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This article describes the analysis, design and development of an Intelligent Learning System (ILS). The design of the ILS is based on a multi-agent architecture. This architecture includes reactive agents which represent the expertise of each of the necessary sub-skills in learning the application domain, which in the study case is structured programming. The ILS utilizes artificial intelligence techniques to implement the teaching-learning process using an inference engine based on a general didactic model. As a result, this system is termed as Intelligent Learning System with Learning Objects (ProgEst). ProgEst is carried out with the objective of providing the user with self-regulated learning strategies in addition to the knowledge of a determined domain. The case study includes situations related to: learning styles, knowledge domain (errors made) and affective-motivational state. The assessments shall determine: 1) what is to be explained, 2) level of detail and timing, 3) how and when to interrupt the student, and 4) the information to provide during the interaction.
This research is framed
within the affective computing, which explains the importance of emotions in
human cognition (decision making, perception, interaction and human
intelligence). Applying this approach to a pedagogical agent is an essential
part to enhance the effectiveness of the teaching-learning process of an
intelligent learning system. This work focuses on the design of the inference
engine that will give life to the interface, where the latter is represented by a pedagogical agent. The inference engine is
based on an affective-motivational model. This model is implemented by using
artificial intelligence technique called fuzzy cognitive maps.