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A Survey of Machine Learning for Assessing and Estimating Student Performance

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Proceedings of International Conference on Recent Innovations in Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1001))

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Abstract

Educational data mining (EDM) contributes cutting-edge methodologies, strategies, and applications to the advancement of the education system, hence playing a crucial part in its development. Utilising machine learning and data mining approaches to explore and utilise educational data, the current advancement gives essential tools for comprehending the student learning environment. Academic institutions in the twenty-first century operate in a highly competitive and complicated environment. Among the prevalent issues faced by universities are performance analysis, the provision of a high-quality education, systems for evaluating the performance of students, and the planning of future activities. Student intervention programmes must be created in these universities in order to address the academic difficulties encountered by students. From 2009 through 2021, the relevant EDM literature relative to predicting student attrition and students at risk is examined in this review. According to the review’s results, several machine learning (ML) methodologies are used to discover and address the fundamental challenges of forecasting students at risk and student withdrawal rate. Furthermore, the bulk of studies make use of data from student college/university database and online learning portals. It was determined that ML techniques play crucial roles in forecasting students at risk and withdrawal rates, hence boosting student performance.

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References

  1. Romero C, Ventura S (2013) Data mining in education. Wiley Interdisc Rev: Data Mining Knowl Discovery 3(1):12–27

    Google Scholar 

  2. Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier

    Google Scholar 

  3. Bengio Y, Lecun Y, Hinton G (2021) Deep learning for AI. Commun ACM 64(7):58–65

    Article  Google Scholar 

  4. Hellas A, Ihantola P, Petersen A, Ajanovski VV, Gutica M, Hynninen T, Knutas A, Leinonen J, Messom C, Liao SN (2018) Predicting academic performance: a systematic literature review. In: Proceedings companion of the 23rd annual ACM conference on innovation and technology in computer science education, pp 175–199

    Google Scholar 

  5. Alyahyan E, Düştegör D (2020) Predicting academic success in higher education: literature review and best practices. Int J Educ Technol Higher Educ 17(10:1–21

    Google Scholar 

  6. Kotsiantis S, Pierrakeas C, Pintelas P (2004) Predicting students’ performance in distance learning using machine learning techniques. Appl Artif Intell 18(5):411–426

    Article  Google Scholar 

  7. Navamani JMA, Kannammal A (2015) Predicting performance of schools by applying data mining techniques on public examination results. Res J Appl Sci Eng Technol 9(4):262–271

    Article  Google Scholar 

  8. Learning SM (2007) A review of classification techniques, sb kotsiantis. Informatica 31:249–268

    Google Scholar 

  9. Moseley LG, Mead DM (2008) Predicting who will drop out of nursing courses: a machine learning exercise. Nurse Educ Today 28(4):469–475

    Article  Google Scholar 

  10. Nandeshwar A, Menzies T, Nelson A (2011) Learning patterns of university student retention. Expert Syst Appl 38(12):14984–14996

    Google Scholar 

  11. Dewan MAA, Lin F, Wen D et al (2015) Predicting dropout-prone students in e-learning education system. In: 2015 IEEE 12th international conference on Ubiquitous Intelligence and Computing and 2015 IEEE 12th international conference on Autonomic and Trusted Computing and 2015 IEEE 15th international conference on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), IEEE, pp 1735–1740

    Google Scholar 

  12. Tan M, Shao P (2015) Prediction of student dropout in e-learning program through the use of machine learning method. Int J Emerging Technol Learn 10(1)

    Google Scholar 

  13. Sultana S, Khan S, Abbas MA (2017) Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts. Int J Electr Eng Educ 54(2):105–118

    Article  Google Scholar 

  14. Huang S, Fang N (2013) Predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models. Comput Educ 61:133–145

    Article  Google Scholar 

  15. Slim A, Heileman GL, Kozlick J, Abdallah CT (2014) Predicting student success based on prior performance. In: 2014 IEEE symposium on Computational Intelligence and Data Mining (CIDM), IEEE, pp 410–415

    Google Scholar 

  16. Chaudhury P, Mishra S, Tripathy HK, Kishore B (2016) Enhancing the capabilities of student result prediction system. In: Proceedings of the second international conference on information and communication technology for competitive strategies, pp 1–6

    Google Scholar 

  17. Gentleman R, Carey VJ (2008) Unsupervised machine learning. In: Bioconductor case studies. Springer, Heidelberg, pp 137–157

    Google Scholar 

  18. Guo B, Zhang R, Xu G, Shi C, Yang L (2015) Predicting students performance in educational data mining. Int Symp Educ Technol (ISET) 2015:125–128

    Google Scholar 

  19. Rana S, Garg R (2017) Prediction of students performance of an institute using classification via clustering and classification via regression. In: Proceedings of international conference on communication and networks. Springer, Heidelberg, pp 333–343

    Google Scholar 

  20. Anand V, Rahiman SA, George EB, Huda A (2018) Recursive clustering technique for students’ performance evaluation in programming courses. In: 2018 Majan International Conference (MIC), IEEE, pp 1–5

    Google Scholar 

  21. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132

    Google Scholar 

  22. Bydžovská H (2015) Student performance prediction using collaborative filtering methods. In: International conference on artificial intelligence in education. Springer, Heidelberg, pp 550–553

    Google Scholar 

  23. Bydžovská H (2015) Are collaborative filtering methods suitable for student performance prediction? In: Portuguese conference on artificial intelligence. Springer, Heidelberg, pp 425–430

    Google Scholar 

  24. Park Y (2018) Predicting personalized student performance in computing-related majors via collaborative filtering. In: Proceedings of the 19th annual SIG conference on information technology education, pp 151–151

    Google Scholar 

  25. Liou C-H (2016) Personalized article recommendation based on student’s rating mechanism in an online discussion forum. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), IEEE, pp 60–65

    Google Scholar 

  26. Elbadrawy A, Karypis G (2016) Domain-aware grade prediction and top-n course recommendation. In: Proceedings of the 10th ACM conference on recommender systems, pp 183–190

    Google Scholar 

  27. Pero S, Horváth T (2015) Comparison of collaborative-filtering techniques for small-scale student performance prediction task. In: Innovations and advances in computing, informatics, systems sciences, networking and engineering. Springer, Heidelberg, pp 111–116

    Google Scholar 

  28. Song Y, Jin Y, Zheng X, Han H, Zhong Y, Zhao X (2015) Psfk: A student performance prediction scheme for first encounter knowledge in its. In: International conference on knowledge science, engineering and management. Springer, Heidelberg, pp 639–650

    Google Scholar 

  29. Xu K, Liu R, Sun Y, Zou K, Huang Y, Zhang X (2017) Improve the prediction of student performance with hint’s assistance based on an efficient non-negative factorization. IEICE Trans Inf Syst 100(4):768–775

    Article  Google Scholar 

  30. Sheehan M, Park Y (2012) pgpa: a personalized grade prediction tool to aid student success. In: Proceedings of the sixth ACM conference on recommender systems, pp 309–310

    Google Scholar 

  31. Lorenzen S, Pham N, Alstrup S (2017) On predicting student performance using low-rank matrix factorization techniques

    Google Scholar 

  32. Houbraken M, Sun C, Smirnov E, Driessens K (2017) Discovering hidden course requirements and student competences from grade data. In: Adjunct publication of the 25th conference on user modeling, adaptation and personalization, pp 147–152

    Google Scholar 

  33. Gómez-Pulido JA, Cortés-Toro E, Durán-Domínguez A, Crawford B, Soto R (2018) Novel and classic metaheuristics for tunning a recommender system for predicting student performance in online campus. In: International conference on intelligent data engineering and automated learning. Springer, Heidelberg, pp 125–133

    Google Scholar 

  34. Chavarriaga O, Florian-Gaviria B, Solarte O (2014) A recommender system for students based on social knowledge and assessment data of competences. In: European conference on technology enhanced learning. Springer, Heidelberg, pp 56–69

    Google Scholar 

  35. Adewale AM, Bamidele AO, Lateef UO (2018) Predictive modelling and analysis of academic performance of secondary school students: artificial neural network approach. Int J Sci Technol Educ Res 9(1):1–8

    Article  Google Scholar 

  36. Arsad PM, Buniyamin N et al (2013) A neural network students’ performance prediction model (nnsppm). In: 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), IEEE, pp 1–5

    Google Scholar 

  37. Iyanda AR, Ninan OD, Ajayi AO, Anyabolu OG (2018) Predicting student academic performance in computer science courses: a comparison of neural network models. Int J Modern Educ Comput Sci 10(6)

    Google Scholar 

  38. Dharmasaroja P, Kingkaew N (2016) Application of artificial neural networks for prediction of learning performances. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), IEEE, pp 745–751

    Google Scholar 

  39. Quadri MM, Kalyankar N (2010) Drop out feature of student data for academic performance using decision tree techniques. Global J Comput Sci Technol

    Google Scholar 

  40. Walia N, Kumar M, Nayar N, Mehta G (2020) Student’s academic performance prediction in academic using data mining techniques. In: Proceedings of the International Conference on Innovative Computing & Communications (ICICC)

    Google Scholar 

  41. Thammasiri D, Delen D, Meesad P, Kasap N (2014) A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition. Expert Syst Appl 41(2):321–330

    Article  Google Scholar 

  42. Liu D, Richards D, Froissard C, Atif A (2015) Validating the effectiveness of the moodle engagement analytics plugin to predict student academic performance In: 21st Americas Conference on Information Systems, AMCIS 2015. Americas Conference on Information Systems, pp 1–10

    Google Scholar 

  43. Saqr M, Fors U, Tedre M (2017) How learning analytics can early predict under-achieving students in a blended medical education course. Med Teach 39(7):757–767

    Article  Google Scholar 

  44. Chen Y, Johri A, Rangwala H (2018) Running out of stem: a comparative study across stem majors of college students at-risk of dropping out early. In: Proceedings of the 8th international conference on learning analytics and knowledge, pp 270–279

    Google Scholar 

  45. Sara N-B, Halland R, Igel C, Alstrup S (2015) High-school dropout prediction using machine learning: a danish large-scale study. In: ESANN 2015 proceedings, European symposium on artificial neural networks, computational intelligence, pp 319–24

    Google Scholar 

  46. Kostopoulos G, Kotsiantis S, Pintelas P (2015) Estimating student dropout in distance higher education using semisupervised techniques. In: Proceedings of the 19th Panhellenic conference on informatics, pp 38–43

    Google Scholar 

  47. Howard E, Meehan M, Parnell A (2018) Contrasting prediction methods for early warning systems at undergraduate level. Internet Higher Educ 37:66–75

    Article  Google Scholar 

  48. Gray CC, Perkins D (2019) Utilizing early engagement and machine learning to predict student outcomes. Comput Educ 131:22–32

    Article  Google Scholar 

  49. Thaker K, Huang Y, Brusilovsky P, Daqing H (2018) Dynamic knowledge modeling with heterogeneous activities for adaptive textbooks. In: The 11th international conference on educational data mining, pp 592–595

    Google Scholar 

  50. Ahadi A, Lister R, Haapala H, Vihavainen A (2015) Exploring machine learning methods to automatically identify students in need of assistance. In: Proceedings of the eleventh annual international conference on international computing education research, pp 121–130

    Google Scholar 

  51. Adán-Coello JM, Tobar CM (2016) Using collaborative filtering algorithms for predicting student performance. In: International conference on electronic government and the information systems perspective. Springer, Heidelberg, pp 206–218

    Google Scholar 

  52. Kovacic Z (2010) Early prediction of student success: mining students’ enrolment data

    Google Scholar 

  53. Kotsiantis S, Patriarcheas K, Xenos M (2010) A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education. Knowl-Based Syst 23(6):529–535

    Article  Google Scholar 

  54. Villegas-Ch W, Lujan-Mora S, Buenaño-Fernandez D, Roman-Canizares M (2017) Analysis of web-based learning systems by data mining. In: 2017 IEEE second Ecuador Technical Chapters Meeting (ETCM), IEEE, pp 1–5

    Google Scholar 

  55. Karagiannis I, Satratzemi M (2018) An adaptive mechanism for moodle based on automatic detection of learning styles. Educ Inf Technol 23(3):1331–1357

    Article  Google Scholar 

  56. Masci C, Johnes G, Agasisti T (2018) Student and school performance across countries: a machine learning approach. Eur J Oper Res 269(3):1072–1085

    Article  MathSciNet  MATH  Google Scholar 

  57. Johnson WG (2018) Data mining and machine learning in education with focus in undergraduate CS student success. In: Proceedings of the 2018 ACM conference on international computing education research, pp 270–271

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Lovely Professional University, Punjab, India

    Avneet Kaur

  2. Department of Computer Applications, NIT Kurukshetra, Kurukshetra, India

    Munish Bhatia

Authors
  1. Avneet Kaur
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  2. Munish Bhatia
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Correspondence to Munish Bhatia .

Editor information

Editors and Affiliations

  1. Computer Science and IT Department, Central University of Jammu, Jammu, Jammu and Kashmir, India

    Yashwant Singh

  2. Department of Computer Science, KIET Group of Institutions, Ghaziabad, Uttar Pradesh, India

    Pradeep Kumar Singh

  3. Department of Electrical Engineering, Indian Institute of Technology Patna, Patna, Bihar, India

    Maheshkumar H. Kolekar

  4. School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Arpan Kumar Kar

  5. IDMEC, Polytechnic Institute of Castelo Branco, Castelo Branco, Portugal

    Paulo J. Sequeira Gonçalves

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Kaur, A., Bhatia, M. (2023). A Survey of Machine Learning for Assessing and Estimating Student Performance. In: Singh, Y., Singh, P.K., Kolekar, M.H., Kar, A.K., Gonçalves, P.J.S. (eds) Proceedings of International Conference on Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 1001. Springer, Singapore. https://doi.org/10.1007/978-981-19-9876-8_48

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