Structure matters: Adoption of structured classification approach in the context of cognitive presence classification

Publisher:
Springer
Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 9460 pp. 227 - 238
Issue Date:
2015-01-01
Full metadata record
Files in This Item:
Filename Description Size
Waltersetal.-2015-Structurematters.pdfAccepted Manuscript258.35 kB
Adobe PDF
© Springer International Publishing Switzerland 2015. Within online learning communities, receiving timely and meaningful insights into the quality of learning activities is an important part of an effective educational experience. Commonly adopted methods–such as the Community of Inquiry framework–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 real-time analysis of interactions occurring within online learning communities. This paper investigates the importance of incorporating features that utilise the structure of online discussions for the classification of “cognitive presence”–the central dimension of the Community of Inquiry framework focusing on the quality of students’ 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.
Please use this identifier to cite or link to this item: