Difficulties in Learning the Data Structures and Algorithms Course: Literature Review

Abstract

Data Structures and Algorithms (DSA) is a highly demanding course for computer science students. DSA content is highly interconnected and therefore the cognitive load is very high. Due to its high cognitive load, DSA is associated with higher failure and high dropout rates. This article analyses the main causes of student’s difficulties in learning DSA concepts as reported in CS literature. The study used desktop review methods to examine the causes of difficulties in learning DSA from several sources published in CS education pertaining to teaching and learning programming. The cognitive load theory (CLT) is used as a lens to analyze these difficulties in learning DSA. Our review reveals that difficulties associated with DSA course can be traced on varied of factors including abstract and dynamic nature of DSA concepts, poor instructional methods, curricula organization, instructional materials, background knowledge, the multidimensional nature of the DSA, lack of conceptual understanding, poor problem-solving and planning skills, faulty mental models, and low student motivation. Based on these findings we recommend instructors to improve teaching strategies and adopt new instructional approaches that stimulate student’s motivations and minimize learning overheads for novices. The study provides a reference and roadmap for both course designers and developers of computer programming teaching tools towards effective development of programming teaching tools. It also provides insights for both designers and developers of education supporting tools such as program visualization tools towards effective design appropriate