The Effects of Learning Attributes on Students’ Writing Performance

Main Article Content

Kantima Techadisai
Wanpen Worawongpongsa
Phatdanai Nanya

Abstract

This research investigates how three students’ learning attributes—attitudes, behavior, and English proficiency background contribute to the students’ writing performance. Statistical methods explored three primary areas: (1) the influence of students’ attitudes towards the students’ choice of teaching methods and learning behavior, (2) the benefits of consultations in enhancing students’ writing performance, and (3) the impact of the three factors—English proficiency background, online learning duration, and numbers of consultation—on students’ writing performance. The study involved 29 first-year undergraduate engineering students. The results showed that the students with positive attitude towards English learning exhibited higher satisfactions and engagement, regardless of whether they followed a teacher-directed or self-directed method. This positive attitude had a substantial positive correlation with the satisfactions of both self-directed (r = 0.637) and teacher-directed (r = 0.447) methods. Additionally, the satisfactions of the self-directed method significantly correlated with the satisfactions of the teacher-directed method (r = 0.707) and with learning behavior through the teacher-directed method (r = 0.581). With notable differences in pre-test and post-test scores, the consultations were pivotal in enhancing writing performance of the students who participated in the optional extra-session (t = 8.846) when comparing to those who did not (t = 5.138). The data analysis using techniques namely Feature Importance and Univariate Selection indicated that online learning duration (the time spent on the teaching materials) had the most significant impact on the students’ writing performance.

Article Details

How to Cite
Techadisai, K., Worawongpongsa, W., & Nanya, P. (2024). The Effects of Learning Attributes on Students’ Writing Performance. REFLections, 31(3), 1044–1064. https://doi.org/10.61508/refl.v31i3.276987
Section
Research articles

References

Abdallah, N., & Abdallah, O. (2022). Investigating factors affecting students’ satisfaction with e-learning: An empirical case study. Journal of Educators Online, 19(1), Article 3. https://doi.org/10.9743/JEO.2022.19.1.3

Akbarialiabad, H., Zarifsanaiey, N., Taghrir, M. H., Roushenas, S., Panahandeh, S. M., Abdolrahimzadeh-fard, H., Shayan, Z., Kavousi, S., & Paydar, S. (2021). The impact of flipped learning in surgical education: A mixed-method study. Knowledge Management & E-Learning: An International Journal, 13(3), 273–289. https://doi.org/10.34105/j.kmel.2021.13.015

Alammary, A., Sheard, J., & Carbone, A. (2014). Blended learning in higher education: Three different design approaches. Australasian Journal of Educational Technology, 30(4), 440–454. https://doi.org/10.14742/ajet.693

Banditvilai, C. (2016). Enhancing students’ language skills through blended learning. The Electronic Journal of e-Learning, 14(3), 220–229. https://academic-publishing.org/index.php/ejel/article/view/1757

Betsch, T. (2011). The stability of preferences – A social-cognition view. Frontiers in Psychology, 2, Article 290. https://doi.org/10.3389/fpsyg.2011.00290

Blass, L., & Vargo, M. (2018). Pathways: Reading, writing, and critical thinking split 2A (2nd ed.). National Geographic Learning.

Casey, K., Shaw, E., Whittingham, J., & Gallavan, N. (2021). Improving online instruction: A study of online course delivery methods. Journal of Educators Online, 18(1), 53–60. https://doi.org/10.9743/jeo.2021.18.1.8

Díez-Palomar, J., García-Carrión, R., Hargreaves, L., & Vieites, M. (2020). Transforming students’ attitudes towards learning through the use of succe ssful educational actions. PLOS ONE, 15(10), Article e0240292. https://doi.org/10.1371/journal.pone.0240292

Ding, L., & Yang, X. (2023). Attitudes, preference and personality in relation to behavioral intention of autonomous vehicle use: An SEM analysis. PLOS ONE, 18(2), Article e0262899. https://doi.org/10.1371/journal.pone.0262899

Dobre, I. (2015). Learning management systems for higher education - An overview of available options for higher education organizations. Procedia - Social and Behavioral Sciences, 180, 313–320. https://doi.org/10.1016/j.sbspro.2015.02.122

Ferris, D. R. (2003). Response to student writing: Implications for second language students. Lawrence Erlbaum Associates.

Folse, K. S., Muchmore-Vokoun, A., & Solomon, E. V. (1999). Great paragraphs: An introduction to writing paragraphs. Houghton Mifflin.

George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Allyn & Bacon.

Giannoulas, A., Stampoltzis, A., Kounenou, K., & Kalamatianos, A. (2021). How Greek students experienced online education during Covid-19 pandemic, in order to adjust to a post-lockdown period. The Electronic Journal of e-Learning, 19(4), 222–232. https://doi.org/10.34190/ejel.19.4.2347

Goldstein, L. M., & Conrad, S. M. (1990). Student input and negotiation of meaning in ESL writing conferences. TESOL Quarterly, 24(3), 443–460. https://doi.org/10.2307/3587229

Hogue, A. (1996). First steps in academic writing. Addison Wesley Publishing.

Huiying, B. (2012). On interactive EFL teaching based on web-based platform. IEEE Symposium on Robotics and Applications, 2012, 68–70. https://doi.org/10.1109/ISRA.2012.6219121

Hyland, K. (2013). Second language writing. Cambridge University Press.

Im, T. (2021). Online and blended learning in vocational training institutions in South Korea. Knowledge Management & E-Learning: An International Journal, 13(2), 194–208. https://doi.org/10.34105/j.kmel.2021.13.011

Karasneh, R., Al-Azzam, S., Muflih, S., Hawamdeh, S., Muflih, M., & Khader, Y. (2021). Attitudes and practices of educators towards e-learning during the COVID-19 pandemic. The Electronic Journal of e-Learning, 19(4), 252–261. https://doi.org/10.34190/ejel.19.4.2350

Laer, S. V., & Elen, J. (2017). In search of attributes that support self-regulation in blended learning environments. Education and Information Technologies, 22, 1395–1454.

Li, L. Y., & Tsai, C. C. (2017). Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance. Computers & Education, 114, 286–297.

Lo, C. K., & Hew, K. F. (2017). A critical review of flipped classroom challenges in K-12 education: Possible solutions and recommendations for future research. Research and Practice in Technology Enhanced Learning, 12, Article 4. http://doi.org/10.1186/s41039-016-0044-2

Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599. https://doi.org/https://doi.org/10.1016/j.compedu.2009.09.008

Muljana, P. S. & Luo, T. (2019). Factors contributing to student retention in online learning and recommended strategies for improvement: A systematic literature review. Journal of Information Technology Education: Research, 18, 19–57. https://doi.org/10.28945/4182

Nouri, J. (2016). The flipped classroom: For active, effective and increased learning – especially for low achievers. International Journal of Educational Technology in Higher Education, 13, Article 33. https://doi.org/10.1186/s41239-016-0032-z

Parker, N., Mahler, B. P., & Edwards, M. (2021). Humanizing online learning experiences. Journal of Educators Online, 18(2), 119–129.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2021). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

Porter, W. W., Graham, C. R., Spring, K. A., & Welch, K. R. (2014). Blended learning in higher education: Institutional adoption and implementation. Computers & Education, 75, 185–195. https://doi.org/https://doi.org/10.1016/j.compedu.2014.02.011

Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144, Article 103701. https://doi.org/https://doi.org/10.1016/j.compedu.2019.103701

Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2020). Social isolation and acceptance of the Learning Management System (LMS) in the time of COVID-19 Pandemic: An expansion of the UTAUT model. Journal of Educational Computing Research, 59(2), 183–208. https://doi.org/10.1177/0735633120960421

Rensis, L. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140, 1–55.

Savage, A., & Shafiei, M. (2016). Effective academic writing (2nd ed.). Oxford University Press.

Wang, R. (2014). Design of web-based English learning support system. 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), 2014, 771–773.

Weldon, A., Ma, W. W. K., Ho, I. M. K., & Li, E. (2021). Online learning during a global pandemic: Perceived benefits and issues in higher education. Knowledge Management & E-Learning: An International Journal, 13(2), 161–181. https://doi.org/10.34105/j.kmel.2021.13.009

Zarrinfard, S., Rahimi, M., & Mohseny, A. (2021). Flipping an on-campus general English course: A focus on technology complexity of instruction and learners’ levels of impulsivity. International Journal of Educational Technology in Higher Education, 18(1), Article 45. https://doi.org/10.1186/s41239-021-00280-z

Zhi-ying, G., & Hong, L. (2010). Effective English teaching and learning via web-based electronic English lesson plan design. 2010 Second International Workshop on Education Technology and Computer Science, 2010, 358–361.