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You Are How You Behave – Spatiotemporal Representation Learning for College Student Academic Achievement

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Abstract

Scholarships are a reflection of academic achievement for college students. The traditional scholarship assignment is strictly based on final grades and cannot recognize students whose performance trend improves or declines during the semester. This paper develops the Trajectory Mining on Clustering for Scholarship Assignment and Academic Warning (TMS) approach to identify the factors that affect the academic achievement of college students and to provide decision support to help low-performing students attain better performance. Specifically, we first conduct feature engineering to generate a set of features to characterize the lifestyles patterns, learning patterns, and Internet usage patterns of students. We then apply the objective and subjective combined weighted k-means (Wosk-means) algorithm to perform clustering analysis to identify the characteristics of different student groups. Considering the difficulty in obtaining the real global positioning system (GPS) records of students, we apply manually generated spatiotemporal trajectories data to quantify the direction of trajectory deviation with the assistance of the PrefixSpan algorithm to identify low-performing students. The experimental results show that the silhouette coefficient and Calinski-Harabasz index of the Wosk-means algorithm are both approximately 1.5 times to that of the best baseline algorithm, and the sum of the squared error of the Wosk-means algorithm is only the half of the best baseline algorithm.

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References

  1. Petrides K V, Frederickson N, Furnham A. The role of trait emotional intelligence in academic performance and deviant behavior at school. Personality and Individual Differences, 2004, 36(2): 277-293.

    Google Scholar 

  2. Alloway T P, Alloway R G. Investigating the predictive roles of working memory and IQ in academic attainment. Journal of Experimental Child Psychology, 2010, 106(1): 20-29.

    Google Scholar 

  3. Rampersaud G C, Pereira M A, Girard B L et al. Breakfast habits, nutritional status, body weight, and academic performance in children and adolescents. Journal of the American Dietetic Association, 2005, 105(5): 743-760.

    Google Scholar 

  4. Pilcher J J, Morris D M, Donnelly J et al. Interactions between sleep habits and self-control. Frontiers in Human Neuroscience, 2015, 9: 284.

    Google Scholar 

  5. Macan T H, Shahani C, Dipboye R L et al. College students’ time management: Correlations with academic performance and stress. Journal of Educational Psychology, 1990, 82(4): 760-768.

    Google Scholar 

  6. Stadler M, Aust M, Becker N et al. Choosing between what you want now and what you want most: Self-control explains academic achievement beyond cognitive ability. Personality and Individual Differences, 2016, 94: 168-172

    Google Scholar 

  7. Lundstrom S. The impact of family income on child achievement: evidence from the earned income tax credit: Comment. American Economic Review, 2017, 107(2): 623-28.

    Google Scholar 

  8. Figlio D, Karbownik K, Roth J et al. School quality and the gender gap in educational achievement. American Economic Review, 2016, 106(5): 289-295.

    Google Scholar 

  9. Jia J, Li D, Li X et al. Psychological security and deviant peer affiliation as mediators between teacher-student relationship and adolescent Internet addiction. Computers in Human Behavior, 2017, 73: 345-352.

    Google Scholar 

  10. Leung K C. Preliminary empirical model of crucial determinants of best practice for peer tutoring on academic achievement. Journal of Educational Psychology, 2015, 107(2): 558-579.

    Google Scholar 

  11. Pei J, Han J, Mortazavi-Asl B et al. Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In Proc. the 17th International Conference on Data Engineering, April 2001, pp.215-224.

  12. Duckworth A L, Seligman M E P. Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychological Science, 2005, 16(12): 939-944.

    Google Scholar 

  13. Wu Y, Gong R, Cao Y et al. EduCircle: Visualizing spatial temporal features of student performance from campus activity and consumption data. In Proc. International Conference on Cooperative Design, Visualization and Engineering, October 2016, pp.313-321.

  14. Guan C, Lu X, Li X et al. Discovery of college students in financial hardship. In Proc. IEEE International Conference on Data Mining, November 2015, pp.141-150.

  15. Ye H J, Zhan D C, Li X et al. College student scholarships and subsidies granting: A multi-modal multi-label approach. In Proc. the 16th IEEE International Conference on Data Mining, December 2016, pp.559-568.

  16. Liu J, Wang D, Feng S et al. Learning distributed representations for community search using node embedding. Frontiers of Computer Science, 2019, 13(2): 437-439.

    Google Scholar 

  17. Zheng Y. Trajectory data mining: An overview. ACM Trans. Intelligent Systems and Technology, 2015, 6(3): 1-41.

    Google Scholar 

  18. Singh A, Uijtdewilligen L, Twisk J W R et al. Physical activity and performance at school: A systematic review of the literature including a methodological quality assessment. Archives of Pediatrics & Adolescent Medicine, 2012, 166(1): 49-55.

    Google Scholar 

  19. Forrest C B, Bevans K B, Riley A W et al. Health and school outcomes during children’s transition into adolescence. Journal of Adolescent Health, 2013, 52(2): 186-194.

    Google Scholar 

  20. Skryabin M, Zhang J J, Liu L et al. How the ICT development level and usage influence student achievement in reading, mathematics, and science. Computers & Education, 2015, 85: 49-58.

    Google Scholar 

  21. Belo R, Ferreira P, Telang R. Broadband in school: Impact on student performance. Management Science, 2013, 60(2): 265-282.

    Google Scholar 

  22. Han J, Pei J, Kamber M. Data Mining: Concepts and Techniques (3rd edition). Morgan Kaufmann Publishers, Massachusetts, USA, 2011.

  23. Liu H, Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowledge and Data Engineering, 2005, 17(4): 491-502.

    Google Scholar 

  24. Wettschereck D, Aha D W, Mohri T. A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review, 1997, 11(1/2/3/4/5): 273-314.

  25. Tsai C Y, Chiu C C. Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm. Computational Statistics and Data Analysis, 2008, 52(10): 4658-4672.

    MathSciNet  MATH  Google Scholar 

  26. Modha D S, Spangler W S. Feature weighting in k-means clustering. Machine Learning, 2003, 52(3): 217-237.

    MATH  Google Scholar 

  27. Huang J Z, Ng M K, Rong H et al. Automated variable weighting in k-means type clustering. IEEE Trans. Pattern Analysis & Machine Intelligence, 2005, 27(5): 657-668.

    Google Scholar 

  28. Lord E, Willems M, Lapointe F J et al. Using the stability of objects to determine the number of clusters in datasets. Information Sciences, 2017, 393: 29-46.

    Google Scholar 

  29. Kushnir D, Jalali S, Saniee I. Towards clustering high-dimensional Gaussian mixture clouds in linear running time. In Proc. the 22nd International Conference on Artificial Intelligence and Statistics, April 2019, pp.1379-1387.

  30. Basch C E. Healthier students are better learners: A missing link in school reforms to close the achievement gap. Journal of School Health, 2011, 81(10): 593-598.

    Google Scholar 

  31. Ma Y, Cui C, Nie X et al. Pre-course student performance prediction with multi-instance multi-label learning. Science China Information Sciences, 2019, 62(29101): 1-3.

    Google Scholar 

  32. Lakkaraju H, Aguiar E, Shan C et al. A machine learning framework to identify students at risk of adverse academic outcomes. In Proc. the 21th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, August 2015, pp.1909-1918.

  33. Buniyamin N, bin Mat U, Arshad P M. Educational data mining for prediction and classification of engineering students’ achievement. In Proc. the 7th IEEE Int. Conf. Engineering Education, November 2015, pp.49-53.

  34. Hang M, Pytlarz I, Neville J. Exploring student check-in behavior for improved point-of-interest prediction. In Proc. the 24th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, July 2018, pp.321-330.

  35. Li Q, Zheng Y, Xie X et al. Mining user similarity based on location history. In Proc. the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2008, pp.1-10.

  36. Xiao X, Zheng Y, Luo Q et al. Finding similar users using category-based location history. In Proc. the 18th SIGSPATIAL International Conference on Advances In Geographic Information Systems, November 2010, pp.442-445.

  37. Hung W L, Chang Y C, Lee E S. Weight selection in W-K-means algorithm with an application in color image segmentation. Computers & Mathematics with Applications, 2011, 62(2): 668-676.

    MathSciNet  MATH  Google Scholar 

  38. Zhou Z H. Abductive learning: Towards bridging machine learning and logical reasoning. Science China Information Sciences, 2019, 62(7): 76101.

    MathSciNet  Google Scholar 

  39. Duckworth A L, Seligman M E P. Self-discipline gives girls the edge: Gender in self-discipline, grades, and achievement test scores. Journal of Educational Psychology, 2006, 98(1): 198-208.

    Google Scholar 

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Authors and Affiliations

  1. School of Business, Nanjing University, Nanjing, 210093, China

    Xiao-Lin Li & Li Ma

  2. Information Technology Services Center, Nanjing University, Nanjing, 210093, China

    Xiang-Dong He

  3. School of Business, Rutgers University, Newark, NJ, 07102, U.S.A.

    Hui Xiong

Authors
  1. Xiao-Lin Li
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  2. Li Ma
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  3. Xiang-Dong He
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  4. Hui Xiong
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Correspondence to Xiang-Dong He.

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Li, XL., Ma, L., He, XD. et al. You Are How You Behave – Spatiotemporal Representation Learning for College Student Academic Achievement. J. Comput. Sci. Technol. 35, 353–367 (2020). https://doi.org/10.1007/s11390-020-9971-x

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  • DOI: https://doi.org/10.1007/s11390-020-9971-x

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