Problem solving and student performance in data structures and algorithms

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.

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

“…One of the few studies in computer science analyzing how students solve problems used Polya's problem-solving framework to investigate how students solve problems in data structures and algorithms. Chinn et al analyzed the verbal protocols to compare "good" and "bad" problem solvers and to investigate how problem solving ability correlates to grades in the course [8].…”