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Enriching student experience with student driven content while teaching an online data mining class

Published: 16 October 2008 Publication History

Abstract

An online class provides a unique opportunity for the students to participate in the class at their own convenience. However, the students have to be kept motivated by providing the tools to enable discussions related to the class topics. This paper implements and presents a model coupled with data mining techniques that will predict how the students are faring in the classroom discussions. The model provides feedback on which students are active in their online discussions, what topics they are discussing, the relevance of those discussions, and share the information with all those involved in the class.
The techniques presented in this work can be applied to any online class. However, presenting this work in the context of a data mining class is especially rewarding. Students can use their discussions themselves as a case study and apply them in the context of various data mining and text mining algorithms covered in the class. The framework not only aides an instructor in understanding the student interest and participation in an online class, but also helps the students with feedback on their posts with respect to the others.

References

[1]
E. Frank, "Predicting Library of Congress Classifications from Library of Congress Subject Headings," Journal of the American Society for Information Science and Technology, 55(3), 2003, pp. 214--227.
[2]
E. Loper and S. Bird, "NLTK: The Natural Language Toolkit," Proceedings of the ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, Philadelphia, July 2002.
[3]
P. Tan, M. Steinbach, and V. Kumar, "Introduction to Data Mining," Addison-Wesley, 2005.
[4]
R. Valdes-Perez, "Introducing Clustering 2.0", 2008. http://searchdoneright.com/2008/01/introducing-clustering-20/

Cited By

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  • (2018)Qualitative Findings from an Online Course on Machine Learning2018 IEEE Frontiers in Education Conference (FIE)10.1109/FIE.2018.8658554(1-9)Online publication date: Oct-2018
  • (2010)Effectiveness of blog for programming course in supporting engineering students2010 International Symposium on Information Technology10.1109/ITSIM.2010.5561591(1347-1350)Online publication date: Jun-2010

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cover image ACM Conferences
SIGITE '08: Proceedings of the 9th ACM SIGITE conference on Information technology education
October 2008
280 pages
ISBN:9781605583297
DOI:10.1145/1414558
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 October 2008

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Author Tags

  1. data mining
  2. data modeling
  3. discussion groups
  4. online courses

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Overall Acceptance Rate 176 of 429 submissions, 41%

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Cited By

View all
  • (2018)Qualitative Findings from an Online Course on Machine Learning2018 IEEE Frontiers in Education Conference (FIE)10.1109/FIE.2018.8658554(1-9)Online publication date: Oct-2018
  • (2010)Effectiveness of blog for programming course in supporting engineering students2010 International Symposium on Information Technology10.1109/ITSIM.2010.5561591(1347-1350)Online publication date: Jun-2010

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