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

  • Jie Kong
  • Jiaxin Han
  • Junping Ding
  • Haiyang Xia
  • Xin Han
Brain- Inspired computing and Machine learning for Brain Health
  • 13 Downloads

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.

Keywords

Contrast frequent patterns Data mining Learning feature Psychological feature 

Notes

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).

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):700–710CrossRefGoogle Scholar
  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):13–29Google Scholar
  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):1432–1462CrossRefGoogle Scholar
  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 29:1–11CrossRefGoogle Scholar
  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):220–238CrossRefGoogle Scholar
  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 664–670Google Scholar
  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):25–29Google Scholar
  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):36CrossRefGoogle Scholar
  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 362–367Google Scholar
  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 6:42729–42739CrossRefGoogle Scholar
  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):99–116Google Scholar
  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):85–106CrossRefGoogle Scholar
  13. 13.
    Han J, Cheng H, Xin D, Yan X (2007) Frequent pattern mining: current status and future directions. Data Min Knowl Disc 15:55–86MathSciNetCrossRefGoogle Scholar
  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):25–33CrossRefGoogle Scholar
  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):62–84CrossRefGoogle Scholar
  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):128–146CrossRefGoogle Scholar
  17. 17.
    Jong BS, Chan TY, Wu YL (2007) Learning log explorer in e-learning diagnosis. IEEE Trans Educ 50(3):216–228CrossRefGoogle Scholar
  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 101:29–42CrossRefGoogle Scholar
  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 749–758Google Scholar
  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):100–113CrossRefGoogle Scholar
  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 155–158Google Scholar
  22. 22.
    Gara GPP, Padao FRF (2015) mining association rules on students’ profiles and personality types. Lect Not Eng Comput Sci 2215(1):307–312Google Scholar
  23. 23.
    Bay SD, Pazzani MJ (2001) Detecting group differences: mining contrast sets. Data Min Knowl Disc 5(3):213–246CrossRefGoogle Scholar
  24. 24.
    Wu X, Zhang C, Zhang S (2004) Efficient mining of both positive and negative association rules. ACM Trans Inf Syst 22(3):381–405CrossRefGoogle Scholar
  25. 25.
    Maier SF, Seligman ME (2016) Learned helplessness at fifty: insights from neuroscience. Psychol Rev 123(4):349–367CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Jie Kong
    • 1
  • Jiaxin Han
    • 1
  • Junping Ding
    • 2
  • Haiyang Xia
    • 1
  • Xin Han
    • 1
  1. 1.School of Computer ScienceXi’an Shiyou UniversityXi’anChina
  2. 2.Newegg Inc.City of IndustryUSA

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