Mining data for student models

Studies in Computational Intelligence

Volumn 308, Issue , 2010, Pages 323-337

  Baker, Ryan S J D ^a  

^a WORCESTER POLYTECHNIC INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77957280338     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-14363-2_16     Document Type: Article

References (62)

1. Alcala-Fdez, J., Sánchez, L., Garcia, S., del Jesús, M., Ventura, S., Garrell, J., Otero, J., Romero, C., Bacardit, J., Rivas, V., Fernández, J., Herrera, F.: KEEL: A software tool to assess evolutionary algorithms for data mining problems. Soft Computing 13(3), 307-318 (2008)
2. Aleven, V., McLaren, B. M., Roll, I., Koedinger, K. R.: Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In: Lester, J. C., Vicari, R. M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 227-239. Springer, Heidelberg (2004) (Pubitemid 39245899)
3. Aleven, V., McLaren, B., Roll, I., Koedinger, K.: Toward meta-cognitive tutoring: A model of help seeking with a Cognitive Tutor. International Journal of Artificial Intelligence and Education 16, 101-128 (2006) (Pubitemid 39245899)
4. Amershi, S., Conati, C.: Combining Unsupervised and Supervised Classification to Build User Models for Exploratory Learning Environments. Journal of Educational Data Mining 1(1), 18-71 (2009)
5. Arroyo, I., Cooper, D., Burleson, W., Woolf, B. P., Muldner, K., Christopherson, R.: Emotion Sensors Go To School. In: Proceedings of the International Conference on Artificial Intelligence in Education, pp. 17-24 (2009)
6. Baker, R. S. J.d.: Data Mining for Education. In: McGaw, B., Peterson, P., Baker, E. (eds.) To appear in International Encyclopedia of Education, 3rd edn. Elsevier, Oxford (in press)
7. Baker, R. S. J.d.: Modeling and Understanding Students' Off-Task Behavior in Intelligent Tutoring Systems. In: Proceedings of ACM CHI, Computer-Human Interaction, pp. 1059-1068 (2007)
8. Baker, R. S. J.d., de Carvalho, A. M. J. A.: Labeling Student Behavior Faster and More Precisely with Text Replays. In: Proceedings of the 1st International Conference on Educational Data Mining, pp. 38-47 (2008)
9. Baker, R. S. J. d., Yacef, K.: The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining 1(1), 3-17 (2009)
10. Baker, R. S. J.d., Corbett, A. T., Aleven, V.: More Accurate Student Modeling Through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing. In: Woolf, B. P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 406-415. Springer, Heidelberg (2008)
11. Baker, R. S., Corbett, A. T., Koedinger, K. R.: Detecting Student Misuse of Intelligent Tutoring Systems. In: Lester, J. C., Vicari, R. M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 531-540. Springer, Heidelberg (2004) (Pubitemid 39245927)
12. Baker, R. S. J.d., Corbett, A. T., Koedinger, K. R., Evenson, S., Roll, I., Wagner, A. Z., Naim, M., Raspat, J., Baker, D. J., Beck, J.: Adapting to when students game an intelligent tutoring system. In: Ikeda, M., Ashley, K. D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 392-401. Springer, Heidelberg (2006) (Pubitemid 44117611)
13. Baker, R. S. J.d., Corbett, A. T., Roll, I., Koedinger, K. R.: Developing a Generalizable Detector of When Students Game the System. User Modeling and User-Adapted Interaction 18(3), 287-314 (2008)
14. Baker, R. S. J.d., Corbett, A. T., Wagner, A. Z.: Human Classification of Low-Fidelity Replays of Student Actions. In: Proceedings of the Educational Data Mining Workshop at the 8th International Conference on Intelligent Tutoring Systems, pp. 29-36 (2006)
15. Baker, R. S. J.d., Mitrovic, A., Mathews, M.: Detecting Gaming the System in Constraint-Based Tutors. To appear in Proceedings of the 18th International Conference on User Modeling, Adaptation, and Personalization (in press)
16. Beal, C. R., Qu, L., Lee, H.: Classifying learner engagement through integration of multiple data sources. In: Proceedings of the 21st National Conference on Artificial Intelligence, Boston, pp. 2-8 (2006) (Pubitemid 44705278)
17. Beck, J.: Engagement tracing: using response times to model student disengagement. In: Proceedings of the 12th International Conference on Artificial Intelligence in Education (AIED 2005), pp. 88-95 (2005)
18. Borgman, C. L., Abelson, H., Dirks, L., Johnson, R., Koedinger, K. R., Linn, M. C., Lynch, C. A., Oblinger, D. G., Pea, R. D., Salen, K., Smith, M. S., Szalay, A.: Fostering Learning in the Networked World: The Cyberlearning Opportunity and Challenge. In: A 21st Century Agenda for the National Science Foundation, National Science Foundation, Washington, DC (2008)
19. Bradley, P. S., Fayyad, U., Reina, C.: Scaling EM (expectation maximization) clustering to large databases. Technical Report MSR-TR-98-35, Microsoft Research. Redmond, WA (1998)
20. Buckley, B. C., Gobert, J., Horwitz, P.: Using log files to track students' model-based inquiry. In: Proceedings of the 7th International Conference on Learning Sciences: 57-63 (2006)
21. Cetintas, S., Si, L., Xin, Y. P., Hord, C.: Automatic Detection of Off-Task Behaviors in Intelligent Tutoring Systems with Machine Learning Techniques. IEEE Transactions on Learning Technologies (2009)
22. Chaffar, S., Derbali, L., Frasson, C.: Inducing positive emotional state in Intelligent Tutoring Systems. In: Proceedings of the 14th International Conference on Artificial Intelligence in Education (2009)
23. Cocea, M., Hershkovitz, A., Baker, R. S. J.d.: The Impact of Off-task and Gaming Behaviors on Learning: Immediate or Aggregate? In: Proceedings of the 14th International Conference on Artificial Intelligence in Education, pp. 507-514 (2009)
24. Cohen, J.: A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1), 37-46 (1960)
25. Conati, C., Maclaren, H.: Empirically building and evaluating a probabilistic model of user affect. User Modeling and User-Adapted Interaction 19(3), 267-303 (2009)
26. Corbett, A. T., Anderson, J. R.: Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction 4, 253-278 (2007)
27. Dai, W., Yang, Q., Xue, G., Yu, Y.: Boosting for transfer learning. In: Proceedings of the International Conference on Machine Learning (2007) (Pubitemid 47275065)
28. Daume, H.: Frustratingly easy domain adaptation. In: the Proceedings of the Association for Computational Linguistics (2007)
29. de Vicente, A., Pain, H.: Informing the detection of the students' motivational state: an empirical study. In: Cerri, S. A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 933-943. Springer, Heidelberg (2002)
30. D'Mello, S. K., Craig, S. D., Witherspoon, A. W., McDaniel, B. T., Graesser, A. C.: Automatic Detection of Learner's Affect from Conversational Cues. User Modeling and User-Adapted Interaction 18 (1-2), 45-80 (2008)
31. D'Mello, S. K., Picard, R., Graesser, A. C.: Towards an Affect Sensitive Auto-Tutor. IEEE Intelligent Systems 22(4), 53-61 (2007)
32. Feigenbaum, E. A., McCorduck, P.: The fifth generation: Artificial Intelligence and Japan's Computer Challenge to the World. Addison-Wesley, Reading (1983)
33. Grossberg, S.: Nonlinear neural networks: principles, mechanisms and architectures. Neural Networks 1, 17-61 (1988)
34. Hartigan, J. A., Wong, M. A.: A K-means clustering algorithm. Applied Statistics 28, 100-108 (1979)
35. Hawkins, D. M.: The Problem of Overfitting. Journal of Chemical Infomration and Modeling 44, 1-12 (2004)
36. Hershkovitz, A., Nachmias, R.: Developing a Log-Based Motivation Measuring Tool. In: Proceedings of the First International Conference on Educational Data Mining, pp. 226-233 (2008)
37. Johns, J., Woolf, B.: A Dynamic Mixture Model to Detect Student Motivation and Proficiency. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI 2006), pp. 163-168 (2006) (Pubitemid 44705280)
38. Kay, J., Maisonneuve, N., Yacef, K., Reimann, P.: The Big Five and Visualisations of Team Work Activity. In: Ikeda, M., Ashley, K. D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 197-206. Springer, Heidelberg (2006) (Pubitemid 44117592)
39. Koedinger, K. R., Baker, R. S. J.d., Cunningham, K., Skogsholm, A., Leber, B., Stamper, J.: A Data Repository for the EDM community: The PSLC DataShop. In: Romero, C., Ventura, S., Pechenizkiy, M., Baker, R. S. J.d. (eds.) To appear in Handbook of Educational Data Mining. CRC Press, Boca Raton (in press)
40. Marder, M., Bansal, D.: Flow and diffusion of high-stakes test scores. Proceedings of the National Academy of Sciences of the United States 106(41), 17267-17270 (2009)
41. Maris, E.: Psychometric Latent Response Models. Psychometrika 60(4), 523-547 (1995) (Pubitemid 126010709)
42. McQuiggan, S., Mott, B., Lester, J.: Modeling Self-Efficacy in Intelligent Tutoring Systems: An Inductive Approach. User Modeling and User-Adapted Interaction 18, 81-123 (2008)
43. Merceron, A., Yacef, K.: Educational Data Mining: a Case Study. In: Proceedings of the International Conference on Artificial Intelligence in Education (AIED 2005), pp. 467-474. IOS Press, Amsterdam (2005) (Pubitemid 40911909)
44. Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), pp. 935-940 (2006) (Pubitemid 44535615)
45. Montalvo, O., Baker, R. S. J.d., Sao Pedro, M., Nakama, A., Gobert, J. D.: Recognizing Student Planning with Machine Learning (paper under review)
46. Nkambou, R., Nguifo, E. M., Couturier, O., Fourier-Vigier, P.: Problem-Solving Knowledge Mining from Users' Actions in an Intelligent Tutoring System. In: Kobti, Z., Wu, D. (eds.) Canadian AI 2007. LNCS (LNAI), vol. 4509, pp. 393-404. Springer, Heidelberg (2007) (Pubitemid 350269943)
47. Quinlan, J. R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (2008)
48. Reynolds, D. A.: Gaussian Mixture Models. Encyclopedia of Biometric Recognition. Springer, Heidelberg (2008)
49. Roll, I., Baker, R. S., Aleven, V., McLaren, B. M., Koedinger, K. R.: Modeling Students' Metacognitive Errors in Two Intelligent Tutoring Systems. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 379-388. Springer, Heidelberg (2005)
50. Romero, C., Ventura, S.: Educational Data Mining: A Survey from 1995 to 2005. Expert Systems with Applications 33, 125-146 (2007) (Pubitemid 44959924)
51. Sao Pedro, M., Baker, R. S. J.d., Montalvo, O., Nakama, A., Gobert, J. D.: Using Text Replay Tagging to Produce Detectors of Systematic Experimentation Behavior Patterns (Paper under review)
52. Shih, B., Koedinger, K. R., Scheines, R.: A response time model for bottom-out hints as worked examples. In: Baker, R. S. J.d., Barnes, T., Beck, J. E. (eds.) Proceedings of the First International Conference on Educational Data Mining, pp. 117-126 (2008)
53. Steinwart, I., Christmann, A.: Support Vector Machines. Springer, Berlin (2008)
54. Studer, R., Benjamins, V. R., Fensel, D.: Knowledge engineering: Principles and methods. Data and Knowledge Engineering (DKE) 25 (1-2), 161-197 (1998) (Pubitemid 128421870)
55. Tang, T., McCalla, G.: Smart recommendation for an evolving e-learning system: architecture and experiment. International Journal on E-Learning 4(1), 105-129 (2005)
56. Teigen, K. H.: Yerkes-Dodson: a law for all seasons. Theory & Psychology 4(4), 525-547 (1994)
57. Walonoski, J. A., Heffernan, N. T.: Detection and Analysis of Off-Task Gaming Behavior in Intelligent Tutoring Systems. In: Ikeda, M., Ashley, K. D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 382-391. Springer, Heidelberg (2006) (Pubitemid 44117610)
58. Walonoski, J. A., Heffernan, N. T.: Prevention of Off-Task Gaming Behavior in Intelligent Tutoring Systems. In: Ikeda, M., Ashley, K. D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 722-724. Springer, Heidelberg (2006) (Pubitemid 44117652)
59. Witten, I. H., Frank, E.: Data mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
60. Woolf, B. P., Arroyo, I., Muldner, K., Burleson, W., Cooper, D., Dolan, R., Christopherson, R. M.: The Effect of Motivational Learning Companions on Low-Achieving Students and Students with Learning Disabilities. To appear in the Proceedings of the International Conference on Intelligent Tutoring Systems (in press)
61. Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the International Conference on Machine Learning, pp. 856-863 (2003)
62. Zaïane, O.: Building a recommender agent for e-learning systems. In: Proceedings of the International Conference on Computers in Education, pp. 55-59 (2002)