International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Thao-Trang Huynh-Cam 1, Long-Sheng Chen 1*, Van-Canh Nguyen 2, Thanh-Huy Nguyen 3, Tzu-Chuen Lu 1

1 Department of Information Management, Chaoyang University of Technology, Taichung City 413310, Taiwan

2 Quality Assurance Office, Dong Thap University 783, Cao Lanh City, Dong Thap Province, Vietnam

3 Foreign Language Faculty, Dong Thap University 783, Cao Lanh City, Dong Thap Province, Vietnam

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ABSTRACT

Due to advances in wireless technology, e-learning programs and e-courses have increasingly been employed as a mainstream educational mechanism and potentially become crucial incomes of higher education (HE) worldwide. In practice, e-students are required to have highly qualified e-learning programs and satisfied services. It is also extremely difficult for HE to maintain e-students retention as e-students, especially first-year e-students, easily exit from their e-learning programs or shift from one HE to another HE owing to dissatisfaction. However, the dissatisfaction of first-year e-students has gained limited theoretical and practical attention. Thus, it is essential to explore what features make first-year e-students dissatisfied so that HE may have enough time to issue preventive strategies at the early stages for sustainable e-learning adoption. Thus, this study aimed to extract important features using machine learning methods. Data was obtained by using a 5-point Likert e-questionnaire between May and June 2022, generating 499 valid responses from first-year e-students in a Vietnamese public university. The results showed that DT (90.4%) was superior to SVM (88.8%), LR (88.8%), and MLP (85.0%). The most important features included “easy access e-courses via the school e-learning platform”, “adequate personal internet skills”, “feeling stimulated to attend e-courses, “stable and uninterrupted e-learning platform”, “adequate personal digital devices”, “teachers’ great efforts to improve students’ learning”, and “timely responses provisions to students’ inquiries”. The findings of this study are expected to assist HE policy-makers in minimizing e-students’ dissatisfaction and maximizing their satisfaction in order to enhance e-student recruitment and retention, and enhance the quality of e-educational programs.


Keywords: E-student retention, First-year e-student dissatisfaction, Important features for e-student dissatisfaction, Machine learning methods, Vietnam.


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REFERENCES

  1. Alnagar, D.K.F. 2020. Using artificial neural network to predicted student satisfaction in e-learning. American Journal of Applied Mathematics and Statistics, 8, 90–95.

  2. Amkor, A., Aboulkacem, A., El Bannay, O. El Barbri, N. 2024. Electronic tool coupled with machine learning algorithms for the detection of deltamethrin residues in Mentha Spicata L. International Journal of Applied Science and Engineering, 21(1), 2023265.

  3. Anderson, R.E., Srinivasan, S.S. 2003. E‐satisfaction and e‐loyalty: A contingency framework. Psychology & Marketing, 20, 123–138.

  4. Baber, H. 2020. Determinants of students’ perceived learning outcome and satisfaction in online learning during the pandemic of COVID-19. Journal of Education and E-learning Research, 7, 285–292.

  5. Bervell, B., Umar, I.N. 2020. Blended learning or face-to-face? Does Tutor anxiety prevent the adoption of Learning Management Systems for distance education in Ghana?. Open Learning: The Journal of Open, Distance and E-learning, 35, 159–177.

  6. Bossman, A., Agyei, S.K. 2022. Technology and instructor dimensions, e-learning satisfaction, and academic performance of distance students in Ghana. Heliyon, 8.

  7. Browne, M.W. 2000. Cross-validation methods. Journal of Mathematical Psychology, 44, 108–132.

  8. Chang, J.-R., Chen, L.-S., Chang, C.-W. 2020. New term weighting methods for classifying textual sentiment data. International Journal of Applied Science and Engineering, 17(3), 257–268.

  9. Chen, L.-S., Su, C.-T. 2008. Using granular computing model to induce scheduling knowledge in dynamic manufacturing environments. International Journal of Computer Integrated Manufacturing, 21(5), 569–583.

  10. Chen, W.-K., Chen, L.-S., Pan, Y.-T. 2021. A text mining-based framework to discover the important factors in text reviews for predicting the views of live streaming. Applied Soft Computing, 111, 107704.

  11. De Melo Pereira, F.A., Ramos, A.S.M., Gouvêa, M.A., da Costa, M.F. 2015. Satisfaction and continuous use intention of e-learning service in Brazilian public organizations. Computers in Human Behavior, 46, 139–148.

  12. Douglas, J.A., Douglas, A., McClelland, R.J., Davies, J. 2015. Understanding student satisfaction and dissatisfaction: An interpretive study in the UK higher education context. Studies in Higher Education, 40, 329–349.

  13. Elliot, K.M. 2003. Key determinants of student satisfaction. Journal of College Student Retention: Research, Theory and Practice, 4, 271–279.

  14. Fu, F.L. 2010. Comparison of students’ satisfaction and dissatisfaction factors in different classroom types in higher education. International Conference on Hybrid Learning, 415–426.

  15. Ho, I.M.K., Cheong, K.Y., Weldon, A. 2021. Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques. PLOS ONE, 16, e0249423.

  16. Hong, K.S. 2002. Relationships between students’ and instructional variables with satisfaction and learning from a Web-based course. Internet and Higher Education, 5, 267–281.

  17. Huynh-Cam, T.-T., Chen, L.-S., Le, H., 2021. Using decision trees and random forest algorithms to predict and determine factors contributing to first-year university students’ learning performance, Algorithms, 14(11), 318.

  18. Huynh-Cam, T.T., Chen, L.S., Ho, V.T. Lu, T.C. 2023. Identifying crucial factors for e-students’ satisfaction in Vietnamese emergency remote learning using machine learning methods. In 2023 12th International Conference on Awareness Science and Technology (iCAST), 16–21.

  19. Jameel, A.S., Hamdi, S.S., Karem, M.A., Raewf, M.B. 2021. E-satisfaction based on E-service quality among university students. Journal of Physics: Conference Series, 1804, 012039.

  20. Johnson, J.B., Reddy, P., Chand, R., Naiker, M. 2021. Attitudes and awareness of regional Pacific Island students towards e-learning. International Journal of Educational Technology in Higher Education, 18, 1–20.

  21. Jon-Chao, H., Chan-Jer, H., Chien-Yun, D., Ming-Yueh, H., Pei-Hsin, L., Lee, C.C. 2012. Technology anxiety and implicit learning ability affect technology leadership to promote the use of information technology at elementary schools. Procedia-Social and Behavioral Sciences, 64, 555–563.

  22. Kumar, N., Yadav, S.C. 2023. Comprehensive analysis of deep learning-based text classification models and applications. International Journal of Applied Science and Engineering, 20(3), 2022342.

  23. Lu, D.N., Le, H.Q., Vu, T.H., 2020. The factors affecting acceptance of e-learning: A machine learning algorithm approach. Education Sciences, 10, 270.

  24. Martín Rodríguez, Ó., González-Gómez, F., Guardiola, J. 2019. Do course evaluation systems have an influence on e-learning student satisfaction? Higher Education Evaluation and Development, 13, 18–32.

  25. Masadeh, R.E., Almajali, D., Alrowwad, A.A., Alkhawaldeh, R., Khwaldeh, S., Obeid, B., 2023. Evaluation of factors affecting university students' satisfaction with e-learning systems used during Covid-19 crisis: A field study in Jordanian higher education institutions. International Journal of Data and Network Science, 7, 199–214.

  26. Muljana, P.S., Luo, T., 2019. Factors contributing to student retention in online learning and recommended strategies for improvement: A systematic literature review. Journal of Information Technology Education: Research, 18, 19–57.

  27. Nevill, A., Rhodes, C. 2004. Academic and social integration in higher education: a survey of satisfaction and dissatisfaction within a first‐year education studies cohort at a new university. Journal of Further and Higher Education, 28, 179–193.

  28. Pérez-Pérez, M., Serrano-Bedia, A.M., García-Piqueres, G. 2020. An analysis of factors affecting students’ perceptions of learning outcomes with Moodle. Journal of Further and Higher Education, 44, 1114–1129.

  29. Pham, L., Limbu, Y.B., Bui, T.K., Nguyen, H.T., Pham, H.T. 2019. Does e-learning service quality influence e-learning student satisfaction and loyalty? Evidence from Vietnam. International Journal of Educational Technology in Higher Education, 16, 1–26.

  30. Pham, P.T., Duong, T.B., Phan, T.T.T., Nguyen, T.H., Nguyen, M.T., Tuyet, T.L.T., Hoang, N.D., Yen, D.H., Nguyen, T.T. 2020. Managing continuing education via distance learning and face-to-face courses for human resource development in the Mekong Delta of Vietnam. International Journal of Learning, Teaching and Educational Research, 19, 150–171.

  31. Puška, A., Puška, E., Dragić, L., Maksimović, A., Osmanović, N. 2021. Students’ satisfaction with e-learning platforms in Bosnia and Herzegovina. Technology, Knowledge and Learning, 26, 173–191.

  32. Sandiwarno, S., Niu, Z., Nyamawe, A.S. 2023. A novel hybrid machine learning model for analyzing e-learning users’ satisfaction. International Journal of Human-Computer Interaction, 1–22.

  33. Schulz, M., 2023. E-learning as a development Tool. Sustainability, 15, 15012.
    Skrbinjek, V., Dermol, V., 2019. Predicting students’ satisfaction using a decision tree. Tertiary Education and Management, 25, 101–113.

  34. Sung, P.C. 2022. Efficacy of real-time audio biofeedback on physiological strains for simulated tasks with medium and heavy loads. International Journal of Applied Science and Engineering, 19(4), 2022308.

  35. Ullah, M.A., Alam, M.M., Mahiuddin, M., Rahman, M.M. 2019. Predicting factors of students dissatisfaction for retention. Emerging Technologies in Data Mining and Information Security, Springer, Singapore.

  36. Violante, M.G., Vezzetti, E. 2015. Virtual interactive e‐learning application: An evaluation of the student satisfaction. Computer Applications in Engineering Education, 23, 72–91.

  37. Wang, X, Hassan, A.B., Pyng, H.S., Ye, H. 2023. Development and empirical study of international student satisfaction model of online course learning interaction in Chinese universities. Education and Information Technologies, 28, 16951–16977.

  38. Yang, Z., Becerik-Gerber, B., Mino, L. 2013. A study on student perceptions of higher education classrooms: Impact of classroom attributes on student satisfaction and performance. Building and Environment, 70, 171–188.

  39. Yoke, L.B. 2018. Interrelationship between perceived instructor power, student dissatisfaction, and complaint behaviors in the context of higher education. International Education Studies, 11, 12–25.


ARTICLE INFORMATION

Received: 2023-12-27
Revised: 2024-02-19
Accepted: 2024-03-12


Cite this article:

Huynh-Cam, T.T., Chen, L.S., Nguyen, V.C., Nguyen, T.H., Lu, T.C. 2024. Why first-year e-students are dissatisfied: Machine learning methods for enhancing retention. International Journal of Applied Science and Engineering, 21, 2023532. https://doi.org/10.6703/IJASE.202406_21(3).002

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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