Quantitative Analysis of Barriers to Undergraduate Computer Science Learning

Abstract

There is a long-standing discrepancy between planned student learning and student achievement in undergraduate computer science. This research examined the role played by cognitive and affective variables in determining the learning and liking of computer science amongst students. In particular, it evaluated a Built-In Computer Science Learning Difficulty Model (ICSLDM) through the analysis of the correlation between basic computational readiness, programming anxiety, learning strategies, perceived difficulty of the subject and academic achievement. The research used a cross-sectional survey design which was descriptive in nature. The sample population included science students of all years in the district of Mardan and 112 students were sampled with the help of a 5-point Likert-scale tool. A diagnostic assessment was also used to enhance the findings by triangulation. The data were analysed with the help of one-sample t-test, paired t-tests, and Pearson correlation coefficients. Findings showed that the computer science seemed easier to students with better background knowledge (e.g. logical thinking and simple mathematics as applied to programming). It was discovered that there was a disparity in self-reported readiness of students to complete computing tasks and their diagnostic evaluation scores. On the whole, the students perceived computer science as a difficult subject. Although the students stated that the overall orientation towards self-regulated learning was positive, they expressed little confidence in learning programs and had low self-efficacy. Reduced self-efficacy was correlated with reduced cognitive and affective performance. The results uphold the main aspects of the ICSLDM, where the cognitive resources and affective factors significantly forecast the perceived difficulty and student strategic orientations to learning computer science. The paper suggests specific measures to alleviate programming anxiety and enhance the basic computational ability.