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Analysis of students’ learning and psychological features by contrast frequent patterns mining on academic performance

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Abstract

In recent years, data mining techniques have been widely applied in education. However, studies on analyzing the similarity or difference of the same learning pattern in different student groups are still rare. In this study, a data mining method which combines the concepts of contrast sets mining and association rules mining is introduced. It could provide quantitative analysis for the similarity and difference of association rules obtained from the academic records datasets of multiple grades. On this basis, student psychological features are deduced without being sensitive to privacy. The work in this study can help educators understand the learning and psychological states of students in different grades, so as to formulate teaching plans that are more targeted to improve their academic performance.

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References

  1. 1.

    Larson D, Chang V (2016) A review and future direction of agile, business intelligence, analytics and data science. Int J Inf Manag 36(5):700–710

  2. 2.

    Avella JT, Kebritchi M, Nunn SG, Kanai T (2016) Learning analytics methods, benefits, and challenges in higher education: a systematic literature review. Online Learn 20(2):13–29

  3. 3.

    Peña-Ayala A (2014) Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst Appl 41(4):1432–1462

  4. 4.

    Park Y, Yu JH, Jo I-H (2016) Clustering blended learning courses by online behavior data: a case study in a Korean higher education institute. Internet High Educ 29:1–11

  5. 5.

    Blikstein P, Worsley M (2016) Multimodal learning analytics and education data mining: using computational technologies to measure complex learning tasks. J Learn Anal 3(2):220–238

  6. 6.

    Tian F, Zheng Q, Zheng D (2010) Mining patterns of e-learner emotion communication in turn level of Chinese interactive texts: experiments and findings. In: 14th international conference on computer supported cooperative work in design, pp 664–670

  7. 7.

    Borkar S, Rajeswari K (2014) Attributes selection for predicting students’ academic performance using education data mining and artificial neural network. Int J Comput Appl 86(10):25–29

  8. 8.

    Mueen A, Zafar B, Manzoor U (2016) Modeling and predicting students’ academic performance using data mining techniques. Int J Mod Educ Comput Sci 8(11):36

  9. 9.

    Paiva ROA, Bittencourt Santa Pinto II, Da Silva AP, Isotani S, Jaques P (2014) A systematic approach for providing personalized pedagogical recommendations based on educational data mining, vol 8474. Lecture notes in computer science. Springer, Berlin, pp 362–367

  10. 10.

    Zhu H, Ni Y, Tian F, Feng P, Chen Y, Zheng Q (2018) A group-oriented recommendation algorithm based on similarities of personal learning generative networks. IEEE Access 6:42729–42739

  11. 11.

    Lee JE, Recker MM, Choi H et al (2015) Applying data mining methods to understand user interactions within learning management systems: approaches and lessons learned. J Educ Technol Dev Exch 8(2):99–116

  12. 12.

    Slater S, Joksimović S, Kovanovic V, Baker RS, Gasevic D (2017) Tools for educational data mining: a review. J Educ Behav Stat 42(1):85–106

  13. 13.

    Han J, Cheng H, Xin D, Yan X (2007) Frequent pattern mining: current status and future directions. Data Min Knowl Disc 15:55–86

  14. 14.

    Rai S, Saini P, Jain AK (2014) Factors affecting the dropout students using discriminant analysis and association rule. Int J Data Min Emerg Technol 4(1):25–33

  15. 15.

    Tsai YR, Ouyang CS, Chang Y (2016) Identifying engineering students’ English sentence reading comprehension errors: applying a data mining technique. J Educ Comput Res 54(1):62–84

  16. 16.

    Herawan T, Vitasari P, Abdullah Z (2013) Mining critical least association rules of student suffering language and social anxieties. Int J Contin Eng Educ Life Long Learn 23(2):128–146

  17. 17.

    Jong BS, Chan TY, Wu YL (2007) Learning log explorer in e-learning diagnosis. IEEE Trans Educ 50(3):216–228

  18. 18.

    Howard SK, Ma J, Yang J (2016) Student rules: exploring patterns of students’ computer-efficacy and engagement with digital technologies in learning. Comput Educ 101:29–42

  19. 19.

    Mane RV, Ghorpade VR (2018) Association rule mining for finding admission tendency of engineering student with pattern growth approach. Big data analytics. Springer, Singapore, pp 749–758

  20. 20.

    Huang X, Xu Y, Zhang S, Zhang W (2018) Association rule mining for selecting proper students to take part in proper discipline competition: a case study of Zhejiang University of Finance and Economics. Int J Emerg Technol Learn 13(03):100–113

  21. 21.

    Huang J, Zhu A, Luo Q (2007) Personality mining method in web based education system using data mining. In: IEEE international conference on grey systems and intelligent services. GSIS 2007, pp 155–158

  22. 22.

    Gara GPP, Padao FRF (2015) mining association rules on students’ profiles and personality types. Lect Not Eng Comput Sci 2215(1):307–312

  23. 23.

    Bay SD, Pazzani MJ (2001) Detecting group differences: mining contrast sets. Data Min Knowl Disc 5(3):213–246

  24. 24.

    Wu X, Zhang C, Zhang S (2004) Efficient mining of both positive and negative association rules. ACM Trans Inf Syst 22(3):381–405

  25. 25.

    Maier SF, Seligman ME (2016) Learned helplessness at fifty: insights from neuroscience. Psychol Rev 123(4):349–367

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Acknowledgements

This work is supported by the Science Research Project of Shaanxi Provincial Department of Education (CN) (Grant No: 17JK0614) and the Youth Science and Technology Innovation Fund of X’ian Shiyou University (CN) (Grant No: 2013BS025).

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Correspondence to Jie Kong.

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Kong, J., Han, J., Ding, J. et al. Analysis of students’ learning and psychological features by contrast frequent patterns mining on academic performance. Neural Comput & Applic 32, 205–211 (2020). https://doi.org/10.1007/s00521-018-3802-9

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Keywords

  • Contrast frequent patterns
  • Data mining
  • Learning feature
  • Psychological feature