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Identifying priority antecedents of educational data mining acceptance using importance-performance matrix analysis

  • Muslihah WookEmail author
  • Suhaila Ismail
  • Nurhafizah Moziyana Mohd Yusop
  • Siti Rohaidah Ahmad
  • Arniyati Ahmad
Article
  • 23 Downloads

Abstract

Previous studies on educational data mining (EDM) acceptance were focused on antecedents that were adopted from various models and theories. However, the ways in which such antecedents became the most important tools for educational improvement have not been researched in detail. This study aims to identify the priority antecedents of EDM acceptance, particularly among undergraduate students since they are the most affected by this technology. Therefore, six antecedents with 11 variables have been formulated based on positive and negative readiness acquired from the technology readiness index (TRI). Meanwhile, cognition, emotion, internal control belief, and external control belief were obtained from the technology acceptance model 3 (TAM3). The Importance-Performance Matrix Analysis (IPMA) was used to identify priority antecedents of EDM acceptance, which was run using the SmartPLS 3.0 software. The findings revealed that perceived usefulness (PU) is the most important antecedent, followed by perceived ease of use (PEOU), and optimism (OPT). This study contributes to the literature by offering new insights on the field of EDM and extending existing knowledge on how cognition, positive readiness, negative readiness, emotion, internal control belief, and external control belief were combined for identifying priority antecedents of EDM acceptance.

Keywords

Educational data mining Importance-performance matrix analysis Priority antecedents Technology acceptance model 3 Technology readiness index 

Notes

Acknowledgements

The authors acknowledge the Universiti Pertahanan Nasional Malaysia and the Ministry of Higher Education Malaysia for being supporters of this work. We also acknowledge the participation and cooperation received from all undergraduate students in the Klang Valley area.

References

  1. Ali, L., Asadi, M., Gašević, D., Jovanović, J., & Hatala, M. (2013). Factors influencing beliefs for adoption of a learning analytics tool: An empirical study. Computers & Education, 62, 130–148.CrossRefGoogle Scholar
  2. Arnold, K. E., Tanes, Z., & King, A. S. (2010). Administrative perceptions of data-mining software signals: Promoting student success and retention. The Journal of Academic Administration in Higher Education, 6(2), 29–39.Google Scholar
  3. Asif, R., Merceron, A., Abbas, S., & Ghani, N. (2017). Analyzing undergraduate students' performance using educational data mining. Computers & Education, 113, 177–194.CrossRefGoogle Scholar
  4. Babbie, E. R. (1990). Survey Research Methods hlm.2nd Edisi. Belmont: Wadsworth Cengage Learning.Google Scholar
  5. Baker, R. S. J. d. (2010). Mining Data for Student Models. In R. Nkambou, J. Bourdeau, & R. Mizoguchi (Eds.), Advances in intelligent tutoring systems (Vol. 308, pp. 323–337). Berlin: Springer Berlin Heidelberg.CrossRefGoogle Scholar
  6. Bousbia, N., & Belamri, I. (2014). Which contribution does EDM provide to computer-based learning environments? In A. Peña-Ayala (Ed.), Educational data mining (Vol. 524, pp. 3–28). Cham: Springer International Publishing.CrossRefGoogle Scholar
  7. Calders, T., & Pechenizkiy, M. (2012). Introduction to the special section on educational data mining. ACM SIGKDD Explorations Newsletter, 13(2), 3.CrossRefGoogle Scholar
  8. Campagni, R., Merlini, D., Sprugnoli, R., & Verri, M. C. (2015). Data mining models for student careers. Expert Systems with Applications, 42(13), 5508–5521.CrossRefGoogle Scholar
  9. Chin, W. W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), 1–14.CrossRefGoogle Scholar
  10. Clark, L. A., & Watson, D. (1995). Constructing validity : Basic issues in objective scale development the centrality of psychological measurement. Psychological Assessment, 7(3), 309–319.CrossRefGoogle Scholar
  11. Costa, E. B., Fonseca, B., Almeida, M., Ferreira, F., Araújo, D., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students ’ academic failure in introductory programming courses. Computers in Human Behavior, 73, 247–256.CrossRefGoogle Scholar
  12. Dahlan N., Ramayah T., & Mei L. L. (2002) Readiness to Adopt Data Mining Technologies: An Exploratory Study of Telecommunication Employees in Malaysia. In D. Karagiannis, U. Reimer (eds), Practical Aspects of Knowledge Management. PAKM 2002. Lecture Notes in Computer Science, vol 2569. Heidelberg: Springer.Google Scholar
  13. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  14. García, E., Romero, C., Ventura, S. & de Castro, C. (2011). A collaborative educational association rule mining tool. The Internet and Higher Education, 14(2), 77–88.Google Scholar
  15. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12.CrossRefGoogle Scholar
  16. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014a). A primer on partial least squares structural equation modeling (PLS-SEM). Los Angeles: Sage Publications.zbMATHGoogle Scholar
  17. Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014b). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121.CrossRefGoogle Scholar
  18. Henseler, J., Ringle, C. M. & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. (J. Henseler, C. M. Ringle, & R. R. Sinkovics, Eds.), Advances in International Marketing, Advances in International Marketing 20(2009), 277–319.Google Scholar
  19. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135.CrossRefGoogle Scholar
  20. Huang, T. C.-K., Liu, C.-C., & Chang, D.-C. (2012). An empirical investigation of factors influencing the adoption of data mining tools. International Journal of Information Management, 32(3), 257–270.CrossRefGoogle Scholar
  21. Jarvis, C. B., Mackenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218.CrossRefGoogle Scholar
  22. Jin, C. (2013). The perspective of a revised TRAM on social capital building: The case of Facebook usage. Information & Management, 50(4), 162–168.CrossRefGoogle Scholar
  23. Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling hlm.3rd Edisi. New York: The Guilford Press.Google Scholar
  24. Li, Q. (2007). Student and teacher views about technology: A tale of two cities? Journal of Research on Technology in Education, 39(4), 377–397.CrossRefGoogle Scholar
  25. Martens, M., & Roll, O. (2017). Testing the technology readiness and acceptance model for mobile payments across Germany and South Africa. International Journal of Innovation and Technology Management, 14(6), 1750033.CrossRefGoogle Scholar
  26. Motaghian, H., Hassanzadeh, A., & Moghadam, D. K. (2013). Factors affecting university instructors’ adoption of web-based learning systems: Case study of Iran. Computers & Education, 61, 158–167.CrossRefGoogle Scholar
  27. Parasuraman, A. (2000). Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320.CrossRefGoogle Scholar
  28. Park, Y., Yu, J. H., & Jo, I. H. (2016). Clustering blended learning courses by online behavior data case study in a Korean higher education institute. Internet and Higher Education, 29, 1–11.CrossRefGoogle Scholar
  29. Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications, 41(4), 1432–1462.  https://doi.org/10.1016/j.eswa.2013.08.042.CrossRefGoogle Scholar
  30. Pomeroy, W. L. (2014). Academic analytics in higher education: Barriers to adoption. Walden University.Google Scholar
  31. Ranjan, R., Ranjan, J., & Bhatnagar, V. (2013). Critical success factor for implementing data mining in higher education: Indian perspective. International Journal of Computational Systems Engineering, 1(3), 151–161.CrossRefGoogle Scholar
  32. Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865–1886.CrossRefGoogle Scholar
  33. Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, 40(6), 601–618.CrossRefGoogle Scholar
  34. Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27.Google Scholar
  35. Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115.CrossRefGoogle Scholar
  36. Sekaran, U., & Bougie, R. (2010). Research Methods for Business - A Skill Building Approach hlm.5th Edisi. West Sussex: Wiley.Google Scholar
  37. Sukhija, K., Jindal, M. & Aggarwal, N. (2015). The recent state of educational data mining: A survey and future visions. 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE), hlm.354–359. IEEE.Google Scholar
  38. Teo, T. (2011). Factors in fl uencing teachers ’ intention to use technology : Model development and test. Computers & Education, 57(4), 2432–2440.CrossRefGoogle Scholar
  39. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.CrossRefGoogle Scholar
  40. Wang, W.-T., & Wang, C.-C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761–774.CrossRefGoogle Scholar
  41. Wook, M., Yusof, Z. M., & Nazri, M. Z. A. (2017). Educational data mining acceptance among undergraduate students. Education and Information Technologies, 22(3), 1195–1216.Google Scholar
  42. Zhang, P., Li, N. & Sun, H. (2006). Affective quality and cognitive absorption: Extending technology acceptance research. Proceedings of the 39th Annual Hawaii International Conference on System Sciences, hlm.1–11.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Muslihah Wook
    • 1
    Email author
  • Suhaila Ismail
    • 1
  • Nurhafizah Moziyana Mohd Yusop
    • 1
  • Siti Rohaidah Ahmad
    • 1
  • Arniyati Ahmad
    • 1
  1. 1.Faculty of Defence Science and Technology, Kem Perdana Sungai BesiKuala LumpurMalaysia

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