Gender and race in predicting achievement in computer science

In the study described here, 65 prospective computer or information science majors worked through a tutorial on the basics of Perl. Eighteen students were African American. All actions were recorded and time-stamped, allowing us to investigate the relationship among six factors that we believed would predict performance in an introductory computer science (CS) course (as measured by course grade) and how much students would learn from the tutorial (as measured by the difference between pre-test and post-test performance). These factors are: preparation (SAT score, number of previous CS courses taken, and pretest score), time spent on the tutorial as a whole and on individual chapters, amount and type of experimentation, programming accuracy and/or proficiency, approach to materials that involve mathematical formalisms, and approach to learning highly unfamiliar material (pattern-matching procedures). Gender and race differences with respect to these factors were also investigated.