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

Gulgun AFACAN ADANIR

doi:https://doi.org/10.17718/tojde.522398

Makale detayları

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

Yazarlar:

Gulgun AFACAN ADANIR

Yayıncı:

Anadolu University

Ana Başlıklar:

Sosyoloji & antropoloji

Dergi:

Turkish Online Journal of Distance Education 2019 Vol.20 Issue 1, pp.96-114

Belge Tipi:

Makale

Yayın Tarihi:

12/2018

Belge Dili:

İngilizce

Document ID (Org No.):

2000781027

Sıklık:

Üç Aylık

pISSN:

1302-6488

DOI:

https://doi.org/10.17718/tojde.522398

Özet:

 


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.

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Makale detayları

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

Yazarlar: Gulgun AFACAN ADANIR

Yayıncı: Anadolu University

Ana Başlıklar: Sosyoloji & antropoloji

Dergi: Turkish Online Journal of Distance Education 2019 Vol.20 Issue 1, pp.96-114

Belge Tipi: Makale

Yayın Tarihi: 12/2018

Belge Dili: İngilizce

Document ID (Org No.): 2000781027

Sıklık: Üç Aylık

pISSN: 1302-6488

DOI: https://doi.org/10.17718/tojde.522398

Sayı

Yazarlar:

Gulgun AFACAN ADANIR

Yayıncı:

Anadolu University

Ana Başlıklar:

Sosyoloji & antropoloji

Dergi:

Turkish Online Journal of Distance Education 2019 Vol.20 Issue 1, pp.96-114

Belge Tipi:

Makale

Yayın Tarihi:

12/2018

Belge Dili:

İngilizce

Document ID (Org No.):

2000781027

Sıklık:

Üç Aylık

pISSN:

1302-6488

DOI:

https://doi.org/10.17718/tojde.522398