Název: Goal setting and goal orientation as predictors of learning satisfaction and online learning behavior in higher education blended courses
Zdrojový dokument: Studia paedagogica. 2023, roč. 28, č. 3, s. [39]-58
Rozsah
[39]-58
-
ISSN1803-7437 (print)2336-4521 (online)
Trvalý odkaz (DOI): https://doi.org/10.5817/SP2023-3-2
Trvalý odkaz (handle): https://hdl.handle.net/11222.digilib/digilib.79678
Type: Článek
Jazyk
Licence: Neurčená licence
Přístupová práva
otevřený přístup
Upozornění: Tyto citace jsou generovány automaticky. Nemusí být zcela správně podle citačních pravidel.
Abstrakt(y)
This study investigated how goal setting and goal orientation are related to student learning behavior and engagement in an online learning environment, and how learning behavior, goal setting, and goal orientation are related to student satisfaction with the course they are studying. A total of 882 students from 76 different courses participated in this study, which used both self-reported data from a questionnaire and indicators based on digital traces in an online learning environment. The results of multilevel regression analyses showed that student ability to set learning goals (i.e., goal setting) was positively related to both student learning satisfaction and student learning behavior. Intrinsic goal orientation positively predicted student satisfaction with the course. Extrinsic goal orientation did not show a significant effect in any of the observed relationships. The analyzed indicators of student learning behavior showed no statistically significant association with learning satisfaction. Possible explanations for these findings are discussed, and limitations and directions for future research are suggested.
Note
This work was supported by the Czech Science Foundation [grant number 21-08218S, Multimodal learning analytics to study self-regulated learning processes within learning management systems].
Reference
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[3] Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S.-L. (2009). Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 12(1), 1-6. https://doi.org/10.1016/j.iheduc.2008.10.005
[4] Barnard, L., Paton, V., & Lan, W. (2008). Online self-regulatory learning behaviors as a mediator in the relationship between online course perceptions with achievement. The International Review of Research in Open and Distributed Learning, 9(2). https://doi.org/10.19173/irrodl.v9i2.516
[5] Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1-48. https://doi.org/10.18637/jss.v067.i01
[6] Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1-13. https://doi.org/10.1016/j.iheduc.2015.04.007
[7] El-Sayad, G., Saad, N. H. M., & Thurasamy, R. (2021). How higher education students in Egypt perceived online learning engagement and satisfaction during the COVID-19 pandemic. Journal of Computers in Education, 8, 527-550. https://doi.org/10.1007/s40692-021-00191-y
[8] Gao, B. W., Jiang, J., & Tang, Y. (2020). The effect of blended learning platform and engagement on students' satisfaction—the case from the tourism management teaching. Journal of Hospitality, Leisure, Sport & Tourism Education, 27, 100272. https://doi.org/10.1016/j.jhlste.2020.100272
[9] Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68-84. https://doi.org/10.1016/j.iheduc.2015.10.002
[10] Heck, R. H., & Thomas, S. L. (2015). An introduction to multilevel modeling techniques: MLM and SEM approaches using mplus. Taylor & Francis.
[11] Hox, J. J. (2010). Multilevel analysis: Techniques and applications. Routledge.
[12] Huang, J.-C. (2023). Implementation effect of integrating cooperative inquiry into blended learning: Analysis of students' goal setting, task value, and well-being. Interactive Learning Environments, 1-16. https://doi.org/10.1080/10494820.2023.2205896
[13] Ifenthaler, D., & Schumacher, C. (2016). Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64, 923-938. https://doi.org/10.1007/s11423-016-9477-y
[14] Jo, I.-H., Kim, D., & Yoon, M. (2015). Constructing proxy variables to measure adult learners' time management strategies in LMS. Journal of Educational Technology & Society, 18(3), 214-225. Retrieved from http://www.jstor.org/stable/jeductechsoci.18.3.214
[15] Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19, 141-184. https://doi.org/10.1007/s10648-006-9012-5
[16] Kasser, T., & Ryan, R. M. (1996). Further examining the american dream: Differential correlates of intrinsic and extrinsic goals. Personality and Social Psychology Bulletin, 22(3), 280-287. https://doi.org/10.1177/0146167296223006
[17] Ke, F., & Kwak, D. (2013). Online learning across ethnicity and age: A study on learning interaction participation, perception, and learning satisfaction. Computers & Education, 61, 43-51. https://doi.org/10.1016/j.compedu.2012.09.003
[18] Kim, D., Park, Y., Yoon, M., & Jo, I.-H. (2016). Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments. The Internet and Higher Education, 30, 30-43. https://doi.org/10.1016/j.iheduc.2016.03.002
[19] Kim, D., Yoon, M., Jo, I.-H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women's university in south korea. Computers & Education, 127, 233-251. https://doi.org/10.1016/j.compedu.2018.08.023
[20] Klein, H. J., Noe, R. A., & Wang, C. (2006). Motivation to learn and course outcomes: The impact of delivery mode, learning goal orientation, and perceived barriers and enablers. Personnel Psychology, 59(3), 665-702. https://doi.org/10.1111/j.1744-6570.2006.00050.x
[21] Kovanović, V., Gašević, D., Dawson, S., Joksimović, S., Baker, R. S., & Hatala, M. (2015a). Does time-on-task estimation matter? Implications on validity of learning analytics findings. Journal of Learning Analytics, 2(3), 81-110. https://doi.org/10.18608/jla.2015.23.6
[22] Kovanović, V., Gašević, D., Joksimović, S., Hatala, M., & Adesope, O. (2015b). Analytics of communities of inquiry: Effects of learning technology use on cognitive presence in asynchronous online discussions. The Internet and Higher Education, 27, 74-89. https://doi.org/10.1016/j.iheduc.2015.06.002
[23] Lee, J. Q., McInerney, D. M., Liem, G. A. D., & Ortiga, Y. P. (2010). The relationship between future goals and achievement goal orientations: An intrinsic-extrinsic motivation perspective. Contemporary Educational Psychology, 35(4), 264-279. https://doi.org/10.1016/j.cedpsych.2010.04.004
[24] Lee, S. J., Srinivasan, S., Trail, T., Lewis, D., & Lopez, S. (2011). Examining the relationship among student perception of support, course satisfaction, and learning outcomes in online learning. The Internet and Higher Education, 14(3), 158-163. https://doi.org/10.1016/j.iheduc.2011.04.001
[25] Lindfors, E. (2021). Students' goal orientations during a pedagogical innovation process: A study in craft, design and technology teacher education. Techne Serien - Forskning i Slöjdpedagogik Och Slöjdvetenskap, 28(2), 221-232. Retrieved from https://journals.oslomet.no/index.php/techneA/article/view/4381
[26] Ma, L., & She, L. (2023). Self-regulated learning and academic success in online college learning. The Asia-Pacific Education Researcher. https://doi.org/10.1007/s40299-023-00748-8
[27] Miller, A. L., Fassett, K. T., & Palmer, D. L. (2021). Achievement goal orientation: A predictor of student engagement in higher education. Motivation and Emotion, 45, 327-344. https://doi.org/10.1007/s11031-021-09881-7
[28] Murillo-Zamorano, L. R., López Sánchez, J. Á., & Godoy-Caballero, A. L. (2019). How the flipped classroom affects knowledge, skills, and engagement in higher education: Effects on students' satisfaction. Computers & Education, 141, 103608. https://doi.org/10.1016/j.compedu.2019.103608
[29] Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.00422
[30] Pintrich, P. R. (2000). Chapter 14 - the role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451-502). Academic Press. https://doi.org/10.1016/B978-012109890-2/50043-3
[31] Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385-407. https://doi.org/10.1007/s10648-004-0006-x
[32] Pintrich, P. R., Smith, D. A. F., García, T., & McKeachie, W. J. (1991). A manual for the use of the motivated strategies questionnaire (MSLQ). University of Michigan.
[33] Posit team. (2023). RStudio: Integrated development environment for R. Posit Software, PBC. Retrieved from http://www.posit.co/
[34] R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
[35] Rajabalee, Y. B., & Santally, M. I. (2021). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies, 26, 2623-2656. https://doi.org/10.1007/s10639-020-10375-1
[36] Sánchez-Cardona, I., Ortega-Maldonado, A., Salanova, M., & Martínez, I. M. (2021). Learning goal orientation and psychological capital among students: A pathway to academic satisfaction and performance. Psychology in the Schools, 58(7), 1432-1445. https://doi.org/10.1002/pits.22505
[37] Schwam, D., Greenberg, D., & Li, H. (2021). Individual differences in self-regulated learning of college students enrolled in online college courses. American Journal of Distance Education, 35(2), 133-151. https://doi.org/10.1080/08923647.2020.1829255
[38] She, L., Ma, L., Jan, A., Sharif Nia, H., & Rahmatpour, P. (2021). Online learning satisfaction during COVID-19 pandemic among Chinese university students: The serial mediation model. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.743936
[39] Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Sage Publications.
[40] Soyer, M. K., & Kirikkanat, B. (2019). Undergraduates' achievement goal orientations, academic self-efficacy and hope as the predictors of their learning approaches. European Journal of Educational Research, 8(1), 99-106. https://doi.org/10.12973/eu-jer.8.1.99
[41] Vieira, C., Parsons, P., & Byrd, V. (2018). Visual learning analytics of educational data: A systematic literature review and research agenda. Computers & Education, 122, 119-135. https://doi.org/10.1016/j.compedu.2018.03.018
[42] Wilson, A., Watson, C., Thompson, T. L., Drew, V., & Doyle, S. (2017). Learning analytics: Challenges and limitations. Teaching in Higher Education, 22(8), 991-1007. https://doi.org/10.1080/13562517.2017.1332026
[43] Winne, P. H. (2010). Improving measurements of self-regulated learning. Educational Psychologist, 45(4), 267-276. https://doi.org/10.1080/00461520.2010.517150
[44] Winne, P. H. (2013). Self-regulated learning viewed from models of information processing. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement (pp. 145-178). Routledge.
[45] Winne, P. H. (2017). Learning Analytics for Self-Regulated Learning. In C. Lang, G. Siemens, A. F. Wise, & D. Gaševic (Eds.), The Handbook of Learning Analytics (1st ed., pp. 241-249). Society for Learning Analytics Research (SoLAR).
[46] Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 5-13. https://doi.org/10.18608/jla.2015.22.2
[47] Wong, J., Baars, M., Davis, D., Zee, T. V. D., Houben, G.-J., & Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human-Computer Interaction, 35(4-5), 356-373. https://doi.org/10.1080/10447318.2018.1543084
[48] Zeidner, M., & Stoeger, H. (2019). Self-Regulated Learning (SRL): A guide for the perplexed. High Ability Studies, 30(1-2), 9-51. https://doi.org/10.1080/13598139.2019.1589369
[49] Zhang, S., & Liu, Q. (2019). Investigating the relationships among teachers' motivational beliefs, motivational regulation, and their learning engagement in online professional learning communities. Computers & Education, 134, 145-155. https://doi.org/10.1016/j.compedu.2019.02.013
[50] Zhang, Y., Zhang, J., & Li, J. (2018). The effect of intrinsic and extrinsic goals on work performance: Prospective and empirical studies on goal content theory. Personnel Review, 47(4), 900-912. https://doi.org/10.1108/PR-03-2017-0086
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