Paper Info
Title
Quantifying The Effects Of Prior Knowledge In Entry-Level Programming Courses
Abstract
Computer literacy and programming are being taught increasingly at the K-12 level with more students than ever matriculating in college with prior programming experience. Accurately assessing student programming skills acquired in high school can inform college faculty about the range of competencies in introductory programming courses. The tool predominantly-used for assessing past CS knowledge and skills is a survey, which lacks quantitative rigor. This study aims to (1) quantify the effects of prior knowledge in entry-level programming courses and (2) compare the different measurement approaches of student prior knowledge in programming, including surveys and aptitude tests. The results of this study reveal that a discrepancy exists between the results of surveys and aptitude tests. Consistent with prior survey studies, our survey results showed that the effects of student prior programming knowledge faded gradually during the course period. In contrast, the aptitude test results indicated that the effects of student prior knowledge did not weaken over time. The accuracy of both measurements and implications for instructors were further discussed.
Year
DOI
Venue
2019
10.1145/3300115.3309503
PROCEEDINGS OF THE ACM CONFERENCE ON GLOBAL COMPUTING EDUCATION (COMPED '19)
Keywords
DocType
Citations 
CS1, prior knowledge, assessment, performance prediction
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
David H. Smith100.34
IV200.34
Qiang Hao301.35
Filip Jagodzinski47114.83
Yan Liu524173.08
Vishal Gupta684.94