MENTOR: A Physiologically Controlled Tutoring System

Chaouachi, Maher; Jraidi, Imène; Frasson, Claude

doi:10.1007/978-3-319-20267-9_5

Cited by 21 publications

(10 citation statements)

References 21 publications

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“…It indicates the amount of effort invested as well as users' involvement level. To extract the CW from the EEG headset, this research uses a third-party software called Mentor [10]. This software is a module from the NCO software, a proprietary software of the BMU lab that represents the convergence of multiple years of research into one extensive program called NCO [11].…”

Section: Cognitive Workloadmentioning

confidence: 99%

Cognitive Workload Assessment of Aircraft Pilots

Antoine¹,

Abdessalem²,

Frasson³

2022

JBBS

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In this research, we study the cognitive workload of aircraft pilots during a simulated takeoff procedure. We propose a proof-of-concept setup environment to gather heart rate, pupil dilation, and brain cognitive workload data during an A320 takeoff within a simulator. Experiments were performed during which we collected 136 takeoffs across 13 pilots for more than 9 hours of time-series data. Moreover, this paper investigates the correlations between heart rate, pupil dilation, and cognitive workload during such exercise and found that a spike in cognitive load during a critical moment, such as an engine failure, augments a pilot's heart rate and pupil dilation. Results show that a critical moment within a takeoff procedure increases a pilot's cognitive load. Next, we used a stacked-LSTM model to predict cognitive workload 5 seconds into the future. The model was able to produce accurate predictions.

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Section: Cognitive Workloadmentioning

confidence: 99%

Cognitive Workload Assessment of Aircraft Pilots

Antoine¹,

Abdessalem²,

Frasson³

2022

JBBS

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“…MENTOR (MENtal tuTOR) is a tutoring system that uses two brain indicators, namely engagement and cognitive load extracted from the EEG physiological data to adjust the learning strategy according to the learner's mental state [31], [32]. The overall objective of the system is to maintain the learners in an appropriate state.…”

Section: System Designmentioning

confidence: 99%

Enhancing the Learning Experience Using Real-Time Cognitive Evaluation

Chaouachi¹,

Jraidi²,

Lajoie³

et al. 2019

IJIET

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There is increasing evidence that learners' affective and cognitive states play a key role in the learning process. This suggests that systems which are able to detect these states can dynamically use adapted strategies to increase the pace of the learners' skill acquisition and improve their learning experience. In this work, we present a novel approach for automatically adapting the learning strategy in real-time according to the learner's detected mental state. The main goal of the approach is to maintain the learner in a positive state during a lesson by adaptively selecting the best interaction strategy between either using problem solving or worked examples. Two mental indexes, namely, cognitive load and mental engagement were extracted from electroencephalogram (EEG) signals, and used to adapt the system's interaction. The cognitive load index was developped by training and validating a prediction model on various types of memory and logical tasks. The engagement index was directly computed from the EEG signal frequency bands. An experiment with 14 learners was performed in order to evaluate this approach. The obtained results showed that using the learner's mental state to adapt the system's interaction has a positive impact on the learning outcomes, the learning experience and the learners' reported emotional states.

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“…In their study, it was found that the user's performance improved when an EEG index is used as a criterion for switching between manual and automated piloting mode. This index is computed from three EEG frequency bands: α (8)(9)(10)(11)(12), β (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22) and θ (4-8 Hz):…”

Section: Emotion Detectionmentioning

confidence: 99%

“…Since its development by Pope and his colleagues, this engagement index has become a very important and popular technique for real time or offline tracking and analysis of individuals' engagement in several laboratory studies. In the educational sittings for example, this engagement index was used for monitoring learners' engagement and adapting learning activities according to their level of mental engagement by [44] and [11]. In robotics, this index was also used to leverage the interaction between a robot and a user by providing the robot real-time information about the user's engagement while the robot is speaking to him by [43].…”

Section: Emotion Detectionmentioning

confidence: 99%

Emotions and personality traits in argumentation: An empirical evaluation1

Villata

Cabrio

Jraidi

et al. 2017

AAC

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Abstract. 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.

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MENTOR: A Physiologically Controlled Tutoring System

Cited by 21 publications

References 21 publications

Cognitive Workload Assessment of Aircraft Pilots

Cognitive Workload Assessment of Aircraft Pilots

Enhancing the Learning Experience Using Real-Time Cognitive Evaluation

Emotions and personality traits in argumentation: An empirical evaluation1

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