Predicting the persistence of information technology students
Date of Award
Doctor of Philosophy (Ph.D.)
First Committee Member
James D. McKinney, Committee Chair
The purpose of this study was to determine whether student attributes identified by Tinto (1993), including student perceptions of involvement and achievement, reasons for attending college, self ratings of ability, student perceptions of institutional commitment and extracurricular activities, are useful in identifying students who will continue their college studies after taking an introductory computer programming course. The literature reviewed revealed that much of the research on persistence focused on undergraduate students broadly and there were relatively few studies of students in specific areas of study such as information technology.A single-sample survey design was used in this study in which a multivariate model was derived for predicting persistence in college defined as continuous enrollment from one academic year to the next. The sample was composed of 491 community college students who were enrolled in one of two introductory computer courses. The data were collected in the Spring term of 2003 and whether the participants re-enrolled in the college was determined during the following Fall term of 2003.Factor analysis was used to form 10 scales from the survey items that measured varied aspects of student goals, interactions with peers and faculty, student commitment, and perceptions of institutional commitment. A discriminant function analysis was used to test the relative predictive value of a model composed of the 10 factors and selected student characteristics in predicting persistence and classifying persisting and non-persisting students.Of the 324 participants, 243 (75%) persisted and 81 (25%) did not reenroll. The discriminant function analysis was performed using six factors identified as relating to persistence. Only four of the six variables predicted persistence collectively, including; student perceptions of institutional commitment and the frequency and helpfulness of academic advisement, student's cumulative college GPA, and student's enrollment status parttime/full time). This combination of predictors correctly classified 217 (66.9%) of the sample as either persisting or non-persisting. These findings were discussed in terms of the existing knowledge base on the retention of community college students, implications for placement strategies for information technology students; and what additional research might be needed to expand the knowledge about factors that might influence the persistence of community college students who are interested in careers in technology.
Education, Guidance and Counseling; Education, Educational Psychology
White, Richard, "Predicting the persistence of information technology students" (2004). Dissertations from ProQuest. 2166.