Imène Jraidi

Modeling mental workload using EEG features for intelligent systems

Endowing systems with abilities to assess a users mental state in an operational environment could be useful to improve communication and interaction methods. In this work we seek to model user mental workload using spectral features extracted from electroencephalography (EEG) data. In particular, data were gathered from 17 participants who performed different cognitive tasks. We also explore the application of our model in a non laboratory context by analyzing the behavior of our model in an educational context. Our findings have implications for intelligent tutoring systems seeking to continuously assess and adapt to a learners state.

MENTOR: A Physiologically Controlled Tutoring System

In this paper we present a tutoring system that automatically sequences the learning content according to the learners’ mental states. The system draws on techniques from Brain Computer Interface and educational psychology to automatically adapt to changes in the learners’ mental states such as attention and workload using electroencephalogram (EEG) signals. The objective of this system is to maintain the learner in a positive mental state throughout the tutoring session by selecting the next pedagogical activity that fits the best to his current state. An experimental evaluation of our approach involving two groups of learners showed that the group who interacted with the mental state-based adaptive version of the system obtained higher learning outcomes and had a better learning experience than the group who interacted with a non-adaptive version.

Assessing Learners’ Reasoning Using Eye Tracking and a Sequence Alignment Method

In this paper we aim to assess students’ reasoning in a clinical problem-solving task. We propose to use students’ eye movements to measure the scan path followed while resolving medical cases, and a sequence alignment method, namely, the pattern searching algorithm to evaluate their analytical reasoning. Experimental data were gathered from 15 participants using an eye tracker. We present by using gaze data that the proposed approach can be reliably applied to eye movement sequence comparison. Our results have implications for improving novice clinicians’ reasoning abilities in particular and enhancing students’ learning outcomes in general.

Emotions and personality traits in argumentation: An empirical evaluation1

Argumentation is a mechanism to support different forms of reasoning such as decision making and persuasion and always cast under the light of critical thinking. In the latest years, several computational approaches to argumentation have been proposed to detect conflicting information, take the best decision with respect to the available knowledge, and update our own beliefs when new information arrives. The common point of all these approaches is that they assume a purely rational behavior of the involved actors, be them humans or artificial agents. However, this is not the case as humans are proved to behave differently, mixing rational and emotional attitudes to guide their actions. Some works have claimed that there exists a strong connection between the argumentation process and the emotions felt by people involved in such process. We advocate a complementary, descriptive and experimental method, based on the collection of emotional data about the way human reasoners handle emotions during debate interactions. Across different debates, people’s argumentation in plain English is correlated with the emotions automatically detected from the participants, their engagement in the debate, and the mental workload required to debate. Results show several correlations among emotions, engagement and mental workload with respect to the argumentation elements. For instance, when two opposite opinions are conflicting, this is reflected in a negative way on the debaters’ emotions. Beside their theoretical value for validating and inspiring computational argumentation theory, these results have applied value for developing artificial agents meant to argue with human users or to assist users in the management of debates.

Self-Esteem Conditioning for Learning Conditioning

In this paper, we propose to introduce the self-esteem component within learning process. More precisely, we explore the effects of learner self-esteem conditioning in a tutoring system. Our approach is based on a subliminal priming method aiming at enhancing implicit self-esteem. An experiment was conducted while participants were outfitted with biofeedback device. Three physiological sensors were used to continuously monitor learners’ affective reactions namely electroencephalogram, skin conductance and blood volume pulse sensors. The purpose of this work is to analyze the impact of self-esteem conditioning on learning performance on one hand and learners’ emotional and mental states on the other hand.