Cognitive Presence in Web-Based Learning: A Content Analysis of Students' Online Discussions.

Abstract

This first phase of a content analysis of online, asynchronous, educational discussions is designed to generate a method for automatically categorizing messages into cognitive categories using neural network software. This phase of research answers two questions regarding the method of automatically analyzing discussion messages: Can a neural network reliably categorize messages under optimum circumstances, and how can the method be improved to generate greater reliability? To determine whether neural network software can reliably categorize messages, two trials were conducted. The first, “best fit” trial, a proof of concept trial comprised only of messages which best fit the categorization model, generated strong reliability figures (CR = 0.84; k = 0.76), and the second, systematic sample, a sample much more indicative of the messages generated in an online educational discussion, produced formative reliability figures (CR = 0.68; k = 0.31) from which the method of analysis may be optimized. This analysis also provides a distribution based on cognitive presence categories and subcategories of one semester of graduate online educational messages. Many universities and K-12 educational settings have adopted online, web-based instruction as a tool for delivering instruction. According to Green (2000, para 7), “Today, 75 percent of twoand four-year colleges offer some form of online education. By next year, that number will reach 90 percent.” Hamm (2000, para 8) makes a slightly more conservative claim by quoting a study performed by the Chronicle of Higher Education: “60% of American colleges and universities offer online-learning programs, and 8% more plan on doing so in the next year.” He also notes that the e-learning market is expected to grow from $1.2 billion in 2000 to $7 billion in 2003. Certainly, online delivery of instruction is growing as are fora whereby students engage each other. WebCT, one of the more

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