Towards Automated Content Analysis of Discussion Transcripts: A Cognitive Presence Case

Vitomir Kovanovic, Srecko Joksimovic, Watters Zak, Dragan Gasevic, Kirsty Kitto, Marek Hatala, George Siemens

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features , together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.
Original languageEnglish
Title of host publicationProceedings of the 6th International Conference on Learning Analytics & Knowledge (LAK 2016)
PublisherACM Press
Pages15-24
ISBN (Electronic)978-1-4503-4190-5/16/04
DOIs
Publication statusPublished - 27 Apr 2016

Fingerprint

Dive into the research topics of 'Towards Automated Content Analysis of Discussion Transcripts: A Cognitive Presence Case'. Together they form a unique fingerprint.

Cite this