EXCEL through IoT (Exploring Cognitive and Emotional Learning through IOT)

Cognitive Learning is a process that involves learner’s knowledge into consideration. It involves the use of human brain. These days understanding students’s emotional state of mind is one of the research area where student face problems in tackling academic tasks. It has been observed that emotions are a crucial part of students' psychosomatic life, and that they may strongly influence academic motivation, cognitive strategies of learning and achieving the desired results.So, our research is to augment student learning and teacher instruction by giving the real-time reaction of students' state of mind, so that teacher can engage students in the learning processes, help them to learn or use the brain in much and far better way to relate thing with the previous one while learning something new.

Cognitive and Affective Mechanisms of Immersive Virtual Reality Learning Environments

Students and educators value the potential use of immersive virtual reality (IVR) in the classroom to teach academic content as it may increase interest and motivation to learn, which in turn may increase learning outcomes. However, one criticism is that features of IVR, such as extraneous sounds, animations, and interactions, that are not relevant to the content of the lesson, as well as the affective arousal associated with the use of IVR, may be distracting to learning processes. To examine this distraction hypothesis, two experiments were conducted in which students viewed a biology (Experiment 1) or history (Experiment 2) lesson either in IVR or in a desktop lesson containing the same content, with or without practice questions. In both experiments, students who viewed the lesson on the desktop in a PowerPoint (Experiment 1) or an interactive video (Experiment 2) outperformed those who viewed the IVR lessons on transfer tests. The desktop lessons led to higher cognitive engagement based on EEG measures in both experiments, and less self-reported extraneous cognitive load in Experiment 1. Participants also reported more high-arousal positive emotions after the IVR lessons in both experiments, and experienced higher physiological arousal based on heart-rate measures after the IVR lessons compared to the desktop lessons in Experiment 2. The same pattern of results was found when adjunct practice questions were or were not included in the lessons. Across both experiments, mediation analyses suggest that the negative relationship between instructional media and learning outcomes can be explained by self-reported cognitive processing or emotional arousal, particularly for retention test performance. Overall, immersive environments may create high emotional arousal and cognitive distraction during learning, which leads to poorer learning outcomes than desktop environments

EEG-based measurement system for monitoring student engagement in learning 4.0

A wearable system for the personalized EEG-based detection of engagement in learning 4.0 is proposed. In particular, the effectiveness of the proposed solution is assessed by means of the classification accuracy in predicting engagement. The system can be used to make an automated teaching platform adaptable to the user, by managing eventual drops in the cognitive and emotional engagement. The effectiveness of the learning process mainly depends on the engagement level of the learner. In case of distraction, lack of interest or superficial participation, the teaching strategy could be personalized by an automatic modulation of contents and communication strategies. The system is validated by an experimental case study on twenty-one students. The experimental task was to learn how a specific human-machine interface works. Both the cognitive and motor skills of participants were involved. De facto standard stimuli, namely (1) cognitive task (Continuous Performance Test), (2) music background (Music Emotion Recognition-MER database), and (3) social feedback (Hermans and De Houwer database), were employed to guarantee a metrologically founded reference. In within-subject approach, the proposed signal processing pipeline (Filter bank, Common Spatial Pattern, and Support Vector Machine), reaches almost 77% average accuracy, in detecting both cognitive and emotional engagement

Differential Executive Functioning, Impulsivity, And Motivation In Adulthood As A Function Of Experienced Child Abuse

Introduction: While child abuse can only occur before the ages 18, there are long term neurological repercussions that can cause severe detriment to the abused individual. More specifically, normative neurological development during childhood is impeded upon, thus resulting in cognitive and emotional abnormalities in adulthood. Methods: The present study recruited 43 students from the University of North Dakota (Females = 23) with an age range of 18-23 years of age (M = 19.6 years, SD = 1.545). The participants were administered several prescreening measures, including a measure of physical child abuse, emotional child abuse, and sexual child abuse. Based on responses to these measures, participants were assigned to either a no child abuse group, a mild child abuse group, or a moderate-to-severe child abuse group. Next, three measures of executive functioning skill were administered while electroencephalographic data was collected. Results: There was a statistically significant main effect of child abuse group (F = 3.712, p = .034) for the probability of cognitive workload. Drug abuse, which served as a covariate, was also found to be significantly attenuated (F = 6.33, p = .016) during measures of attention. Conclusion: Individuals that have been abused as children must use significantly more mental effort to complete tasks as compared to their non-abused counterparts. Increased neurological effort could be used to explain poor decision making skills that is common within the population. Further analysis must be conducted on behavior data that collected for the present study. Likewise, new research should explore the relationship between drug use and abuse and neurological deficits in this population

Human Factors and Neurophysiological Metrics in Air Traffic Control: a Critical Review

International audienceThis article provides the reader a focused and organised review of the research progresses on neurophysiological indicators, also called “neurometrics”, to show how neurometrics could effectively address some of the most important Human Factors (HFs) needs in the Air Traffic Management (ATM) field. The state of the art on the most involved HFs and related cognitive processes (e.g. mental workload, cognitive training) is presented together with examples of possible applications in the current and future ATM scenarios, in order to better understand and highlight the available opportunities of such neuroscientific applications. Furthermore, the paper will discuss the potential enhancement that further research and development activities could bring to the efficiency and safety of the ATM service

Model Individualization for Real-Time Operator Functional State Assessment

Proper assessment of Operator Functional State (OFS) and appropriate workload modulation offer the potential to improve mission effectiveness and aviation safety in both overload and under-load conditions. Although a wide range of research has been devoted to building OFS assessment models, most of the models are based on group statistics and little or no research has been directed towards model individualization, i.e., tuning the group statistics based model for individual pilots. Moreover, little emphasis has been placed on monitoring whether the pilot is disengaged during low workload conditions. The primary focus of this research is to provide a real-time engagement assessment technique considering individual variations in an aviation environment. This technique is based on an advanced machine learning technique, called enhanced committee machine. We have investigated two different model individualization approaches: similarity-based and dynamic ensemble selection-based. The basic idea of the similarity-based technique is to find similar subjects from the training data pool and use their data together with the limited training data from the test subject to build an individualized OFS assessment model. The dynamic ensemble selection dynamically select data points in a validation dataset (with labels) that are adjacent to each test sample, and evaluate all the trained models using the identified data points. The best performing models will be selected and maximum voting can be applied to perform individualized assessment for the test sample. To evaluate the developed approaches, we have collected data from a high fidelity Boeing 737 simulator. The results show that the performance of the dynamic ensemble selection approach is comparable to that achieved from an individual model (assuming sufficient data is available from each individual)

Détection et amélioration de l'état cognitif de l'apprenant

Cette thèse vise à détecter et améliorer l’état cognitif de l’apprenant. Cet état est défini par la capacité d’acquérir de nouvelles connaissances et de les stocker dans la mémoire. Nous nous sommes essentiellement intéressés à améliorer le raisonnement des apprenants, et ceci dans trois environnements : environnement purement cognitif Logique, jeu sérieux LewiSpace et jeu sérieux intelligent Inertia. La détection de cet état se fait essentiellement par des mesures physiologiques (en particulier les électroencéphalogrammes) afin d’avoir une idée sur les interactions des apprenants et l’évolution de leurs états mentaux. L’amélioration des performances des apprenants et de leur raisonnement est une clé pour la réussite de l’apprentissage. Dans une première partie, nous présentons l’implémentation de l’environnement cognitif logique. Nous décrivons des statistiques faites sur cet environnement. Nous avons collecté durant une étude expérimentale les données sur l’engagement, la charge cognitive et la distraction. Ces trois mesures se sont montrées efficaces pour la classification et la prédiction des performances des apprenants. Dans une deuxième partie, nous décrivons le jeu Lewispace pour l’apprentissage des diagrammes de Lewis. Nous avons mené une étude expérimentale et collecté les données des électroencéphalogrammes, des émotions et des traceurs de regard. Nous avons montré qu’il est possible de prédire le besoin d’aide dans cet environnement grâce à ces mesures physiologiques et des algorithmes d’apprentissage machine. Dans une troisième partie, nous clôturons la thèse en présentant des stratégies d’aide intégrées dans un jeu virtuel Inertia (jeu de physique). Cette dernière s’adapte selon deux mesures extraites des électroencéphalogrammes (l’engagement et la frustration). Nous avons montré que ce jeu permet d’augmenter le taux de réussite dans ses missions, la performance globale et par conséquent améliorer l’état cognitif de l’apprenant.This thesis aims at detecting and enhancing the cognitive state of a learner. This state is measured by the ability to acquire new knowledge and store it in memory. Focusing on three types of environments to enhance reasoning: environment Logic, serious game LewiSpace and intelligent serious game Inertia. Physiological measures (in particular the electroencephalograms) have been taken in order to measure learners’ engagement and mental states. Improving learners’ reasoning is key for successful learning process. In a first part, we present the implementation of logic environment. We present statistics on this environment, with data collected during an experimental study. Three types of data: engagement, workload and distraction, these measures were effective and can predict and classify learner’s performance. In a second part, we describe the LewiSpace game, aimed at teaching Lewis diagrams. We conducted an experimental study and collected data from electroencephalograms, emotions and eye-tracking software. Combined with machine learning algorithms, it is possible to anticipate a learner’s need for help using these data. In a third part, we finish by presenting some assistance strategies in a virtual reality game called Inertia (to teach Physics). The latter adapts according to two measures extracted from electroencephalograms (frustration and engagement). Based on our study, we were able to enhance the learner’s success rate on game missions, by improving its cognitive state