Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory

Computers in Human Behavior

Volumn 47, Issue , 2015, Pages 168-181

  Xing, Wanli ^a   Guo, Rui ^a   Petakovic, Eva ^a   Goggins, Sean ^a  

^a UNIVERSITY OF MISSOURI   (United States)

Author keywords

Activity theory; CSCL; Educational data mining; Genetic Programming; Learning analytics; Prediction

Indexed keywords

ACTIVITY COEFFICIENTS; ALGORITHMS; DATA HANDLING; DATA MINING; EDUCATION; EDUCATION COMPUTING; FORECASTING; GENETIC ALGORITHMS; GENETIC PROGRAMMING; PROBLEM SOLVING; SEMANTICS; SOCIAL NETWORKING (ONLINE); STUDENTS; TEACHING;

ACTIVITY THEORY; ADVANCED MODELING TECHNIQUES; COMPUTER SUPPORTED COLLABORATIVE LEARNING; CSCL; EDUCATIONAL DATA MINING; EDUCATIONAL DATA MININGS (EDM); LEARNING ANALYTICS; PERFORMANCE PREDICTION MODELS;

E-LEARNING;

EID: 84924252472     PISSN: 07475632     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chb.2014.09.034     Document Type: Article

References (76)

1. W. Afzal, R. Torkar, R. Feldt, and T. Gorschek Genetic programming for cross-release fault count predictions in large and complex software projects Evolutionary Computation and Optimization Algorithms in Software Engineering 2010 94 126
2. Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2013). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior.
3. R.S.J.D. Baker, and K. Yacef The state of educational data mining in 2009: A review and future visions Journal of Educational Data Mining 1 1 2009 3 17
4. S.A. Barab, M. Barnett, L. Yamagata-Lynch, K. Squire, and T. Keating Using activity theory to understand the systemic tensions characterizing a technology-rich introductory astronomy course Mind, Culture, and Activity 9 2 2002 76 107
5. Barber, R., & Sharkey, M. (2012). Course correction: Using analytics to predict course success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 259-262). ACM.
6. O.K. Basharina An activity theory perspective on student-reported contradictions in international telecollaboration Language Learning & Technology 11 2 2007 82 103
7. D. Bernardo, H. Hagras, and E. Tsang A genetic type-2 fuzzy logic based system for the generation of summarised linguistic predictive models for financial applications Soft Computing 17 12 2013 2185 2201
8. Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief. US Department of Education, Office of Educational Technology, pp. 1-57.
9. M.D. Calvo-Flores, E.G. Galindo, M.P. Jiménez, and O.P. Piñeiro Predicting students' marks from Moodle logs using neural network models Current Developments in Technology-Assisted Education 1 2006 586 590
10. J.P. Campbell, P.B. DeBlois, and D.G. Oblinger Academic analytics: A new tool for a new era Educause Review 42 4 2007 40
11. Cano, A., Zafra, A., & Ventura, S. (2010). Solving classification problems using genetic programming algorithms on GPUs. In Hybrid Artificial Intelligence Systems (pp. 17-26). Springer, Berlin Heidelberg.
12. A. Cano, A. Zafra, and S. Ventura An interpretable classification rule mining algorithm Information Sciences 240 2013 1 20
13. Cetintas, S., Si, L., Xin, Y. P., & Hord, C. (2009). Predicting correctness of problem solving from low-level log data in intelligent tutoring systems. In EDM (pp. 230-239).
14. Deegalla, S., & Bostrom, H. (2006). Reducing high-dimensional data by principal component analysis vs. random projection for nearest neighbor classification. In International conference on machine learning and applications (pp. 245-250).
15. P. Domingos, and M. Pazzani On the optimality of the simple Bayesian classifier under zero-one loss Machine Learning 29 2-3 1997 103 130
16. Y. Engeström Activity theory and individual and social transformation Perspectives on Activity Theory 1999 19 38
17. Espejo, P. G., Romero, C., Ventura, S., & Herrera, F. (2005). Induction of classification rules with grammar-based genetic programming. In Proceedings of the 2nd int. conf. mach. intell. (ACIDCA ICMI) (pp. 596-601), Tozeur, Tunisha.
18. P.G. Espejo, S. Ventura, and F. Herrera A survey on the application of genetic programming to classification Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 40 2 2010 121 144
19. Essa, A., & Ayad, H. (2012). Student success system: risk analytics and data visualization using ensembles of predictive models. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 158-161). ACM.
20. Fancsali, S. E. (2011). Variable construction for predictive and causal modeling of online education data. In Proceedings of the 1st international conference on learning analytics and knowledge (pp. 54-63). ACM.
21. Ferguson, R. (2012). The state of learning analytics in 2012: A review and future challenges. Knowledge Media Institute, Technical Report KMI-2012 (Vol. 1), 2012.
22. A.A. Freitas Data mining and knowledge discovery with evolutionary algorithms 2002 Springer
23. A.A. Freitas, D.C. Wieser, and R. Apweiler On the importance of comprehensible classification models for protein function prediction IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 7 1 2010 172 182
24. V. Fromkin, R. Rodman, and N.M. Hyams An introduction to language 2009 Cengage Learning
25. S.P. Goggins, and G. Dyke Network analytic techniques for online chat D. Suthers, K. Lund, C. Teplovs, C.P. Rose, Productive multivocality in the analysis of group interactions 2013 Springer 541 559
26. Goggins, S., Laffey, J., & Galyen, K. (2009). Social ability in online groups: Representing the quality of interactions in social computing environments. In Proceedings from IEEE conference on computer science and engineering, Vancouver, BC.
27. Goggins, S. P., Laffey, J., Amelung, C., & Gallagher, M. (2010). Social intelligence in completely online groups. In Proceedings from IEEE international conference on social computing, Minneapolis, MN.
28. S.P. Goggins, C. Mascaro, and G. Valetto Group informatics: A methodological approach and ontology for sociotechnical group research Journal of the American Society for Information Science and Technology 64 3 2013 516 539 10.1002/asi.22802
29. S. Goggins, G. Valetto, C. Mascaro, and K. Blincoe Creating a model of the dynamics of socio-technical groups User Modeling and User-Adapted Interaction 23 4 2013 345 379 Retrieved http://link.springer.com/article/10.1007/s11257-012-9122-3
30. D.E. Goldberg, and J.H. Holland Genetic algorithms and machine learning Machine Learning 3 2 1988 95 99
31. C.L. Gress, M. Fior, A.F. Hadwin, and P.H. Winne Measurement and assessment in computer-supported collaborative learning Computers in Human Behavior 26 5 2010 806 814
32. Gunnarsson, B. L., & Alterman, R. (2012). Predicting failure: A case study in co-blogging. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 263-266). ACM.
33. C.A. Halverson Activity theory and distributed cognition: Or what does CSCW need to DO with theories? Computer Supported Cooperative Work (CSCW) 11 1-2 2002 243 267
34. Hämäläinen, W., & Vinni, M. (2006). Comparison of machine learning methods for intelligent tutoring systems. In Intelligent tutoring systems (pp. 525-534). Springer, Berlin, Heidelberg.
35. Henery, R. J. (1994). Classifiction. In D. Michie, D. J. Spiegelhalter, & C. C. Taylor (Eds.), Machine Learning, Neural and Statistical Classification (pp. 6-16). Ellis Horwood.
36. F.E. Harrell Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis 2001 Springer
37. Hellerstein, J. L., Jayram, T. S., & Rish, I. (2000). Recognizing end-user transactions in performance management. IBM TJ Watson Research Center.
38. S. Hrastinski A theory of online learning as online participation Computers & Education 52 1 2009 78 82
39. J. Huysmans, K. Dejaeger, C. Mues, J. Vanthienen, and B. Baesens An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models Decision Support Systems 51 1 2011 141 154
40. Ibrahim, Z., & Rusli, D. (2007). Predicting students' Academic Performance: Comparing artificial neural network, decision tree and linear regression. In Proceedings of the 21 Annual SAS Malaysia Forum, Kuala Lumpur, Malaysia (pp. 1-6).
41. D.H. Jonassen, and S.M. Land Preface D.H. Jonassen, S.M. Land, Theoretical foundations of learning environments 2000 Lawrence Erlbaum New Jersey 3 9
42. R. Kohavi, and F. Provost Glossary of terms Machine Learning 30 2-3 1998 271 274
43. Kotsiantis, S. B., & Pintelas, P. E. (2005). Predicting students marks in hellenic open university. In Advanced learning technologies, 2005. ICALT 2005. fifth IEEE international conference on (pp. 664-668). IEEE.
44. Korb, K. B., & Nicholson, A. E. (2003). Bayesian artificial intelligence. CRC press.
45. Koza, J. R. (1992). Genetic programming: vol. 1, On the programming of computers by means of natural selection (Vol. 1). MIT press.
46. I. Lawrence, and K. Lin A concordance correlation coefficient to evaluate reproducibility Biometrics 1989 255 268
47. A.N. Leont'ev The problem of activity in psychology Journal of Russian and East European Psychology 13 2 1974 4 33
48. S. Luke Two fast tree-creation algorithms for genetic programming Evolutionary Computation IEEE Transactions on 4 3 2000 274 283
49. C. Márquez-Vera, A. Cano, C. Romero, and S. Ventura Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data Applied Intelligence 2013 1 16
50. R.I. McKay, N.X. Hoai, P.A. Whigham, Y. Shan, and M. O'Neill Grammar-based genetic programming: A survey Genetic Programming and Evolvable Machines 11 3-4 2010 365 396
51. H. Mintrop, and G.L. Sunderman Predictable failure of federal sanctions-driven accountability for school improvement - and why we may retain it anyway Educational Researcher 38 5 2009 353 364
52. Myller, N., Suhonen, J., & Sutinen, E. (2002). Using data mining for improving web-based course design. In Computers in education, 2002. Proceedings. International Conference on (pp. 959-963). IEEE.
53. Nasiri, M., & Minaei, B. (2012). Predicting GPA and academic dismissal in LMS using educational data mining: A case mining. In E-Learning and E-Teaching (ICELET), 2012 Third International Conference on (pp. 53-58). IEEE.
54. Nebot, A., Castro, F., Vellido, A., & Mugica, F. (2006). Identification of fuzzy models to predict students performance in an e-learning environment. In The Fifth IASTED international conference on web-based education, WBE (pp. 74-79).
55. Q. Ni, L. Wang, B. Zheng, and M. Sivakumar Evolutionary algorithm for water storage forecasting response to climate change with small data sets: The Wolonghu Wetland, China Environmental Engineering Science 29 8 2012 814 820
56. Pardos, Z. A., Heffernan, N. T., Anderson, B., & Heffernan, C. L. (2007). The effect of model granularity on student performance prediction using Bayesian networks. In User modeling 2007 (pp. 435-439). Springer, Berlin Heidelberg.
57. Pardos, Z. A., Beck, J. E., Ruiz, C., & Heffernan, N. T. (2008). The composition effect: Conjunctive or compensatory? An analysis of multi-skill math questions in ITS.
58. Roberge, D., Rojas, A., & Baker, R. (2012). Does the length of time off-task matter?. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 234-237). ACM.
59. Romero, C., Ventura, S., Espejo, P. G., & Hervás, C. (2008). Data Mining Algorithms to Classify Students. In EDM (pp. 8-17).
60. C. Romero, M.I. López, J.M. Luna, and S. Ventura Predicting students' final performance from participation in on-line discussion forums Computers & Education 68 2013 458 472
61. C. Romero, and S. Ventura Educational data mining: A review of the state of the art Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 40 6 2010 601 618
62. S.B. Shum, and R. Ferguson Social learning analytics Journal of Educational Technology & Society 15 3 2012
63. Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.
64. M.M.A. Tair, and A.M. El-Halees Mining educational data to improve students' performance: A case study International Journal of Information 2 2 2012
65. E.H. Thomas, and N. Galambos What satisfies students? Mining student-opinion data with regression and decision tree analysis Research in Higher Education 45 3 2004 251 269
66. Vanneschi, L., & Poli, R. (2012). Genetic Programming - introduction, applications, theory and open issues. In Handbook of natural computing (pp. 709-739). Springer, Berlin Heidelberg.
67. S. Ventura, C. Romero, A. Zafra, J.A. Delgado, and C. Hervás JCLEC: a Java framework for evolutionary computation Soft Computing 12 4 2008 381 392
68. L.L.S. Vygotski Mind in society: The development of higher psychological processes 1978 Harvard University Press
69. E. Wenger Communities of practice: Learning, meaning, and identity 1998 Cambridge University Press
70. Wolff, A., Zdrahal, Z., Nikolov, A., & Pantucek, M. (2013). Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment. In Proceedings of the third international conference on learning analytics and knowledge (pp. 145-149). ACM.
71. Xing, W.L., Wadholm, B., & Goggins, S. (2014a). Learning analytics in CSCL with a focus on assessment: An exploratory study of activity theory-informed cluster analysis. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 59-67). ACM.
72. Xing, W. L. Wadholm, B. & Goggins, S. (2014b). Assessment analytics in CSCL: Activity theory based method. In Proceedings of the international conference on learning sciences'14 (pp. 1535-1536), Boulder, Colorado, USA.
73. Xing, W. L., Wadholm, B., & Goggins, S. (2014). Group Learning assessment in CSCL: Developing a theory-informed analytics. Educational Technology & Society (in press).
74. C. Xu, W. Wang, and P. Liu A genetic programming model for real-time crash prediction on freeways Intelligent Transportation System, IEEE Transactions on 14 2 2013 574 586
75. A. Zafra, and S. Ventura Predicting student grades in learning management systems with multiple instance genetic programming EDM 9 2009 309 319
76. Y. Zhang, and S. Bhattacharyya Genetic programming in classifying large-scale data: An ensemble method Information Sciences 163 1 2004 85 101