Introduction to algorithms
CS1 and CS2 (panel session): foundations of computer science and discrete mathematics
Proceedings of the thirty-first SIGCSE technical symposium on Computer science education
Discrete mathematics: support of and preparation for the study of computer science
Proceedings of the eighth annual consortium on Computing in Small Colleges Rocky Mountain conference
Math proficiency: a key to success for computer science students
Communications of the ACM
Introduction to Algorithms: A Creative Approach
Introduction to Algorithms: A Creative Approach
Learning styles and performance in the introductory programming sequence
SIGCSE '02 Proceedings of the 33rd SIGCSE technical symposium on Computer science education
Mathematics preparation for undergraduate degrees in computer science
SIGCSE '02 Proceedings of the 33rd SIGCSE technical symposium on Computer science education
Introduction to the Design and Analysis of Algorithms
Introduction to the Design and Analysis of Algorithms
Math educators, computer science educators: working together
SIGCSE '03 Proceedings of the 34th SIGCSE technical symposium on Computer science education
Equilibriating instructional media for cognitive styles
Proceedings of the 8th annual conference on Innovation and technology in computer science education
Panel discussion: mathematical thinking in computer science
Proceedings of the 2nd annual conference on Mid-south college computing
Learning computer programming and role of errors in design
ACM SIGCSE Bulletin
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In Introduction to Analysis of Algorithms, students' first experience applying a combination of computer science theory and mathematics to paper-based problem solving, analysis of pre-developed algorithms, and proofs of algorithmic run-times. In rare cases, students implement (efficient) variations of existing algorithms. Student's have difficulty transitioning from programming to problem solving in the first weeks of course. In this paper, we explore the reasons that Analysis might be inaccessible to the computer science student: we define accessible course (content), compare programmer style with requirements of analytic problem solving and determine techniques to make mathematical problem solving accessible. Since they are not (as yet) supported by empirical evidence, these discussions do not lead to definitive claims about analysis, accessibility or performance; on the other hand, they do generate theories for research and suggest ideas for improving the accessibility of analysis, computer science theory and applied mathematics for computer science students.