Skip to main content
SpringerLink
Log in

Educational Data Mining in Prediction of Students’ Learning Performance: A Scoping Review

  • Conference paper
  • First Online:
Towards a Collaborative Society Through Creative Learning (WCCE 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 685))

Included in the following conference series:

  • 530 Accesses

Abstract

Students’ academic achievement is always a target of concern for educational institutions. Nowadays, the rapid development of digital transformation has resulted in huge amounts of data being generated by Learning Management Systems. The deployment of Educational Data Mining (EDM) is becoming increasingly significant in discovering ways to improve student learning outcomes. Those approaches effectively facilitate dealing with students’ massive amounts of data. The purpose of this review is to evaluate and discuss the state-of-art EDM for predicting students’ learning performance among higher education institutions. A scoping review was conducted on twelve peer-reviewed publications that were indexed in ACM, IEEE Xplore, Science Direct and Scopus between 2012 and 2021. This study comprehensively reviewed the final inclusion literature on EDM in terms of tools, techniques, machine learning algorithms and application schemes. We have found that WEKA (tool) and classification (technique) were chosen in most of the selected studies carried out in their EDM settings. This review suggested that Tree Structured algorithms as supervised learning approaches can better predict students’ learning performance, as it has been validated in several comparative analyses of other algorithms. In the present study, we demonstrate a future trend toward improving the generalizability of prediction models that can deal with a diverse population and the predictive results can be easily interpreted and explained by educators in the general market.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chalaris, M., Gritzalis, S., Maragoudakis, M., Sgouropoulou, C., Tsolakidis, A.: Improving quality of educational processes providing new knowledge using data mining techniques. In: 3rd International Conference on Integrated Information (IC-ININFO), vol. 147, pp. 390–397 (2014)

    Google Scholar 

  2. Khanna, L., Singh, S.N., Alam, M.: Educational data mining and its role in determining factors affecting students academic performance: a systematic review. In: Proceedings of the 2016 1st India International Conference on Information Processing (IICIP), Delhi, India. IEEE (2016)

    Google Scholar 

  3. Moscoso-Zea, O., Saa, P., Luján-Mora, S.: Evaluation of algorithms to predict graduation rate in higher education institutions by applying educational data mining. Australas. J. Eng. Educ. 24(1), 4–13 (2019)

    Article  Google Scholar 

  4. Francis, B.K., Babu, S.S.: Predicting academic performance of students using a hybrid data mining approach. J. Med. Syst. 43(6), 162 (2019)

    Article  Google Scholar 

  5. Alzafari, K., Kratzer, J.: Challenges of implementing quality in European higher education: an expert perspective. Qual. High. Educ. 25(3), 261–288 (2019)

    Article  Google Scholar 

  6. Tsai, Y.R., Ouyang, C.S., Chang, Y.K.: Identifying engineering students’ English sentence reading comprehension errors: applying a data mining technique. J. Educ. Comput. Res. 54(1), 62–84 (2016)

    Article  Google Scholar 

  7. Li, C., Herbert, N., Yeom, S., Montgomery, J.: Retention factors in STEM education identified using learning analytics: a systematic review. Educ. Sci. 12(11), 781 (2022)

    Article  Google Scholar 

  8. Gupta, S.B., Yadav, R.K., Shivani: Analysis of popular techniques used in educational data mining. Int. J. Next-Gener. Comput. 11(2), 137–162 (2020)

    Google Scholar 

  9. Jin, Y., Yang, X., Yu, C., Yang, L.: Educational data mining: discovering principal factors for better academic performance. In: Proceedings of the 2021 the 3rd International Conference on Big Data Engineering and Technology (BDET), Singapore, Singapore (2021)

    Google Scholar 

  10. Pradeep, A., Das, S., Kizhekkethottam, J.J.: Students dropout factor prediction using EDM techniques. In: Proceedings of the 2015 International Conference on Soft-Computing and Networks Security (ICSNS), Coimbatore, India. IEEE (2015)

    Google Scholar 

  11. Dabhade, P., Agarwal, R., Alameen, K.P., Fathima, A.T., Sridharan, R., Gopakumar, G.: Educational data mining for predicting students’ academic performance using machine learning algorithms. Mater. Today-Proc. 47, 5260–5267 (2021)

    Article  Google Scholar 

  12. Amrieh, E.A., Hamtini, T., Aljarah, I.: Mining educational data to predict student’s academic performance using ensemble methods. Int. J. Database Theory Appl. 9(8), 119–136 (2016)

    Article  Google Scholar 

  13. Zoric, A.B.: Benefits of educational data mining. In: Proceedings of the 44th International Scientific Conference on Economic and Social Development, Split, Croatia (2019)

    Google Scholar 

  14. Tricco, A.C., et al.: PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann. Int. Med. 169(7), 467–473 (2018)

    Article  Google Scholar 

  15. Almarabeh, H.: Analysis of students’ performance by using different data mining classifiers. Int. J. Mod. Educ. Comput. Sci. 9(8), 9–15 (2017)

    Article  Google Scholar 

  16. El Guabassi, I., Bousalem, Z., Marah, R., Qazdar, A.: Comparative analysis of supervised machine learning algorithms to build a predictive model for evaluating students’ performance. Int. J. Online Biomed. Eng. (iJOE). 17(2), 90–105 (2021)

    Article  Google Scholar 

  17. El Guabassi, I., Bousalem, Z., Marah, R., Qazdar, A.: A recommender system for predicting students’ admission to a graduate program using machine learning algorithms. Int. J. Online Biomed. Eng. (iJOE) 17, 135–147 (2021)

    Article  Google Scholar 

  18. Ayinde, A., Omidiora, E., Adetunji, A.: Comparative analysis of selected classifiers in mining students’ educational data. Commun. Appl. Electron. (CAE) 1(5), 5–8 (2015)

    Article  Google Scholar 

  19. Saheed, Y., Oladele, T., Akanni, A., Ibrahim, W.: Student performance prediction based on data mining classification techniques. Niger. J. Technol. 37(4), 1087–1091 (2018)

    Article  Google Scholar 

  20. Blasi, A.H., Alsuwaiket, M.: Analysis of students’ misconducts in higher education using decision tree and ANN algorithms. Eng. Technol. Appl. Sci. Res. 10(6), 6510–6514 (2020)

    Article  Google Scholar 

  21. Salal, Y., Abdullaev, S.: Optimization of classifiers ensemble construction: case study of educational data mining. Comput. Technol. Autom. Control Radio Electron. 19(4), 139–143 (2019)

    Google Scholar 

  22. Kaunang, F.J., Rotikan, R.: Students’ academic performance prediction using data mining. In: Proceedings of the 2018 Third International Conference on Informatics and Computing (ICIC), Palembang, Indonesia. IEEE (2018)

    Google Scholar 

  23. Kiu, C.-C.: Data mining analysis on student’s academic performance through exploration of student’s background and social activities. In: Proceedings of the 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA), Subang Jaya, Malaysia. IEEE (2018)

    Google Scholar 

  24. Chapman, P., et al.: CRISP-DM 1.0: step-by-step data mining guide. SPSS inc. 78, 1–78 (2000)

    Google Scholar 

  25. Adekitan, A.I., Salau, O.: The impact of engineering students’ performance in the first three years on their graduation result using educational data mining. Heliyon 5(2), e01250 (2019)

    Article  Google Scholar 

  26. Kabakchieva, D.: Predicting student performance by using data mining methods for classification. Cybern. Inf. Technol. 13(1), 61–72 (2013)

    MathSciNet  Google Scholar 

  27. Shafiq, D.A., Marjani, M., Habeeb, R.A.A., Asirvatham, D.: Student retention using educational data mining and predictive analytics: a systematic literature review. IEEE Access 10, 72480–72503 (2022)

    Article  Google Scholar 

  28. Chaovalit, P., Zhou, L.: Movie review mining: a comparison between supervised and unsupervised classification approaches. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences, Big Island, HI, USA. IEEE (2005)

    Google Scholar 

  29. Toivonen, T., Jormanainen, I.: Evolution of decision tree classifiers in open ended educational data mining. In: Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality, León, Spain (2019)

    Google Scholar 

  30. Rodrigues, M.W., Isotani, S., Zarate, L.E.: Educational data mining: a review of evaluation process in the e-learning. Telematics Inform. 35(6), 1701–1717 (2018)

    Article  Google Scholar 

  31. Baradwaj, B.K., Pal, S.: Mining educational data to analyze students’ performance. Int. J. Adv. Comput. Sci. Appl. 2(6), 63–66 (2012)

    Google Scholar 

  32. Parmar, K., Vaghela, D., Sharma, P.: Performance prediction of students using distributed data mining. In: Proceedings of the 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India. IEEE (2015)

    Google Scholar 

Download references

Acknowledgement

This research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP). Grant funded by the Korean government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub).

Author information

Authors and Affiliations

  1. University of Tasmania, Hobart, Australia

    Chunping Li, Mingxi Li, Yi-Tong Tseng & Soonja Yeom

  2. National Chung Hsing University, Taichung City, Taiwan

    Chuan-Liang Huang

  3. Chonnam National University, Gwangju, South Korea

    Soo-Hyung Kim

Authors
  1. Chunping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingxi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chuan-Liang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yi-Tong Tseng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Soo-Hyung Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Soonja Yeom
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chunping Li .

Editor information

Editors and Affiliations

  1. School of Education, La Trobe University, Deepdene, VIC, Australia

    Therese Keane

  2. Education and Social Research Institute, Manchester Metropolitan University, Manchester, UK

    Cathy Lewin

  3. University of Duisburg-Essen, Essen, Germany

    Torsten Brinda

  4. Istituto Tecnologie Didattiche, Consiglio Nazioanale delle Ricerche, Genoa, Genova, Italy

    Rosa Bottino

Rights and permissions

Reprints and permissions

Copyright information

© 2023 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, C., Li, M., Huang, CL., Tseng, YT., Kim, SH., Yeom, S. (2023). Educational Data Mining in Prediction of Students’ Learning Performance: A Scoping Review. In: Keane, T., Lewin, C., Brinda, T., Bottino, R. (eds) Towards a Collaborative Society Through Creative Learning. WCCE 2022. IFIP Advances in Information and Communication Technology, vol 685. Springer, Cham. https://doi.org/10.1007/978-3-031-43393-1_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43393-1_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43392-4

  • Online ISBN: 978-3-031-43393-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation