Implicit Strategies for Intelligent Tutoring Systems

Jraidi, Imène; Chalfoun, Pierre; Frasson, Claude

doi:10.1007/978-3-642-30950-2_1

Cited by 17 publications

(10 citation statements)

References 19 publications

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“…uncertainty modeling, emotional state classification) [10] [11], performance assessment (e.g. outcome prediction, learners' classification) [12] [13] [14] [15] [16] and users' mental activity assessment [17] [18] [19].…”

Section: Introductionmentioning

confidence: 99%

Tracking Students’ Mental Engagement Using EEG Signals during an Interaction with a Virtual Learning Environment

Khedher¹,

Jraidi²,

Frasson³

2019

JILSA

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Monitoring students' level of engagement during learning activities is an important challenge in the development of tutoring interventions. In this paper, we explore the feasibility of using electroencephalographic signals (EEG) as a tool to monitor the mental engagement index of novice medicine students during a reasoning process. More precisely, the objectives were first, to track students' mental engagement evolution in order to investigate whether there were particular sections within the learning environment that aroused the highest engagement level among the students, and, if so, did these sections have an impact on learners' performance. Experimental analyses showed the same trends in the different resolution phases as well as across the different regions of the environments. However, we noticed a higher engagement index during the treatment identification phase since it aroused more mental effort. Moreover statistically significant effects were found between mental engagement and students' performance.

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Section: Introductionmentioning

confidence: 99%

Tracking Students’ Mental Engagement Using EEG Signals during an Interaction with a Virtual Learning Environment

Khedher¹,

Jraidi²,

Frasson³

2019

JILSA

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“…For instance, in case of a favorable interaction (i.e., a high probability of flow), the tutoring system would let the learner free to go through the materials without interruption. Implicit interventions such as affective or cognitive priming can be made to enhance the interaction experience without interrupting the learner's immersion (see [80] for more details). If the learner is about to get stuck (i.e., a high probability for stuck), an explicit intervention would be initiated, while taking into account the learner's emotional changes.…”

Section: Learners' Interaction Experiencementioning

confidence: 99%

A Hierarchical Probabilistic Framework for Recognizing Learners’ Interaction Experience Trends and Emotions

Jraidi

Chaouachi

Frasson

2014

Advances in Human-Computer Interaction

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We seek to model the users’ experience within an interactive learning environment. More precisely, we are interested in assessing the relationship between learners’ emotional reactions and three trends in the interaction experience, namely,flow: the optimal interaction (a perfect immersion within the task),stuck: the nonoptimal interaction (a difficulty to maintain focused attention), andoff-task: the noninteraction (a dropout from the task). We propose a hierarchical probabilistic framework using a dynamic Bayesian network to model this relationship and to simultaneously recognize the probability of experiencing each trend as well as the emotional responses occurring subsequently. The framework combines three modalitydiagnostic variablesthat sense the learner’s experience including physiology, behavior, and performance,predictive variablesthat represent the current context and the learner’s profile, and adynamic structurethat tracks the evolution of the learner’s experience. An experimental study, with a specifically designed protocol for eliciting the targeted experiences, was conducted to validate our approach. Results revealed that multiple concurrent emotions can be associated with the experiences of flow, stuck, and off-task and that the same trend can be expressed differently from one individual to another. The evaluation of the framework showed promising results in predicting learners’ experience trends and emotional responses.

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“…Several researches used textbased emotion to predict and classify the emotion types, such as [1] [2] [3] and [4]. Jraidi et al [5] show the impact of using emotion in intelligent system and show how these emotions oriented toward developing emotionally sensitive tutors. [6] and Appraisal Method [7] to classify a text to a suitable emotion.…”

Section: Introductionmentioning

confidence: 99%

Text-Based Intelligent Learning Emotion System

Razek¹,

Frasson²

2017

JILSA

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Nowadays, millions of users use many social media systems every day. These services produce massive messages, which play a vital role in the social networking paradigm. As we see, an intelligent learning emotion system is desperately needed for detecting emotion among these messages. This system could be suitable in understanding users' feelings towards particular discussion. This paper proposes a text-based emotion recognition approach that uses personal text data to recognize user's current emotion. The proposed approach applies Dominant Meaning Technique to recognize user's emotion. The paper reports promising experiential results on the tested dataset based on the proposed algorithm.

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Implicit Strategies for Intelligent Tutoring Systems

Cited by 17 publications

References 19 publications

Tracking Students’ Mental Engagement Using EEG Signals during an Interaction with a Virtual Learning Environment

Tracking Students’ Mental Engagement Using EEG Signals during an Interaction with a Virtual Learning Environment

A Hierarchical Probabilistic Framework for Recognizing Learners’ Interaction Experience Trends and Emotions

Text-Based Intelligent Learning Emotion System

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