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

Yessane Shrrie Nagendhra Rao; Chwen Jen Chen

doi:10.30935/cedtech/14333

Yessane Shrrie Nagendhra Rao ¹, Chwen Jen Chen ¹ ^*

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CONT ED TECHNOLOGY, Volume 16, Issue 2, Article No: ep502

https://doi.org/10.30935/cedtech/14333

Submitted: 17 November 2023, Published Online: 08 March 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

References

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
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.
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
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21^st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49. https://doi.org/10.1016/j.tele.2019.01.007
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
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
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
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
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
International Educational Data Mining Society. (2011). EDM. https://educationaldatamining.org/
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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 7^th International Conference on Cloud Computing and Big Data Analytics (pp. 507-512). https://doi.org/10.1109/ICCCBDA55098.2022.9778874
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
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

Citation

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

Rao, Y. S. N., and 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

Rao YSN, Chen CJ. Bibliometric insights into data mining in education research: A decade in review. CONT ED TECHNOLOGY. 2024;16(2), ep502. https://doi.org/10.30935/cedtech/14333

Rao, Yessane Shrrie Nagendhra, and Chwen Jen Chen. "Bibliometric insights into data mining in education research: A decade in review". Contemporary Educational Technology 2024 16 no. 2 (2024): ep502. https://doi.org/10.30935/cedtech/14333

Rao, Yessane Shrrie Nagendhra et al. "Bibliometric insights into data mining in education research: A decade in review". Contemporary Educational Technology, vol. 16, no. 2, 2024, ep502. https://doi.org/10.30935/cedtech/14333

Rao YSN, Chen CJ. Bibliometric insights into data mining in education research: A decade in review. CONT ED TECHNOLOGY. 2024;16(2):ep502. https://doi.org/10.30935/cedtech/14333