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Analyzing cognitive presence in online courses using an artificial neural network
DISSERTATION

, Georgia State University, United States

Georgia State University . Awarded

Abstract

This work outlines the theoretical underpinnings, method, results, and implications for constructing a discussion list analysis tool that categorizes online, educational discussion list messages into levels of cognitive effort.

Purpose. The purpose of such a tool is to provide evaluative feedback to instructors who facilitate online learning, to researchers studying computer-supported collaborative learning, and to administrators interested in correlating objective measures of students' cognitive effort with other measures of student success. This work connects computer-supported collaborative learning, content analysis, and artificial intelligence.

Method. Broadly, the method employed is a content analysis in which the data from the analysis is modeled using artificial neural network (ANN) software. A group of human coders categorized online discussion list messages, and inter-rater reliability was calculated among them. That reliability figure serves as a measuring stick for determining how well the ANN categorizes the same messages that the group of human coders categorized. Reliability between the ANN model and the group of human coders is compared to the reliability among the group of human coders to determine how well the ANN performs compared to humans.

Findings. Two experiments were conducted in which artificial neural network (ANN) models were constructed to model the decisions of human coders, and the experiments revealed that the ANN, under noisy, real-life circumstances codes messages with near-human accuracy. From experiment one, the reliability between the ANN model and the group of human coders, using Cohen's kappa, is 0.519 while the human reliability values range from 0.494 to 0.742 (M=0.6). Improvements were made to the human content analysis with the goal of improving the reliability among coders. After these improvements were made, the humans coded messages with a kappa agreement ranging from 0.816 to 0.879 (M=0.848), and the kappa agreement between the ANN model and the group of human coders is 0.70.

Citation

McKlin, T.E. Analyzing cognitive presence in online courses using an artificial neural network. Ph.D. thesis, Georgia State University. Retrieved May 25, 2019 from .

This record was imported from ProQuest on October 23, 2013. [Original Record]

Citation reproduced with permission of ProQuest LLC.

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