Skip to main content
Log in

Identifying at-risk students based on the phased prediction model

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Identifying at-risk students is one of the most important issues in online education. During different stages of a semester, students display various online learning behaviors. Therefore, we propose a phased prediction model to predict at-risk students at different stages of a semester. We analyze students’ individual characteristics and online learning behaviors, extract features that are closely related to their learning performance, and propose combined feature sets based on a time window constraint strategy and a learning time threshold constraint strategy. The results of our experiments show that the precision of the proposed model in different phases is from 90.4 to 93.6%.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. The College of Distance Learning of Xi’an Jiaotong University. http://www.xjtudlc.com/

  2. Daniel B (2015) Big Data and analytics in higher education: opportunities and challenges. Br J Edu Technol 46(5):904–920

    Article  Google Scholar 

  3. Morris LV, Finnegan C, Wu SS (2005) Tracking student behavior, persistence, and achievement in online courses. Internet High Educ 8(3):221–231

    Article  Google Scholar 

  4. Michinov N, Brunot S, Le Bohec O, Juhel J, Delaval M (2011) Procrastination, participation, and performance in online learning environments. Comput Educ 56(1):243–252

    Article  Google Scholar 

  5. Zhu H, Zhang X, Wang X, Chen Y, Zeng B (2014) A case study of learning action and emotion from a perspective of learning analytics. In: Computational science and engineering (CSE), 2014 IEEE 17th international conference on. IEEE, pp 420–424

  6. Jovanovic M, Vukicevic M, Milovanovic M, Minovic M (2012) Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study. Int J Comput Intell Syst 5(3):597–610

    Article  Google Scholar 

  7. Agrawal R, Golshan B, Terzi E (2014) Grouping students in educational settings. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 1017–1026

  8. Anderson A, Huttenlocher D, Kleinberg J, Leskovec J (2014) Engaging with massive online courses. In: Proceedings of the 23rd international conference on World wide web. ACM, pp 687–698

  9. Brown E, Stewart C, Brailsford T (2006) Adapting for visual and verbal learning styles in AEH. In: Advanced learning technologies, 2006. Sixth international conference on. IEEE, pp 1145–1146

  10. Shahiri AM, Husain W (2015) A review on predicting student’s performance using data mining techniques. Procedia Comput Sci 72:414–422

    Article  Google Scholar 

  11. Angeline DMD (2013) Association rule generation for student performance analysis using apriori algorithm. SIJ Trans Comput Sci Eng Appl (CSEA) 1(1):12–16

    Google Scholar 

  12. Li KF, Rusk D, Song F (2013) Predicting student academic performance. In: 2013 seventh international conference on complex, intelligent, and software intensive systems. IEEE, pp 27–33

  13. Oladokun VO, Adebanjo AT, Seih AR, Charles-Owaba OE (2008) Predicting students academic performance using artificial neural network: a case study of an engineering course. Pac J Sci Technol 9(1):72–79

  14. Ramesh VAMANAN, Parkavi P, Ramar K (2013) Predicting student performance: a statistical and data mining approach. Int J Comput Appl 63(8):35–39

    Google Scholar 

  15. Gray G, McGuinness C, Owende P (2014) An application of classification models to predict learner progression in tertiary education. In: 2014 IEEE international advance computing conference (IACC). IEEE, pp 549–554

  16. Hidayah I, Permanasari AE, Ratwastuti N (2013) Student classification for academic performance prediction using neuro fuzzy in a conventional classroom. In: 2013 international conference on information technology and electrical engineering (ICITEE). IEEE, pp 221–225

  17. Alharbi Z, Cornford J, Dolder L, De La Iglesia B (2016) Using data mining techniques to predict students at risk of poor performance. In: 2016 SAI computing conference (SAI). IEEE, pp 523–531

  18. Hu YH, Lo CL, Shih SP (2014) Developing early warning systems to predict students’ online learning performance. Comput Hum Behav 36:469–478

    Article  Google Scholar 

  19. You JW (2016) Identifying significant indicators using LMS data to predict course achievement in online learning. Internet High Educ 29:23–30

    Article  Google Scholar 

  20. Kupczynski L, Gibson AM, Ice P, Richardson J, Challoo L (2011) The impact of frequency on achievement in online courses: a study from a South Texas University. J Interact Online Learn 10(3):141–149

  21. Cerezo R, Sánchez-Santillán M, Paule-Ruiz MP, Núñez JC (2016) Students’ LMS interaction patterns and their relationship with achievement: a case study in higher education. Comput Educ 96:42–54

    Article  Google Scholar 

  22. Jiang S, Williams A, Schenke K, Warschauer M, O’dowd D (2014) Predicting MOOC performance with week 1 behavior. In: Educational data mining 2014

  23. Romero C, López MI, Luna JM, Ventura S (2013) Predicting students’ final performance from participation in on-line discussion forums. Comput Educ 68:458–472

    Article  Google Scholar 

  24. Ramesh A, Goldwasser D, Huang B, Daumé III H, Getoor L (2013) Modeling learner engagement in MOOCs using probabilistic soft logic. In: NIPS workshop on data driven education. vol 21, p 62

  25. Lara JA, Lizcano D, Martınez MA, Pazos J, Riera T (2014) A system for knowledge discovery in e-learning environments within the European Higher Education Area-application to student data from Open University of Madrid, UDIMA. Comput Educ 72:23–36

    Article  Google Scholar 

  26. Wu F, Zhijia M (2016) The design research of learning outcomes prediction based on the model of personalized behavior analysis for learners. China Educ Technol 1:009

    Google Scholar 

  27. Guo B, Zhang R, Xu G, Shi C, Yang L (2015) Predicting students performance in educational data mining. In: Educational technology (ISET), 2015 international symposium on. IEEE, pp 125–128

  28. Hung JL, Wang MC, Wang S, Abdelrasoul M, Li Y, He W (2017) Identifying at-risk students for early interventions–a time-series clustering approach. IEEE Trans Emerg Top Comput 5(1):45–55

    Article  Google Scholar 

  29. Asif R, Merceron A, Ali SA, Haider NG (2017) Analyzing undergraduate students’ performance using educational data mining. Comput Educ 113:177–194

    Article  Google Scholar 

  30. Marbouti F, Diefes-Dux HA, Madhavan K (2016) Models for early prediction of at-risk students in a course using standards-based grading. Comput Educ 103:1–15

    Article  Google Scholar 

  31. Wenhui Peng (2013) Analysis and modeling of e-learning behavior. Science Press, Beijing

    Google Scholar 

  32. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    MATH  Google Scholar 

  33. Yen S-J, Lee Y-S (2009) Cluster-based under-sampling approaches for imbalanced data distributions. Expert Syst Appl 36(3):5718–5727

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work is supported by National Key Research and Development Program of China (2018YFB1004500), National Nature Science Foundation of China (61877048, 61472315), Innovative Research Group of the National Natural Science Foundation of China (61721002), Innovation Research Team of Ministry of Education (IRT_17R86), Project of China Knowledge Center for Engineering Science and Technology, Project of Chinese academy of engineering “The Online and Offline Mixed Educational Service System for ‘The Belt and Road’ Training in MOOC China.” Natural Science Basic Research Plan in Shaanxi Province of China (2019JM-458).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Feng Tian.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Zheng, Q., Ji, S. et al. Identifying at-risk students based on the phased prediction model. Knowl Inf Syst 62, 987–1003 (2020). https://doi.org/10.1007/s10115-019-01374-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-019-01374-x

Keywords

Navigation