Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Addressing Emotions within E-Learning Systems

Addressing Emotions within E-Learning Systems
View Sample PDF
Author(s): Valentino Zurloni (CESCOM, University of Milan - Bicocca, Italy), Fabrizia Mantovani (CESCOM, University of Milan - Bicocca, Italy and ATN-P LAB, Istituto Auxologico Italiano, Italy), Marcello Mortillaro (CESCOM, University of Milan - Bicocca, Italy and CISA - University of Geneva, Switzerland), Antonietta Vescovo (CESCOM, University of Milan - Bicocca, Italy) and Luigi Anolli (CESCOM, University of Milan - Bicocca, Italy)
Copyright: 2008
Pages: 14
Source title: Handbook of Research on Instructional Systems and Technology
Source Author(s)/Editor(s): Terry T. Kidd (Texas A&M University, USA) and Holim Song (Texas Southern University, USA)
DOI: 10.4018/978-1-59904-865-9.ch057


View Addressing Emotions within E-Learning Systems on the publisher's website for pricing and purchasing information.


Emotions are attracting growing attention within the instructional design research community. However, clarification is still required as to how exactly to address emotions within the field of e-learning. The aim of this chapter is twofold. Firstly, we will focus on the reasons for including emotions within the instructional technology domain, and in particular, on the relevance of emotions to computer-based learning. The need for specific theory in this regard is heightened by the current drive to design instructional devices that interact with learners in a motivating, engaging, and helpful way. Secondly, within the framework affective computing paradigm, the different modalities for detecting emotions in instructional technology contexts will be systematically reviewed, and the strengths and limits of each will be discussed on the basis of the most up to date research outcomes. Finally, a tentative architecture for emotion recognition in computer-based learning will be proposed, focusing on the adoption of a multimodal approach to emotion recognition, in order to overcome the limitations and the difficulties associated with individual modalities.

Related Content

Vierne Placide, Michelle M. Vance. © 2022. 23 pages.
Robert Earl McKinney, Anne D. Halli-Tierney, Allyson E. Gold, Rebecca S. Allen, Dana G. Carroll. © 2022. 19 pages.
Lindsey E. Moseley, Lauren C. McConnell, Sydney Meadows, Justin Carter, Bradley M. Wright. © 2022. 23 pages.
Tyan Thomas, Alice Lim Scaletta, Sharon K. Park. © 2022. 30 pages.
Teresa Seefeldt, Omathanu Perumal, Hemachand Tummala. © 2022. 22 pages.
Elizabeth A. Sheaffer, Katie Boyd, Cheryl D. Cropp. © 2022. 21 pages.
Erika L. Kleppinger, Kevin N. Astle, Amber M. Hutchison, Channing R. Ford. © 2022. 23 pages.
Body Bottom