Salta al menu principale di navigazione Salta al contenuto principale Salta al piè di pagina del sito
Ricerche di Psicologia/Psychological Research Journal - Open Access

Articoli

N. 2 (2022)

Metacognition and Approaches Regarding Internet-Based Learning in Taiwanese University Students

DOI
https://doi.org/10.3280/rip2022oa14576
Inviata
12 settembre 2022
Pubblicato
14-09-2022

Abstract

While learning in Internet-based environments, students rely on metacognitive knowledge to organize, record, monitor, and review their learning path. In this experience, they may reveal either a “surface” or “deep” approach.
In this study, 509 university students were administered the adapted versions of the ‘Metacognitive Knowledge regarding Internet-based Learning’ questionnaire and of the ‘Approaches to Internet-based Learning’ questionnaire.
Positive correlations between metacognitive knowledge and approaches to Internet-based learning environments emerged: The metacognitive attitude was associated to a concerned and critical approach to learning whereas the negative attitude about Internet-based learning was associated to the surface approach.

Students showed a global understanding of the peculiarities and opportunities of Internet-based learning environments rather than empathize a single cognitive or metacognitive feature.

Riferimenti bibliografici

  1. Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133-148.
  2. Antonietti, A., Colombo, B., & Lozotsev, Y. (2008). Undergraduates’ metacognitive knowledge about the psychological effects of different kinds of computer-supported instructional tools. Computers in Human Behavior, 24, 2172-2198.
  3. Antonietti, A., & Giorgetti, M. (2004). Students’ conceptions about learning from multimedia. In H. Niegemann, R. Brunken, & D. Leutner (Eds.), Instructional design for multimedia learning (pp. 249-265). Munster-New York: Waxmann.
  4. Antonietti, A., & Giorgetti, M. (2006). Teachers’ beliefs about psychological aspects of learning through multimedia. Computers in Human Behavior, 22, 267-282.
  5. Azevedo, R. (2005). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educational Psychologist, 40, 199-209.
  6. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Academic of Marketing Science, 16(1), 76-94.
  7. Bekele, T. A., & Menchaca, M. P. (2008). Research on Internet-supported learning: A review. Quarterly Review of Distance Education, 9(4), 373.
  8. Bennett, L., & Scholes, R. (2001). Goals and attitudes related to technology use in a social studies method course. Contemporary Issues in Technology and Teacher Education, 1, 373-385.
  9. Biggs, J. (1994). Approaches to learning: Nature and measurement of. In T. Husen, T. N. Postlethwaite (Eds.), The international encyclopedia of education (2 ed., Vol. 1, pp. 319-322). Oxford: Pergamon Press.
  10. Binali, T., Tsai, C.C., & Hsin-Yi Chang, H.Y. (2021). University students’ profiles of online learning and their relation to online metacognitive regulation and internet-specific epistemic justification. Computers & Education, 175, 104315. DOI: 10.1016/j.compedu.2021.104315.
  11. Carvalho, A. R., & Santos, C. (2022). Developing peer mentors’ collaborative and metacognitive skills with a technology-enhanced peer learning program. Computers and Education Open, 3, 100070. DOI: 10.1016/j.caeo.2021.100070.
  12. Celik, I., Muukkonen, H., & Dogan, S. (2021). A model for understanding new media literacy: Epistemological beliefs and social media use. Library & Information Science Research, 43(4), 101125.
  13. Chang, B. (2021). Incorporating Eastern and Western Learning Perspectives into a Western Learning Environment. Journal of Interdisciplinary Studies in Education, 10(1), 6-40. Retrieved from https://www.ojed.org/index.php/jise/article/view/2695.
  14. Chin, C., & Brown, D. E. (2000). Learning in science: A comparison of deep and surface approaches. Journal of Research in Science Teaching, 37, 109-138.
  15. Chiu, Y.-L., Liang, J.-C., & Tsai, C.-C. (2013). Internet-specific epistemic beliefs and self-regulated learning in online academic information searching. Metacognition and Learning, 8, 235-260. DOI: 10.1007/s11409-013-9103-x.
  16. Cho, M.-H., & Heron, M.L. (2015). Self-regulated learning: The role of motivation, emotion, and use of learning strategies in studentsʼ learning experiences in a self-paced online mathematics course. Distance Education, 36(1), 80-99. DOI: 10.1080/01587919.2015.1019963.
  17. Chuang, S.-C., & Tsai, C.-C. (2005). Preferences toward the constructivist Internet-based learning environments among high school students in Taiwan. Computers in Human Behavior, 21, 255-272.
  18. Ellis, R. A., Goodyear, P., Bliuc, A. M., & Ellis, M. (2011). High school studentsʼ experiences of learning through research on the Internet. Journal of Computer Assisted Learning, 27, 503-515.
  19. Eltahir, M., Al-Qatawneh, S., Al-Ramahi, N., & Alsalhi, N. (2019). The Perspective of Students and Faculty Members on the Efficiency and
  20. Usability of E-Learning Courses at Ajman University: A Case Study. Journal of Technology and Science Education, 9(3), 388-403.
  21. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34, 906-911.
  22. Gao, T., & Lehman, J. D. (2003). The effects of different levels of interaction on the achievement and motivational perceptions of college students in a Webbased learning environment. Journal of Interactive Learning Research, 14, 367-386.
  23. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (6th ed.). New York: Prentice Hall.
  24. Henderson, M., Selwyn, N., & Aston, R. (2017). What works and why? Student perceptions of ‘useful’ digital technology in university teaching and learning. Studies in Higher Education, 42(8), 1567-1579.
  25. Karatas, K., & Arpaci, I. (2021). The role of self-directed learning, metacognition, and 21st century skills predicting the readiness for online learning. Contemporary Educational Technology, 13 (3). DOI: 10.30935/cedtech/10786.
  26. Kember, D., Biggs, J., Leung, D. Y. P. (2004). Examining the multidimensionality of approaches to learning through the development of a
  27. revised version of the Learning Process Questionnaire. British Journal of Educational Psychology, 74, 261-280.
  28. Lee, M.-H. (2016). Exploring the relationships between Taiwan university students’ perceived teacher authority toward Internet-based learning environments and their approaLeeches to Internet-based learning. Paper presented at the International Symposium on Emerging Technologies for Education (SETE’16), Roma: Italy.
  29. Lee, M.-H., Johanson, R. E., & Tsai, C. C. (2008). Exploring Taiwanese high school students’ conceptions of and approaches to learning science through a structural equation modeling analysis. Science Education, 92, 191-220.
  30. Lee, W.-C., Chiu, Y.-L., Liang, J.-C., & Tsai, C.-C. (2014). Exploring the structural relationships between high school students’ internet-specific epistemic beliefs and their utilization of online academic help seeking. Computers in Human Behavior, 36, 391-400. DOI: 10.1016/j.chb.2014.03.069.
  31. Lee, S. W.-Y., & Tsai, C.-C. (2011). Students’ perception of collaboration, selfregulated learning, and information seeking in the context of Internet-based learning and traditional learning. Computers in Human Behavior, 27, 905-914. DOI: 10.1016/j.chb.2010.11.016.
  32. Macedo-Rouet, M., Potocki, A., Scharrer, L., Ros, C., Stadtler, M., Salmerón, L., & Rouet, J. F. (2019). How good is this page? Benefits and limits of prompting on adolescents’ evaluation of web information quality. Reading Research Quarterly, 54(3), 299-321.
  33. Marton, F., & Säljö, R. (1976). On qualitative differences in learning: I-outcome and process. British Journal of Educational Psychology, 46, 4-11.
  34. Mason, L., Boldrin, A., & Ariasi, N. (2010). Epistemic metacognition in context: Evaluating and learning online information. Metacognition and Learning, 5(1), 67-90. DOI: 10.1007/s11409-009-9048-2.
  35. Molin, F., Haelermans, C., Cabus, S., & Groot, W. (2020). The effect of feedback on metacognition-a randomized experiment using polling
  36. technology. Computers Education, 152, 103885. DOI: 10.1016/j.compedu.2020.103885.
  37. Moos, D. C., & Azevedo, R. (2008). Exploring the fluctuation of motivation and use of self-regulatory processes during learning with hypermedia. Instructional Science, 36, 203-231.
  38. Ohtani, K., & Hisasaka, T. (2018). Beyond intelligence: A meta-analytic review of the relationship among metacognition, intelligence, and academic performance. Metacognition and Learning, 13, 179-212. DOI: 10.1007/s11409-018-9183-8.
  39. Palvia, S., Aeron, P., Gupta, P., Mahapatra, D., Parida, R., Rosner, R., & Sindhi, S. (2018). Online education: Worldwide status, challenges, trends, and implications. Journal of Global Information Technology Management, 21(4), 233-241.
  40. Pieschl, S., Stahl, E., & Bromme, R. (2008). Epistemological beliefs and selfregulated learning with hypertext. Metacognition and Learning, 3, 17-37.
  41. Shea, P., & Bidjerano, T. (2009). Community of inquiry as a theoretical framework to foster “epistemic engagement” and “cognitive presence” in online education. Computers Education, 52, 543-553.
  42. Shen, K.-M., Lee, M.-H., Tsai, C.-C., & Chang, C.-Y. (2016). Undergraduate students’ earth science learning: relationships among conceptions, approaches, and learning self-efficacy in Taiwan. International Journal of Science Education, 38, 1527-1547.
  43. Stevens, J. (1996). Applied multivariate statistics for the social science (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.
  44. Strømsø, H. I., & Bråten, I. (2010). The role of personal epistemology in the self-regulation of internet-based learning. Metacognition and Learning, 5(1), 91-111. DOI: 10.1007/s11409-009-9043-7.
  45. Teng, M. F. (2021). Interactive-whiteboard-technology-supported collaborative writing: Writing achievement, metacognitive activities, and co-regulation patterns. System, 97, 102426. DOI: 10.1016/j.system.2020.102426.
  46. Thompson, B., & Daniel., L. G. (1996). Factor analytic evidence for the construct validity of scores: A historical overview and some guidelines. Educational and Psychological Measurement, 56, 197-208.
  47. Tsai, C.-C. (2004). Beyond cognitive and metacognitive tools: The use of the internet as anʼ epistemologicalʼ tool for instruction. British Journal of Educational Technology, 35(5), 525-536. DOI: 10.1111/j.0007-1013.2004.00411.x.
  48. Tsai, C.-C. (2005). Preferences toward Internet-based learning environments: High school students’ perspectives for science learning. Educational Technology and Society, 8, 203-213.
  49. Tsai, C.-C., & Chuang, S.-C. (2005). The correlation between epistemological beliefs and preferences toward Internet-based learning environments. British Journal of Educational Technology, 36, 97-100.
  50. Tsao, Y.-C., Wu, C.-L., & Lee, M.-H. (2014). Development and validation of an instrument for exploring Taiwanese undergraduates’ approaches to Internetbased learning. International Conference on Computers in Education (ICCE), Nara, Japan.
  51. Zhao, L. & Ye, C. (2020). Time and performance in online learning: Applying the theoretical perspective of metacognition. Decision Sciences Journal of Innovative Education, 18(3), 435-455. DOI: 10.1111/dsji.12216.
  52. Zhu, Y., Zhang, J.H., Au, W., & Yates, G. (2020). University students’ online learning attitudes and continuous intention to undertake online courses: a self-regulated learning perspective. Education Tech Research Dev, 68, 1485-1519. DOI: 10.1007/s11423-020-09753-w.

Metriche

Caricamento metriche ...