Title
Affecting off-task behaviour: how affect-aware feedback can improve student learning.
Abstract
This paper describes the development and evaluation of an affect-aware intelligent support component that is part of a learning environment known as iTalk2Learn. The intelligent support component is able to tailor feedback according to a student's affective state, which is deduced both from speech and interaction. The affect prediction is used to determine which type of feedback is provided and how that feedback is presented (interruptive or non-interruptive). The system includes two Bayesian networks that were trained with data gathered in a series of ecologically-valid Wizard-of-Oz studies, where the effect of the type of feedback and the presentation of feedback on students' affective states was investigated. This paper reports results from an experiment that compared a version that provided affect-aware feedback (affect condition) with one that provided feedback based on performance only (non-affect condition). Results show that students who were in the affect condition were less bored and less off-task, with the latter being statically significant. Importantly, students in both conditions made learning gains that were statistically significant, while students in the affect condition had higher learning gains than those in the non-affect condition, although this result was not statistically significant in this study's sample. Taken all together, the results point to the potential and positive impact of affect-aware intelligent support.
Year
DOI
Venue
2016
10.1145/2883851.2883936
LAK '16 CONFERENCE PROCEEDINGS: THE SIXTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE,
Keywords
Field
DocType
Affect,Feedback,Exploratory Learning Environments
Computer science,Knowledge management,Bayesian network,Human–computer interaction,Artificial intelligence,Learning environment,Affect (psychology),Peer feedback,Machine learning,Student learning
Conference
Citations 
PageRank 
References 
3
0.40
20
Authors
6
Name
Order
Citations
PageRank
Beate Grawemeyer118417.60
Manolis Mavrikis227341.97
Wayne Holmes3255.74
Sergio Gutiérrez Santos4508.24
Michael Wiedmann5172.27
Nikol Rummel645657.13