Learning groups’ conversations in computer supported collaborative learning (CSCL) environmentsresulting 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 similarit.
Bantu dong kak buat conditional sentences type beeapa aja seterah​