Learning Styles in a Collaborative Algorithmic Problem-Based Learning

Nowadays, university teaching can no longer rely solely on the pillar of traditional teaching, research on new teaching/learning methods is becoming more and more numerous, especially with the integration of new information and communication technologies, which play an important role in our daily lives. In the case of our university, all the algorithmic courses taught in the first year of computer science at our university are face-to-face; our research aims to present the improvements that online adaptive training can bring to the learning style of learners. In particular, in terms of learners’ subjective satisfaction and learning speed and performance. The objective of this research is to find the contribution that problem-based learning can make to the learner’s learning style within a social network. The technique proposed in this paper aims to personalize learning by applying Felder–Silverman’s model of learning styles and intelligent technologies, for example, such as ontology and data mining methods to improve the quality and sustainability of learning. The PBL process does not focus on problem solving with a defined solution, but takes into consideration the improvement of other attractive abilities and qualities. This will include learning, improved collaboration and group communication.