Perceived control over attitude: the psychological drivers of sustainable smart courses learning among economically disadvantaged undergraduates
DOI:
https://doi.org/10.26499/bebasan.v12i1.273Abstract
Smart courses, being the effective tool for education equity, are faced with high dropout. This study investigates the factors affecting the complete of the smart courses of economically disadvantaged undergraduates from the psychology drive perspective. Nvivo tool was used to encode the screened 392 literature from Web of Science and the Engineering Index to explore the generic relationship among free nodes. According to the Theory of Planned Behavior, 17 hypotheses and corresponding Structural Equation Modeling were established, and 257 valid questionnaires（age of participants from 19 to 24）were collected for analyzing, and experts were invited to do an interview. The results indicated that behavioral attitude, subjective norms, and perceived behavioral control all had significant positive effects on students’ intention to engage in sustainable learning. Among them, perceived behavioral control demonstrated the strongest path coefficient (β = 0.499, p < 0.001), highlighting students’ self-efficacy and perceived resource controllability as key driving forces in promoting continuous learning. Although the effects of behavioral attitude (β = 0.343, p < 0.001) and subjective norms (β = 0.274, p < 0.001) were relatively weaker, the clarity of learning goals, expectations from others, and social support still played a positive role in motivating learning engagement. Different from the conventional view, this paper finds out that the most effective point is perceived behavioral control instead of attitudes for economically disadvantaged undergraduates for persistent learning behavior. This study provides a novel perspective on explaining economically disadvantaged undergraduates' sustainable smart courses learning behavior and offers practical recommendations to enhance course completion rates.
References
Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behavior. Springer Berlin Heidelberg.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior & Human Decision Processes 50(2), 179–211.
Ajzen, I., and Fishbein, M. (1970). The prediction of behavior from attitudinal and normative variables. Journal of Experimental Social Psychology 6(4), 466-487.
Armitage, C.J., and Conner, M. (2001a). Efficacy of the Theory of Planned Behaviour: a meta-analytic review. The British journal of social psychology 40(Pt 4), 471-499. doi: 10.1348/014466601164939.
Armitage, C.J., and Conner, M. (2001b). Efficacy of the theory of planned behaviour: A meta‐analytic review. British journal of social psychology 40(4), 471-499.
Armitage, C.J., and Conner, M. (2001c). Efficacy of the Theory of Planned Behaviour: A meta‐analytic review. British Journal of Social Psychology 40(4).
Bagozzi (1981). Evaluating Structural Equation Models With Unobservable Variables and Meas Error: A Comment, in: Journal of Marketing Research.
Chen, S.Y. (2016). Using the sustainable modified TAM and TPB to analyze the effects of perceived green value on loyalty to a public bike system. TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 88, 58-72. doi: 10.1016/j.tra.2016.03.008.
Dai, H.M., Teo, T., Rappa, N.A., and Huang, F. (2020). Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Computers & Education 150, 103850.
Gómez-Ramirez, I., Valencia-Arias, A., and Duque, L. (2019). Approach to M-learning Acceptance Among University Students: An Integrated Model of TPB and TAM. INTERNATIONAL REVIEW OF RESEARCH IN OPEN AND DISTRIBUTED LEARNING 20(3), 141-164.
Hadadgar, A., Changiz, T., Masiello, I., Dehghani, Z., Mirshahzadeh, N., and Zary, N. (2016). Applicability of the theory of planned behavior in explaining the general practitioners eLearning use in continuing medical education. BMC MEDICAL EDUCATION 16. doi: 10.1186/s12909-016-0738-6.
Haleem, A., Sushil, Qadri, M.A., and Kumar, S. (2012). Analysis of critical success factors of world-class manufacturing practices: an application of interpretative structural modelling and interpretative ranking process. PRODUCTION PLANNING & CONTROL 23(10-11), 722-734. doi: 10.1080/09537287.2011.642134.
Hogg, M.A., and Terry, D.J. (1996). Group Norms and the Attitude-Behavior Relationship.
Huang, T. (2023). Factors affecting students’ online courses learning behaviors. Education and Information Technologies 28(12), 16485-16507.
Jalilvand, M.R., and Samiei, N. (2012). The impact of electronic word of mouth on a tourism destination choice Testing the theory of planned behavior (TPB). INTERNET RESEARCH 22(5), 591-612. doi: 10.1108/10662241211271563.
Jung, Y., and Lee, J. (2018). Learning Engagement and Persistence in Massive Open Online Courses (MOOCS). COMPUTERS & EDUCATION 122, 9-22. doi: 10.1016/j.compedu.2018.02.013.
Kara, M. (2022). Revisiting online learner engagement: exploring the role of learner characteristics in an emergency period. JOURNAL OF RESEARCH ON TECHNOLOGY IN EDUCATION 54, S236-S252. doi: 10.1080/15391523.2021.1891997.
Knauder, H., and Koschmieder, C. (2019). Individualized student support in primary school teaching: A review of influencing factors using the Theory of Planned Behavior (TPB). TEACHING AND TEACHER EDUCATION 77, 66-76. doi: 10.1016/j.tate.2018.09.012.
Li, Z., and Liu, Y. (2023a). Analysis of the current situation of the research on the influencing factors of online learning behavior and suggestions for teaching improvement. Sustainability 15(3), 2119.
Li, Z.G., and Liu, Y. (2023b). Analysis of the Current Situation of the Research on the Influencing Factors of Online Learning Behavior and Suggestions for Teaching Improvement. SUSTAINABILITY 15(3). doi: 10.3390/su15032119.
Lin, K.-M. (2011). e-Learning continuance intention: Moderating effects of user e-learning experience. Computers & education 56(2), 515-526.
Locke, E.A., and Latham, G.P. (2002). Building a practically useful theory of goal setting and task motivation. A 35-year odyssey. The American psychologist 57(9), 705-717. doi: 10.1037/0003-066X.57.9.705.
Park, J.-H., and Choi, H.J. (2009a). Factors influencing adult learners' decision to drop out or persist in online learning. Journal of Educational Technology & Society 12(4), 207-217.
Park, J.H., and Choi, H.J. (2009b). Factors Influencing Adult Learners' Decision to Drop Out or Persist in Online Learning. EDUCATIONAL TECHNOLOGY & SOCIETY 12(4), 207-217.
Pintrich, P.R., and Groot, E.V.D. (1990). Motivational and Self-Regulated Learning Components of Classroom Academic Performance. Journal of Educational Psychology 82(1), 33-40.
Rahmani, A.M., Groot, W., and Rahmani, H. (2024). Dropout in online higher education: a systematic literature review. INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION 21(1). doi: 10.1186/s41239-024-00450-9.
Reilly, C., and Reeves, T.C. (2024). Refining active learning design principles through design-based research. Active Learning in Higher Education 25(1), 81-100.
Straub, D., and Gefen, D. (2004). Validation Guidelines for IS Positivist Research. Communications of the Association for Information Systems 3.
Teo, T., and Beng Lee, C. (2010). Explaining the intention to use technology among student teachers: An application of the Theory of Planned Behavior (TPB). Campus-Wide Information Systems 27(2), 60-67.
Wang, B., Wang, X.M., Guo, D.X., Zhang, B., and Wang, Z.H. (2018). Analysis of factors influencing residents' habitual energy-saving behaviour based on NAM and TPB models: Egoism or altruism? ENERGY POLICY 116, 68-77. doi: 10.1016/j.enpol.2018.01.055.
Wang, W., and Zhan, J. (2020a). The relationship between English language learner characteristics and online self-regulation: A structural equation modeling approach. Sustainability 12(7), 3009.
Wang, W., and Zhan, J. (2020b). The Relationship between English Language Learner Characteristics and Online Self-regulation: A Structural Equation Modeling Approach. SUSTAINABILITY 12(7). doi: 10.3390/su12073009.
Willging, P., and Johnson, S. (2019). FACTORS THAT INFLUENCE STUDENTS’ DECISION TO DROPOUT OF ONLINE COURSES. Online Learning 8. doi: 10.24059/olj.v8i4.1814.
Wu, H.-Y., Wu, H.-S., and Su, Y.-P. (Year). "Exploring the Critical Factors Affecting College Students’ Intelligent Learning", in: EdMedia+ Innovate Learning: Association for the Advancement of Computing in Education (AACE)), 806-817.
Wu, M.Y., Zhai, J.Q., Wall, G., and Li, Q.C. (2021). Understanding international students' motivations to pursue higher education in mainland China. EDUCATIONAL REVIEW 73(5), 580-596. doi: 10.1080/00131911.2019.1662772.
Xavier, M., and Meneses, J. (2021). The tensions between student dropout and flexibility in learning design: The voices of professors in open online higher education. International Review of Research in Open and Distributed Learning 22(4), 72-88.
Xu, L., Zhang, J., Ding, Y., Zheng, J., Sun, G., Zhang, W., et al. (2023a). Understanding the role of peer pressure on engineering students' learning behavior: A TPB perspective. Frontiers in Public Health 10, 1069384.
Xu, L., Zhang, J.X., Ding, Y.Y., Zheng, J.W., Sun, G.Z., Zhang, W., et al. (2023b). Understanding the role of peer pressure on engineering students' learning behavior: A TPB perspective. FRONTIERS IN PUBLIC HEALTH 10. doi: 10.3389/fpubh.2022.1069384.
Yağcı, M. (2022). Educational data mining: prediction of students' academic performance using machine learning algorithms. Smart Learning Environments 9(1), 11.
Yu, B. (2023). Self-regulated learning: A key factor in the effectiveness of online learning for second language learners. FRONTIERS IN PSYCHOLOGY 13. doi: 10.3389/fpsyg.2022.1051349.
Zheng, H., Qian, Y., Wang, Z.R., and Wu, Y.H. (2023). Research on the Influence of E-Learning Quality on the Intention to Continue E-Learning: Evidence from SEM and fsQCA. SUSTAINABILITY 15(6). doi: 10.3390/su15065557.
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