Discovering and recognizing student interaction patterns in exploratory learning environments

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6094 LNCS, Issue PART 1, 2010, Pages 125-134

  Bernardini, Andrea ^a   Conati, Cristina ^b  

^a FONDAZIONE UGO BORDONI   (Italy)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Educational data mining; Exploratory learning environments; Student modeling

Indexed keywords

ADAPTIVE SUPPORT; AI ALGORITHMS; ASSOCIATION RULE MINING; EDUCATIONAL DATA MINING; EXPLORATORY LEARNING; EXPLORATORY LEARNING ENVIRONMENTS; INTERACTION PATTERN; ON-LINE CLASSIFICATION; STUDENT INTERACTIONS; STUDENT MODELING; STUDENT MODELS;

ASSOCIATION REACTIONS; ASSOCIATION RULES; COMPUTER AIDED INSTRUCTION; DATA MINING;

STUDENTS;

EID: 79957481240     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13388-6_17     Document Type: Conference Paper

References (18)

1. Shute, V.J.: A comparison of learning environments: All that glitters.. In: Computers as Cognitive Tools, pp. 47-73. Lawrence Erlbaum Associates, Hillsdale (1993).
2. Merten, C., Conati, C.: Eye-Tracking to Model and Adapt to User Meta-Cognition in Intelligent Learning Environments. In: Proceedings of Intelligent User Interfaces (2006).
3. Baker, R.S.J.d., et al.: 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)
4. Amershi, S., Carenini, G., Conati, C., Mackworth, A., Poole, D.: Pedagogy and Usability in Interactive Algorithm Visualisations: Designing and Evaluating CIspace. Interacting with Computers 20(1), 64-96 (2008). (Pubitemid 350267205)
5. Merceron, A., Yacef, K.: A web-based tutoring tool with mining facilities to Improve Teaching and Learning. In: AIED 2003, pp. 201-208 (2003).
6. Freyberger, J., Heffernan, N., Ruiz, C.: Using association rules to guide a search for best fitting transfer models of student learning. In: Workshop on analyzing student-tutor interactions logs to improve educational outcomes at ITS conference, pp. 1-4 (2004).
7. García, E., Romero, C., Ventura, S., Castro, C.D.: An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. UMUAI 19, 99-132 (2009).
8. Hunt, E., Madhyastha, T.: Data Mining Patterns of Thought. In: Proceedings of the AAAI Workshop on Educational Data Mining (2005).
9. Merceron, A., Yacef, K.: TADA-Ed for Educational Data Mining. Interactive Mulitmedia Electronic Journal of Computer-Enhanced Learning (2005).
10. Talavare, L., Gaudioso, E.: Mining Student Data to Characterize Similar Behavior Groups in Unstructured Collaboration Spaces. In: Proceedings of the European Conference on AI Workshop on AI in CSCL (2004).
11. Amershi, S., Conati, C.: Combining Unsupervised and Supervised Machine Learning to Build User Models for Exploratory Learning Environments. The Journal of Educational Data Mining 1(1), 18-71 (2009).
12. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in large Databases. In: VLDB 1994, pp. 487-499 (1994).
13. Thabtah, F.A.: A review of associative classification mining. Knowl. Eng. Rev. 22(1), 37- 65 (2007).
14. Duda, R.O., Hart, P.E., Stork, D.: Pattern Classification. Wiley- Interscience, New York (2001).
15. Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7, 179-188 (1936).
16. Weka project, http://www.cs.waikato.ac.nz/~ml/weka/.
17. Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Proceedings of the 12th International Conference on Machine Learning, pp. 194-202 (1995).
18. Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: Proceedings of the Fifth ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, KDD 1999, pp. 337-341. ACM, New York (1999).