- Class 30
- Practice 30
- Independent work 120
Lecturers and associates
The objectives of this module are to enable students to:
• Interpret the challenges in human-human affective and communicative interaction (
• Demonstrate knowledge in current theories and trends in designing emotionally and socially sensitive interactive technology, using machine learning and pattern recognition techniques
• Comprehend and apply (appropriate) methods for collection, analysis, representation and evaluation of human affective and communicative behaviour data
• Demonstrate ability to computationally analyse, recognise and evaluate human affective and social behaviour
• Demonstrate critical thinking, analysis and synthesis while making a decision on 'when' and 'how' to incorporate emotions and social signals in a specific application context
Students learn students to develop approaches and use technologies that help people measure and communicate emotion, that respectfully read and that intelligently respond to emotion, and have internal mechanisms inspired by the useful roles emotions play.
It is important for students to take this module in order to learn the knowledge and have an understanding of affective computing and to enable them to independently apply methods of analysis, synthesis and recognition of affective states.
1. Algebra University College (2020) Affective Computing Workbook, Zagreb: Algebra University College
1. Calvo, R.A., D'Mello, S. K., Gratch, J., Kappas, A. (2014) The Oxford Handbook of Affective Computing, Oxford: Oxford University Press
2. Scherer, K.R., Bänziger, T., Roesch, E.A. (2010) Blueprint for Affective Computing, Oxford: Oxford University Press
3. Davidson, R.J., Scherer, K.R., Goldsmith, H.H. (2009) Handbook of Affective Sciences, Oxford: Oxford University Press
4. Picard, R.W. (2000) Affective Computing. Cambridge: MIT Press
Minimal learning outcomes
- Identify methods of affective computing
- Describe variety in representation of affective states
- Explain most common features of psychophysiological expressions
- Explain most common features of vocal and verbal expressions.
- Explain most common features of facial expressions.
- Describe steps in developing model for the analysis or synthesis of affect.
- Explain team roles and responsibilities in project of development of system for analysis or synthesis of affect.
Preferred learning outcomes
- Judge the application of the method of affective computing to solve a particular problem.
- Select the optimal representation of affective states for a particular problem.
- Critically judge the features of psychophysiological expressions.
- Critically judge the features of vocal and verbal expressions.
- Critically judge the features of facial expressions.
- Develop a model basis for the analysis or synthesis of affect.
- Development of a complete model / system for analysis or synthesis of affect.