: A literature review. System, 23(2), 165-174.
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
• Garrison, D. R. (1997). Self-directed Learning: Toward a comprehensive model. Adult Education Quarterly, 48(1), 18-33.
• Garrison, D.R. (2003). Self-directed learning and distance education. In M.G. Moore, W.G. Anderson (Eds.), Handbook of distance education (pp. 161-168), Erlbaum, Mahwah, NJ.
• Giesbers, B., Rienties, B., Tempelaar, D., & Gijselaers, W. (2014). A dynamic analysis of the interplay between asynchronous and synchronous communication in online learning: The impact of motivation. Journal of Computer Assisted Learning, 30, 30-50.
• Goodyear, P., & Ellis, R. A. (2008). University students’ approaches to learning: rethinking the place of technology. Distance Education, 29(2), 141-152.
• Holec, H. (1981). Autonomy and foreign language learning. Oxford: Pergamon Press.
• Hubackova, S., Semradova, I., & Klimova, B. F. (2011). Blended learning in a foreign language teaching. Procedia—Social and Behavioral Sciences, 28, 281-285.
• Jones, R. G. (2011). Emerging technologies autonomous language learning. Language Learning & Technology, 15, 4-11.
• Lai, C. (2013). A framework of developing self-directed technology use for language learning. Language Learning & Technology, 17(2), 100-122.
• Lai, C., & Gu, M.Y. (2011). Self-regulated out-of-class language learning with technology. Computer Assisted Language Learning, 24(4), 317–335.
• Lai, C., Shum, M., & Tian, Y. (2016). Enhancing learners’ self-directed use of technology for language learning: the effectiveness of an online training platform. Computer Assisted Language Learning, 29(1), 40-60.
• Lai, C., Wang, Q., & Lei, J. (2012). What factors predict undergraduate students’ use of technology for learning? A case from Hong Kong. Computers & Education, 59(2), 569-579.
• Niemiec, C. P., & Ryan, M. (2009). Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice. Theory & Research in Education, 7(2), 133-144.
• Oxford, R. (2009). The influence of technology on second language writing. In R. Oxford & J. Oxford (Eds.), Second language teaching and learning in the Net generation (pp. 9-21). Manoa, HI: National Foreign Language Resource Center.
• Peng, J.-E., & Woodrow, L. (2010). Willingness to communicate in English: A model in the Chinese EFL classroom context. Language Learning, 60, 834-876.
• Reeve, J. (2013). How students create motivationally supportive learning environments for themselves: The concept of agentic engagement. Journal of Educational Psychology, 105(3), 579-595.
• Reeve, J., & Jang, H. (2006). What teachers say and do to support students’ autonomy during a learning activity. Journal of Educational Psychology, 98(1), 209-218.
• Reinders, H., & Darasawang, P. (2012). Diversity in language support. In G. Stockwell (Ed.), Computer-assisted language learning: Diversity in research and practice (pp. 49-70). Cambridge: Cambridge University Press.
• Reinders, H., & White, C. (2011). Learner autonomy and new learning environments. Language Learning & Technology, 15(3), 1-3.
• Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67.
• Shu, D., & Zhuang, Z. (2008). Modern foreign language teaching: Theory, practice and method (Revised Edition). Shanghai: Shanghai Foreign Language Education Press.
• Song, L., & Hill, J.R. (2007). A conceptual model for understanding self-directed learning in online environments. Journal of Interactive Online Learning, 6(1), 27-41.
• Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302-312.
• Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440.
• Teo, T., & Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: A multi-group analysis of the Unified Theory of Acceptance and Use of Technology. Interactive Learning Environments, 22(1), 51-66.
• Venkatesh, V., Morris, M., Davis, G., & Davis, F. D. (2003). User-acceptance of Information Technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.