Structure matters: Adoption of structured classification Approach in the context of cognitive presence classification Within online learning communities, receiving timely  :6,insights into the quality of learning activities is an important part of an effective educational experience. Commonly adopted methods xe2x80x93 such as the Community of Inquiry framework xe2x80x93 rely on manual coding of online discussion transcripts, which is a costly and time consuming process. There are several efforts underway to enable the automated classification of online discussion messages using supervised machine learning, which would enable the realtime analysis of interactions occurring within online learning communities. This paper  :84,importance of incorporating features that utilise the structure of online discussions for the classification of cognitive presence xe2x80x93 the central dimension of the Community of Inquiry framework focusing on the quality of studentsu0027 critical thinking within online learning communities. We implemented a Conditional Random Field classification solution, which incorporates structural features that may be useful in increasing classification performance over other implementations. Our approach leads to an improvement in classification accuracy of 5.8% over current existing techniques when tested on the same  dataset, with a precision and recall of 0.630 and 0.504 respectively.

Conversational Structure Aware and Context Sensitive Topic Model for Online Discussions. Millions of online discussions are generated everyday on social media platforms. Topic modelling is an efficient way of better understanding large text datasets at scale. Conventional topic models have had limited success in online discussions, and to overcome their limitations, we use the discussion thread tree structure and propose a popularity metric to quantify the number of replies to a comment to extend the frequency of word occurrences, and the transitivity concept to characterize topic dependency among nodes in a nested discussion thread. We build a Conversational Structure Aware Topic Model (CSATM) based on popularity and transitivity to infer topics and their assignments to comments. Experiments on real forum datasets are used to demonstrate improved performance for topic extraction with six different measurements of coherence and impressive accuracy for topic assignments.