Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Predicting Students Performance Using Educational Data Mining and Learning Analytics: A Systematic Literature Review

  • Conference paper
  • First Online:
Innovative Data Communication Technologies and Application

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 59))

  • 2158 Accesses

Abstract

With technological advancement, data is proliferating. Therefore, a need has been felt for advanced analytic techniques, which can help in extracting useful information, patterns, and relationships for decision making. The same is true for the educational domain. For analyzing the data relating to the educational field, educational data mining, and learning analytics techniques have been used. One of the crucial objectives of educational research is to predict the performance of students to bring about changes in educational outcomes. The research in this field aims to find the techniques/methods that can improve prediction, to identify the attributes that can be used to make a prediction or to assess performance. This paper aims to conduct a systematic literature review of predicting student’s performance using educational data mining and learning analytics to identify techniques, attributes, metrics used, and to define the current state of the art.

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
$34.99 /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 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
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

Similar content being viewed by others

References

  1. Cook DJ, Das SK (2012) Pervasive computing at scale: transforming the state of the art. Pervasive Mob Comput 8(1):22–35

    Article  Google Scholar 

  2. Ferguson R (2012) The state of learning analytics in 2012: a review and future challenges (Technical Report KMI-2012)

    Google Scholar 

  3. Dhankhar A, Solanki K (2020) State of the art of learning analytics in higher education. Int J Emerg Trends Eng Res 8(3):868–877

    Article  Google Scholar 

  4. Kitchenham BA (2007) Guidelines for performing systematic literature reviews in software engineering (version 2.3). Software Engineering Group, School of Computer Science and Mathematics, Keele University, and Department of Computer Science, The University of Durham

    Google Scholar 

  5. Kitchenham B, Pretorius R, Budgen D, Pearl Brereton O, Turner M, Niazi M, Linkman S (2010) Systematic literature reviews in software engineering—a tertiary study. Inf Softw Technol 52(8):792–805 (2010)

    Google Scholar 

  6. Coussement K, Phan M, De Caigny A, Benoit DF, Raes A (2020) Predicting student dropout in subscription-based online learning environments: the beneficial impact of the logit leaf model. Dec Support Syst 113325

    Google Scholar 

  7. Rizvi S, Rienties B, Khoja SA (2019) The role of demographics in online learning; a decision tree based approach. Comput Educ 137:32–47

    Article  Google Scholar 

  8. Xing W, Chen X, Stein J, Marcinkowski M (2016) Temporal predication of dropouts in MOOCs: reaching the low hanging fruit through stacking generalization. Comput Hum Behav 58:119–129

    Article  Google Scholar 

  9. Ashraf M, Zaman M, Ahmed M (2020) An Intelligent prediction system for educational data mining based on ensemble and filtering approaches. Procedia Comput Sci 167:1471–1483

    Article  Google Scholar 

  10. Ortigosa A, Carro RM, Bravo-Agapito J, Lizcano D, Alcolea JJ, Blanco O (2019) From lab to production: lessons learnt and real-life challenges of an early student-dropout prevention system. IEEE Trans Learn Technol 12(2):264–277

    Article  Google Scholar 

  11. Figueroa-Cañas J, Sancho-Vinuesa T (2020) Early prediction of dropout and final exam performance in an online statistics course. IEEE Revista Iberoamericana De Tecnologias Del Aprendizaje 15(2):86–94

    Article  Google Scholar 

  12. Moreno-Marcos PM, Pong TC, Muñoz-Merino PJ, Kloos CD (2020) Analysis of the factors influencing learners’ performance prediction with learning analytics. IEEE Access 8:5264–5282

    Article  Google Scholar 

  13. Rubiano SMM, Garcia JAD (2016) Analysis of data mining techniques for constructing a predictive model for academic performance. IEEE Latin Am Trans 14(6):2783–2788

    Article  Google Scholar 

  14. Baneres D, Rodríguez-Gonzalez ME, Serra M (2019) An early feedback prediction system for learners at-risk within a first-year higher education course. IEEE Trans Learn Technol 12(2):249–263

    Article  Google Scholar 

  15. Wakelam E, Jefferies A, Davey N, Sun Y (2020) The potential for student performance prediction in small cohorts with minimal available attributes. Br J Edu Technol 51(2):347–370

    Article  Google Scholar 

  16. Huang AY, Lu OH, Huang JC, Yin CJ, Yang SJ (2020) Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs. Interactive Learn Environ 28(2):206–230

    Article  Google Scholar 

  17. Guerrero-Higueras ÁM, Fernández Llamas C, Sánchez González L, Gutierrez Fernández A, Esteban Costales G, González MÁC (2020) Academic success assessment through version control systems. Appl Sci 10(4):1492

    Article  Google Scholar 

  18. López-Zambrano J, Lara JA, Romero C (2020) Towards portability of models for predicting students’ final performance in university courses starting from moodle logs. Appl Sci 10(1):354

    Article  Google Scholar 

  19. Bedregal-Alpaca N, Cornejo-Aparicio V, Zárate-Valderrama J, Yanque-Churo P (2020) Classification models for determining types of academic risk and predicting dropout in university students. (IJACSA) Int J Adv Comput Sci Appl 11(1)

    Google Scholar 

  20. Alhassan A, Zafar B, Mueen A (2020) Predict students academic performance based on their assessment grades and online activity data. Int J Adv Comput Sci Appl 11(4)

    Google Scholar 

  21. Alhakami H, Alsubait T, Aliarallah A (2020) Data mining for student advising. Int J Adv Comput Sci Appl 11(3)

    Google Scholar 

  22. Hoque MI, Kalam Azad A, Tuhin MAH, Salehin ZU (2020) University students result analysis and prediction system by decision tree algorithm. Adv Sci Technol Eng Syst J 5(3):115–122

    Google Scholar 

  23. Xu X, Wang J, Peng H, Wu R (2019) Prediction of academic performance associated with internet usage behaviors using machine learning algorithms. Comput Hum Behav 98:166–173

    Article  Google Scholar 

  24. Al-Sudani S, Palaniappan R (2019) Predicting students’ final degree classification using an extended profile. Educ Inf Technol 24(4):2357–2369

    Article  Google Scholar 

  25. Francis BK, Babu SS (2019) Predicting academic performance of students using a hybrid data mining approach. J Med Syst 43(6):162

    Article  Google Scholar 

  26. Buenaño-Fernández D, Gil D, Luján-Mora S (2019) Application of machine learning in predicting performance for computer engineering students: a case study. Sustainability 11(10):2833

    Article  Google Scholar 

  27. Mimis M, El Hajji M, Es-Saady Y, Guejdi AO, Douzi H, Mammass D (2019) A framework for smart academic guidance using educational data mining. Educ Inf Technol 24(2):1379–1393

    Article  Google Scholar 

  28. Adekitan AI, Noma-Osaghae E (2019) Data mining approach to predicting the performance of first year student in a university using the admission requirements. Educ Inf Technol 24(2):1527–1543

    Article  Google Scholar 

  29. Rodrigues RL, Ramos JLC, Silva JCS, Dourado RA, Gomes AS (2019) Forecasting Students’ performance through self-regulated learning behavioral analysis. Int J Dist Educ Technol (IJDET) 17(3):52–74

    Article  Google Scholar 

  30. Sadiq HM, Ahmed SN (2019) Classifying and predicting students’ performance using improved decision tree C4.5 in Higher Education Institutes. J Comput Sci 15(9):1291–1306

    Google Scholar 

  31. Yaacob WFW, Nasir SAM, Yaacob WFW, Sobri NM (2019) Supervised data mining approach for predicting student performance. Indones J Electr Eng Comput Sci 16:1584–1592

    Google Scholar 

  32. Jorda ER, Raqueno AR (2019) Predictive model for the academic performance of the engineering students using CHAID and C 5.0 algorithm. Int J Eng Res Technol 12(6): 917–928. ISSN 0974–3154

    Google Scholar 

  33. Livieris IE, Tampakas V, Karacapilidis N, Pintelas P (2019) A semi-supervised self-trained two-level algorithm for forecasting students’ graduation time. Intell Dec Technol 13(3):367–378

    Google Scholar 

  34. Helal S, Li J, Liu L, Ebrahimie E, Dawson S, Murray DJ, Long Q (2018) Predicting academic performance by considering student heterogeneity. Knowl-Based Syst 161:134–146

    Article  Google Scholar 

  35. Nguyen HQ, Pham TT, Vo V, Vo B, Quan TT (2018) The predictive modeling for learning student results based on sequential rules. Int J Innov Comput Inf Control 14(6):2129–2140

    Google Scholar 

  36. Bucos M, Drăgulescu B (2018) Predicting student success using data generated in traditional educational environments. TEM J 7(3):617

    Google Scholar 

  37. Zhou Q, Quan W, Zhong Y, Xiao W, Mou C, Wang Y Predicting high-risk students using Internet access logs. Knowl Inf Syst 55(2):393–413

    Google Scholar 

  38. Adejo OW, Connolly T (2018) Predicting student academic performance using multi-model heterogeneous ensemble approach. J Appl Res High Educ

    Google Scholar 

  39. Shanthini A, Vinodhini G, Chandrasekaran RM (2018) Predicting students’ academic performance in the university using meta decision tree classifiers. J Comput Sci 14(5):654–662

    Article  Google Scholar 

  40. Jamil JM, Mohd Pauzi NF, Shahara Nee INM (2018) An analysis on student academic performance by using decision tree models. J Soc Sci Res (6):615–620. ISSN(e): 2411–9458, ISSN(p): 2413–6670

    Google Scholar 

  41. Mishra S, Panda AR (2018) Predictive evaluation of student’s performance using decision tree approach. J Adv Res Dyn Control Syst 14(special issue):511–516

    Google Scholar 

  42. Kostopoulos G, Kotsiantis S, Pierrakeas C, Koutsonikos G, Gravvanis GA (2018) Forecasting students’ success in an open university. Int J Learn Technol 13(1):26–43

    Article  Google Scholar 

  43. 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 

  44. Seidel E, Kutieleh S (2017) Using predictive analytics to target and improve first year student attrition. Austr J Educ 61(2):200–218

    Article  Google Scholar 

  45. Tran TO, Dang HT, Dinh VT, Phan XH (2017) Performance prediction for students: a multi-strategy approach. Cybern Inf Technol 17(2):164–182

    Google Scholar 

  46. Evale D (2016) Learning management system with prediction model and course-content recommendation module. J Inf Technol Educ Res 16(1):437–457

    Google Scholar 

  47. Dhankhar A, Solanki K, Rathee A, Ashish (2019) Predicting student’s performance by using classification methods. Int J Adv Trends Comput Sci Eng 8(4):1532–1536

    Google Scholar 

  48. Waheed H, Hassan SU, Aljohani NR, Hardman J, Alelyani S, Nawaz R (2020) Predicting academic performance of students from VLE big data using deep learning models. Comput Hum Behav 104:106189

    Article  Google Scholar 

  49. Injadat M, Moubayed A, Nassif AB, Shami A (2020) Systematic ensemble model selection approach for educational data mining. Knowledge Based Syst 105992

    Google Scholar 

  50. Qu S, Li K, Zhang S, Wang Y (2018) Predicting achievement of students in smart campus. IEEE Access 6:60264–60273

    Article  Google Scholar 

  51. Olive DM, Huynh DQ, Reynolds M, Dougiamas M, Wiese D (2019) A quest for a one-size-fits-all neural network: early prediction of students at risk in online courses. IEEE Trans Learn Technol 12(2):171–183

    Article  Google Scholar 

  52. Yang TY, Brinton CG, Joe-Wong C, Chiang M (2017) Behavior-based grade prediction for MOOCs via time series neural networks. IEEE J Select Topics Signal Process 11(5):716–728

    Google Scholar 

  53. Hung JL, Shelton BE, Yang J, Du X (2019) Improving predictive modeling for at-risk student identification: a multistage approach. IEEE Trans Learn Technol 12(2):148–157

    Article  Google Scholar 

  54. Al-Shabandar R, Hussain AJ, Liatsis P, Keight R (2019) Detecting at-risk students with early interventions using machine learning techniques. IEEE Access 7:149464–149478

    Article  Google Scholar 

  55. Tsai SC, Chen CH, Shiao YT, Ciou JS, Wu TN (2020) Precision education with statistical learning and deep learning: a case study in Taiwan. Int J Educ Technol High Educ 17:1–13

    Article  Google Scholar 

  56. Qu S, Li K, Wu B, Zhang X, Zhu K (2019) Predicting student performance and deficiency in mastering knowledge points in MOOCs using multi-task learning. Entropy 21(12):1216

    Article  Google Scholar 

  57. Aljohani NR, Fayoumi A, Hassan SU (2019) Predicting at-risk students using clickstream data in the virtual learning environment. Sustainability 11(24):7238

    Google Scholar 

  58. Pal VK, Bhatt VKK (2019) Performance prediction for post graduate students using artificial neural network. Int J Innov Technol Explor Eng (IJITEE). ISSN 2278-3075

    Google Scholar 

  59. Crivei LM, Ionescu VS, Czibula G (2019) An analysis of supervised learning methods for predicting students’ performance in academic environments. ICIC Exp Lett 13:181–190

    Google Scholar 

  60. Ramanathan L, Parthasarathy G, Vijayakumar K, Lakshmanan L, Ramani S (2019) Cluster-based distributed architecture for prediction of student’s performance in higher education. Cluster Comput 22(1):1329–1344

    Article  Google Scholar 

  61. Kokoç M, Altun A (2019) Effects of learner interaction with learning dashboards on academic performance in an e-learning environment. Behav Inf Technol 1–15

    Google Scholar 

  62. Vora DR, Rajamani K (2019) A hybrid classification model for prediction of academic performance of students: a big data application. Evol Intell 1–14

    Google Scholar 

  63. Raj JS, Ananthi JV (2019) Recurrent neural networks and nonlinear prediction in support vector machines. J Soft Comput Paradigm (JSCP) 1(01):33–40

    Google Scholar 

  64. Bashar A (2019) Survey on evolving deep learning neural network architectures. J Artif Intell 1(02):73–82

    Google Scholar 

  65. Gašević D, Dawson S, Rogers T, Gasevic D (2016) Learning analytics should not promote one size fits all: the effects of instructional conditions in predicting academic success. Internet High Educ 28:68–84

    Article  Google Scholar 

  66. Burgos C, Campanario ML, de la Peña D, Lara JA, Lizcano D, Martínez MA (2018) Data mining for modeling students’ performance: a tutoring action plan to prevent academic dropout. Comput Electr Eng 66:541–556

    Article  Google Scholar 

  67. Qiu L, Liu Y, Liu Y (2018) An integrated framework with feature selection for dropout prediction in massive open online courses. IEEE Access 6:71474–71484

    Article  Google Scholar 

  68. Gitinabard N, Xu Y, Heckman S, Barnes T, Lynch CF (2019) How widely can prediction models be generalized? Performance prediction in blended courses. IEEE Trans Learn Technol 12(2):184–197

    Article  Google Scholar 

  69. Sothan S (2019) The determinants of academic performance: evidence from a Cambodian University. Stud High Educ 44(11):2096–2111

    Article  Google Scholar 

  70. Raveendran Pillai B, Gautham J (2019) Deep regressor: cross subject academic performance prediction system for university level students. Int J Innov Technol Explor Eng (IJITEE) 8(11S). ISSN: 2278-3075

    Google Scholar 

  71. Rajalaxmi RR, Natesan P, Krishnamoorthy N, Ponni S (2019) Regression model for predicting engineering students academic performance. Int J Recent Technol Eng 71–75

    Google Scholar 

  72. Singh K, Maloney T (2019) Using validated measures of high school academic achievement to predict university success. New Zealand Econ Pap 53(1):89–106

    Article  Google Scholar 

  73. Zhang X, Sun G, Pan Y, Sun H, He Y, Tan J (2018) Students performance modeling based on behavior pattern. J Ambient Intell Human Comput 9(5):1659–1670

    Article  Google Scholar 

  74. Yang SJ, Lu OH, Huang AY, Huang JC, Ogata H, Lin AJ (2018) Predicting students’ academic performance using multiple linear regression and principal component analysis. J Inf Process 26:170–176

    Google Scholar 

  75. Christensen BC, Bemman B, Knoche H, Gade R (2018) Pass or fail? Prediction of students? Exam outcomes from self-reported measures and study activities. ixD&A 39:44–60

    Article  Google Scholar 

  76. Ellis RA, Han F, Pardo A (2017) Improving learning analytics–combining observational and self-report data on student learning. J Educ Technol Soc 20(3):158–169

    Google Scholar 

  77. Strang KD (2017) Predicting student satisfaction and outcomes in online courses using learning activity indicators. Int J Web-Based Learn Teach Technol (IJWLTT) 12(1):32–50

    Article  Google Scholar 

  78. Gershenfeld S, Ward Hood D, Zhan M (2016) The role of first-semester GPA in predicting graduation rates of underrepresented students. J Col Stud Reten Res Theory Pract 17(4):469–488

    Google Scholar 

  79. Gutiérrez L, Flores V, Keith B, Quelopana A (2019) Using the Belbin method and models for predicting the academic performance of engineering students. Comput Appl Eng Educ 27(2):500–509

    Article  Google Scholar 

  80. Gutiérrez F, Seipp K, Ochoa X, Chiluiza K, De Laet T, Verbert K (2020) LADA: a learning analytics dashboard for academic advising. Comput Hum Behav 107:105826

    Article  Google Scholar 

  81. Delen D, Topuz K, Eryarsoy E (2020) Development of a Bayesian belief network-based DSS for predicting and understanding freshmen student attrition. Eur J Oper Res 281(3):575–587

    Article  Google Scholar 

  82. Ramesh A, Goldwasser D, Huang B, Daume H, Getoor L (2018) Interpretable engagement models for MOOCs using Hinge-loss markov random fields. IEEE Trans Learn Technol

    Google Scholar 

  83. Lan AS, Waters AE, Studer C, Baraniuk RG (2017) BLAh: boolean logic analysis for graded student response data. IEEE J Select Topics Signal Process 11(5):754–764

    Article  Google Scholar 

  84. Wan H, Liu K, Yu Q, Gao X (2019) Pedagogical intervention practices: improving learning engagement based on early prediction. IEEE Trans Learn Technol 12(2):278–289

    Article  Google Scholar 

  85. Xu J, Moon KH, Van Der Schaar M (2017) A machine learning approach for tracking and predicting student performance in degree programs. IEEE J Select Topics Sig Process 11(5):742–753

    Article  Google Scholar 

  86. Bhagavan KS, Thangakumar J, Subramanian DV (2020) Predictive analysis of student academic performance and employability chances using HLVQ algorithm. J Ambient Intell Human Comput 1–9

    Google Scholar 

  87. Kamal P, Ahuja S (2019) An ensemble-based model for prediction of academic performance of students in undergrad professional course. J Eng Des Technol

    Google Scholar 

  88. Chui KT, Fung DCL, Lytras MD, Lam TM (2020) Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Comput Hum Behav 107:105584

    Article  Google Scholar 

  89. Zollanvari A, Kizilirmak RC, Kho YH, Hernández-Torrano D (2017) Predicting students’ GPA and developing intervention strategies based on self-regulatory learning behaviors. IEEE Access 5:23792–23802

    Article  Google Scholar 

  90. Almutairi FM, Sidiropoulos ND, Karypis G (2017) Context-aware recommendation-based learning analytics using tensor and coupled matrix factorization. IEEE J Select Top Sig Process 11(5):729–741

    Article  Google Scholar 

  91. Popescu E, Leon F (2018) Predicting academic performance based on learner traces in a social learning environment. IEEE Access 6:72774–72785

    Article  Google Scholar 

  92. Ramanathan L, Geetha A, Khalid L, Swarnalatha P (2016) A novel genetic nand paft model for enhancing the student grade performance system in higher educational institutions. IIOABJ 7(5)

    Google Scholar 

  93. Siemens G, Baker RSJD (2012) Learning analytics and educational data mining: towards communication and collaboration. In: Buckingham Shum S, Gasevic D, Ferguson R (eds) Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, New York, NY, pp 252–254

    Google Scholar 

  94. Chatti MA, Dyckhoff AL, Schroeder U, Thüs H (2012) A reference model for learning analytics. Int J Technol Enhanced Learn 4(5):318–331

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak, Haryana, India

    Amita Dhankhar, Kamna Solanki &  Omdev

  2. Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, Haryana, India

    Sandeep Dalal

Authors
  1. Amita Dhankhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamna Solanki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sandeep Dalal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Omdev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amita Dhankhar .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, Tamil Nadu, India

    Jennifer S. Raj

  2. College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

    Abdullah M. Iliyasu

  3. Telecommunications, Czech Technical University in Prague, Prague, Czech Republic

    Robert Bestak

  4. School of Information Technology, Deakin University, Geelong, Australia

    Zubair A. Baig

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dhankhar, A., Solanki, K., Dalal, S., Omdev (2021). Predicting Students Performance Using Educational Data Mining and Learning Analytics: A Systematic Literature Review. In: Raj, J.S., Iliyasu, A.M., Bestak, R., Baig, Z.A. (eds) Innovative Data Communication Technologies and Application. Lecture Notes on Data Engineering and Communications Technologies, vol 59. Springer, Singapore. https://doi.org/10.1007/978-981-15-9651-3_11

Download citation

Publish with us

Policies and ethics