Ramla Ghali

Emotional Strategies for Vocabulary Learning

Emotions have a crucial role in our social life in general and in our professional life and education in particular. Besides, they proved essential in information processing systems, more specifically in Intelligent Tutoring Systems (ITS). In this article, we present new strategies integrated into an ITS’ tutor module and intended to teach children the vocabulary of a foreign language, namely English. These strategies can generate emotions for students making words memorization easier. We describe the experimentation carried out to validate these emotional strategies as well as the results obtained.

Using Electrophysiological Features in Cognitive Tasks: An Empirical Study

Learners’ performances in intelligent tutoring systems or e-learning environments depend on various factors such as the nature of the task presented, their cognitive and affective abilities, etc. In this paper, we focus on studying in detail the variation of these different factors and more specifically the electroencephalogram (EEG metrics) and how they differ according to a category and a type of cognitive tasks. We also studied the possibility of predicting a learner’s performance using feature selection and multiple regressions. Primarily, results shows that learners’ scores could be predicted using in descending order the difficulty level of the task, the type of a task, the duration of a task and the EEG workload metric by building a multiple regression model that fit our data.

LewiSpace: An Educational Puzzle Game Combined with a Multimodal Machine Learning Environment

In this paper, we will present an educational game that we developed in order to teach a chemistry lesson, namely drawing a Lewis diagram. We also conducted an experiment to gather data about the cognitive and emotional states of the learners as well as their behaviour throughout our game by using three types of sensors (electroencephalography, eye tracking, and facial expression recognition with an optical camera). Primary results show that a machine learning model (logistic regression) can predict with some success whether the learner will give a correct or a wrong answer to a task presented in the game, and paves the way for an adaptive version of the game. This latter will challenge or assist learners based on some features extracted from our data in order to provide real-time adaptation specific to the user.

Motivational Strategies to Support Engagement of Learners in Serious Games

The use of Video Games as learning tool is becoming increasingly widespread. Indeed, these games are well known as educational games or serious games. They mainly aim at providing to the learner an interactive, motivational and educational environment at the same time. In order to better study the necessary characteristics for the development of an effective serious game (both motivational and educational), we evaluated the physiological responses of participants during their interaction with our serious game, called HeapMotiv. We essentially measured a physiological index of engagement through an EEG wifi headset and studied the evolution of this index with the different missions and motivational strategies of HeapMotiv. Focusing on the gaming aspects, the analysis of this engagement index behavior showed the significant impact of motivational strategies on skills acquisition and motivational experience.

Persuasive Argumentation and Emotions: An Empirical Evaluation with Users

In everyday life discussion, people try to persuade each other about the goodness of their viewpoint regarding a certain topic. This persuasion process is usually affected by several elements, like the ability of the speaker in formulating logical arguments, her confidence with respect to the discussed topic, and the emotional solicitation that certain arguments may cause in the audience. In this study, we compare the effect of using one of the three well-known persuasion strategies (Logos, Ethos and Pathos) in the argumentation process. These strategies are used by a moderator who influences the participants during the debates. We study which persuasion strategy is the most effective, and how they vary according to two mental metrics extracted from electroencephalograms: Engagement and workload. Results show that the right hemisphere has the highest engagement when Logos arguments are proposed to participants with Neutral opinion during the debate. We show also that the Logos strategy solicits the highest mental Workload, and the Pathos strategy is the most effective to use in argumentation and to convince the participants.

An Exploratory Study of Learners’ Brain States

In Intelligent Tutoring Systems, continuous analysis of learner’s brain states is essential. Several studies have proposed different methods to evaluate learner’s mental states in cognitive tasks. However, these studies do not take into account the nature of the cognitive task. In this paper, we have developed various categories of brain games in order to study the variation of some specific brain states (engagement, workload and distraction) depending on the type and difficulty of the game. The preliminary results showed a close relationship between the category of game, the workload mental state and learner’s performance.