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Mining in Educational Data: Review and Future Directions

  • Said A. SalloumEmail author
  • Muhammad Alshurideh
  • Ashraf Elnagar
  • Khaled Shaalan
Conference paper
  • 58 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)

Abstract

One of the developing fields of the present times is educational data mining that pertains to developing methods that help in examining various kinds of data obtained from the educational field. A vital part is played by data mining in the education field, particularly when behavior is being assessed in an online learning setting. This is because data mining is capable of analyzing and identifying the hidden information regarding the data itself, which is very difficult and takes up a lot of time if performed manually. This review has the objective of examining the way data mining was handled by researchers in the past and the most recent trends on data mining in educational research, as well as to evaluate the likelihood of employing machine learning in the field of education. The various limitations inherent in the current research are examined and recommendations are made for future research.

Keywords

Educational data ming Online learning Machine learning 

References

  1. 1.
    Saa, A.A., Al-Emran, M., Shaalan, K.: Factors affecting students’ performance in higher education: a systematic review of predictive data mining techniques. Technol. Knowl. Learn. 24, 567–598 (2019)CrossRefGoogle Scholar
  2. 2.
    Salloum, S.A., AlHamad, A.Q., Al-Emran, M., Shaalan, K.: A survey of Arabic text mining, vol. 740 (2018)Google Scholar
  3. 3.
    Mhamdi, C., Al-Emran, M., Salloum, S.A.: Text mining and analytics: a case study from news channels posts on Facebook, vol. 740 (2018)Google Scholar
  4. 4.
    Hassanien, A.E., Darwish, A., El-Askary, H.: Machine Learning and Data Mining in Aerospace Technology. Springer, Cham (2020)CrossRefGoogle Scholar
  5. 5.
    Hassanien, A.E.: Machine Learning Paradigms: Theory and Application. Springer, Cham (2019)CrossRefGoogle Scholar
  6. 6.
    Ismail, F.H., Hassanien, A.E.: Extracting valuable associations among textural features of medical images. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), pp. 605–608 (2018)Google Scholar
  7. 7.
    Ahuja, R., Jha, A., Maurya, R., Srivastava, R.: Analysis of educational data mining. In: Harmony Search and Nature Inspired Optimization Algorithms, pp. 897–907. Springer (2019)Google Scholar
  8. 8.
    Sarra, A., Fontanella, L., Di Zio, S.: Identifying students at risk of academic failure within the educational data mining framework. Soc. Indic. Res. 146(1–2), 41–60 (2019)CrossRefGoogle Scholar
  9. 9.
    Mohamad, S.K., Tasir, Z.: Educational data mining: a review. Procedia-Soc. Behav. Sci. 97, 320–324 (2013)CrossRefGoogle Scholar
  10. 10.
    Baker, R.S.J.D., Yacef, K.: The state of educational data mining in 2009: a review and future visions. JEDM: J. Educ. Data Min. 1(1), 3–17 (2009)Google Scholar
  11. 11.
    Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33(1), 135–146 (2007)CrossRefGoogle Scholar
  12. 12.
    Salloum, S.A., Alhamad, A.Q.M., Al-Emran, M., Monem, A.A., Shaalan, K.: Exploring students’ acceptance of E-learning through the development of a comprehensive technology acceptance model. IEEE Access 7, 128445–128462 (2019)CrossRefGoogle Scholar
  13. 13.
    Alshurideh, M., Salloum, S.A., Al Kurdi, B., Al-Emran, M.: Factors affecting the social networks acceptance: an empirical study using PLS-SEM approach. In: 8th International Conference on Software and Computer Applications (2019)Google Scholar
  14. 14.
    Alshurideh, M.T., Salloum, S.A., Al Kurdi, B., Monem, A.A., Shaalan, K.: Understanding the quality determinants that influence the intention to use the mobile learning platforms: a practical study. Int. J. Interact. Mob. Technol. 13(11), 157–183 (2019)CrossRefGoogle Scholar
  15. 15.
    Mitrofanova, Y.S., Sherstobitova, A.A., Filippova, O.A.: Modeling smart learning processes based on educational data mining tools. In: Smart Education and e-Learning 2019, pp. 561–571. Springer (2019)Google Scholar
  16. 16.
    Menaka, M.S., Kesavaraj, G.: A study on e-learning system to analyse student performance using data mining (2019)Google Scholar
  17. 17.
    Cerezo, R., Bogarín, A., Esteban, M., Romero, C.: Process mining for self-regulated learning assessment in e-learning. J. Comput. High. Educ. 32, 74–88 (2020)CrossRefGoogle Scholar
  18. 18.
    Keskin, S., Şahin, M., Yurdugül, H.: Online learners’ navigational patterns based on data mining in terms of learning achievement. In: Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment, pp. 105–121. Springer (2019)Google Scholar
  19. 19.
    Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., Van Erven, G.: Educational data mining: predictive analysis of academic performance of public school students in the capital of Brazil. J. Bus. Res. (2018)Google Scholar
  20. 20.
    Salloum, S.A., Al-Emran, M., Monem, A.A., Shaalan, K.: Using text mining techniques for extracting information from research articles. In: Studies in Computational Intelligence, vol. 740. Springer (2018)Google Scholar
  21. 21.
    Salloum, S.A., Al-Emran, M., Abdallah, S., Shaalan, K.: Analyzing the Arab gulf newspapers using text mining techniques. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 396–405 (2017)Google Scholar
  22. 22.
    Salloum, S.A., Al-Emran, M., Shaalan, K.: Mining social media text: extracting knowledge from Facebook. Int. J. Comput. Digit. Syst. 6(2), 73–81 (2017)CrossRefGoogle Scholar
  23. 23.
    Salloum, S.A., Mhamdi, C., Al-Emran, M., Shaalan, K.: Analysis and classification of Arabic newspapers’ Facebook pages using text mining techniques. Int. J. Inf. Technol. Lang. Stud. 1(2), 8–17 (2017)Google Scholar
  24. 24.
    Cummins, M.R.: Nonhypothesis-driven research: data mining and knowledge discovery. In: Clinical Research Informatics, pp. 341–356. Springer (2019)Google Scholar
  25. 25.
    Salloum, S.A., Al-Emran, M., Monem, A.A., Shaalan, K.: A survey of text mining in social media: Facebook and Twitter perspectives. Adv. Sci. Technol. Eng. Syst. J 2(1), 127–133 (2017)CrossRefGoogle Scholar
  26. 26.
    Alomari, K.M., AlHamad, A.Q., Salloum, S.: Prediction of the digital game rating systems based on the ESRB (2019)Google Scholar
  27. 27.
    Arunachalam, A.S., Velmurugan, T.: Analyzing student performance using evolutionary artificial neural network algorithm. Int. J. Eng. Technol. 7(2.26), 67–73 (2018)CrossRefGoogle Scholar
  28. 28.
    Romero, C., Ventura, S., García, E.: Data mining in course management systems: moodle case study and tutorial. Comput. Educ. 51(1), 368–384 (2008)CrossRefGoogle Scholar
  29. 29.
    Sachin, R.B., Vijay, M.S.: A survey and future vision of data mining in educational field. In: 2012 Second International Conference on Advanced Computing & Communication Technologies, pp. 96–100 (2012)Google Scholar
  30. 30.
    Salloum, S.A., Shaalan, K.: Factors affecting students’ acceptance of e-learning system in higher education using UTAUT and structural equation modeling approaches. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 469–480 (2018)Google Scholar
  31. 31.
    Salloum, S.A., Al-Emran, M., Habes, M., Alghizzawi, M., Ghani, M.A., Shaalan, K.: Understanding the impact of social media practices on e-learning systems acceptance. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 360–369 (2019)Google Scholar
  32. 32.
    Salloum, S.A., Mhamdi, C., Al Kurdi, B., Shaalan, K.: Factors affecting the adoption and meaningful use of social media: a structural equation modeling approach. Int. J. Inf. Technol. Lang. Stud. 2(3), 96–109 (2018)Google Scholar
  33. 33.
    Salloum, S.A., Maqableh, W., Mhamdi, C., Al Kurdi, B., Shaalan, K.: Studying the social media adoption by university students in the United Arab Emirates. Int. J. Inf. Technol. Lang. Stud. 2(3), 83–95 (2018)Google Scholar
  34. 34.
    Salloum, S.A.S., Shaalan, K.: Investigating students’ acceptance of e-learning system in higher educational environments in the UAE: applying the extended technology acceptance model (TAM). The British University in Dubai (2018)Google Scholar
  35. 35.
    Habes, M., Alghizzawi, M., Khalaf, R., Salloum, S.A., Ghani, M.A.: The relationship between social media and academic performance: Facebook perspective. Int. J. Inf. Technol. Lang. Stud. 2(1), 12–18 (2018)Google Scholar
  36. 36.
    Salloum, S.A., Al-Emran, M., Shaalan, K., Tarhini, A.: Factors affecting the E-learning acceptance: a case study from UAE. Educ. Inf. Technol. 24, 509–530 (2019)CrossRefGoogle Scholar
  37. 37.
    Al-Emran, M., Salloum, S.A.: Students’ attitudes towards the use of mobile technologies in e-evaluation. Int. J. Interact. Mob. Technol. 11(5), 195–202 (2017)CrossRefGoogle Scholar
  38. 38.
    Kabakchieva, D.: Predicting student performance by using data mining methods for classification. Cybern. Inf. Technol. 13(1), 61–72 (2013)MathSciNetGoogle Scholar
  39. 39.
    Durairaj, M., Vijitha, C.: Educational data mining for prediction of student performance using clustering algorithms. Int. J. Comput. Sci. Inf. Technol. 5(4), 5987–5991 (2014)Google Scholar
  40. 40.
    Francis, B.K., Babu, S.S.: Predicting academic performance of students using a hybrid data mining approach. J. Med. Syst. 43(6), 162 (2019)CrossRefGoogle Scholar
  41. 41.
    Akram, A., et al.: Predicting students’ academic procrastination in blended learning course using homework submission data. IEEE Access 7, 102487–102498 (2019)CrossRefGoogle Scholar
  42. 42.
    Rojanavasu, P.: Educational data analytics using association rule mining and classification. In: 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), pp. 142–145 (2019)Google Scholar
  43. 43.
    Sana, B., Siddiqui, I.F., Arain, Q.A.: Analyzing students’ academic performance through educational data mining. 3c Tecnol. glosas innovación Apl. a la pyme 8(29), 402–421 (2019)Google Scholar
  44. 44.
    Bharara, S., Sabitha, S., Bansal, A.: Application of learning analytics using clustering data Mining for Students’ disposition analysis. Educ. Inf. Technol. 23(2), 957–984 (2018)CrossRefGoogle Scholar
  45. 45.
    Nurhayati, O.D., Bachri, O.S., Supriyanto, A., Hasbullah, M.: Graduation prediction system using artificial neural network. Int. J. Mech. Eng. Technol. 9(7), 1051–1057 (2018)Google Scholar
  46. 46.
    Rao, K.S., Swapna, N., Kumar, P.P.: Educational data mining for student placement prediction using machine learning algorithms. Int. J. Eng. Technol. Sci. 7(1.2), 43–46 (2018)Google Scholar
  47. 47.
    Okubo, F., Yamashita, T., Shimada, A., Ogata, H.: A neural network approach for students’ performance prediction. In: LAK 2017, pp. 598–599 (2017)Google Scholar
  48. 48.
    Almarabeh, H.: Analysis of students’ performance by using different data mining classifiers. Int. J. Mod. Educ. Comput. Sci. 9(8), 9 (2017)CrossRefGoogle Scholar
  49. 49.
    Alban, M., Mauricio, D.: Neural networks to predict dropout at the universities. Int. J. Mach. Learn. Comput. 9(2), 149–153 (2019)CrossRefGoogle Scholar
  50. 50.
    Feng, J.: Predicting students’ academic performance with decision tree and neural network (2019)Google Scholar
  51. 51.
    Jie, W., Hai-yan, L., Biao, C., Yuan, Z.: Application of educational data mining on analysis of students’ online learning behavior. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 1011–1015 (2017)Google Scholar
  52. 52.
    Lara, J.A., Lizcano, D., Martínez, M.A., Pazos, J., Riera, T.: 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 (2014)CrossRefGoogle Scholar
  53. 53.
    Chakraborty, B., Chakma, K., Mukherjee, A.: A density-based clustering algorithm and experiments on student dataset with noises using Rough set theory. In: 2016 IEEE International Conference on Engineering and Technology (ICETECH), pp. 431–436 (2016)Google Scholar
  54. 54.
    Chauhan, N., Shah, K., Karn, D., Dalal, J.: Prediction of student’s performance using machine learning (2019). SSRN 3370802Google Scholar
  55. 55.
    Pechenizkiy, M., Calders, T., Vasilyeva, E., De Bra, P.: Mining the student assessment data: lessons drawn from a small scale case study. In: Educational Data Mining 2008 (2008)Google Scholar
  56. 56.
    Shih, Y.-C., Huang, P.-R., Hsu, Y.-C., Chen, S.Y.: A complete understanding of disorientation problems in Web-based learning. Turkish Online J. Educ. Technol. 11(3), 1–13 (2012)Google Scholar
  57. 57.
    Talavera, L., Gaudioso, E.: Mining student data to characterize similar behavior groups in unstructured collaboration spaces. In: Workshop on Artificial Intelligence in CSCL. 16th European Conference on Artificial Intelligence, pp. 17–23 (2004)Google Scholar
  58. 58.
    Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaïane, O.R.: Clustering and sequential pattern mining of online collaborative learning data. IEEE Trans. Knowl. Data Eng. 21(6), 759–772 (2008)CrossRefGoogle Scholar
  59. 59.
    Dutt, A., Aghabozrgi, S., Ismail, M.A.B., Mahroeian, H.: Clustering algorithms applied in educational data mining. Int. J. Inf. Electron. Eng. 5(2), 112 (2015)Google Scholar
  60. 60.
    Bogarín, A., Romero, C., Cerezo, R., Sánchez-Santillán, M.: Clustering for improving educational process mining. In: Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, pp. 11–15 (2014)Google Scholar
  61. 61.
    Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., Van Erven, G.: Educational data mining: predictive analysis of academic performance of public school students in the capital of Brazil. J. Bus. Res. 94, 335–343 (2019)CrossRefGoogle Scholar
  62. 62.
    Palomo-Duarte, M., Berns, A., Yañez Escolano, A., Dodero, J.-M.: Clustering analysis of game-based learning: worth it for all students? J. Gaming Virtual Worlds 11(1), 45–66 (2019)CrossRefGoogle Scholar
  63. 63.
    Ahmed, A.B.E.D., Elaraby, I.S.: Data mining: a prediction for student’s performance using classification method. World J. Comput. Appl. Technol. 2(2), 43–47 (2014)Google Scholar
  64. 64.
    Anjewierden, A., Kolloffel, B., Hulshof, C.: Towards educational data mining: using data mining methods for automated chat analysis to understand and support inquiry learning processes (2007)Google Scholar
  65. 65.
    Adebayo, A.O., Chaubey, M.S.: Data mining classification techniques on the analysis of student’s performance. GSJ 7(4), 45–52 (2019)Google Scholar
  66. 66.
    Kay, J., Maisonneuve, N., Yacef, K., Zaïane, O.: Mining patterns of events in students’ teamwork data. In: Proceedings of the Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems (ITS 2006), pp. 45–52 (2006)Google Scholar
  67. 67.
    Tiwari, A.K., Ramakrishna, G., Sharma, L.K., Kashyap, S.K.: Academic performance prediction algorithm based on fuzzy data mining. Int. J. Artif. Intelegence 8(1), 26–32 (2019)Google Scholar
  68. 68.
    Merceron, A., Yacef, K.: Revisiting interestingness of strong symmetric association rules in educational data. In: Proceedings of the International Workshop on Applying Data Mining in e-Learning, Creete, Greece, pp. 3–12 (2007)Google Scholar
  69. 69.
    García, E., Romero, C., Ventura, S., Calders, T.: Drawbacks and solutions of applying association rule mining in learning management systems. In: Proceedings of the International Workshop on Applying Data Mining in e-Learning (ADML 2007), Crete, Greece, pp. 13–22 (2007)Google Scholar
  70. 70.
    Samuel, A.L.: Some studies in machine learning using the game of checkers. II—recent progress. IBM J. Res. Dev. 11(6), 601–617 (1967)CrossRefGoogle Scholar
  71. 71.
    Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  72. 72.
    Kučak, D., Juričić, V., Đambić, G.: Machine learning in education-a survey of current research trends. In: Annals of DAAAM and Proceedings, vol. 29 (2018)Google Scholar
  73. 73.
    Stahl, F., Jordanov, I.: An overview of the use of neural networks for data mining tasks. Wiley Interdiscip Rev. Data Min. Knowl. Discov. 2(3), 193–208 (2012)CrossRefGoogle Scholar
  74. 74.
    Coelho, O.B., Silveira, I.: Deep learning applied to learning analytics and educational data mining: a systematic literature review. In: Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), vol. 28, no. 1, p. 143 (2017)Google Scholar
  75. 75.
    Vellido, A., Castro, F., Nebot, A.: Clustering educational data. In: Handbook of Educational Data Mining, pp. 75–92 (2010)Google Scholar
  76. 76.
    Li, J., Wong, Y., Kankanhalli, M.S.: Multi-stream deep learning framework for automated presentation assessment. In: 2016 IEEE International Symposium on Multimedia (ISM), pp. 222–225 (2016)Google Scholar
  77. 77.
    Gross, E., Wshah, S., Simmons, I., Skinner, G.: A handwriting recognition system for the classroom. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, pp. 218–222 (2015)Google Scholar
  78. 78.
    Guo, B., Zhang, R., Xu, G., Shi, C., Yang, L.: Predicting students performance in educational data mining. In: 2015 International Symposium on Educational Technology (ISET), pp. 125–128 (2015)Google Scholar
  79. 79.
    Tang, S., Peterson, J.C., Pardos, Z.A.: Deep neural networks and how they apply to sequential education data. In: Proceedings of the Third (2016) ACM Conference on Learning @ Scale, pp. 321–324 (2016)Google Scholar
  80. 80.
    Wang, L., Sy, A., Liu, L., Piech, C.: Deep knowledge tracing on programming exercises. In: Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, pp. 201–204 (2017)Google Scholar
  81. 81.
    Craven, M.W., Shavlik, J.W.: Using neural networks for data mining. Futur. Gener. Comput. Syst. 13(2–3), 211–229 (1997)CrossRefGoogle Scholar
  82. 82.
    Anozie, N., Junker, B.W.: Predicting end-of-year accountability assessment scores from monthly student records in an online tutoring system (2006)Google Scholar
  83. 83.
    Khan, I., Al Sadiri, A., Ahmad, A.R., Jabeur, N.: Tracking student performance in introductory programming by means of machine learning. In: 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1–6 (2019)Google Scholar
  84. 84.
    Livieris, I.E., Drakopoulou, K., Tampakas, V.T., Mikropoulos, T.A., Pintelas, P.: Predicting secondary school students’ performance utilizing a semi-supervised learning approach. J. Educ. Comput. Res. 57(2), 448–470 (2019)CrossRefGoogle Scholar
  85. 85.
    Yadav, S.K., Pal, S.: Data mining: a prediction for performance improvement of engineering students using classification, arXiv Prepr. arXiv:1203.3832 (2012)
  86. 86.
    Yadav, S.K., Bharadwaj, B., Pal, S.: Mining education data to predict student’s retention: a comparative study, arXiv Prepr. arXiv:1203.2987 (2012)
  87. 87.
    Akinola, O.S., Akinkunmi, B.O., Alo, T.S.: A data mining model for predicting computer programming proficiency of computer science undergraduate students (2012)Google Scholar
  88. 88.
    Luckin, R., Holmes, W., Griffiths, M., Forcier, L.B.: Intelligence unleashed: an argument for AI in education (2016)Google Scholar
  89. 89.
    Meseguer-Brocal, G., Cohen-Hadria, A., Peeters, G.: DALI: a large dataset of synchronized audio, lyrics and notes, automatically created using teacher-student machine learning paradigm, arXiv Prepr. arXiv:1906.10606 (2019)
  90. 90.
    El-Alfy, E.-S.M., Abdel-Aal, R.E.: Construction and analysis of educational tests using abductive machine learning. Comput. Educ. 51(1), 1–16 (2008)CrossRefGoogle Scholar
  91. 91.
    Đambić, G., Krajcar, M., Bele, D.: Machine learning model for early detection of higher education students that need additional attention in introductory programming courses. Int. J. Digit. Technol. Econ. 1(1), 1–11 (2016)Google Scholar
  92. 92.
    Celar, S., Stojkic, Z., Seremet, Z., Marusic, Z., Zelenika, D.: Classification of test documents based on handwritten student ID’s characteristics. Procedia Eng. 100, 782–790 (2015)CrossRefGoogle Scholar
  93. 93.
    Pechenizkiy, M., Trcka, N., Vasilyeva, E., Van der Aalst, W., De Bra, P.: Process mining online assessment data. In: International Working Group on Educational Data Mining (2009)Google Scholar

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Authors and Affiliations

  1. 1.Research Institute of Sciences and EngineeringUniversity of SharjahSharjahUAE
  2. 2.Faculty of Engineering and ITThe British University in DubaiDubaiUAE
  3. 3.Faculty of BusinessUniversity of JordanAmmanJordan
  4. 4.Management DepartmentUniversity of SharjahSharjahUAE
  5. 5.Department of Computer ScienceUniversity of SharjahSharjahUAE

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