Identifying student profiles in CSCL systems for programming learning using quality in use analysis

Abstract

In the digital age, computer programming skills are in high demand, and collaborative learning is essential for its development. Computer-Supported Collaborative Learning (CSCL) systems enable real-time collaboration among students, regardless of their location, by offering resources and tools for programming tasks. To optimize the learning experience in CSCL systems, user profiling can be used to tailor educational content, adapt learning activities, provide personalized feedback, and facilitate targeted interventions based on individual learners' needs, preferences, and performance patterns. This paper describes a framework that can be applied to profile students of CSCL systems. By analysing log files, computational models, and quality measures, the framework captures various dimensions of the learning process and generates user profiles based on the Myers-Briggs Type Indicator (MBTI) personality. The work also conducts a case study that applies this framework to COLLECE 2.0, a CSCL system that supports programming learning.