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Title
Abstract
Introduction
Mining Assessment Data
Generic Frameworks, Methods and Approaches for Edm
Mining Browsing or Interaction Data
Summary of Research Work
Conclusion
References

A Survey on Research work in Educational Data Mining

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https://doi.org/10.9790/0661-17224349
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Abstract

Educational Data Mining is an emerging discipline that focuses on applying Data Mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and institutional effectiveness. A literature review on educational data mining follows, which covers topics such as student retention and attrition, personal recommender systems with in education and how data mining can be used to analyze course management system data. Gaps in the current literature and opportunities for further research are presented.

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References (80)

  1. C. Romero, S. Ventura, Educational data mining: A survey from 1995 to 2005
  2. Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on management of data, Washington, DC (pp. 207-216).
  3. Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In Eleventh international conference on data engineering (pp. 3-14). Taipei, Taiwan: IEEE Computer Society Press.
  4. Arroyo, I., Murray, T., Woolf, B., & Beal, C. (2004). Inferring unobservable learning variables from students' help seeking behaviour. In Intelligent tutoring systems (pp. 782-784).
  5. Baker, R., Corbett, A., & Koedinger, K. (2004). Detecting student misuse of intelligent tutoring systems. In Intelligent tutoring systems (pp. 531-540).
  6. Beck, J., & Woolf, B. (2000). High-level student modelling with machine learning. In Intelligent tutoring systems (pp. 584-593).
  7. Becker, K., Ghedini, C., & Terra, E. (2000). Using kdd to analyze the impact of curriculum revisions in a Brazilian university. In Eleventh international conference on data engineering. Proceedings of the SPIE 14th annual international conference on aerospace/defense, sensing, simulation and controls, Orlando (pp. 412-419).
  8. Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education, 13, 156-169.
  9. Chen, G., Liu, C., Ou, K., & Liu, B. (2000). Discovering decision knowledge from web log portfolio for managing classroom processes by applying decision tree and data cube technology. Journal of Educational Computing Research, 23(3), 305-332.
  10. Chen, J., Li, Q., Wang, L., & Jia, W. (2004). Automatically generating an e-textbook on the web. In International conference on advances in web based learning (pp. 35-42).
  11. Farzan, R. (2004). Adaptive socio-recommender system for open-corpus e-learning. In Doctoral consortium of the third international conference on adaptive hypermedia and adaptive web-based systems.
  12. Feng, M., Heffernan, N., & Koedinger, K. (2005). Looking for sources of error in predicting student's knowledge. In Proceedings of AAAI'05 workshop on educational data mining.
  13. Freyberger, J., Heffernan, N., & Ruiz, C. (2004). 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.
  14. Grob, H., Bensberg, F., & Kaderali, F. (2004). Controlling open source intermediaries -a web log mining approach. In Proceedings of the 26 th international conference on information technology interfaces (pp. 233-242).
  15. Hanna, M. (2004). Data mining in the e-learning domain. Computers & Education Journal, 42(3), 267-287.
  16. Heiner, C., Beck, J., & Mostow, J. (2004). Lessons on using its data to answer educational research questions. In Proceedings of the ITS2004 workshop on analyzing student-tutor interaction logs to improve educational outcomes (pp. 1-9).
  17. Hwang, W., Chang, C., & Chen, G. (2004). The relationship of learning traits, motivation and performance-learning response dynamics. Computers & Education Journal, 42(3), 267-287.
  18. Iksal, S., & Choquet, C. (2005). Usage analysis driven by models in a pedagogical context. Ingram, A. (1999). Using web server logs in evaluating instructional web sites. Journal of Educational Technology Systems, 28(2), 137-157.
  19. Johnson, S., Arago, S., Shaik, N., & Palma-Rivas, N. (2000). Comparative analysis of learner satisfaction and learning outcomes in online and face-to-face learning environments. Journal of Interactive Learning Research, 11(1), 29-49.
  20. Klosgen, W., & Zytkow, J. (2002). Handbook of data mining and knowledge discovery. New York: Oxford University Press.
  21. Koutri, M., Avouris, N., & Daskalaki, S. (2004). Ch. A survey on web usage mining techniques for web-based adaptive hypermedia systems.
  22. Lu, J. (2004). Personalized e-learning material recommender system. In International conference on information technology for application (pp. 374-379).
  23. Luan, J. (2002). Data mining, knowledge management in higher education, potential applications. In Workshop associate of institutional research international conference, Toronto (pp. 1-18).
  24. Ma, Y., Liu, B., Wong, C., Yu, P., & Lee, S. (2000). Targeting the right students using data mining. In KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 457-464).
  25. Markham, S., Ceddia, J., Sheard, J., Burvill, C., Weir, J., Field, B., et al. (2003). Applying agent technology to evaluation tasks in e- learning environments. In Proceedings of the exploring educational technologies conference.
  26. Mazza, R., & Milani, C. (2005). Exploring usage analysis in learning systems: Gaining insights from visualisations. In Workshop on usage analysis in learning systems at 12th international conference on artificial intelligence in education.
  27. Merceron, A., & Yacef, K. (2003). A web-based tutoring tool with mining facilities to improve learning and teaching. In Proceedings of 11 th international conference on artificial intelligence in education (pp. 201-208).
  28. Merceron, A., & Yacef, K. (2004). Mining student data captured from a web-based tutoring tool: Initial exploration and results. Journal of Interactive Learning Research, 15(4), 319-346.
  29. Merceron, A., & Yacef, K. (2005). Tada-ed for educational data mining. Interactive Multimedia Electronic Journal of Computer- Enhanced Learning, 7(1), 267-287.
  30. Minaei-Bidgoli, B., & Punch, W. (2003). Using genetic algorithms for data mining optimization in an educational web-based system. In GECCO (pp. 2252-2263).
  31. Mor, E., & Minguillon, J. (2004). E-learning personalization based on itineraries and long-term navigational behaviour. In Proceedings of the 13th international World Wide Web conference (pp. 264-265).
  32. Mostow, J., Beck, J., Cen, H., Cuneo, A., Gouvea, E., & Heiner, C. (2005). An educational data mining tool to browse tutor-student interactions: Time will tell! In Proceedings of the workshop on educational data mining (pp. 15-22).
  33. Nilakant, K., & Mitrovic, A. (2005). Application of data mining in constraint-based intelligent tutoring systems. In Proceedings of the artificial intelligence in education, AIED (pp. 896-898).
  34. Peled, A., & Rashty, D. (1999). Logging for success: Advancing the use of www logs to improve computer mediated distance learning. Journal of Educational Computing Research, 21(4), 413-431.
  35. Rahkila, M., & Karjalainen, M. (1999). Evaluation of learning in computer based education using log systems. In ASEE/IEEE frontiers in education conference, San Juan, Puerto Rico (pp. 16-21).
  36. Romero, C., Ventura, S., & Bra, P. D. (2004). Knowledge discovery with genetic programming for providing feedback to courseware author. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, 14(5), 425-464.
  37. Sanjeev, P., & Zytkow, J. M. (1995). Discovering enrolment knowledge in university databases. In KDD (pp. 246-251).
  38. Shen, R., Yang, F., & Han, P. (2002). Data analysis center based on e-learning platform. In Proceedings of the 5th international workshop on the internet challenge: Technology and applications (pp. 19-28).
  39. Silva, D., & Vieira, M. (2002). Using data warehouse and data mining resources for ongoing assessment in distance learning. In IEEE international conference on advanced learning technologies, Kazan, Russia (pp. 40-45).
  40. Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. (2000). Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1(2), 12-23.
  41. Talavera, L., & Gaudioso, E. (2004). Mining student data to characterize similar behaviour groups in unstructured collaboration spaces. In Workshop on artificial intelligence in CSCL. 16th European conference on artificial intelligence (pp. 17-23).
  42. Tane, J., Schmitz, C., & Stumme, G. (2004). Semantic resource management for the web: An e-learning application. In Proceedings of the WWW conference, New York, USA (pp. 1-10).
  43. Tang, C., Yin, H., Li, T., Lau, R., Li, Q., & Kilis, D. (2000). Personalized courseware construction based on web data mining. In Proceedings of the first international conference on web information systems engineering, Washington, DC, USA (pp. 204-211).
  44. Tang, T., & McCalla, G. (2002). Student modelling for a web-based learning environment: A data mining approach. In Eighteenth national conference on artificial intelligence, Menlo Park, CA, USA (pp. 967-968).
  45. Tang, T., & McCalla, G. (2005). Smart recommendation for an evolving e-learning system. International Journal on E-Learning, 4(1), 105-129.
  46. Ueno, M. (2004b). Online outlier detection system for learning time data in e-learning and its evaluation. In International conference on computers and advanced technology in education (pp. 248-253).
  47. Urbancic, T., Skrjanc, M., & Flach, P. (2002). Web-based analysis of data mining and decision support education. AI Communications, 15, 199-204.
  48. Wang, F. (2002). On using data-mining technology for browsing log file analysis in asynchronous learning environment. In Conference on educational multimedia, hypermedia and telecommunications (pp. 2005-2006).
  49. Wang, W., Weng, J., Su, J., & Tseng, S. (2004). Learning portfolio analysis and mining in SCORM compliant environment. In ASEE/IEEE frontiers in education conference (pp. 17-24).
  50. Zaıane, O. (2002). Building a recommender agent for e-learning systems. In ICCE (pp. 55-59).
  51. Zaıane, O., & Luo, J. (2001). Web usage mining for a better web-based learning environment. In Proceedings of conference on advanced technology for education, Banff, Alberta (pp. 60-64).
  52. Zaıane, O., Xin, M., & Han, J. (1998). Discovering web access patterns and trends by applying OLAP and data mining technology on web logs. In Advances in digital libraries (pp. 19-29).
  53. Kenneth R. Koedinger, Elizabeth A. McLaughlin and John C. Stamper, "Automated Student Model Improvement ",In Fifth international conference on Educational Data Mining-2012, (PP:17-24).
  54. Vasile Rus, Arthur Graesser, Cristian Moldovan and Nobal Niraula, "Automatic Discovery of Speech Act Categories in Educational Games", In Fifth international conference on Educational Data Mining-2012,(PP: 25-32).
  55. Shubhendu Trivedi, Zachary Pardos, Gábor Sárközy and Neil Heffernan, "Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction", In Fifth international conference on Educational Data Mining-2012, (PP: 33-40).
  56. Yanbo Xu and Jack Mostow,"Comparison of methods to trace multiple subskills: Is LR-DBN best? ", In Fifth international conference on Educational Data Mining-2012, (PP: 41-48).
  57. Jose Gonzalez-Brenes and Jack Mostow, "Dynamic Cognitive Tracing: Towards Unified Discovery of Student and Cognitive Models", In Fifth international conference on Educational Data Mining-2012, (PP: 49-56).
  58. John Kinnebrew and Gautam Biswas, "Identifying Learning Behaviours by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution", In Fifth international conference on Educational Data Mining-2012, (PP: 57 -64).
  59. François Bouchet, John Kinnebrew, Gautam Biswas and Roger Azevedo, " Identifying Student's Characteristic Learning Behaviours in an Intelligent Tutoring System Fostering Self-Regulated Learning", In Fifth international conference on Educational Data Mining-2012, (PP: 65-72).
  60. Ilya Goldin, Kenneth Koedinger and Vincent Aleven, "Learner Differences in Hint Processing", In Fifth international conference on Educational Data Mining-2012, (PP: 73-80).
  61. Behzad Beheshti, Michel Desmarais and Rhouma Naceur, "Methods to find the number of latent skills", In Fifth international conference on Educational Data Mining-2012, (PP: 81-86).
  62. Terry Peckham and Gordon McCalla, "Mining Student Behavior Patterns in Reading Comprehension Tasks", In Fifth international conference on Educational Data Mining-2012, (PP: 87-94).
  63. Yoav Bergner, Stefan Droschler, Gerd Kortemeyer, Saif Rayyan, Daniel Seaton and David Pritchard, " Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response Theory", In Fifth international conference on Educational Data Mining-2012, (PP: 95 -102).
  64. Tomas Obsivac, Lubos Popelinsky, Jaroslav Bayer, Jan Geryk and Hana Bydzovska, "Predicting drop-out from social behaviour of students", In Fifth international conference on Educational Data Mining-2012, (PP: 103 -109).
  65. Martina Rau and Richard Scheines, "Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations", In Fifth international conference on Educational Data Mining-2012, (PP:110-117).
  66. Jung In Lee and Emma Brunskill, "The Impact on Individualizing Student Models on Necessary Practice Opportunities ",In Fifth international conference on Educational Data Mining-2012, (PP:118-125).
  67. Leigh Ann Sudol, Kelly Rivers and Thomas K. Harris, "Calculating Probabilistic Distance to Solution in a Complex Problem Solving Domain", In Fifth international conference on Educational Data Mining-2012, (PP:144-147).
  68. Manuel Ignacio Lopez, Cristobal Romero, Sebastián Ventura and J.M. Luna, "Classification via clustering for predicting final marks starting from the student participation in Forums", In Fifth international conference on Educational Data Mining-2012, (PP:148-151).
  69. Ma. Mercedes Rodrigo, Ryan S. J. D. Baker, Bruce McLaren, Alejandra Jayme and Thomas Dy, "Development of a Workbench to Address the Educational Data Mining Bottleneck", In Fifth international conference on Educational Data Mining-2012, (PP: 152- 155).
  70. Jennifer Sabourin, Bradford Mott and James Lester, "Early Prediction of Student Self-Regulation Strategies by Combining Multiple Models", In Fifth international conference on Educational Data Mining-2012, (PP:156-159).
  71. Judi Mccuaig and Julia Baldwin, "Identifying Successful Learners from Interaction Behaviour", In Fifth international conference on Educational Data Mining-2012, (PP:160-163).
  72. Michael Eagle, Matthew Johnson and Tiffany Barnes, "Interaction Networks: Generating High Level Hints Based on Network Community Clustering", In Fifth international conference on Educational Data Mining-2012, (PP: 164-167).
  73. Martina Rau and Zachary Pardos, "Interleaved Practice with Multiple Representations: Analyses with Knowledge Tracing Based Techniques", In Fifth international conference on Educational Data Mining-2012, (PP: 168-171).
  74. Carol Forsyth, Philip Pavlik Jr, Arthur Graesser, Zhiqiang Cai, Mae-Lynn Germany, Keith Millis, Heather Butler, Diane Halpern and Robert Dolan, " Learning Gains for Core Concepts in a Serious Game on Scientific Reasoning", In Fifth international conference on Educational Data Mining-2012, (PP: 172-175).
  75. Yutao Wang and Neil Heffernan, "Leveraging First Response Time into the Knowledge Tracing Model", In Fifth international conference on Educational Data Mining-2012, (PP: 176-179).
  76. Jin Soung Yoo and Moon-Heum Cho, "Mining Concept Maps to Understand University Students' Learning", In Fifth international conference on Educational Data Mining-2012, (PP:184-187).
  77. Michael Yudelson and Emma Brunskill, "Policy Building -An Extension to User Modeling", In Fifth international conference on Educational Data Mining-2012, (PP: 188-191).
  78. Zachary Pardos, Qing Yang Wang and Shubhendu Trivedi, "The real world significance of performance prediction", In Fifth international conference on Educational Data Mining-2012, (PP: 192-195).
  79. John Stamper, Derek Lomas, Dixie Ching, Steven Ritter, Kenneth Koedinger and Jonathan Steinhart, " The Rise of the Super Experiment", In Fifth international conference on Educational Data Mining-2012, (PP: 196-199).
  80. Yutao Wang and Joseph Beck, "Using Student Modeling to Estimate Student Knowledge Retention", In Fifth international conference on Educational Data Mining-2012, (PP:200-203).

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