Emotion Measurement in Intelligent Tutoring Systems: What, When and How to Measure

Feidakis, Michalis; Daradoumis, Τhanasis; Caballé, Santi

doi:10.1109/incos.2011.82

Cited by 57 publications

(56 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Drawing upon various published works [4], [10], [31], [2], [30], main system elements from various similar system have been identified. The main elements of each system to evaluate facial expression are:…”

Section: Automatic System For Classification Emotiontheoretical mentioning

confidence: 99%

Real Time Facial Expression Recognition Using Webcam and SDK Affectiva

Magdin¹,

Prikler²

2018

IJIMAI

View full text Add to dashboard Cite

Section: Automatic System For Classification Emotiontheoretical mentioning

confidence: 99%

Real Time Facial Expression Recognition Using Webcam and SDK Affectiva

Magdin¹,

Prikler²

2018

IJIMAI

View full text Add to dashboard Cite

“…Although there is no consensus about their number or nature, the so-called basic emotions are commonly assumed to be: anger, happiness, fear, sadness, surprise, disgust and love, anticipation, joy and trust [9]. The dimensional approach identifies the characteristics that allow us to differentiate between the different emotions.…”

Section: Measuring Emotionsmentioning

confidence: 99%

“…Regarding the terminology to be used, "emotion" refers to a strong physical and mental response, deriving from a particular stimulus (person, situation or event), which is generalized and occupies the person as a whole for a short period of time [9]. Although sometimes they are used interchangeably, it is important to consider the difference between the terms of "emotion" and others such as "affect", "feeling", or "mood", which are different in degree of consciousness, duration, and distinctiveness ability [10].…”

Section: Introductionmentioning

confidence: 99%

Using the Internet of Things to Support Emotional Health

Eriksson¹,

Russo²,

Marín³

2018

ICST Transactions on Ambient Systems

View full text Add to dashboard Cite

A first step towards emotional well-being is to monitor, understand and reflect upon one's feelings and emotions. A number of personal emotion-tracking applications are available today. In this paper we describe an examination of these applications which indicates that many of the applications do not provide sufficient support for monitoring a full spectrum of emotional data or for analysing or using the data that is provided. To design applications that better support emotional well-being, the full capabilities of the Internet of Things should be utilized. The paper concludes with a description of how Internet of Things technologies can enable the development of systems that can more accurately capture emotional data and support personal learning in the area of emotional health.

show abstract

“…Drawing upon various published works [20], [16], [48], [1], [43], main system elements from various similar system have been identified. The main elements of each system to evaluate facial expression are: The proposition can be extended by access to emotions.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the Emotional State of a User Using a Webcam

Magdin¹,

Turčáni²,

Hudec³

2016

IJIMAI

View full text Add to dashboard Cite

-In online learning is more difficult for teachers identify to see how individual students behave. Student's emotions like self-esteem, motivation, commitment, and others that are believed to be determinant in student's performance can not be ignored, as they are known (affective states and also learning styles) to greatly influence student's learning. The ability of the computer to evaluate the emotional state of the user is getting bigger attention. By evaluating the emotional state, there is an attempt to overcome the barrier between man and non-emotional machine. Recognition of a real time emotion in e-learning by using webcams is research area in the last decade. Improving learning through webcams and microphones offers relevant feedback based upon learner's facial expressions and verbalizations. The majority of current software does not work in real time -scans face and progressively evaluates its features. The designed software works by the use neural networks in real time which enable to apply the software into various fields of our lives and thus actively influence its quality. Validation of face emotion recognition software was annotated by using various experts. These expert findings were contrasted with the software results. An overall accuracy of our software based on the requested emotions and the recognized emotions is 78%. Online evaluation of emotions is an appropriate technology for enhancing the quality and efficacy of e-learning by including the learner´s emotional states.

show abstract

Emotion Measurement in Intelligent Tutoring Systems: What, When and How to Measure

Cited by 57 publications

References 15 publications

Real Time Facial Expression Recognition Using Webcam and SDK Affectiva

Real Time Facial Expression Recognition Using Webcam and SDK Affectiva

Using the Internet of Things to Support Emotional Health

Evaluating the Emotional State of a User Using a Webcam

Contact Info

Product

Resources

About