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Variables predicting achievement in introductory computer science courses

University of British Columbia

This study examined the problem of predicting achievement for introductory computer science courses at the college or university level. A questionnaire was developed based on 22 predictor variables that were identified in the review of the literature to predict the final course examination score and the final course grade. The predictor variables were grouped into secondary subject areas and other prior characteristics of each student. The total score on the questionnaire, the score on the KSW Test (Konvalina, Stephens and Wileman, 1983) and the 22 variables were correlated with the final course examination scores and the final course grades using the Pearson product-moment correlation coefficient. Five secondary subject areas correlated significantly (p ≤ 0.003) with the final course examination score and the final course grade: prior computer science achievement, secondary English achievement, secondary languages other than English, secondary mathematics achievement and secondary science achievement. Eight other characteristics correlated significantly with the final examination scores: number of mathematics courses completed after grade 12, overall grade 12 percentage/grade point average, current college/university overall percentage/grade point average, prior computer programming experience, expected grade of current computer course, number of time-sharing and networking systems previously used, number of different programming languages used for programming, and number of different types of computers previously used for programming. Eleven further characteristics correlated significantly with the final course grades: current age, year/grade level, number of years after secondary graduation, number of mathematics courses completed after grade 12, overall grade 12 percentage/grade point average, current college/university overall percentage/grade point average, prior computer programming experience, expected grade of current computer course, number of time-sharing and networking systems previously used, number of different programming languages used for programming, and number of different types of computers previously used for programming. The factor analysis calculations showed that there were five main factors (each composed of a combination of predictor variables) that were labelled as Computer Literacy, Scholastic Achievement and Language Learning, Mathematical Reasoning and Scholastic Achievement, and Finger Dexterity. The questionnaire developed in this study is as good as the KSW Test and is faster to complete (10 minutes versus 40 minutes). Interpretations of the findings, conclusions, limitations of the study, and implications of the study are discussed.

Text

2010-09-16

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http://hdl.handle.net/2429/28487

Science Education

University of British Columbia

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