Towards automated analysis of cognitive presence in MOOC discussions
Yuanyuan Hu, Claire Donald, Nasser Giacaman, Zexuan Zhu
Abstract
This paper reports on early stages of a machine learning research project, where phases of cognitive presence in MOOC discussions were manually coded in preparation for training automated cognitive classifiers. We present a manual-classification rubric combining Garrison, Anderson and Archer's [11] coding scheme with Park's [25] revised version for a target MOOC. The inter-rater reliability between two raters achieved 95.4% agreement with a Cohen's weighted kappa of 0.96, demonstrating our classification rubric is plausible for the target MOOC dataset. The classification rubric, originally intended for for-credit, undergraduate courses, can be applied to a MOOC context. We found that the main disagreements between two raters lay on adjacent cognitive phases, implying that additional categories may exist between cognitive phases in such MOOC discussion messages. Overall, our results suggest a reliable rubric for classifying cognitive phases in discussion messages of the target MOOC by two raters. This indicates we are in a position to apply machine learning algorithms which can also cater for data with inter-rater disagreements in future automated classification studies.