To monitor and enhance the learning performance of learning groups in a Web learning system, teachers need to know the learning status of the group and determine the key influences affecting group learning outcomes. Teachers can achieve this goal by observing the group discussions and learning behavior from Web logs and analyzing the Web log data to obtain the relevant information. However, Web logs are not systematically organized and the discussions are extensive. Consequently, teachers must struggle to extract information from logs and intuitively apply teaching rules based on experience when managing the groups. Rather than using statistics packages to evaluate hypotheses, this work presents a methodology of applying existing data and text mining tools to automatically gather learning status and predict performance of learning groups from the contents of discussions and from log records of learning behaviors. Meanwhile, the methodology infers a causal network exists between learning features and learning performance. Knowledge is inferred based on statistics and probability reasoning and social interdependency theory. The causal network can suggest means of enhancing learning performance to teachers. Simultaneously, teachers can use the knowledge of learning groups obtained to manage group learning process on the Web. Experimental results of applying the novel methodology to manage a group learning class organized over the Web and containing 706 students are also presented. (Contains 11 tables and 3 figures.)