Using data mining as a strategy for assessing asynchronous discussion forums

Abstract

The purpose of this paper is to show how data mining may offer promise as a strategy for discovering and building alternative representations for the data underlying asynchronous discussion forums. Presently, the instructor's view of the output of a threaded forum is limited to reviewing a transcript or print version of the written dialogue produced by participants. With potentially hundreds of contributions to review for an entire online course, the instructor lacks a comprehensive view of the information embedded in the transcript. In this context, the authors attempt to sort out the question, "what is data from an online forum?" among other key questions. The present work seeks to intersect the information (i.e., participation indicators) an instructor may wish to extract from the forum with viewable and useful information that the system could produce from the instructor's query. Temporal participation indicators are used to show how using data and text mining techniques in the query process could improve the instructor's ability to evaluate the progress of a threaded discussion. © 2004 Elsevier Ltd. All rights reserved.