Problem solving and student performance in data structures and algorithms

Authors:
Donald Chinn;Catherine Spencer;Kristofer Martin
Affiliations:
University of Washington;University of Washington;University of Washington
Venue:
Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education
Year:
2007

Citing 6
Cited 3

Data Structures and Algorithm Analysis in Java

Data Structures and Algorithm Analysis in Java
Algorithm Design

Algorithm Design
Collaborative, problem-based learning in computer science

Journal of Computing Sciences in Colleges
Treisman workshops and student performance in CS

Proceedings of the 38th SIGCSE technical symposium on Computer science education
Workshops vs. tutoring: how ASU's minority engineering program is changing the way engineering students learn

FIE '01 Proceedings of the Frontiers in Education Conference, 2001. 31st Annual - Volume 02
Algorithm Design: Foundations, Analysis and Internet Examples

Algorithm Design: Foundations, Analysis and Internet Examples

Using Bloom's taxonomy to code verbal protocols of students solving a data structure problem

Proceedings of the 47th Annual Southeast Regional Conference
Empirical evidence for the existence and uses of metacognition in computer science problem solving

Proceedings of the 41st ACM technical symposium on Computer science education
Application of non-programming focused treisman-style workshops in introductory computer science

Proceedings of the 43rd ACM technical symposium on Computer Science Education

Quantified Score

Hi-index	0.00

Visualization

Abstract

Active and problem-based learning environments strive to improve students' problem solving skills. To better understand students' problem solving processes and thus guide the structure and development of such environments, we asked students to solve data structures and algorithms problems and to verbalize their thoughts as they solved them. In this paper, we discuss methodological issues associated with the analysis of their verbalizations. We then analyze and discuss the relationship between statistics that describe students' problem solving process and their performance in the course they were taking at the time, either the data structures or algorithms course.