The role of schemata in the Information Systems Success Model in bridging online learning equity
DOI:
https://doi.org/10.24059/olj.v30i3.5294Keywords:
Schemata theory, online learning, IS Success Model, learning equity, quality educationAbstract
Initial cognitive structures (schemata) play a crucial role in the success of Information Systems (IS) in online learning environments, particularly in efforts to support conditions associated with learning equity. However, the IS Success Model has yet to define the role of schemata clearly. This study investigates the influence and position of schemata within the DeLone and McLean framework in the context of online learning equity, with the focus on system-related benefits that may support equitable learning conditions. While learning equity is not measured directly, the model examines perceived benefits, such as accessibility, flexibility, and participation, that are theoretically linked to fair learning opportunities. Data were collected via questionnaires administered to 252 students from two state universities, all of whom had at least one year of online learning experience. The instrument was based on the IS Success Model, which includes system quality, information quality, service quality, intention to use, user satisfaction, net benefits, and schemata. Structural Equation Modeling (SEM) was used to analyze the model. The findings show that key components of the IS Success Model are positively and significantly related to perceived system-related benefits associated with conditions that may support equitable online learning, with schemata functioning as a moderating variable. It is suggested that IS implementations should incorporate support strategies such as scaffolding, personalized guidance, and introductory content to enhance the positive impact of schemata.
References
Abdullah, M. S. A. M., Aizuddin, A. N., & Manaf, M. R. A. (2025). The impact of provider’s quality of information system on user satisfaction and perceived net benefits in Malaysian public Hospitals. Malaysian Journal of Medical Sciences, 32(2), 108–121. https://doi.org/10.21315/mjms-12-2024-952
Alkhawaja, M. I., Halim, M. S. A., Abumandil, M. S. S., & Al-Adwan, A. S. (2022). System quality and student’s acceptance of the e-learning system: The serial mediation of perceived usefulness and intention to use. Contemporary Educational Technology, 14(2), ep350. https://doi.org/10.30935/cedtech/11525
Al-Maroof, R., Al-Qaysi, N., Salloum, S. A., & Al-Emran, M. (2022). Blended learning acceptance: A systematic review of information systems models. Technology, Knowledge and Learning, 27(3), 891–926. https://doi.org/10.1007/s10758-021-09519-0
Alotaibi, R. S., & Alshahrani, S. M. (2022). An extended DeLone and McLean’s model to determine the success factors of e-learning platform. PeerJ Computer Science, 8, e876.
Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28–38. https://doi.org/10.1016/j.compedu.2014.08.006
Anderson, R. C., Spiro, R. J., & Anderson, M. C. (1978). Schemata as scaffolding for the representation of information in connected discourse. American Educational Research Journal, 15(3), 433–440. https://doi.org/10.3102/00028312015003433
Axelrod, R. (1973). Schema theory: An information processing model of perception and cognition. American Political Science Review, 67(4), 1248–1266. https://doi.org/10.2307/1956546
Azevedo, R. (2005). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educational Psychologist, 40(4), 199–209. https://doi.org/10.1207/s15326985ep4004_2
Baloran, E. T., Hernan, J. T., & Taoy, J. S. (2021). Course satisfaction and student engagement in online learning amid COVID-19 pandemic: A structural equation model. Turkish Online Journal of Distance Education, 22(4), 1–12. https://doi.org/10.17718/tojde.1002721
Becher, A., Slobodin, O., Manevich-Malul, G., Abu-Kishk, H., Queder, S. A. R., & Pinson, H. (2025). Flexibility as a form of inequity in emergency remote online learning: the perspective of Israeli university students. Journal of Computing in Higher Education, 1–29. https://doi.org/10.1007/s12528-025-09433-9
Bong, J., Cho, K., Liu, Z., & He, D. (2023). A dual-process motivation mediation model to explain female high school students' cognitive engagement and disengagement in emergency remote teaching and online learning in South Korea. British Journal of Educational Technology, 55(3), 1020–1040. https://doi.org/10.1111/bjet.13415
Bormanaki, H. B., & Khoshhal, Y. (2017). The role of equilibration in Piaget’s theory of cognitive development and its implication for receptive skills: A theoretical study. Journal of Language Teaching and Research, 8(5), 996–1005. https://doi.org/10.17507/jltr.0805.22
Bruning, R., Schraw, G., Norby, M., & Ronning, R. (2004). Cognitive psychology and instruction (4th ed.). Pearson Education Inc.
Buttimer, C. J., Littenberg-Tobias, J., & Reich, J. (2022). Designing online professional learning to support educators to teach for equity during COVID and Black Lives Matter. AERA Open, 8, 233285842110677. https://doi.org/10.1177/23328584211067789
Çelik, K., & Ayaz, A. (2022). Validation of the Delone and McLean information systems success model: a study on student information system. Education and Information Technologies, 27(4), 4709–4727. https://doi.org/10.1007/s10639-021-10798-4
Chaku, N., Kelly, D. P., & Beltz, A. M. (2021). Individualized learning potential in stressful times: How to leverage intensive longitudinal data to inform online learning. Computers in Human Behavior, 121, 106772. https://doi.org/10.1016/j.chb.2021.106772
Chalmers, P. A. (2003). The role of cognitive theory in human-computer interface. Computers in Human Behavior, 19(5), 593–607. https://doi.org/10.1016/S0747-5632(02)00086-9
Chen, V., Sandford, A., LaGrone, M., Charbonneau, K., Kong, J., & Ragavaloo, S. (2022). An exploration of instructors’ and students’ perspectives on remote delivery of courses during the COVID-19 pandemic. British Journal of Educational Technology, 53(3), 512–533. https://doi.org/10.1111/bjet.13205
Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), vii–xvi. https://www.jstor.org/stable/249674
Cole, A. W., Lennon, L., & Weber, N. L. (2021). Student perceptions of online active learning practices and online learning climate predict online course engagement. Interactive Learning Environments, 29(5), 866–880. https://doi.org/10.1080/10494820.2019.1619593
D’Souza, C., Kappelides, P., Sithole, N., Chu, M., Taghian, M., & Tay, R. (2023). Learning self-efficacies influence on E-servicescapes: Rethinking post-pandemic pedagogy. Journal of Services Marketing, 37(5), 636–649. https://doi.org/10.1108/jsm-05-2022-0179
DeFeo, D. J., & Caparas, F. (2014). Tutoring as transformative work: A phenomenological case study of tutors’ experiences. Journal of College Reading and Learning, 44(2), 141–163. https://doi.org/10.1080/10790195.2014.906272
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748
Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018
Duke, C. R. (2002). Learning outcomes: Comparing student perceptions of skill level and importance. Journal of Marketing Education, 24(3), 203–217. https://doi.org/10.1177/0273475302238043
Elmunsyah, H., Nafalski, A., Wibawa, A. P., & Dwiyanto, F. A. (2023). Understanding the impact of a learning management system using a novel modified DeLone and McLean model. Education Sciences, 13(3), 235. https://doi.org/10.3390/educsci13030235
Flynn, S. (2021). Education, digital natives, and inequality. Irish Journal of Sociology, 29(2), 248–253. https://doi.org/10.1177/07916035211004815
Fornell, C., & Larcker, D. F. (1981a). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Fornell, C., & Larcker, D. F. (1981b). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3150980
Fuchs, L. S., Seethaler, P. M., Sterba, S. K., Craddock, C., Fuchs, D., Compton, D. L., Geary, D. C., & Changas, P. (2021). Closing the word-problem achievement gap in first grade: Schema-based word-problem intervention with embedded language comprehension instruction. Journal of Educational Psychology, 113(1), 86. https://doi.org/10.1037/edu0000467
Ghozali, I., & Latan, H. (2012). Partial least square: Konsep, teknik dan aplikasi SmartPLS 2.0 M3. Semarang: Badan Penerbit Universitas Diponegoro, 115–126.
Goodman, J., Melkers, J., & Pallais, A. (2018). Can online delivery increase access to education? Journal of Labor Economics, 37(1), 1–34. https://doi.org/10.1086/698895
Gorman, C. A., & Rentsch, J. R. (2017). Retention of assessment center rater training: Improving performance schema accuracy using frame-of-reference training. Journal of Personnel Psychology, 16, 1–11.
Gu, J. (2022). Semiprivate space and access to online education during COVID-19: Empirical tests from China. Online Information Review, 46(4), 771–786. https://doi.org/10.1108/OIR-03-2021-0150
Gurlitt, J., Dummel, S., Schuster, S., & Nückles, M. (2012). Differently structured advance organizers lead to different initial schemata and learning outcomes. Instructional Science, 40(2), 351–369. https://doi.org/10.1007/s11251-011-9180-7
Hair Jr, J. F., Matthews, L. M., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi.org/10.1504/IJMDA.2017.087624
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). SAGE Publications.
Halkias, G. (2015). Mental representation of brands: a schema-based approach to consumers’ organization of market knowledge. Journal of Product & Brand Management, 24(5), 438–448. https://doi.org/10.1108/JPBM-02-2015-0818
He, G. (2020). Schema interaction visual teaching based on smart classroom environment in art course. International Journal of Emerging Technologies in Learning (IJET), 15(17), 252–267. https://doi.org/10.3991/ijet.v15i17.16441
Huang, T.-L., Liao, G.-Y., Huang, H.-Y., Dennis, A. R., & Teng, C.-I. (2025). Learning to be loyal: A schema theory perspective on game use. European Journal of Information Systems, 1–19. https://doi.org/10.1080/0960085X.2025.2499272
James, P. C. (2021). What determines student satisfaction in an e-learning environment? A comprehensive literature review of key success factors. Higher Education Studies, 11(3), 1–9. https://doi.org/10.5539/hes.v11n3p1
Jung, E., Lim, R., & Kim, D. (2022). A schema-based instructional design model for self-paced learning environments. Education Sciences, 12(4), 271. https://doi.org/10.3390/educsci12040271
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31. https://doi.org/10.1207/S15326985EP3801_4
Kock, N. (2023). Using logistic regression in PLS-SEM: Dichotomous endogenous variables. Data Analysis Perspectives Journal, 4(4), 1–6.
Koh, J. H. L. (2017). Designing and integrating reusable learning objects for meaningful learning: Cases from a graduate programme. Australasian Journal of Educational Technology, 33(5). https://doi.org/10.14742/ajet.3072
Li, X., & Zhu, W. (2022). System quality, information quality, satisfaction and acceptance of online learning platform among college students in the context of online learning and blended learning. Frontiers in Psychology, 13, 1054691. https://doi.org/10.3389/fpsyg.2022.1054691
Lin, L., Wang, J., & Meng, X. (2022). Influencing factors of learners’ cognitive engagement in an online learning environment. International Journal of Emerging Technologies in Learning, 17(17), 127–139. https://doi.org/10.3991/ijet.v17i17.33851
Liu, T., & Cheng, L. (2025). The effects of image-schema-based instruction on the learning of collocations of near-synonyms among Chinese EFL learners. Social Sciences & Humanities Open, 12. https://doi.org/10.1016/j.ssaho.2025.101728
Lutfi, A. (2023). Factors affecting the success of accounting information system from the lens of DeLone and McLean IS model. International Journal of Information Management Data Insights, 3(2), 100202.
Magsamen-Conrad, K., & Dillon, J. M. (2020). Mobile technology adoption across the lifespan: A mixed methods investigation to clarify adoption stages, and the influence of diffusion attributes. Computers in Human Behavior, 112, 106456. https://doi.org/10.1016/j.chb.2020.106456
Mahande, R. D., Abdal, N. M., & Nasir, N. (2024). Investigating the effect of learning styles on HyFlex learning towards equity of learning in higher education. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-10-2023-0203
Malureanu, A., Panisoara, G., & Lazar, I. (2021). The relationship between self-confidence, self-efficacy, grit, usefulness, and ease of use of elearning platforms in corporate training during the COVID-19 pandemic. Sustainability, 13(12), 6633. https://doi.org/10.3390/su13126633
Margolis, A. A. (2020). Zone of Proximal Development (ZPD) and organization of students learning activity. Psychological Science and Education, 25(4), 6–27. https://doi.org/10.17759/pse.2020250402
Martin, F., Wang, C., & Sadaf, A. (2018). Student perception of helpfulness of facilitation strategies that enhance instructor presence, connectedness, engagement and learning in online courses.
Nichuhovska, L., Nikolenko, L., Bondarenko, Z., Motorina, V., & Prykhodko, T. (2023). Challenges and prospects of online education in the context of barrier-free access. Multidisciplinary Reviews, 6. https://10.31893/multirev.2023spe020
Noreña, P., Suescún, E., Zapata, C. M., Scaunasu, B., Elliot, G., Jacobsen, H.-A., & Mosquera, D. (2025). Continuous learning of event-based systems by using pre-conceptual schemas. Journal of Systems and Software, 112529. http://dx.doi.org/10.2139/ssrn.5035177
Nunnally, J., & Bernstein, I. (1994). Psychometric theory (3rd ed.). McGrawHill.
Phillips, R., McNaught, C., & Kennedy, G. (2012). Evaluating e-learning: Guiding research and practice (1st ed.). Routledge. https://doi.org/10.4324/9780203813362
Piaget, J. (1970). Science of education and the psychology of the child. Viking Press.
Rahmi, U., & Azrul, A. (2022). Optimizing the discussion methods in blended learning to improve student’s high order thinking skills. Pegem Journal of Education and Instruction, 12(3), 190–196. https://doi.org/10.47750/pegegog.12.03.20
Rahmi, U., Azrul, A., & Mahande, R. D. (2022). The prototype of blended learning’s support system to improve the pre-service teacher’s digital literacy. The Journal of Educators Online, 19(3). https://doi.org/10.9743/JEO.2022.19.3.5
Ramírez-Hurtado, J. M., Hernández-Díaz, A. G., López-Sánchez, A. D., & Pérez-León, V. E. (2021). Measuring online teaching service quality in higher education in the COVID-19 environment. International Journal of Environmental Research and Public Health, 18(5), 2403. https://doi.org/10.3390/ijerph18052403
Reedy, A. K. (2019). Rethinking online learning design to enhance the experiences of Indigenous higher education students. Australasian Journal of Educational Technology, 35(6), 132–149. https://doi.org/10.14742/ajet.5561
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor’s comments: a critical look at the use of PLS-SEM in “MIS Quarterly”. MIS Quarterly, 36(1), iii–xiv. https://doi.org/10.2307/41410402
Roddy, C., Amiet, D. L., Chung, J., Holt, C., Shaw, L., McKenzie, S., Garivaldis, F., Lodge, J. M., & Mundy, M. E. (2017). Applying best practice online learning, teaching, and support to intensive online environments: An integrative review. Frontiers in Education, 2, 59. https://doi.org/10.3389/feduc.2017.00059
Roh, M., & Won, Y. (2023). Impact of online-delivered ehealth literacy intervention on ehealth literacy and health behavior outcomes among female college students during COVID-19. International Journal of Environmental Research and Public Health, 20(3), 2044. https://doi.org/10.3390/ijerph20032044
Rughoobur-Seetah, S., & Hosanoo, Z. A. (2021). An evaluation of the impact of confinement on the quality of e-learning in higher education institutions. Quality Assurance in Education, 29(4), 422–444. https://doi.org/10.1108/QAE-03-2021-0043
Sabeh, H. N., Husin, M. H., Kee, D. M. H., Baharudin, A. S., & Abdullah, R. (2021). A systematic review of the DeLone and McLean model of information systems success in an e-learning context (2010–2020). IEEE Access, 9, 81210–81235. https://doi.org/10.1109/ACCESS.2021.3084815
Sachan, A., Kumar, R., & Kumar, R. (2018). Examining the impact of e-government service process on user satisfaction. Journal of Global Operations and Strategic Sourcing, 11(3), 321–336.
Sweller, J. (2023). The development of cognitive load theory: Replication crises and incorporation of other theories can lead to theory expansion. Educational Psychology Review, 35, 95. https://doi.org/10.1007/s10648-023-09817-2
Tate, T., & Warschauer, M. (2022). Equity in online learning. Educational Psychologist, 57(3), 192–206. https://doi.org/10.1080/00461520.2022.2062597
Truong, H. M. (2016). Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior, 55, 1185–1193. https://doi.org/10.1016/j.chb.2015.02.014
Tsai, H.-T., Chien, J.-L., & Tsai, M.-T. (2014). The influences of system usability and user satisfaction on continued Internet banking services usage intention: Empirical evidence from Taiwan. Electronic Commerce Research, 14(2), 137–169. https://doi.org/10.1007/s10660-014-9136-5
Turnbull, D., Chugh, R., & Luck, J. (2021). Learning management systems: A review of the research methodology literature in Australia and China. International Journal of Research & Method in Education, 44(2), 164–178. https://doi.org/10.1080/1743727X.2020.1737002
UNESCO. (2016). Education 2030: Incheon declaration and framework for action for the implementation of Sustainable Development Goal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. Unesco.org. https://unesdoc.unesco.org/ark:/48223/pf0000245656
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application (JITTA), 11(2), 2. https://aisel.aisnet.org/jitta/vol11/iss2/2
Vuopala, E., Hyvönen, P., & Järvelä, S. (2016). Interaction forms in successful collaborative learning in virtual learning environments. Active Learning in Higher Education, 17(1), 25–38. https://doi.org/10.1177/1469787415616730
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Wang, C., Yang, Y., Tara, N., Irum, M., & Ahmad, K. (2025). Evaluating the impact and development of digital resources utilization on student performance in distance learning using the DeLone and McLean IS success model. Information Development, 02666669251339828. https://doi.org/10.1177/02666669251339828
Wang, Y.-X., & Yin, B. (2023). A new understanding of the cognitive reappraisal technique: An extension based on the schema theory. Frontiers in Behavioral Neuroscience, 17, 1174585. https://doi.org/10.3389/fnbeh.2023.1174585
Wei, B. (2023). The tendency of the schematic structure to maintain stability can be interpreted as mental inertia. Frontiers in Psychology, 14, 1243711. https://doi.org/10.3389/fpsyg.2023.1243711
Wirzberger, M., Herms, R., Esmaeili Bijarsari, S., Eibl, M., & Rey, G. D. (2018). Schema-related cognitive load influences performance, speech, and physiology in a dual-task setting: A continuous multi-measure approach. Cognitive Research: Principles and Implications, 3(1). https://doi.org/10.1186/s41235-018-0138-z
Xiao, P., Sun, Y., Chen, Y., & Wang, X. (2024). Influence of platform satisfaction on the willingness to use a new platform. Scientific Reports, 14(1), 28607. https://doi.org/10.1038/s41598-024-79085-9
You, W. (2022). Research on the relationship between learning engagement and learning completion of online learning students. International Journal of Emerging Technologies in Learning, 17(01), 102–117. https://doi.org/10.3991/ijet.v17i01.28545
Zheng, B., & Warschauer, M. (2015). Participation, interaction, and academic achievement in an online discussion environment. Computers & Education, 84, 78–89. https://doi.org/10.1016/j.compedu.2015.01.008
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