Self-Assesment tool with topic-driven navigation for algorithms learning

Fernando López-Ostenero, Laura Plaza, Lourdes Araujo, Juan Martínez-Romo

Abstract: Algorithms and data structures are one of the most difficult parts of Computer Science for students to learn. It is therefore essential to develop approaches and tools that facilitate the acquisition of this crucial part of a computer scientist’s training. Based on the hypothesis that making the access to information and self-assessment easier improves the learning of the concepts, in this work we propose a graphical tool that allows the student to navigate through the topics of the subject, and to reach collections of self-assessment exercises on each of the topic. For this purpose, the information of the subject has been organized in a navigable hierarchy of topics that allows students to access exercises in a simple and visual way, on the one hand, but also to easily learn the relationships between the different concepts of the subject. These exercises allow students to self-evaluate and solve their doubts, as well as to find analogies between different topics in the hierarchy. This process is complemented by a web page recommender for the topics of the hierarchy. Results according to a questionnaire about the tool answered by the students indicate that it is considered of high utility and interest.