E-Learning Application Usage in Higher Education with Technology Acceptance Model (TAM) for Students’ Assessment

Andi Padalia (1), Jamilah (2), Syakhruni (3), Yuli Sectio Rini (4), A. Muhammad Idkhan (5)
(1) Department of Art, Dance and Music Education, Universitas Negeri Makassar, Makassar, 90224, Indonesia
(2) Department of Art, Dance and Music Education, Universitas Negeri Makassar, Makassar, 90224, Indonesia
(3) Department of Art, Dance and Music Education, Universitas Negeri Makassar, Makassar, 90224, Indonesia
(4) Department of Dance Education, Universitas Negeri Yogyakarta, Yogyakarta, 55281, Indonesia
(5) Department of Mechanical Engineering Education, Universitas Negeri Makassar, Makassar, 90224, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Padalia, Andi, et al. “E-Learning Application Usage in Higher Education With Technology Acceptance Model (TAM) for Students’ Assessment”. International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 3, June 2023, pp. 1059-67, doi:10.18517/ijaseit.13.3.18691.
Disruptions to daily life and business have occurred in nearly every part of the world due to the COVID-19 pandemic. Multiple sectors, including the educational system, have been hit particularly hard. This study focuses on adopting e-learning by students and explores how it can effectively change the old-style classroom environment. Adopting an approach based on the technology acceptance paradigm, the research aims to investigate the e-learning adoption level among students. The study was conducted on 200 students in the art and design faculty at Universitas Negeri Makassar and used e-learning LMS SYAM-OK. The research examined students' intentions to adopt and use online learning in the future can impact their perceptions of the usefulness and ease of technology. The data were analyzed using the SEM methods with IBM AMOS software. The results of the study indicate that there is a significant impact between the variables of AB to BI (coefficient of 0.514; p < 0.01) and BI to AU (coefficient of 0.617; p < 0.01) of online education platforms during the pandemic. Students are intensely interested in utilizing the e-learning system when they have favorable attitudes. The success or failure of an e-learning program depends on the student's mindset. An individual's opinion of a system is shaped by their experience, particularly its accessibility and practicality. The perceived utility influences the number of individuals willing to return to using e-learning and use it.

M. Mailizar, D. Burg, and S. Maulina, “Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model,” Educ. Inf. Technol., vol. 26, no. 6, pp. 7057-7077, 2021.

S. Suarlin, S. Negi, M. I. Ali, B. A. Bhat, and E. Elpisah, “The Impact of Implication Problem Posing Learning Model on Students in High Schools,” Int. J. Environ. Eng. Educ., vol. 3, no. 2, pp. 69-74, 2021.

Z. Feiyue, “Edutainment Methods in the Learning Process: Quickly, Fun and Satisfying,” Int. J. Environ. Eng. Educ., vol. 4, no. 1, pp. 19-26, 2022.

M. S. Lamada, S. Sanatang, A. Z. Ifani, and D. H. Hidayat, “Evaluation in Assessment of Student Competence: Application of the Indonesian Student Competency Assessment (AKSI) in Elementary Schools,” Int. J. Environ. Eng. Educ., vol. 4, no. 2, pp. 66-75, 2022.

J. L. Moore, C. Dickson-Deane, and K. Galyen, “e-Learning, online learning, and distance learning environments: Are they the same?,” Internet High. Educ., vol. 14, no. 2, pp. 129-135, 2011.

W. W. Song, A. Forsman, and J. Yan, “An e-curriculum based systematic resource integration approach to web-based education,” Int. J. Inf. Educ. Technol., vol. 5, no. 7, p. 495, 2015.

E. Alqurashi, “Predicting student satisfaction and perceived learning within online learning environments,” Distance Educ., vol. 40, no. 1, pp. 133-148, 2019.

M. B. Yilmaz, “The Relation between Academic Procrastination of University Students and Their Assignment and Exam Performances: The Situation in Distance and Face-to-Face Learning Environments.,” J. Educ. Train. Stud., vol. 5, no. 9, pp. 146-157, 2017.

A. A. Yunusa and I. N. Umar, “A scoping review of critical predictive factors (CPFs) of satisfaction and perceived learning outcomes in E-learning environments,” Educ. Inf. Technol., vol. 26, no. 1, pp. 1223-1270, 2021.

S. Lonn and S. D. Teasley, “Saving time or innovating practice: Investigating perceptions and uses of Learning Management Systems,” Comput. Educ., vol. 53, no. 3, pp. 686-694, 2009.

F. Demir, C. Bruce-Kotey, and F. Alenezi, “User Experience Matters: Does One size Fit all? Evaluation of Learning Management Systems,” Technol. Knowl. Learn., pp. 1-19, 2021.

Q. Liu and S. Geertshuis, “Professional identity and the adoption of learning management systems,” Stud. High. Educ., pp. 1-14, 2019.

N. Cavus, “Distance learning and learning management systems,” Procedia-Social Behav. Sci., vol. 191, pp. 872-877, 2015.

N. Cavus and M. M. Al-Momani, “Mobile system for flexible education,” Procedia Comput. Sci., vol. 3, pp. 1475-1479, 2011.

A. Albirini, “Teachers’ attitudes toward information and communication technologies: The case of Syrian EFL teachers,” Comput. Educ., vol. 47, no. 4, pp. 373-398, 2006.

C. De Medio, C. Limongelli, F. Sciarrone, and M. Temperini, “MoodleREC: A recommendation system for creating courses using the moodle e-learning platform,” Comput. Human Behav., vol. 104, p. 106168, 2020.

T. J. McGill and J. E. Klobas, “A task-technology fit view of learning management system impact,” Comput. Educ., vol. 52, no. 2, pp. 496-508, 2009.

D. Weaver, C. Spratt, and C. S. Nair, “Academic and student use of a learning management system: Implications for quality,” Australas. J. Educ. Technol., vol. 24, no. 1, 2008.

A. Padalia and T. Natsir, “End-User Computing Satisfaction (EUCS) Model: Implementation of Learning Management System (LMS) on Students Satisfaction at Universities,” Int. J. Environ. Eng. Educ., vol. 4, no. 3, 2022, doi: 10.55151/ijeedu.v4i3.72.

W. H. Levie and R. Lentz, “Effects of text illustrations: A review of research,” Ectj, vol. 30, no. 4, pp. 195-232, 1982.

Y. Lee, K. A. Kozar, and K. R. T. Larsen, “The technology acceptance model: Past, present, and future,” Commun. Assoc. Inf. Syst., vol. 12, no. 1, p. 50, 2003.

F. Fitria, R. Ruslan, and M. Y. Mappeasse, “Application of E-Learning Based on Enriched Virtual Model in the Subject Database,” Int. J. Environ. Eng. Educ., vol. 3, no. 1, pp. 32-40, 2021.

R. H. Hoyle, Structural Equation Modeling”¯: Concepts, Issues, and Applications. Thousand Oaks, California: SAGE Publications, Inc., 1995.

J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 7th ed. Harlow, England: Pearson New International Edition, 2014.

L. F. DiLalla, “Structural equation modeling: Uses and issues,” Handb. Appl. Multivar. Stat. Math. Model., pp. 439-464, 2000.

F. N. Kerlinger and H. B. Lee, Foundations of Behavioral Research, 4th ed. New York: Holt, Rinehart and Winston, 2000.

R. B. Johnson and L. Christensen, Educational research: Quantitative, qualitative, and mixed approaches. SAGE Publications, Incorporated, 2019.

J. W. Creswell and V. L. P. Clark, Designing and Conducting Mixed Methods Research, 3rd ed. Beverly Hills, CA: SAGE Publications, 2018.

B. M. Byrne, Structural equation modeling with AMOS: Basic concepts, applications, and programming, 2nd ed. New York: Routledge, 2016.

G. D. Garson, Partial Least Squares: Regression & structural equation modeling. Asheboro, USA: Statistical Publishing Associates, 2016.

A. Singh, S. Sharma, and M. Paliwal, “Adoption intention and effectiveness of digital collaboration platforms for online learning: the Indian students’ perspective,” Interact. Technol. Smart Educ., vol. 18, no. 4, pp. 493-514, 2021.

G. Maheshwari, “Factors affecting students’ intentions to undertake online learning: an empirical study in Vietnam,” Educ. Inf. Technol., vol. 26, no. 6, pp. 6629-6649, 2021.

A. Tarhini, K. Hone, and X. Liu, “The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model,” Comput. Human Behav., vol. 41, pp. 153-163, 2014.

C. Ong, “Malaysian undergraduates’ behavioural intention to use LMS: an extended selfdirected learning technology acceptance model (SDLTAM),” J. ELT Res., vol. 4, no. 1, pp. 8-25, 2019.

F. Abdullah, R. Ward, and E. Ahmed, “Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios,” Comput. Human Behav., vol. 63, pp. 75-90, 2016.

F. Abdullah and R. Ward, “Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors,” Comput. Human Behav., vol. 56, pp. 238-256, 2016.

M. A. Al”hawari and S. Mouakket, “The influence of technology acceptance model (TAM) factors on students’e”satisfaction and e”retention within the context of UAE e”learning,” Educ. Bus. Soc. Contemp. Middle East. Issues, vol. 3, no. 4, pp. 299-314, 2010.

C.-T. Chang, J. Hajiyev, and C.-R. Su, “Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach,” Comput. Educ., vol. 111, pp. 128-143, 2017.

G. D. Garson, Structural Equation Modeling, Blue Book. Asheboro, North Corolina: Statistical Associates Publishing, 2012.

D. Gefen, E. E. Rigdon, and D. Straub, “Editor’s comments: an update and extension to SEM guidelines for administrative and social science research,” Mis Q., pp. iii-xiv, 2011.

R. E. Schumacher and R. G. Lomax, A Beginner’s Guide to Structural Equation Modeling: Third Edition, 3rd ed. Mahwah, NJ: Lawrence Erlbaum Associates, 2010.

R. B. Kline, “Promise and pitfalls of structural equation modeling in gifted research.,” 2010.

K. G. Jí¶reskog and D. Sí¶rbom, LISREL 8: Structural equation modeling with the SIMPLIS command language. Scientific Software International, 1993.

J. J. Hox and T. M. Bechger, “An introduction to structural equation modeling,” 2007.

B. Wheaton, B. Muthen, D. F. Alwin, and G. F. Summers, “Assessing reliability and stability in panel models,” Sociol. Methodol., vol. 8, pp. 84-136, 1977.

E. G. Carmines, “Analyzing models with unobserved variables,” Soc. Meas. Curr. issues, vol. 80, 1981.

J. S. Tanaka and G. J. Huba, “A general coefficient of determination for covariance structure models under arbitrary GLS estimation,” Br. J. Math. Stat. Psychol., vol. 42, no. 2, pp. 233-239, 1989.

J. H. Steiger and J. C. Lind, “Statistically based tests for the number of common factors,” 1980.

M. W. Browne and R. Cudeck, “Alternative ways of assessing model fit,” Sage Focus Ed., vol. 154, p. 136, 1993.

L. J. Williams and E. O’Boyle Jr, “The myth of global fit indices and alternatives for assessing latent variable relations,” Organ. Res. Methods, vol. 14, no. 2, pp. 350-369, 2011.

F. Chen, P. J. Curran, K. A. Bollen, J. Kirby, and P. Paxton, “An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models,” Sociol. Methods Res., vol. 36, no. 4, pp. 462-494, 2008.

L. Hu and P. M. Bentler, “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives,” Struct. Equ. Model. a Multidiscip. J., vol. 6, no. 1, pp. 1-55, 1999.

L. R. Tucker and C. Lewis, “A reliability coefficient for maximum likelihood factor analysis,” Psychometrika, vol. 38, no. 1, pp. 1-10, 1973.

P. M. Bentler and L. T. Hu, “Evaluating model fit,” in Structural equation modeling: Concepts, issues, and applications, Thousand Oaks, CA: SAGE Publications, 1995, pp. 76-99.

P. M. Bentler, “SEM with simplicity and accuracy,” J. Consum. Psychol., vol. 20, no. 2, pp. 215-220, 2010, doi: 10.1016/j.jcps.2010.03.002.

T. A. Brown, Confirmatory Factor Analysis for Applied Research, 2nd ed. New York: The Guilford Press, 2015.

K. A. Bollen, “A new incremental fit index for general structural equation models,” Sociol. Methods Res., vol. 17, no. 3, pp. 303-316, 1989.

S. A. Mulaik, L. R. James, J. Van Alstine, N. Bennett, S. Lind, and C. D. Stilwell, “Evaluation of goodness-of-fit indices for structural equation models.,” Psychol. Bull., vol. 105, no. 4, pp. 430-445, 1989.

L. James, S. Mulaik, and J. M. Brett, Causal analysis: Assumptions, models, and data. Beverly Hills: Sage publications, 1982.

K. A. Bollen and J. S. Long, Testing structural equation models, vol. 154. Sage, 1993.

M. S. Khine, L. C. Ping, and D. Cunningham, Application of Structural Equation Modeling in Educational Research and Practice”¯: Contemporary Approaches to Research, 7th ed. Rotterdam, Netherlands: Sense Publishers, 2013.

R. P. Bagozzi and Y. Yi, “On the evaluation of structural equation models,” J. Acad. Mark. Sci., vol. 16, no. 1, pp. 74-94, 1988.

V. Venkatesh and F. D. Davis, “A theoretical extension of the technology acceptance model: Four longitudinal field studies,” Manage. Sci., vol. 46, no. 2, pp. 186-204, 2000.

H. Van der Heijden, “User acceptance of hedonic information systems,” MIS Q., pp. 695-704, 2004.

D.-H. Shin, Y.-J. Shin, H. Choo, and K. Beom, “Smartphones as smart pedagogical tools: Implications for smartphones as u-learning devices,” Comput. Human Behav., vol. 27, no. 6, pp. 2207-2214, 2011.

R. G. Saadí©, “Dimensions of perceived usefulness: Toward enhanced assessment,” Decis. Sci. J. Innov. Educ., vol. 5, no. 2, pp. 289-310, 2007.

C.-L. Hsu and H.-P. Lu, “Why do people play on-line games? An extended TAM with social influences and flow experience,” Inf. Manag., vol. 41, no. 7, pp. 853-868, 2004.

W. R. King and J. He, “A meta-analysis of the technology acceptance model,” Inf. Manag., vol. 43, no. 6, pp. 740-755, 2006.

J. Schepers and M. Wetzels, “A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects,” Inf. Manag., vol. 44, no. 1, pp. 90-103, 2007.

S. Anuar and R. Othman, “Determinants of online tax payment system in Malaysia,” Int. J. Public Inf. Syst., no. 1, pp. 17-32, 2012.

F. Calisir, C. A. Gumussoy, and A. Bayram, “Predicting the behavioral intention to use enterprise resource planning systems: An exploratory extension of the technology acceptance model,” Manag. Res. news, 2009.

Y. Wang, H. Lin, and P. Luarn, “Predicting consumer intention to use mobile service,” Inf. Syst. J., vol. 16, no. 2, pp. 157-179, 2006.

K. Mathieson, E. Peacock, and W. W. Chin, “Extending the technology acceptance model: the influence of perceived user resources,” ACM SIGMIS Database DATABASE Adv. Inf. Syst., vol. 32, no. 3, pp. 86-112, 2001.

T. Robinson, “Using the technology acceptance model to examine technology acceptance of online learning technologies by non-traditional students,” I-Manager’s J. Educ. Technol., vol. 16, no. 1, p. 21, 2019.

H. Baber, “Modelling the acceptance of e-learning during the pandemic of COVID-19-A study of South Korea,” Int. J. Manag. Educ., vol. 19, no. 2, p. 100503, 2021, doi: https://doi.org/10.1016/j.ijme.2021.100503.

N. Marangunić and A. Granić, “Technology acceptance model: a literature review from 1986 to 2013,” Univers. access Inf. Soc., vol. 14, no. 1, pp. 81-95, 2015.

R. Saadí© and B. Bahli, “The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model,” Inf. Manag., vol. 42, no. 2, pp. 317-327, 2005.

R. Scherer, F. Siddiq, and J. Tondeur, “The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education,” Comput. Educ., vol. 128, pp. 13-35, 2019.

D. G. O’Dell and T. Sulastri, “The Impact of Using the Internet for Learning for Students with Technology Acceptance Model ( TAM ),” Int. J. Environ. Eng. Educ., vol. 1, no. 2, pp. 46-52, 2019.

A. Al-Azawei, P. Parslow, and K. Lundqvist, “Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM),” Australas. J. Educ. Technol., vol. 33, no. 2, 2017.

T. Farahat, “Applying the technology acceptance model to online learning in the Egyptian universities,” Procedia-Social Behav. Sci., vol. 64, pp. 95-104, 2012.

X. Liu, “Empirical testing of a theoretical extension of the technology acceptance model: An exploratory study of educational wikis,” Commun. Educ., vol. 59, no. 1, pp. 52-69, 2010.

R. Ibrahim, N. S. Leng, R. C. M. Yusoff, G. N. Samy, S. Masrom, and Z. I. Rizman, “E-learning acceptance based on technology acceptance model (TAM),” J. Fundam. Appl. Sci., vol. 9, no. 4S, pp. 871-889, 2017.

K. A. Pituch and Y. Lee, “The influence of system characteristics on e-learning use,” Comput. Educ., vol. 47, no. 2, pp. 222-244, 2006.

P. C. Sun, R. J. Tsai, G. Finger, Y. Y. Chen, and D. Yeh, “What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction,” Comput. Educ., vol. 50, no. 4, pp. 1183-1202, May 2008, doi: 10.1016/j.compedu.2006.11.007.

T. Govindasamy, “Successful implementation of e-learning: Pedagogical considerations,” internet High. Educ., vol. 4, no. 3-4, pp. 287-299, 2001.

C.-M. Chiu, M.-H. Hsu, S.-Y. Sun, T.-C. Lin, and P.-C. Sun, “Usability, quality, value and e-learning continuance decisions,” Comput. Educ., vol. 45, no. 4, pp. 399-416, 2005.

H. Al-Samarraie, B. K. Teng, A. I. Alzahrani, and N. Alalwan, “E-learning continuance satisfaction in higher education: a unified perspective from instructors and students,” Stud. High. Educ., vol. 43, no. 11, pp. 2003-2019, 2018.

D. Zhang, J. L. Zhao, L. Zhou, and J. F. Nunamaker Jr, “Can e-learning replace classroom learning?,” Commun. ACM, vol. 47, no. 5, pp. 75-79, 2004.

M. Aparicio, F. Bacao, and T. Oliveira, “Grit in the path to e-learning success,” Comput. Human Behav., vol. 66, pp. 388-399, 2017.

C.-S. Ong and J.-Y. Lai, “Gender differences in perceptions and relationships among dominants of e-learning acceptance,” Comput. Human Behav., vol. 22, no. 5, pp. 816-829, 2006, doi: https://doi.org/10.1016/j.chb.2004.03.006.

M. Aparicio, F. Bacao, and T. Oliveira, “Cultural impacts on e-learning systems’ success,” Internet High. Educ., vol. 31, pp. 58-70, 2016.

M. Aparicio, F. Bacao, and T. Oliveira, “An e-learning theoretical framework,” An e-learning Theor. Framew., no. 1, pp. 292-307, 2016.

S. T. Siddiqui, S. Alam, Z. A. Khan, and A. Gupta, “Cloud-based e-learning: using cloud computing platform for an effective e-learning,” in Smart Innovations in Communication and Computational Sciences, Springer, 2019, pp. 335-346.

S. B. Gupta and M. Gupta, “Technology and E-learning in higher education,” Technology, vol. 29, no. 4, pp. 1320-1325, 2020.

U.-D. Ehlers and J. M. Pawlowski, Handbook on quality and standardisation in e-learning. Springer Science & Business Media, 2006.

F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Q., pp. 319-340, 1989.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).