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This paper assesses the potential of one approach to predicting student performance in an introductory computer science class using information about students' preparation, attitudes and study habits. An expert system has been utilized for this purpose. The expert system accepts data related to seven different categories of preparation, belief and attitude and, through the partial activation of multiple rules, predicts an outcome for each student on the post-test (which should correlate with and is used as a surrogate for the student's final course grade). This paper presents two different implementation approaches for the proposed system and then characterizes the performance of the second (chosen) one, demonstrating its suitability for predicting student performance (subject to a level of error).