Student Engagement Mechanism of Online Learning: The Effect of Service Quality on Learning Management System

Hartiwi Prabowo - Bina Nusantara University, Jakarta, 11480, Indonesia
Yuniarty Yuniarty - Bina Nusantara University, Jakarta, 11480, Indonesia
Ridho Bramulya Ikhsan - Bina Nusantara University, Jakarta, 11480, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.3.1263

Abstract


Since typical classrooms do not include discussions, collaborative learning, or interactive learning activities, engagement is a major challenge in distant learning. Online learning satisfaction levels should be measured as evaluation material for future implementation. Although online learning has many advantages, a high dropout rate remains a significant challenge. This study investigates how higher education students' engagement and satisfaction with online learning are enhanced by information, system, and service aspects. The research design was quantitative research, and we used a questionnaire to collect data. The questionnaire was designed on a five-rating interval scale. The sampling technique was simple random sampling. The target minimum sample was counted using the Slovin method, and 206 undergraduate students taking online courses were surveyed online. The model was tested using structural equation modeling partial least squares (SEM PLS). This method is useful for investigating the relationship between constructs. The model was tested with the application of the SmartPLS program. The results revealed a positive and significant effect of system quality, information quality, service quality to student engagements, and their impact on student satisfaction, both direct and indirect. This study answers the literature gap and verifies the importance of online learning quality factors on students’ satisfaction and engagement. These results are expected to help to improve online learning in higher education settings, specifically on students' engagement and satisfaction, leading to perseverance and success.

Keywords


Service quality; online learning; LMS; student; engagement; satisfaction.

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References


R. Nepal and A. M. Rogerson, “From theory to practice of promoting student engagement in business and law-related disciplines: The case of undergraduate economics education,†Educ. Sci., vol. 10, no. 8, pp. 1–13, 2020, doi: 10.3390/educsci10080205.

M. Fanguy, M. Baldwin, E. Shmeleva, K. Lee, and J. Costley, “How collaboration influences the effect of note-taking on writing performance and recall of contents,†Interact. Learn. Environ., 2021, doi: 10.1080/10494820.2021.1950772.

A. Ashrafi, A. Zareravasan, S. Rabiee Savoji, and M. Amani, “Exploring factors influencing students’ continuance intention to use the learning management system (LMS): a multi-perspective framework,†Interact. Learn. Environ., pp. 1–23, Feb. 2020, doi: 10.1080/10494820.2020.1734028.

K. Nesenbergs, V. Abolins, J. Ormanis, and A. Mednis, “Use of augmented and virtual reality in remote higher education: A systematic umbrella review,†Educ. Sci., vol. 11, no. 1, pp. 1–12, 2021, doi: 10.3390/educsci11010008.

C. Barclay, C. Donalds, and K. M. Osei-Bryson, “Investigating critical success factors in online learning environments in higher education systems in the Caribbean*,†Inf. Technol. Dev., vol. 24, no. 3, pp. 582–611, 2018, doi: 10.1080/02681102.2018.1476831.

I. Pozón-López, Z. Kalinic, E. Higueras-Castillo, and F. Liébana-Cabanillas, “A multi-analytical approach to modeling of customer satisfaction and intention to use in Massive Open Online Courses (MOOC),†Interact. Learn. Environ., vol. 0, no. 0, pp. 1–19, 2019, doi: 10.1080/10494820.2019.1636074.

M. Al-Emran, I. Arpaci, and S. A. Salloum, “An empirical examination of continuous intention to use m-learning: An integrated model,†Educ. Inf. Technol., 2020, doi: 10.1007/s10639-019-10094-2.

M. Al-Emran and T. Teo, “Do knowledge acquisition and knowledge sharing really affect e-learning adoption? An empirical study,†Educ. Inf. Technol., vol. 25, no. 3, pp. 1983–1998, 2020, doi: 10.1007/s10639-019-10062-w.

N. Ameen, R. Willis, M. N. Abdullah, and M. Shah, “Towards the successful integration of e-learning systems in higher education in Iraq: A student perspective,†Br. J. Educ. Technol., vol. 50, no. 3, pp. 1434–1446, 2019, doi: 10.1111/bjet.12651.

A. Caruana, A. La Rocca, and I. Snehota, “Learner Satisfaction in Marketing Simulation Games: Antecedents and Influencers,†J. Mark. Educ., vol. 38, no. 2, pp. 107–118, Jun. 2016, doi: 10.1177/0273475316652442.

A. Chavoshi and H. Hamidi, “Social, individual, technological and pedagogical factors influencing mobile learning acceptance in higher education: A case from Iran,†Telemat. Informatics, vol. 38, pp. 133–165, 2019, doi: 10.1016/j.tele.2018.09.007.

I. Pozón-López, E. Higueras-Castillo, F. Muñoz-Leiva, and F. J. Liébana-Cabanillas, Perceived user satisfaction and intention to use massive open online courses (MOOCs), no. 0123456789. Springer US, 2020.

M. Rafiee and S. Abbasian-Naghneh, “E-learning: development of a model to assess the acceptance and readiness of technology among language learners,†Comput. Assist. Lang. Learn., pp. 1–21, Jul. 2019, doi: 10.1080/09588221.2019.1640255.

F. Rejón-Guardia, A. I. Polo-Peña, and G. Maraver-Tarifa, The acceptance of a personal learning environment based on Google apps: the role of subjective norms and social image, vol. 32, no. 2. Springer US, 2020.

N. M. Sabah, “Motivation factors and barriers to the continuous use of blended learning approach using Moodle: students’ perceptions and individual differences,†Behav. Inf. Technol., vol. 0, no. 0, pp. 1–24, 2019, doi: 10.1080/0144929X.2019.1623323.

M. Sheppard and C. Vibert, “Re-examining the relationship between ease of use and usefulness for the net generation,†Educ. Inf. Technol., vol. 24, no. 5, pp. 3205–3218, 2019, doi: 10.1007/s10639-019-09916-0.

M. A. Almaiah and A. Al-Khasawneh, “Investigating the main determinants of mobile cloud computing adoption in university campus,†Educ. Inf. Technol., 2020, doi: 10.1007/s10639-020-10120-8.

N. Thongsri, L. Shen, and Y. Bao, “Investigating factors affecting learner’s perception toward online learning: evidence from ClassStart application in Thailand,†Behav. Inf. Technol., vol. 38, no. 12, pp. 1243–1258, 2019, doi: 10.1080/0144929X.2019.1581259.

M. N. Yakubu, S. I. Dasuki, A. M. Abubakar, and M. M. O. Kah, “Determinants of learning management systems adoption in Nigeria: A hybrid SEM and artificial neural network approach,†Educ. Inf. Technol., 2020, doi: 10.1007/s10639-020-10110-w.

S. K. Sharma, A. Gaur, V. Saddikuti, and A. Rastogi, “Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems,†Behav. Inf. Technol., vol. 36, no. 10, pp. 1053–1066, Oct. 2017, doi: 10.1080/0144929X.2017.1340973.

W. L. Shiau and P. Y. K. Chau, “Understanding behavioral intention to use a cloud computing classroom: A multiple model comparison approach,†Inf. Manag., vol. 53, no. 3, pp. 355–365, 2016, doi: 10.1016/j.im.2015.10.004.

W. H. DeLone and E. R. McLean, “The DeLone and McLean model of information systems success: A ten-year update,†J. Manag. Inf. Syst., vol. 19, no. 4, pp. 9–30, Apr. 2003, doi: 10.1080/07421222.2003.11045748.

I. Balaban, E. Mu, and B. Divjak, “Development of an electronic Portfolio system success model: An information systems approach,†Comput. Educ., vol. 60, no. 1, pp. 396–411, 2013, doi: 10.1016/j.compedu.2012.06.013.

A. Hassanzadeh, F. Kanaani, and S. Elahi, “A model for measuring e-learning systems success in universities,†Expert Syst. Appl., vol. 39, no. 12, pp. 10959–10966, 2012, doi: 10.1016/j.eswa.2012.03.028.

H. Mohammadi, “Investigating users’ perspectives on e-learning: An integration of TAM and IS success model,†Comput. Human Behav., vol. 45, pp. 359–374, 2015, doi: 10.1016/j.chb.2014.07.044.

H. C. Wang and Y. F. Chiu, “Assessing e-learning 2.0 system success,†Comput. Educ., vol. 57, no. 2, pp. 1790–1800, 2011, doi: 10.1016/j.compedu.2011.03.009.

M. A. Uppal, S. Ali, and S. R. Gulliver, “Factors determining e-learning service quality,†Br. J. Educ. Technol., vol. 49, no. 3, pp. 412–426, 2018, doi: 10.1111/bjet.12552.

G. D. Kuh, “What student affairs professionals need to know about student engagement,†J. Coll. Stud. Dev., vol. 50, no. 6, pp. 683–706, 2009, doi: 10.1353/csd.0.0099.

G. D. Kuh, “The national survey of student engagement: Conceptual and empirical foundations.,†New Dir. institutional Res., vol. 141, no. June, pp. 5–20, 2001.

S. Hu and G. D. Kuh, “Being (Dis)Engaged in Educationally Purposeful Activities: The Influences of Student and Institutional Characteristics. Paper presented at the Annual Conference. Seattle, WA, 10–14 April.,†Am. Educ. Res. Assoc., vol. 143, pp. 1–27, 2001, [Online]. Available: https://files.eric.ed.gov/fulltext/ED452776.pdf.

P. J. H. Hu and W. Hui, “Examining the role of learning engagement in technology-mediated learning and its effects on learning effectiveness and satisfaction,†Decis. Support Syst., vol. 53, no. 4, pp. 782–792, 2012, doi: 10.1016/j.dss.2012.05.014.

R. Panigrahi, P. R. Srivastava, and P. K. Panigrahi, “Effectiveness of e-learning: the mediating role of student engagement on perceived learning effectiveness,†Inf. Technol. People, 2020, doi: 10.1108/ITP-07-2019-0380.

S. Büchele, “Evaluating the link between attendance and performance in higher education: the role of classroom engagement dimensions,†Assess. Eval. High. Educ., vol. 46, no. 1, pp. 132–150, 2021, doi: 10.1080/02602938.2020.1754330.

J. Reeve, H. Jang, D. Carrell, S. Jeon, and J. Barch, “Enhancing Students’ Engagement by Increasing Teachers’ Autonomy Support,†Motiv. Emot., vol. 28, no. 2, pp. 147–169, Jun. 2004, doi: 10.1023/B:MOEM.0000032312.95499.6f.

J. Sithipolvanichgul, C. Chen, J. Land, and P. Ractham, “Factors Affecting Cloud Computing Adoption and Continuance Intention of Students in Thailand,†Int. J. Innov. Technol. Manag., Oct. 2021, doi: 10.1142/S0219877021500371.

Z. A. A. Muhisn, M. Ahmad, M. Omar, and S. A. Muhisn, “The impact of socialization on collaborative learning method in e-Learning Management System (eLMS),†Int. J. Emerg. Technol. Learn., vol. 14, no. 20, pp. 137–148, 2019, doi: 10.3991/ijet.v14i20.10992.

A. El Mhouti, A. Nasseh, M. Erradi, and J. M. Vasquèz, “Enhancing collaborative learning in Web 2.0-based e-learning systems: A design framework for building collaborative e-learning contents,†Educ. Inf. Technol., vol. 22, no. 5, pp. 2351–2364, 2017, doi: 10.1007/s10639-016-9545-2.

M. Ã. Herrera-Pavo, “Collaborative learning for virtual higher education,†Learn. Cult. Soc. Interact., vol. 28, no. June 2020, p. 100437, 2021, doi: 10.1016/j.lcsi.2020.100437.

J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 7th ed. Prentice Hall, 2009.

J. F. Hair, C. M. Ringle, and M. Sarstedt, “PLS-SEM: Indeed a silver bullet,†J. Mark. Theory Pract., vol. 19, no. 2, pp. 139–152, 2011, doi: 10.2753/MTP1069-6679190202.

G. D. Garson, Partial Least Squares: Regression & Structural Equation Models. 2016.

A. J. Boevé, R. R. Meijer, R. J. Bosker, J. Vugteveen, R. Hoekstra, and C. J. Albers, “Implementing the flipped classroom: an exploration of study behaviour and student performance,†High. Educ., vol. 74, no. 6, pp. 1015–1032, 2017, doi: 10.1007/s10734-016-0104-y.

Z. Daouk, R. Bahous, and N. N. Bacha, “Perceptions on the effectiveness of active learning strategies,†J. Appl. Res. High. Educ., vol. 8, no. 3, pp. 360–375, 2016, doi: 10.1108/JARHE-05-2015-0037.