Exploring User Satisfaction in a Tutorial Dialogue System

Abstract User satisfaction is a common evaluation metric in task-oriented dialogue systems, whereas tutorial dialogue systems are often evaluated in terms of student learning gain. However, user satisfaction is also important for such systems, since it may predict technology acceptance. We present a detailed satisfaction questionnaire used in evaluating the BEETLE II system (REVU-NL), and explore the underlying components of user satisfaction using factor analysis. We demonstrate interesting patterns of interaction between interpretation quality, satisfaction and the dialogue policy, highlighting the importance of more finegrained evaluation of user satisfaction

Learning style and learning strategies in a multimedia environment

There is a growing realization that it may be expeditious to combine elements from different theories of learning when trying to derive a coherent and usable policy towards computer‐mediated learning. Consideration of the subtle distinction between Computer‐Aided Learning (CAL) and Computer‐Aided Instruction (CAI) conform the basis of a possible classification of computer‐mediated learning, and hence of multimedia tools. This classification enables the development of a continuum upon which to place various strategies for computer‐mediated learning, and hence a means of broadly classifying multimedia learning tools

Reducing risky security behaviours:utilising affective feedback to educate users

Despite the number of tools created to help end-users reduce risky security behaviours, users are still falling victim to online attacks. This paper proposes a browser extension utilising affective feedback to provide warnings on detection of risky behaviour. The paper provides an overview of behaviour considered to be risky, explaining potential threats users may face online. Existing tools developed to reduce risky security behaviours in end-users have been compared, discussing the success rate of various methodologies. Ongoing research is described which attempts to educate users regarding the risks and consequences of poor security behaviour by providing the appropriate feedback on the automatic recognition of risky behaviour. The paper concludes that a solution utilising a browser extension is a suitable method of monitoring potentially risky security behaviour. Ultimately, future work seeks to implement an affective feedback mechanism within the browser extension with the aim of improving security awareness

A model for providing emotion awareness and feedback using fuzzy logic in online learning

Monitoring users’ emotive states and using that information for providing feedback and scaffolding is crucial. In the learning context, emotions can be used to increase students’ attention as well as to improve memory and reasoning. In this context, tutors should be prepared to create affective learning situations and encourage collaborative knowledge construction as well as identify those students’ feelings which hinder learning process. In this paper, we propose a novel approach to label affective behavior in educational discourse based on fuzzy logic, which enables a human or virtual tutor to capture students’ emotions, make students aware of their own emotions, assess these emotions and provide appropriate affective feedback. To that end, we propose a fuzzy classifier that provides a priori qualitative assessment and fuzzy qualifiers bound to the amounts such as few, regular and many assigned by an affective dictionary to every word. The advantage of the statistical approach is to reduce the classical pollution problem of training and analyzing the scenario using the same dataset. Our approach has been tested in a real online learning environment and proved to have a very positive influence on students’ learning performance.Peer ReviewedPostprint (author's final draft