Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Using learning style data in an introductory computer science course
SIGCSE '99 The proceedings of the thirtieth SIGCSE technical symposium on Computer science education
Learning styles and performance in the introductory programming sequence
SIGCSE '02 Proceedings of the 33rd SIGCSE technical symposium on Computer science education
The software engineering capstone: structure and tradeoffs
SIGCSE '02 Proceedings of the 33rd SIGCSE technical symposium on Computer science education
RoboCode & problem-based learning: a non-prescriptive approach to teaching programming
Proceedings of the 11th annual SIGCSE conference on Innovation and technology in computer science education
ASSISTing CS1 students to learn: learning approaches and object-oriented programming
Proceedings of the 11th annual SIGCSE conference on Innovation and technology in computer science education
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Recent research has shown that a student's learning style - essentially, the way a student approaches and masters new material - can affect student performance in introductory computer science courses. We show here that a student's learning style can also affect student performance across the courses in the computer science curriculum.This paper presents the results of a case study in which we collected learning style data for students completing the required courses in a typical computer science curriculum. We then used a wide range of statistical analyses to check for bias in the dataset and to examine the relationships between student learning style and student performance in those courses.Our analysis identified a number of statistically significant relationships between student learning style and performance. We examine potential explanations for those relationships and discuss ways in which the results can be used to enhance student learning.