The objective of this article is to illustrate that text mining and qualitative research are epistemologically compatible. First, like many qualitative research approaches, such as grounded theory, text mining encourages open-mindedness and discourages preconceptions. Contrary to the popular belief that text mining is a linear and fully automated procedure, the text miner might add, delete, and revise the initial categories in an iterative fashion. Second, text mining is similar to content analysis, which also aims to extract common themes and threads by counting words. Although both of them utilize computer algorithms, text mining is characterized by its capability of processing natural languages. Last, the criteria of sound text mining adhere to those in qualitative research in terms of consistency and replicability.

This study investigated the use of asynchronous (mailing lists) and synchronous (chat sessions) Internet-based communication and its impact on teachers' attitudes toward collaboration, activity completion rate, and test performance. In addition, the impact of collaboration on activity completion rate and teacher performance measured by objective tests was investigated. Although it was found that attitudes toward collaboration did not affect test performance, the data suggested a relationship between attitudes toward collaboration and use of Internet-based communication. OBJECTIVESCognitive models of learning stress that learners are active agents in constructing their own learning [1, 2]. Collaborative learning enhances the learning process, allowing learners to engage in the construction of new representations of knowledge and facilitating the transfer of information from short-term to long-term memory [3,4]. Asynchronous and synchronous communication provides users with opportunities to collaborate across distances through such technologies as mailing lists and chat sessions, respectively. It was hypothesized that these modes of communication provide unique features conducive to the development of a 405 Ó 2000, Baywood Publishing Co., Inc.

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Student retention is an important issue for all university policy makers due to the potential negative impact on the image of the university and the career path of the dropouts. Although this issue has been thoroughly studied by many institutional researchers using parametric techniques, such as regression analysis and logit modeling, this article attempts to bring in a new perspective by exploring the issue with the use of three data mining techniques, namely, classification trees, multivariate adaptive regression splines (MARS), and neural networks. Data mining procedures identify transferred hours, residency, and ethnicity as crucial factors to retention. Carrying transferred hours into the university implies that the students have taken college level classes somewhere else, suggesting that they are more academically prepared for university study than those who have no transferred hours. Although residency was found to be a crucial predictor to retention, one should not go too far as to interpret this finding that retention is affected by proximity to the university location. Instead, this is a typical example of Simpson's Paradox. The geographical information system analysis indicates that non-residents from the east coast tend to be more persistent in enrollment than their west coast schoolmates.

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