Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

The Predictive Learning Analytics for Student Dropout Using Data Mining Technique: A Systematic Literature Review

  • Chapter
  • First Online:
Advances in Technology Transfer Through IoT and IT Solutions

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

Abstract

This research aims to make a systematic review of the literature with the theme of predictive learning analytics (PLA) for student dropouts using data mining techniques. The method used in this systematic review research is the literature from empirical research regarding the prediction of dropping out of school. In this phase, a review protocol, selection requirements for potential studies, and methods for analyzing the content of the selected studies are provided. The PLA is a statistical analysis of current data and historical data derived from student learning processes to develop predictions for improving the quality of learning by identifying students who are at risk of failing in their studies. PLA in higher education (HE) is essential to improve knowledge. The failure of the HE to identify the potential factors contributing to student failure rate will risk both the HE images and the student’s life. The systematic literature review conducted in this study was taken from selected journals published from 2016 to 2021.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Similar content being viewed by others

References

  1. Y. Chen, A. Johri, H. Rangwala, Running out of STEM: a comparative study across STEM majors of college students at-risk of dropping out early, in ACM International Conference Proceeding Series (2018), pp. 270–279

    Google Scholar 

  2. F. Jiménez, A. Paoletti, G. Sánchez, G. Sciavicco, Predicting the risk of academic dropout with temporal multi-objective optimization. IEEE Trans. Learn. Technol. 12, 225–236 (2019). https://doi.org/10.1109/TLT.2019.2911070

    Article  Google Scholar 

  3. S. Sultana, S. Khan, M.A. Abbas, Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts. Int. J. Electr. Eng. Educ. 54, 105–118 (2017). https://doi.org/10.1177/0020720916688484

    Article  Google Scholar 

  4. S. Aydin, A. Öztürk, G.T. Büyükköse et al., An investigation of drop-out in open and distance education. Educ. Sci. Theory Pract. 19, 40–57 (2019). https://doi.org/10.12738/estp.2019.2.003

  5. M.S. Larsen, K.P. Kornbeck, R.M. Kristensen et al., Dropout phenomena at universities: what is dropout? Why does dropout occur? What can be done by the universities to prevent or reduce it? A systematic review (2013)

    Google Scholar 

  6. B. Perez, C. Castellanos, D. Correal, Applying data mining techniques to predict student dropout: a case study, in Proceedings of the 2018 IEEE 1st Colombian Conference on Applications in Computational Intelligence (ColCACI 2018) (2018). https://doi.org/10.1109/ColCACI.2018.8484847

  7. J.M. Ortiz-Lozano, A. Rua-Vieites, P. Bilbao-Calabuig, M. Casadesús-Fa, University student retention: best time and data to identify undergraduate students at risk of dropout. Innov. Educ. Teach. Int. 57, 74–85 (2020). https://doi.org/10.1080/14703297.2018.1502090

    Article  Google Scholar 

  8. C. Herodotou, Z. Zdrahal, B. Rienties et al., Implementing predictive learning analytics on a large scale: the teacher’s perspective, in ACM International Conference Proceeding Series (2017), pp. 267–271. https://doi.org/10.1145/3027385.3027397

  9. C. Herodotou, B. Rienties, B. Verdin, A. Boroowa, Predictive learning analytics “at scale”: towards guidelines to successful implementation in higher education based on the case of the Open University UK. J. Learn. Anal. 6, 85–95 (2019). https://doi.org/10.18608/jla.2019.61.5

  10. C. Herodotou, B. Rienties, A. Boroowa et al., A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective. Educ. Technol. Res. Dev. 67, 1273–1306 (2019). https://doi.org/10.1007/s11423-019-09685-0

    Article  Google Scholar 

  11. E. Iwatani, Overview of data mining’s potential benefits and limitations in education research. Pract. Assess., Res. Eval. 23, 1–8 (2018)

    Google Scholar 

  12. M. Alban, D. Mauricio, Predicting university dropout trough data mining: a systematic literature. Indian J. Sci. Technol. 12, 1–12 (2019). https://doi.org/10.17485/ijst/2019/v12i4/139729

  13. O. Viberg, M. Hatakka, O. Bälter, A. Mavroudi, The current landscape of learning analytics in higher education. Comput. Hum. Behav. 89, 98–110 (2018). https://doi.org/10.1016/j.chb.2018.07.027

    Article  Google Scholar 

  14. C. Herodotou, B. Rienties, M. Hlosta et al., The scalable implementation of predictive learning analytics at a distance learning university: insights from a longitudinal case study. Internet High. Educ. 45, 1–13 (2020). https://doi.org/10.1016/j.iheduc.2020.100725

    Article  Google Scholar 

  15. A.A. Mubarak, Predictive learning analytics using deep learning model in MOOCs’ courses videos. Educ. Inf. Technol. (2020)

    Google Scholar 

  16. E.M. Queiroga, J.L. Lopes, K. Kappel et al., A learning analytics approach to identify students at risk of dropout: a case study with a technical distance education course. Appl. Sci. 10 (2020). https://doi.org/10.3390/app10113998

  17. B.T.M. Wong, Learning analytics in higher education: an analysis of case studies. Asian Assoc. Open Univ. J. 12, 21–40 (2017). https://doi.org/10.1108/aaouj-01-2017-0009

    Article  Google Scholar 

  18. L. Aulck, N. Velagapudi, J. Blumenstock, J. West, Predicting student dropout in higher education, in Machine Learning in within the Open Polytechnic of New Zealand, relying Social Good Applications (2016), pp. 16–20

    Google Scholar 

  19. E. Balraj, D. Maalini, A survey on predicting student dropout analysis using data mining algorithms. Int. J. Pure Appl. Math. 118, 621–627 (2018)

    Google Scholar 

  20. A. Pérez, U. Bío-bío, E.E. Grandón et al., Comparative analysis of prediction techniques to determine student dropout: logistic regression vs decision trees, in 37th International Conference of the Chilean Computer Science Society (SCCC) (IEEE, 2018)

    Google Scholar 

  21. P. Boris, Predicting student drop-out rates using data mining techniques: a case study, in Communications in Computer and Information Science (2018), pp. 111–125

    Google Scholar 

  22. S.S. Ahmad Tarmizi, S. Mutalib, N.H. Abdul Hamid et al., A case study on student attrition prediction in higher education using data mining techniques. Commun. Comput. Inf. Sci. 1100, 181–192 (2019). https://doi.org/10.1007/978-981-15-0399-3_15

    Article  Google Scholar 

  23. K. Kang, S. Wang, Analyze and predict student dropout from online programs, in ACM International Conference Proceeding Series (2018), pp. 6–12. https://doi.org/10.1145/3193077.3193090

  24. W.F. Wan Yaacob, N. Mohd Sobri, S.A.M. Nasir et al., Predicting student drop-out in higher institution using data mining techniques. J. Phys. Conf. Ser. 1496 (2020). https://doi.org/10.1088/1742-6596/1496/1/012005

  25. E. Yukselturk, S. Ozekes, Y.K. Türel, Predicting dropout student: an application of data mining methods in an online education program. Eur. J. Open, Distance E-Learn. 17, 118–133 (2018). https://doi.org/10.2478/eurodl-2014-0008

    Article  Google Scholar 

  26. C. Pierrakeas, G. Koutsonikos, A. Lipitakis et al., The variability of the reasons for student dropout in distance learning and the prediction of dropout-prone students, in Machine Learning Paradigms (Springer International Publishing, 2020)

    Google Scholar 

  27. L. Kemper, G. Vorhoff, B.U. Wigger, Predicting student dropout: a machine learning approach. Eur. J. High. Educ. 10, 28–47 (2020). https://doi.org/10.1080/21568235.2020.1718520

    Article  Google Scholar 

  28. R. Da Fonseca Silveira, M. Holanda, M. De Carvalho Victorino, M. Ladeira, Educational data mining: analysis of drop out of engineering majors at the UnB – Brazil, in Proceedings of the 18th International Conference on Machine Learning and Applications (ICMLA 2019) (2019), pp. 259–262. https://doi.org/10.1109/ICMLA.2019.00048

  29. D. Kim, S. Kim, Sustainable education: analyzing the determinants of university student dropout by nonlinear panel data models. Sustainability 10, 1–19 (2018). https://doi.org/10.3390/su10040954

    Article  Google Scholar 

  30. N. Nurmalitasari, Z.A. Long, M. Faizuddin, M. Noor, Data preparation in predictive learning analytics (PLA) for student dropout. Int. J. Innov. Technol. Explor. Eng. 9, 116–120 (2020). https://doi.org/10.35940/ijitee.c1027.0193s20

  31. P.M. Da Silva, M.N.C.A. Lima, W.L. Soares et al., Ensemble regression models applied to dropout in higher education, in Proceedings of the 2019 Brazilian Conference on Intelligent Systems (BRACIS 2019) (2019), pp. 120–125. https://doi.org/10.1109/BRACIS.2019.00030

  32. A. Mayra, D. Mauricio, Factors to predict dropout at the universities: a case of study in Ecuador, in IEEE Global Engineering Education Conference (EDUCON 2018), April 2018, pp. 1238–1242. https://doi.org/10.1109/EDUCON.2018.8363371

  33. T. Hasbun, A. Araya, J. Villalon, Extracurricular activities as dropout prediction factors in higher education using decision trees, in Proceedings of the 16th International Conference on Advanced Learning Technologies (ICALT 2016) (2016), pp. 242–244. https://doi.org/10.1109/ICALT.2016.66

  34. A. Saranya, J. Rajeswari, Enhanced prediction of student dropouts using fuzzy inference system and logistic regression. ICTACT J. Soft Comput. 06, 1157–1162 (2016). https://doi.org/10.21917/ijsc.2016.0161

  35. F.J. da Costa, M. de S. Bispo, R. de C. de F. Pereira, Dropout and retention of undergraduate students in management: a study at a Brazilian Federal University. RAUSP Manag. J. 53, 74–85 (2018). https://doi.org/10.1016/j.rauspm.2017.12.007

  36. N. Tasnim, M.K. Paul, A.H.M.S. Sattar, Identification of drop out students using educational data mining, in 2nd International Conference on Electrical, Computer and Communication Engineering (ECCE 2019) (2019), pp. 7–9. https://doi.org/10.1109/ECACE.2019.8679385

  37. L. Wang, H. Wang, Learning behavior analysis and dropout rate prediction based on MOOCs data, in Proceedings of the 10th International Conference on Information Technology in Medicine and Education (ITME 2019) (2019), pp. 419–423. https://doi.org/10.1109/ITME.2019.00100

  38. A.A. Mubarak, H. Cao, W. Zhang, Prediction of students’ early dropout based on their interaction logs in online learning environment. Interact. Learn. Environ., 1–20 (2020). https://doi.org/10.1080/10494820.2020.1727529

  39. J. Liang, J. Yang, Y. Wu, L. Zheng, Big data application in education: dropout prediction in Edx MOOCs, in Second International Conference on Multimedia Big Data (IEEE, 2016), pp. 6–9

    Google Scholar 

  40. W. Li, M. Gao, H. Li et al., Dropout prediction in MOOCs using behavior features and multi-view semi-supervised learning, in Proceedings of the International Joint Conference on Neural Networks 2016, October 2016, pp. 3130–3137. https://doi.org/10.1109/IJCNN.2016.7727598

  41. C. Burgos, M.L. Campanario, D. de la Peña et al., Data mining for modeling students’ performance: a tutoring action plan to prevent academic dropout. Comput. Electr. Eng. 66, 541–556 (2018). https://doi.org/10.1016/j.compeleceng.2017.03.005

    Article  Google Scholar 

  42. K. Coussement, M. Phan, A. De Caigny et al., Predicting student dropout in subscription-based online learning environments: the beneficial impact of the logit leaf model. Decis. Support Syst. 135, 113325 (2020). https://doi.org/10.1016/j.dss.2020.113325

    Article  Google Scholar 

  43. B.R. Cuji Chacha, W.L. Gavilanes López, V.X. Vicente Guerrero, W.G. Villacis Villacis, Student dropout model based on logistic regression, in Applied Technologies. Communications in Computer and Information Science (CCIS), vol. 1194 (2020), pp. 321–333. https://doi.org/10.1007/978-3-030-42520-3_26

  44. P. Strecht, J. Mendes-Moreira, C. Soares, Merging decision trees: a case study in predicting student performance, in Advanced Data Mining and Applications. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8933 (2014), pp. 535–548. https://doi.org/10.1007/978-3-319-14717-8_42

  45. W. Xing, D. Du, Dropout prediction in MOOCs: using deep learning for personalized intervention. J. Educ. Comput. Res. 57, 1–24 (2018). https://doi.org/10.1177/0735633118757015

    Article  Google Scholar 

  46. T.M. Barros, P.A.S. Neto, I. Silva, L.A. Guedes, Predictive models for imbalanced data: a school dropout perspective. Educ. Sci. 9 (2019). https://doi.org/10.3390/educsci9040275

  47. A. Viloria, J.G. Padilla, C. Vargas-Mercado et al., Integration of data technology for analyzing university dropout. Procedia Comput. Sci. 155, 569–574 (2019). https://doi.org/10.1016/j.procs.2019.08.079

    Article  Google Scholar 

  48. I. Lykourentzou, I. Giannoukos, V. Nikolopoulos et al., Dropout prediction in e-learning courses through the combination of machine learning techniques. Comput. Educ. 53, 950–965 (2009). https://doi.org/10.1016/j.compedu.2009.05.010

    Article  Google Scholar 

  49. A. Behr, M. Giese, K.H.D. Teguim, K. Theune, Early prediction of university dropouts—a random forest approach. Jahrb. Nationaloekon. Stat. (2020). https://doi.org/10.1515/jbnst-2019-0006

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Malaysian Institute Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia

    Nurmalitasari, Zalizah Awang Long & Mohammad Faizuddin Mohd Noor

  2. Faculty of Computer Science, Duta Bangsa University of Surakarta, Surakarta, Indonesia

    Nurmalitasari

Authors
  1. Nurmalitasari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zalizah Awang Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Faizuddin Mohd Noor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nurmalitasari .

Editor information

Editors and Affiliations

  1. Centre for Women Advancement and Leadership, Malaysian Institute of Marine Engineering Technology, Universiti Kuala Lumpur, Lumut, Perak, Malaysia

    Azman Ismail

  2. School of Information Science, College of Computing, Informatics and Media, Universiti Teknologi MARA, Machang, Malaysia

    Fatin Nur Zulkipli

  3. Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia

    Zalizah Awang Long

  4. Faculty of Mechanical Engineering, Esslingen University of Applied Sciences, Esslingen, Germany

    Andreas Öchsner

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Nurmalitasari, Awang Long, Z., Mohd Noor, M.F. (2023). The Predictive Learning Analytics for Student Dropout Using Data Mining Technique: A Systematic Literature Review. In: Ismail, A., Zulkipli, F.N., Awang Long, Z., Öchsner, A. (eds) Advances in Technology Transfer Through IoT and IT Solutions . SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-25178-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-25178-8_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25177-1

  • Online ISBN: 978-3-031-25178-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation