Review Article

Bibliometric insights into data mining in education research: A decade in review

Yessane Shrrie Nagendhra Rao 1 , Chwen Jen Chen 1 *
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1 Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, Sarawak, MALAYSIA* Corresponding Author
Contemporary Educational Technology, 16(2), April 2024, ep502, https://doi.org/10.30935/cedtech/14333
Published Online: 08 March 2024, Published: 01 April 2024
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ABSTRACT

This bibliometric study on data mining in education synonymous with big educational data utilizes VOSviewer and Harzing’s Publish and Perish to analyze the metadata of 1,439 journal articles found in Scopus from 2010 to 2022. As bibliometric analyses in this field are lacking, this study aims to provide a comprehensive outlook on the current developments and impact of research in this field. This study employs descriptive and trends analysis, co-authorship analysis, co-citation analysis, co-occurrences of keywords, terms map analysis, and analysis of the impact and performance of publications. It also partially replicates a similar study conducted by Wang et al. (2022), who used the Web of Science (WoS) database. The study is reported in an article entitled ‘Big data and data mining in education: A bibliometrics study from 2010 to 2022’. Results show that data mining in education is a growing research field. There is also a significant difference between the publications in Scopus and WoS. The study found several research areas and topics, such as student academic performance prediction, e-learning, machine learning, and innovative data mining techniques, to be the core basis for collaborating and continuing current research in this field. These results highlight the importance of continuing research on data mining in education, guiding future research in tackling educational challenges.
Keywords: educational data mining, big data, education, bibliometric analysis, Scopus

CITATION (APA)

Rao, Y. S. N., & Chen, C. J. (2024). Bibliometric insights into data mining in education research: A decade in review. Contemporary Educational Technology, 16(2), ep502. https://doi.org/10.30935/cedtech/14333

REFERENCES

  1. Agaoglu, M. (2016). Predicting instructor performance using data mining techniques in higher education. IEEE Access, 4, 2379-2387. https://doi.org/10.1109/ACCESS.2016.2568756
  2. Ahmi, A. (2021). Bibliometric analysis for beginners: A starter guide to begin with a bibliometric study using Scopus dataset and tools such as Microsoft Excel, Harzing’s Publish or Perish and VOSviewer software. Aidi-Ahmi.
  3. Alachiotis, N. S., Kotsiantis, S., Sakkopoulos, E., & Verykios, V. S. (2022). Supervised machine learning models for student performance prediction. Intelligent Decision Technologies, 16(1). 93-106. https://doi.org/10.3233/IDT-210251
  4. Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49. https://doi.org/10.1016/j.tele.2019.01.007
  5. Baek, C., & Doleck, T. (2022). Educational data mining: A bibliometric analysis of an emerging field. IEEE Access, 10, 31289-31296. https://doi.org/10.1109/ACCESS.2022.3160457
  6. Baker, R. S., & Siemens, G. (2014). Educational data mining and learning analytics. In R. K. Sawyer (Eds.), Cambridge handbook of the learning sciences (pp. 253-274). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.016
  7. Esteban, A., Romero, C., & Zafra, A. (2021). Assignments as influential factor to improve the prediction of student performance in online courses. Applied Sciences, 11(21). 10145. https://doi.org/10.3390/app112110145
  8. Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Slater, S., Baker, R., & Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 44(1), 130-160. https://doi.org/10.3102/0091732X20903304
  9. Hermaliani, E. H., Fanani, A. Z., Santoso, H. A., Affandy, A., Purwanto, P., Muljono, M., Syukur, A., Setiadi, D. R. I. M., & Rafrastara, F. A. (2022). Systematic review of educational data mining for student performance prediction using bibliometric network analysis (SeBriNA). In Proceedings of the 2022 International Seminar on Application for Technology of Information and Communication (pp. 463-468). https://doi.org/10.1109/iSemantic55962.2022.9920477
  10. International Educational Data Mining Society. (2011). EDM. https://educationaldatamining.org/
  11. López-Zambrano, J., Lara, J. A., & Romero, C. (2022). Improving the portability of predicting students’ performance models by using ontologies. Journal of Computing in Higher Education, 34, 1-19. https://doi.org/10.1007/s12528-021-09273-3
  12. López-Zambrano, J., Torralbo, J. A. L., & Romero, C. (2021). Early prediction of student learning performance through data mining: A systematic review. Psicothema, 33(3), 456-465. https://doi.org/10.7334/psicothema2021.62
  13. Maatuk, A. M., Elberkawi, E. K., Aljawarneh, S., Rashaideh, H., & Alharbi, H. (2022). The COVID-19 pandemic and e-learning: Challenges and opportunities from the perspective of students and instructors. Journal of Computing in Higher Education, 34, 21-38. https://doi.org/10.1007/s12528-021-09274-2
  14. Marín-Marín, J., López-Belmonte, J., Fernández-Campoy, J., & Romero-Rodríguez, J. (2019). Big data in education. A bibliometric review. Journal of Social Sciences, 8(8), 223. https://doi.org/10.3390/socsci8080223
  15. Masood, M., & Mokmin, N. A. M. (2017). Case-based reasoning intelligent tutoring system: An application of big data and IoT. In Proceedings of the 2017 International Conference on Big Data Research (pp. 28-32). https://doi.org/10.1145/3152723.3152735
  16. Mengash, H. A. (2020). Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access, 8, 55462-55470. https://doi.org/10.1109/ACCESS.2020.2981905
  17. Menon, A., Gaglani, S., Haynes, M. R., & Tackett, S. (2017). Using “big data” to guide implementation of a web and mobile adaptive learning platform for medical students. Medical Teacher, 39(9), 975-980. https://doi.org/10.1080/0142159X.2017.1324949
  18. Nuankaew, P., & Nuankaew, W. S. (2022). Student performance prediction model for predicting academic achievement of high school students. European Journal of Educational Research, 11(2), 949-963. https://doi.org/10.12973/EU-JER.11.2.949
  19. Nuankaew, P., Nasa-ngium, P., & Nuankaew, W. S. (2021). Application for identifying students achievement prediction model in tertiary education: Learning strategies for lifelong learning. International Journal of Interactive Mobile Technologies, 15(22), 22-43. https://doi.org/10.3991/IJIM.V15I22.24069
  20. Rodrigues, M. W., Isotani, S., & Zárate, L. E. (2018). Educational data mining: A review of evaluation process in the e-learning. Telematics and Informatics, 35, 1701-1717. https://doi.org/10.1016/j.tele.2018.04.015
  21. Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33, 135-146. https://doi.org/10.1016/j.eswa.2006.04.005
  22. Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 40(6), 601-625. https://doi.org/10.1109/TSMCC.2010.2053532
  23. Romero, C., & Ventura, S. (2017). Educational data science in massive open online courses. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(1), e1187. https://doi.org/10.1002/widm.1187
  24. Shahiri, A. M., & Husain, W. (2015). A review on predicting student’s performance using data mining techniques. Procedia Computer Science, 72. 414-422. https://doi.org/10.1016/j.procs.2015.12.157
  25. Sin, K., & Muthu, L. (2015). Application of big data in education data mining and learning analytics–A literature review. ICTACT Journal on Soft Computing, 4(5), 1035-1049. https://doi.org/10.21917/ijsc.2015.0145
  26. Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., & Ragos, O. (2021). Fuzzy-based active learning for predicting student academic performance using autoML: A step-wise approach. Journal of Computing in Higher Education, 33(3), 635-667. https://doi.org/10.1007/s12528-021-09279-x
  27. Wang, C., Dai, J., & Xu, L. (2022). Big data and data mining in education: A bibliometrics study from 2010 to 2022. In Proceedings of the 7th International Conference on Cloud Computing and Big Data Analytics (pp. 507-512). https://doi.org/10.1109/ICCCBDA55098.2022.9778874
  28. Yacoub, M. F., Maghawry, H. A., Helal, N. A., Gharib, T. F., & Ventura, S. (2022). An enhanced predictive approach for students’ performance. International Journal of Advanced Computer Science and Applications, 13(4), 879-883. https://doi.org/10.14569/IJACSA.2022.01304101
  29. Yagci, M. (2022). Educational data mining: Prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9, 11. https://doi.org/10.1186/s40561-022-00192-z