Alicia Heraz

Using machine learning to predict learner emotional state from brainwaves

Intelligent Tutoring Systems (ITS) learner model has progressively evolved. Initially composed of a cognitive module it was extended with a psychological module and an emotional module. The learner model still remains non-exhaustive. Methods of data collection on the cognitive and emotional state of the learner often lack precision and objectivity. In this paper we introduce an emomental agent. It interacts with an ITS to communicate the emotional state of the learner based upon his mental state. The mental state is obtained from the learners brainwaves. The agent learns to predict the learners emotions by using machine learning techniques.

Decision Tree for Tracking Learner's Emotional State Predicted from His Electrical Brain Activity

This paper proposes the use of machine learning techniques to build an efficient learners emotional transition diagram transition. For information Extraction tasks, we led an experimentation in which we exposed a group of 17 learners to a series of pictures from the International Affective Picture System (IAPS). Decision tree classifier has demonstrated the best ability to learn model structure from data collected. Among the emotions involved in learning and according to the picture from IAPS and the current emotional state, we drew up the transition diagram. Our model aims to improve the task of predicting the emotional state in an Intelligent Tutoring System and achieve a prediction accuracy of 63.11%. These results suggest that the implementation of the decision tree algorithm in the intelligent tutoring system we are developing improves the ability for an ITS to track the learners emotional states.

Theoretical model for interplay between some learning situations and brainwaves

There is interplay between brainwaves and learning To describe and understand part of this complex interaction, this paper proffers a new learner model called the LBD Model (or Learning and Brainwaves Dominances Model) Twenty-three participants were recruited to validate this Model Results show distinct instances of the LBD Model regarding three situations of learning: positive learning, unconscious learning and unlearning.

How Do Emotions Induce Dominant Learners’ Mental States Predicted from Their Brainwaves?

In this paper we discuss how learner’s electrical brain activity can be influenced by emotional stimuli. We conducted an experiment in which we exposed 17 learners to a set of pictures from the International Affective Picture System (IAPS) while their electrical brain activity was recorded. We got 33.106 recordings. In an exploratory study we examined the influence of 24 picture categories from the IAPS on the amplitude variations of the 4 brainwaves frequency bands: (, (, ( and (. We used machine learning techniques to track the amplitudes in order to predict the dominant frequency band which inform about the learner mental and emotional states. Correlation and regression analyses show a significant impact of the emotional stimuli on the amplitudes of the brainwave frequency bands. Standard classification techniques were used to assess the reliability of the automatic prediction of the dominant frequency band. The reached accuracy was 90%.