Research Article
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Detecting Topics of Chat Discussions in A Computer Supported Collaborative Learning (CSCL) Environment

Year 2019, Volume: 20 Issue: 1, 96 - 114, 01.01.2019
https://doi.org/10.17718/tojde.522398

Abstract

Learning groups' conversations in computer supported collaborative learning (CSCL) environments result in significant information regarding the content of the course. This information is beneficial for instructors to analyze learners' activities during their collaboration process. In understanding these activities and performance of learners, the topic of conversation is important. The purpose of the study is to detect topics of chat discussions conducted by groups of learners while collaboratively studying in an online CSCL environment called Virtual Math Teams (VMT). We implemented the study in the context of a graduate level course during one term in a large state university in Turkey. Participants are MSc and PhD students registered to the course and divided over five groups of three students. We combined and employed methods of data mining, social network analysis, and topic detection to identify topics of learners' discussions. Our data analysis process aims to identify the task related topics occurred in chat discussion of learning teams. In our analysis we followed the stages of data preprocessing, segmentation analysis, and topic detection. Our purpose with the preprocessing stage was eliminating improper data for the main analysis and making the data ready for analysis stage. Therefore, our final corpus was shaped to involve 95% of initial chat messages. Segmentation analysis aims to explore organization of chat discussion and divides the chat logs into more manageable units according to their corresponding contents. In total, we resulted 294 segments including task related and non-task related ones. The topic detection analysis explored the content of chat segments and revealed the major subject of discussions with the use of latent semantic analysis, which is applied to find content similarity among segments and indicative words produced through the use of two mode network analysis.

References

  • Adams, P. H., & Martell, C. H. (2008, August). Topic detection and extraction in chat. In Semantic Computing, 2008 IEEE International Conference on (pp. 581-588). IEEE. Anjewierden, A., Kolloffel, B., & Hulshof, C. (2007). Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes. In International Workshop on Applying Data Mining in e-Learning (ADML 2007). Bakharia, A., Heathcote, E., & Dawson, S. (2009). Social networks adapting pedagogical practice: SNAPP (Doctoral dissertation, University of Auckland, Auckland University of Technology, and Australasian Society for Computers in Learning in Tertiary Education (ascilite)). Bakhtin, M. (1986). The problem of speech genres (V. McGee, Trans.). In C. Emerson & M. Holquist (Eds.), Speech genres and other late essays (pp. 60-102). Austin: Univ. of Texas Press. Bruckman, A. (2006). Analysis of log file data to understand behavior and learning in an online community. In The International handbook of virtual learning environments (pp. 1449-1465). Springer Netherlands. De Wever, B., Schellens, T., Valcke, M., & Van Keer, H. (2006). Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review. Computers & education, 46(1), 6-28. Dascalu, M., Chioasca, E. V., & Trausan-Matu, S. (2008). ASAP-An Advanced System for Assessing Chat Participants. In Artificial Intelligence: Methodology, Systems, and Applications (pp. 58-68). Springer Berlin Heidelberg. Dong, H., Cheung Hui, S., & He, Y. (2006). Structural analysis of chat messages for topic detection. Online Information Review, 30(5), 496-516. Elnahrawy, E. (2002, November). Log-based chat room monitoring using text categorization: A comparative study. In The International Conference on Information and Knowledge Sharing, US Virgin Islands. Elsner, M., & Charniak, E. (2011, June). Disentangling chat with local coherence models. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1 (pp. 1179-1189). Association for Computational Linguistics. Engestrom, Y., Miettinen, R., & Punamaki, R. L. (1999). Perspectives on activity theory. Cambridge University Press. Erkens, G., & Janssen, J. (2008). Automatic coding of dialogue acts in collaboration protocols. International journal of computer-supported collaborative learning, 3(4), 447-470. Fournier, H., Kop, R., & Sitlia, H. (2011). The value of learning analytics to networked learning on a Personal Learning Environment. Glaser, B. S., & Strauss, A. (1971). A. 1967, The discovery of grounded theory. New york. Gweon, Raj, B., & Rosé, C. P. (2011). The automatic assessment of knowledge integration processes in project teams. In Proceedings of Computer Supported Collaborative Learning (pp. 462-469). Janssen, J., Erkensa, G., Kanselaara, G., & Jaspersa, J. (2007). Visualization of participation: Does it contribute to successful computer-supported collaborative learning? Computers & Education, 49(4), 1037–1065. Khan, F. M., Fisher, T. A., Shuler, L., Wu, T., & Pottenger, W. M. (2002). Mining chat-room conversations for social and semantic interactions. Computer Science and Engineering, Lehigh University. Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse processes, 25(2-3), 259-284. Law, N., Yuen, J., Huang, R., Li, Y., & Pan, N. (2007, July). A learnable content & participation analysis toolkit for assessing CSCL learning outcomes and processes. In Proceedings of the 8th international conference on Computer supported collaborative learning (pp. 411-420). International Society of the Learning Sciences. Li, Y., Wang, J., Liao, J., Zhao, D., & Huang, R. (2007, July).Assessing collaborative process in cscl with an intelligent content analysis toolkit. In Advanced Learning Technologies, 2007. ICALT 2007. Seventh IEEE International Conference on (pp. 257-261). IEEE. Mayfield, E., Adamson, D., & Rosé, C. P. (2012, July). Hierarchical conversation structure prediction in multi-party chat. In Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue (pp. 60-69). Association for Computational Linguistics. Mu, J., Stegmann, K., Mayfield, E., Rosé, C., & Fischer, F. (2012). The ACODEA framework: Developing segmentation and classification schemes for fully automatic analysis of online discussions. International Journal of Computer-Supported Collaborative Learning, 7(2), 285-305. Mukherjee, M., & Holder, L. B. (2004, August). Graph-based data mining on social networks. In Workshop on Link Analysis and Group Detection (LinkKDD2004). Ozyurt, O., & Kose, C. (2010). Chat mining: Automatically determination of chat conversations’ topic in Turkish text based chat mediums. Expert Systems with Applications, 37(12), 8705-8710. Passmore, D. L. (2011). Social network analysis: Theory and applications.Tersedia: http://code. pediapress. com/[12 Juni 2014]. Petropoulou, O., Altanis, I., Retalis, S., Nicolaou, C. A., Kannas, C., Vasiliadou, M., & Pattis, I. (2010). Building a tool to help teachers analyse learners’ interactions in a networked learning environment. Educational Media International, 47(3), 231-246. Pozzi, F., Manca, S., Persico, D., & Sarti, L. (2007). A general framework for tracking and analysing learning processes in computer‐supported collaborative learning environments. Innovations in Education and Teaching International, 44(2), 169-179. Retalis, S., Papasalouros, A., Psaromiligkos, Y., Siscos, S., & Kargidis, T. (2006). Towards Networked Learning Analytics–A concept and a tool. In Proceedings of the fifth international conference on networked learning. Riffe, D., Lacy, S., & Fico, F. (1998). Analyzing media messages: Quantitative content analysis. Mahwah. NJ (USA): Lawrence Erlbaum Associates. Rourke, L., Anderson, T., Garrison, D. R., & Archer, W. (2001). Methodological issues in the content analysis of computer conference transcripts. International journal of artificial intelligence in education (IJAIED), 12, 8-22. Searle, J. R. (1969). Speech Act Theory. Cambridge, MA: Cambridge University Press. Shen, D., Yang, Q., Sun, J. T., & Chen, Z. (2006, August). Thread detection in dynamic text message streams. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval(pp. 35-42). ACM. Strijbos, J. W., & Stahl, G. (2007). Methodological issues in developing a multi-dimensional coding procedure for small-group chat communication.Learning and Instruction, 17(4), 394-404. Trausan-Matu, S., Rebedea, T., Dragan, A., & Alexandru, C. (2007). Visualisation of learners' contributions in chat conversations. Blended learning, 217-226. Trausan-Matu, S., Dascalu, M., & Rebedea, T. (2014). PolyCAFe—automatic support for the polyphonic analysis of CSCL chats. International Journal of Computer-Supported Collaborative Learning, 9(2), 127-156. Uthus, D. C., & Aha, D. W. (2013). Multiparticipant chat analysis: A survey. Artificial Intelligence, 199, 106-121. Wang, L., & Oard, D. W. (2009, May). Context-based message expansion for disentanglement of interleaved text conversations. In Proceedings of human language technologies: The 2009 annual conference of the North American chapter of the association for computational linguistics (pp. 200-208). Association for Computational Linguistics. Wang, Y. C., Joshi, M., Cohen, W. W., & Rosé, C. P. (2008, March). Recovering Implicit Thread Structure in Newsgroup Style Conversations. In ICWSM. Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46(1), 71-95. Zemel, A., Xhafa, F., & Cakir, M. (2007). What's in the mix? Combining coding and conversation analysis to investigate chat-based problem solving. Learning and Instruction, 17(4), 405-415.
Year 2019, Volume: 20 Issue: 1, 96 - 114, 01.01.2019
https://doi.org/10.17718/tojde.522398

Abstract

References

  • Adams, P. H., & Martell, C. H. (2008, August). Topic detection and extraction in chat. In Semantic Computing, 2008 IEEE International Conference on (pp. 581-588). IEEE. Anjewierden, A., Kolloffel, B., & Hulshof, C. (2007). Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes. In International Workshop on Applying Data Mining in e-Learning (ADML 2007). Bakharia, A., Heathcote, E., & Dawson, S. (2009). Social networks adapting pedagogical practice: SNAPP (Doctoral dissertation, University of Auckland, Auckland University of Technology, and Australasian Society for Computers in Learning in Tertiary Education (ascilite)). Bakhtin, M. (1986). The problem of speech genres (V. McGee, Trans.). In C. Emerson & M. Holquist (Eds.), Speech genres and other late essays (pp. 60-102). Austin: Univ. of Texas Press. Bruckman, A. (2006). Analysis of log file data to understand behavior and learning in an online community. In The International handbook of virtual learning environments (pp. 1449-1465). Springer Netherlands. De Wever, B., Schellens, T., Valcke, M., & Van Keer, H. (2006). Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review. Computers & education, 46(1), 6-28. Dascalu, M., Chioasca, E. V., & Trausan-Matu, S. (2008). ASAP-An Advanced System for Assessing Chat Participants. In Artificial Intelligence: Methodology, Systems, and Applications (pp. 58-68). Springer Berlin Heidelberg. Dong, H., Cheung Hui, S., & He, Y. (2006). Structural analysis of chat messages for topic detection. Online Information Review, 30(5), 496-516. Elnahrawy, E. (2002, November). Log-based chat room monitoring using text categorization: A comparative study. In The International Conference on Information and Knowledge Sharing, US Virgin Islands. Elsner, M., & Charniak, E. (2011, June). Disentangling chat with local coherence models. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1 (pp. 1179-1189). Association for Computational Linguistics. Engestrom, Y., Miettinen, R., & Punamaki, R. L. (1999). Perspectives on activity theory. Cambridge University Press. Erkens, G., & Janssen, J. (2008). Automatic coding of dialogue acts in collaboration protocols. International journal of computer-supported collaborative learning, 3(4), 447-470. Fournier, H., Kop, R., & Sitlia, H. (2011). The value of learning analytics to networked learning on a Personal Learning Environment. Glaser, B. S., & Strauss, A. (1971). A. 1967, The discovery of grounded theory. New york. Gweon, Raj, B., & Rosé, C. P. (2011). The automatic assessment of knowledge integration processes in project teams. In Proceedings of Computer Supported Collaborative Learning (pp. 462-469). Janssen, J., Erkensa, G., Kanselaara, G., & Jaspersa, J. (2007). Visualization of participation: Does it contribute to successful computer-supported collaborative learning? Computers & Education, 49(4), 1037–1065. Khan, F. M., Fisher, T. A., Shuler, L., Wu, T., & Pottenger, W. M. (2002). Mining chat-room conversations for social and semantic interactions. Computer Science and Engineering, Lehigh University. Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse processes, 25(2-3), 259-284. Law, N., Yuen, J., Huang, R., Li, Y., & Pan, N. (2007, July). A learnable content & participation analysis toolkit for assessing CSCL learning outcomes and processes. In Proceedings of the 8th international conference on Computer supported collaborative learning (pp. 411-420). International Society of the Learning Sciences. Li, Y., Wang, J., Liao, J., Zhao, D., & Huang, R. (2007, July).Assessing collaborative process in cscl with an intelligent content analysis toolkit. In Advanced Learning Technologies, 2007. ICALT 2007. Seventh IEEE International Conference on (pp. 257-261). IEEE. Mayfield, E., Adamson, D., & Rosé, C. P. (2012, July). Hierarchical conversation structure prediction in multi-party chat. In Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue (pp. 60-69). Association for Computational Linguistics. Mu, J., Stegmann, K., Mayfield, E., Rosé, C., & Fischer, F. (2012). The ACODEA framework: Developing segmentation and classification schemes for fully automatic analysis of online discussions. International Journal of Computer-Supported Collaborative Learning, 7(2), 285-305. Mukherjee, M., & Holder, L. B. (2004, August). Graph-based data mining on social networks. In Workshop on Link Analysis and Group Detection (LinkKDD2004). Ozyurt, O., & Kose, C. (2010). Chat mining: Automatically determination of chat conversations’ topic in Turkish text based chat mediums. Expert Systems with Applications, 37(12), 8705-8710. Passmore, D. L. (2011). Social network analysis: Theory and applications.Tersedia: http://code. pediapress. com/[12 Juni 2014]. Petropoulou, O., Altanis, I., Retalis, S., Nicolaou, C. A., Kannas, C., Vasiliadou, M., & Pattis, I. (2010). Building a tool to help teachers analyse learners’ interactions in a networked learning environment. Educational Media International, 47(3), 231-246. Pozzi, F., Manca, S., Persico, D., & Sarti, L. (2007). A general framework for tracking and analysing learning processes in computer‐supported collaborative learning environments. Innovations in Education and Teaching International, 44(2), 169-179. Retalis, S., Papasalouros, A., Psaromiligkos, Y., Siscos, S., & Kargidis, T. (2006). Towards Networked Learning Analytics–A concept and a tool. In Proceedings of the fifth international conference on networked learning. Riffe, D., Lacy, S., & Fico, F. (1998). Analyzing media messages: Quantitative content analysis. Mahwah. NJ (USA): Lawrence Erlbaum Associates. Rourke, L., Anderson, T., Garrison, D. R., & Archer, W. (2001). Methodological issues in the content analysis of computer conference transcripts. International journal of artificial intelligence in education (IJAIED), 12, 8-22. Searle, J. R. (1969). Speech Act Theory. Cambridge, MA: Cambridge University Press. Shen, D., Yang, Q., Sun, J. T., & Chen, Z. (2006, August). Thread detection in dynamic text message streams. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval(pp. 35-42). ACM. Strijbos, J. W., & Stahl, G. (2007). Methodological issues in developing a multi-dimensional coding procedure for small-group chat communication.Learning and Instruction, 17(4), 394-404. Trausan-Matu, S., Rebedea, T., Dragan, A., & Alexandru, C. (2007). Visualisation of learners' contributions in chat conversations. Blended learning, 217-226. Trausan-Matu, S., Dascalu, M., & Rebedea, T. (2014). PolyCAFe—automatic support for the polyphonic analysis of CSCL chats. International Journal of Computer-Supported Collaborative Learning, 9(2), 127-156. Uthus, D. C., & Aha, D. W. (2013). Multiparticipant chat analysis: A survey. Artificial Intelligence, 199, 106-121. Wang, L., & Oard, D. W. (2009, May). Context-based message expansion for disentanglement of interleaved text conversations. In Proceedings of human language technologies: The 2009 annual conference of the North American chapter of the association for computational linguistics (pp. 200-208). Association for Computational Linguistics. Wang, Y. C., Joshi, M., Cohen, W. W., & Rosé, C. P. (2008, March). Recovering Implicit Thread Structure in Newsgroup Style Conversations. In ICWSM. Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46(1), 71-95. Zemel, A., Xhafa, F., & Cakir, M. (2007). What's in the mix? Combining coding and conversation analysis to investigate chat-based problem solving. Learning and Instruction, 17(4), 405-415.
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Details

Primary Language English
Journal Section Articles
Authors

Gulgun Afacan Adanır This is me 0000-0002-0832-1808

Publication Date January 1, 2019
Submission Date March 1, 2018
Published in Issue Year 2019 Volume: 20 Issue: 1

Cite

APA Afacan Adanır, G. (2019). Detecting Topics of Chat Discussions in A Computer Supported Collaborative Learning (CSCL) Environment. Turkish Online Journal of Distance Education, 20(1), 96-114. https://doi.org/10.17718/tojde.522398