Asia Information Retrieval Symposium

Information Retrieval Technology pp 227-238

Structure Matters: Adoption of Structured Classification Approach in the Context of Cognitive Presence Classification

  • Zak Waters
  • Vitomir Kovanović
  • Kirsty Kitto
  • Dragan Gašević
Conference paper

DOI: 10.1007/978-3-319-28940-3_18

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9460)
Cite this paper as:
Waters Z., Kovanović V., Kitto K., Gašević D. (2015) Structure Matters: Adoption of Structured Classification Approach in the Context of Cognitive Presence Classification. In: Zuccon G., Geva S., Joho H., Scholer F., Sun A., Zhang P. (eds) Information Retrieval Technology. Lecture Notes in Computer Science, vol 9460. Springer, Cham

Abstract

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.

Keywords

Text classification Conditional random fields Online learning Online discussions 

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zak Waters
    • 1
  • Vitomir Kovanović
    • 2
  • Kirsty Kitto
    • 1
  • Dragan Gašević
    • 2
  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.The University of EdinburghEdinburghUK

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