Towards Psychologically based Personalised Modelling of Emotions Using Associative Classifiers

Towards Psychologically based Personalised Modelling of Emotions Using Associative Classifiers

Aladdin Ayesh (De Montfort University, Leicester, UK), Miguel Arevalillo-Herráez (Departament d'Informàtica. Universitat de València, Burjassot, Spain) and Francesc J. Ferri (Departament d'Informàtica. Universitat de València, Burjassot, Spain)
DOI: 10.4018/IJCINI.2016040103


Learning environments, among other user-centred applications, are excellent candidates to trial Computational Emotions and their algorithms to enhance user experience and to expand the system usability. However, this was not feasible because of the paucity in affordable consumer technologies that support the requirements of systems with advanced cognitive capabilities. Microsoft Kinect provides an accessible and affordable technology that can enable cognitive features such as facial expressions extraction and emotions detection. However, it comes with its own additional challenges, such as the limited number of extracted Animation Units (AUs). This paper presents a new approach that attempts at finding patterns of interaction between AUs, and between AUs and a given emotion. By doing so, the authors aim to reach a mechanism to generate a dynamically personified set of rules relating AUs and emotions. These rules will implicitly encode a person's individuality in expressing one's emotions.
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Adaptive learning aims to meet individual learning needs and improve learning efficiency and the level of achievement by providing personalized tutoring which considers individual abilities, interests, needs, learning styles or other data that deemed to be a factor. To provide this type of service, systems collect information about learners that is then used to meet their specific needs e.g. adequate contents to the user’s preferred learning method. The same knowledge can be used to adapt the user interface, to decide on the most suitable input/output devices to be used, to establish an incremental cognitive path, to provide appropriate feedback to the student, or to issue recommendations. However, the system’s ability to provide adaptive learning is limited by the interpretation of the user’s information.

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