Exploring Software Engineering Subjects by Using Visual Learning Analytics Techniques

Title:

Exploring Software Engineering Subjects by Using Visual Learning Analytics Techniques [Download]

Authors:

Conde, Miguel Á. and García-Peñalvo, Francisco J. and Gómez-Aguilar, Diego-Alonso and Therón, Roberto

Index Terms:

Context;Visual analytics;Data visualization;Least squares approximations;Semantics;Analytical models;Visual analytics;Learning Analytics;Decision Making;Learning Management Systems;Visual analytics;learning analytics;decision making;learning management sy

Abstract:

The application of the information and communication technologies to teaching and learning processes is linked to the development of new tools and services that can help students and teachers. Learning platforms are a clear example of this. They are very popular tools in eLearning contexts and provide different types of learning applications and services. In addition, these environments also register most of the interactions between the learning process stakeholders and the system. This information could potentially be used to make decisions, but usually it is stored as raw data, which is very difficult to understand. This paper presents a system that employs visual learning analytic techniques to facilitate the exploitation of that information. The system presented includes several tools that make possible to explore issues, such as when interaction is carried out, which contents are the most important for users, and how they interact with others. The system was tested in the context of a software engineering subject, considering the stored logs of five academic years. From this analysis, it is possible to see how visual analytics can help decision-making, and in this context, how it helps to improve educational processes.

DOI:

10.1109/RITA.2015.2486378

How to cite:
Conde, Miguel Á. and García-Peñalvo, Francisco J. and Gómez-Aguilar, Diego-Alonso and Therón, Roberto, "Exploring Software Engineering Subjects by Using Visual Learning Analytics Techniques" in IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, pp. 242-252, Nov. 2015. doi: 10.1109/RITA.2015.2486378