Using Biometrics to Support Affective eLearning for Users with Special Needs
This paper concerns the use of biometric technology as a means to improve delivery of eLearning material to learners with special needs. We discuss the current state of research and the potential offered by recent advances in and commercialisation of biom
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University College Cork, Cork, Ireland [email protected], [email protected] 2 Trinity College Dublin, Dublin, Ireland [email protected]
Abstract. This paper concerns the use of biometric technology as a means to improve delivery of eLearning material to learners with special needs. We discuss the current state of research and the potential offered by recent advances in and commercialisation of biometric technologies. We then present an ongoing study that examines the application of some of these technologies in learner-assessment of special needs students. Keywords: Affective computing · Biometrics · eLearning · Learners with special needs
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Background
1.1 Affective Computing Affective computing concerns the development of systems that can recognise, interpret and respond to human emotion, thus allowing the creation of applications that support more effective human-machine communication or (when used as a mediating tech‐ nology) human-human-communication. Affective computing uses sensors to gather data of the type humans use to identify emotions in others. The sensors used include video cameras to capture facial expres‐ sions, posture, gestures, etc., microphones to capture speech, and various other sensors to capture physiological data such as skin temperature, galvanic resistance, pulse-rate, etc. The data gathered is then analysed to identify affective states. For example, speech affect recognisers measure features of speech such as pitch, volume and speed, and use this data along with the linguistic content of the speech to infer the emotional state of the user. Research has shown that affect plays a key role in our understanding of phenomena such as attention, memory and aesthetics, and helps us gauge the levels of enjoyment, engagement and frustration that the user experiences. By understanding how a user responds both physically and emotionally, it is possible to develop systems that are more intelligent, adaptive and robust and ultimately more useful. In eLearning applications, for example, affective computing techniques could be used to adjust the presentation style of a teaching package depending upon the learner’s emotional state (bored, engaged, frustrated, etc.). © Springer International Publishing Switzerland 2016 K. Miesenberger et al. (Eds.): ICCHP 2016, Part I, LNCS 9758, pp. 487–490, 2016. DOI: 10.1007/978-3-319-41264-1_66
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I. Pitt et al.
1.2 Affective Computing and Users with Special Needs The ability to analyse emotional state offers additional potential to those with special needs. It is widely recognised that, whilst language is an important primary communi‐ cation channel, a large percentage of the information we receive is communicated nonverbally. Mehrabian [1], for example, argues that only 7 % of a message between two people is communicated verbally, whilst 38 % is communicated through prosody and 55 % through body-language. Someone with a sensory impairment may be unable to access one of the communication channels and thus lose a large percentage of the trans‐ mitted message, while t
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