Modeling affect by mining students’ interactions within learning environments

Manolis Mavrikis, Sidney D’Mello, Kaska Porayska-Pomsta, Mihaela Cocea, Art Graesser

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


In the past decade, research on affect-sensitive learning environments has emerged as an important area in artificial intelligence in education (AIEd) and intelligent tutoring systems (ITS) [1-6]. These systems aspire to enhance the effectiveness of computer-mediated tutorial interactions by dynamically adapting to individual learners’ affective and cognitive states [7] thereby emulating accomplished human tutors [7,8]. Such dynamic adaptation requires the implementation of an affective loop [9], consisting of (1) detection of the learner’s affective states, (2) selection of systems actions that are sensitive to a learner’s affective and cognitive states, and sometimes (3) synthesis of emotional expressions by animated pedagogical agents that simulate human tutors or peer learning companions [9,10].

Original languageEnglish
Title of host publicationHandbook of Educational Data Mining
EditorsCristobal Romero, Sebastian Ventura, Mykola Pechenizkiy, Ryan Baker
PublisherCRC Press Inc
Number of pages14
ISBN (Electronic)9781439804582, 9780429130120
ISBN (Print)9781439804575
Publication statusPublished - 26 Oct 2010


Dive into the research topics of 'Modeling affect by mining students’ interactions within learning environments'. Together they form a unique fingerprint.

Cite this