A Learning Object Recommendation System: Affective-Recommender

A Learning Object Recommendation System: Affective-Recommender

Adriano Pereira (Universidade Federal de Santa Maria, Brazil) and Iara Augustin (Universidade Federal de Santa Maria, Brazil)
DOI: 10.4018/978-1-4666-4542-4.ch014


Emotions play a very important role in the learning process. Affective computing studies try to identify users’ affective state, as emotion, using affect models and affect detection techniques, in order to improve human-computer interactions, as in a learning environment. The Internet explosion makes a huge volume of information, including learning objects data, available. In this scenario, recommendation systems help users by selecting and suggesting probable interesting items, dealing with large data availability and decision making problems, and customizing users’ interaction. In u-learning context, students could learn anywhere and anytime, having different options of data objects available. Since different students have different preferences and learning styles, personalization becomes an important feature in u-learning systems. Considering all this, the authors propose the Affective-Recommender, a learning object recommendation system. In this chapter, they describe the system’s requirements and architecture, focusing on affect detection and the recommendation algorithm, an example of use case, and results of system implementation over Moodle LMS.
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In this section, the main broad topics treated in this work are present. First, it's made a revision about Affective Computing topics, as Affect Detection and affective models, and affective relation to learning. And after, recommendation systems are exposed, including their operation way and classification.

Affective Computing

Picard (1995) defines Affective Computing as the computing that is related to, is arises from or influences emotions in humans, in order to help them, providing ways to make decisions. It is possible due to emotions’ importance in perception and cognition fields. Affective Computing has as objective to construct systems able to recognize user’s affective state, responding automatically to them, improving human-computer interaction (Calvo & D’Mello, 2010).

Affective computing systems are divided in (i) those that detect users affective state; (ii) systems that express something that users identify as an emotion; and (iii) system that “feels” emotions (Picard, 1997).

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