Apprentissage automatique pour l'assistance au suivi d'étudiants en ligne : approches classique et bio-inspirée

Cette thèse a pris la forme d’un partenariat entre l’équipe VORTEX du laboratoire de recherche en informatique IRIT et l’entreprise Andil, spécialisée dans l'informatique pour l'e-learning. Ce partenariat est conclu autour d’une thèse CIFRE, dispositif soutenu par l’État via l’ANRT. La doctorante, Angela Bovo, a travaillé au sein de l'Université Toulouse 1 Capitole. Un partenariat a également été noué avec l'institut de formation Juriscampus, qui nous a fourni des données issues de formations réelles pour nos expérimentations. Notre objectif principal avec ce projet était d'améliorer les possibilités de suivi des étudiants en cours de formation en ligne pour éviter leur décrochage ou leur échec. Nous avons proposé des possibilités de suivi par apprentissage automatique classique en utilisant comme données les traces d'activité des élèves. Nous avons également proposé, à partir de nos données, des indicateurs de comportement des apprenants. Avec Andil, nous avons conçu et réalisé une application web du nom de GIGA, déjà commercialisée et appréciée par les responsables de formation, qui implémente ces propositions et qui a servi de base à de premières expériences de partitionnement de données qui semblent permettre d'identifier les étudiants en difficulté ou en voie d'abandon. Ce projet a également été lancé avec l'objectif d'étudier les possibilités de l'algorithme d'apprentissage automatique inspiré du cerveau humain Hierarchical Temporal Memory (HTM), dans sa version Cortical Learning Algorithm (CLA), dont les hypothèses fondatrices sont bien adaptées à notre problème. Nous avons proposé des façons d'adapter HTM-CLA à des fonctionnalités d'apprentissage automatique classique (partitionnement, classification, régression, prédiction), afin de comparer ses résultats à ceux fournis par les autres algorithmes plus classiques ; mais aussi de l'utiliser comme base d'un moteur de génération de comportement, qui pourrait être utilisé pour créer un tuteur virtuel intelligent chargé de conseiller les apprenants en temps réel. Les implémentations ne sont toutefois pas encore parvenues à produire des résultats probants.This Ph.D. took the shape of a partnership between the VORTEX team in the computer science research laboratory IRIT and the company Andil, which specializes in software for e-learning. This partnership was concluded around a CIFRE Ph.D. This plan is subsidized by the French state through the ANRT. The Ph.D. student, Angela Bovo, worked in Université Toulouse 1 Capitole. Another partnership was built with the training institute Juriscampus, which gave us access to data from real trainings for our experiments. Our main goal for this project was to improve the possibilities for monitoring students in an e-learning training to keep them from falling behind or giving up. We proposed ways to do such monitoring with classical machine learning methods, with the logs from students' activity as data. We also proposed, using the same data, indicators of students' behaviour. With Andil, we designed and produced a web application called GIGA, already marketed and sold, and well appreciated by training managers, which implements our proposals and served as a basis for first clustering experiments which seem to identify well students who are failing or about to give up. Another goal of this project was to study the capacities of the human brain inspired machine learning algorithm Hierarchical Temporal Memory (HTM), in its Cortical Learning Algorithm (CLA) version, because its base hypotheses are well adapted to our problem. We proposed ways to adapt HTM-CLA to classical machine learning functionalities (clustering, classification, regression, prediction), in order to compare its results to those of more classical algorithms; but also to use it as a basis for a behaviour generation engine, which could be used to create an intelligent tutoring system tasked with advising students in real time. However, our implementations did not get to the point of conclusive results

Apprentissage automatique pour l'assistance au suivi d'étudiants en ligne : approches classique et bio-inspirée

Cette thèse a pris la forme d’un partenariat entre l’équipe VORTEX du laboratoire de recherche en informatique IRIT et l’entreprise Andil, spécialisée dans l'informatique pour l'e-learning. Ce partenariat est conclu autour d’une thèse CIFRE, dispositif soutenu par l’État via l’ANRT. La doctorante, Angela Bovo, a travaillé au sein de l'Université Toulouse 1 Capitole. Un partenariat a également été noué avec l'institut de formation Juriscampus, qui nous a fourni des données issues de formations réelles pour nos expérimentations. Notre objectif principal avec ce projet était d'améliorer les possibilités de suivi des étudiants en cours de formation en ligne pour éviter leur décrochage ou leur échec. Nous avons proposé des possibilités de suivi par apprentissage automatique classique en utilisant comme données les traces d'activité des élèves. Nous avons également proposé, à partir de nos données, des indicateurs de comportement des apprenants. Avec Andil, nous avons conçu et réalisé une application web du nom de GIGA, déjà commercialisée et appréciée par les responsables de formation, qui implémente ces propositions et qui a servi de base à de premières expériences de partitionnement de données qui semblent permettre d'identifier les étudiants en difficulté ou en voie d'abandon. Ce projet a également été lancé avec l'objectif d'étudier les possibilités de l'algorithme d'apprentissage automatique inspiré du cerveau humain Hierarchical Temporal Memory (HTM), dans sa version Cortical Learning Algorithm (CLA), dont les hypothèses fondatrices sont bien adaptées à notre problème. Nous avons proposé des façons d'adapter HTM-CLA à des fonctionnalités d'apprentissage automatique classique (partitionnement, classification, régression, prédiction), afin de comparer ses résultats à ceux fournis par les autres algorithmes plus classiques ; mais aussi de l'utiliser comme base d'un moteur de génération de comportement, qui pourrait être utilisé pour créer un tuteur virtuel intelligent chargé de conseiller les apprenants en temps réel. Les implémentations ne sont toutefois pas encore parvenues à produire des résultats probants.This Ph.D. took the shape of a partnership between the VORTEX team in the computer science research laboratory IRIT and the company Andil, which specializes in software for e-learning. This partnership was concluded around a CIFRE Ph.D. This plan is subsidized by the French state through the ANRT. The Ph.D. student, Angela Bovo, worked in Université Toulouse 1 Capitole. Another partnership was built with the training institute Juriscampus, which gave us access to data from real trainings for our experiments. Our main goal for this project was to improve the possibilities for monitoring students in an e-learning training to keep them from falling behind or giving up. We proposed ways to do such monitoring with classical machine learning methods, with the logs from students' activity as data. We also proposed, using the same data, indicators of students' behaviour. With Andil, we designed and produced a web application called GIGA, already marketed and sold, and well appreciated by training managers, which implements our proposals and served as a basis for first clustering experiments which seem to identify well students who are failing or about to give up. Another goal of this project was to study the capacities of the human brain inspired machine learning algorithm Hierarchical Temporal Memory (HTM), in its Cortical Learning Algorithm (CLA) version, because its base hypotheses are well adapted to our problem. We proposed ways to adapt HTM-CLA to classical machine learning functionalities (clustering, classification, regression, prediction), in order to compare its results to those of more classical algorithms; but also to use it as a basis for a behaviour generation engine, which could be used to create an intelligent tutoring system tasked with advising students in real time. However, our implementations did not get to the point of conclusive results

L'apprentissage automatique comme base du suivi d'élèves et de l'amélioration de formations

National audienceCet article vise à présenter un projet de recherche dont le but est d'utiliser des méthodes d'intelligence artificielle et de fouille de données pour l'e-learning. Nous proposons des solutions techniques au problème du suivi des élèves en formation et de l'amélioration des formations proposées. Notre solution prend la forme d'une application qui centralisera des données issues de LMS et permettra de les examiner et de les analyser en utilisant des méthodes de l'intelligence artificielle. Cette application pourra dans un deuxième temps servir de base à la création d'un tuteur virtuel intelligent. Nous détaillons nos propositions concernant les méthodes à employer, l'architecture de l'application et les éléments choisis pour servir d'indicateurs et d'attributs d'apprentissage automatique, et analysons les résultats préliminaires d'un partitionnement de données

Clustering Moodle data as a tool for profiling students

International audienceThis paper describes the first step of a research project with the aim of predicting students' performance during an online curriculum on a LMS and keeping them from falling behind. Our research project aims to use data mining, machine learning and artificial intelligence methods for monitoring students in e-learning trainings. This project takes the shape of a partnership between computer science / artificial intelligence researchers and an IT firm specialized in e-learning software. We wish to create a system that will gather and process all data related to a particular e-learning course. To make monitoring easier, we will provide reliable statistics, behaviour groups and predicted results as a basis for an intelligent virtual tutor using the mentioned methods. This system will be described in this article. In this step of the project, we are clustering students by mining Moodle log data. A first objective is to define relevant clustering features. We will describe and evaluate our proposal. A second objective is to determine if our students show different learning behaviours. We will experiment whether there is an overall ideal number of clusters and whether the clusters show mostly qualitative or quantitative differences. Experiments in clustering were carried out using real data obtained from various courses dispensed by a partner institute using a Moodle platform. We have compared several classic clustering algorithms on several group of students using our defined features and analysed the meaning of the clusters they produced

Analysis of students clustering results based on Moodle log data

International audienceThis paper describes a proposal of relevant clustering features and the results of experiments using them in the context of determining students' learning behaviors by mining Moodle log data. Our clustering experiments tried to show whether there is an overall ideal number of clusters and whether the clusters show mostly qualitative or quantitative differences. They were carried out using real data obtained from various courses dispensed by a partner institute using a Moodle platform. We have compared several classic clustering algorithms on several group of students using our defined features and analysed the meaning of the clusters they produced

Study of a pilot’s heart rate throughout his training

In aviation, the physiological monitoring of pilots can be useful to estimate the pilot’s mental and physical state and to help understand their performance. So far, most of this physiological monitoring takes place inside labs or flight simulators, as some physiological sensors are still rather unwieldy in a plane or uncomfortable for the pilot, or are still expensive, such as EEG or fNIRS. However, the humble cardiac monitoring now offers good quality yet cheap sensors for athletes, which makes it a good fit for the general aviation domain, where pilots have a limited budget. Although physical activity impacts heart rate, this activity is limited inside a cockpit and the metabolic heart rate could be subtracted for better results 1. We have had the opportunity of logging a student pilot’s physiological data throughout his initial PPL training (which is not over yet). During all his flights, he wore a Garmin D2 watch, which synchronizes the heart rate with the GPS position, speed and altitude

Pilot’s head tracking as a proxy for gaze tracking

Eye tracking is often used in aviation to study the pilot’s visual circuits, which are relevant to the first two stages of eye tracking integration 1. However, the sensors can be expensive or difficult to integrate in a cockpit and can be sensitive to light variations. We propose to start studying the movements of the pilot’s head as a proxy of their visual interest, because the sensors are already integrated in many virtual reality setups and are not so expensive. Of course, the data quality can’t be considered as good, because the pilot can move their eyes without moving their head. Therefore, the goal of this study is to qualify the interest of the pilot’s head tracking

O Verso e Reverso do Acesso à Água Como Direito Fundamental:: Vilas Produtivas Rurais - São José de Piranhas –PB

Historicamente a problemática dos recursos hídricos no Nordeste brasileiro é uma questão fundamental para superação dos obstáculos que compromete o processo de desenvolvimento regional, mais especificamente a região semiárida apresenta situações difíceis com a ocorrência de um fenômeno climático cíclico a seca, temperaturas elevadas, altas taxas de evaporação, concentração populacional alta que geram pressões excessivas sobre os recursos hídricos. Por outro lado, a falta de gestão hídrica foi sempre à tônica da manutenção de um cenário crítico a cada seca, sendo fundamental investimentos em obras hídricas, elaboração de políticas públicas e ações implementadas na perspectiva de resolver o problema da escassez da água e acesso enquanto direito fundamental. Diante das questões circunscritas estudo o artigo tem o objetivo de analisar o acesso democrático à água como direito humano fundamental da população nas vilas produtivas rurais - São José de Piranhas – PB. A metodologia de pesquisa aplicada em função dos objetivos é de caráter exploratório e descritivo com abordagem analítica dos dados quanti-qualitativa, no lócus social da pesquisa eixo norte do Projeto de Integração Rio São Francisco em São José de Piranhas – PB, população alvo foi os moradores das 4 Vilas Produtiva Rurais localizadas na cidade de São José de Piranhas com aplicação de entrevistas e questionários feitos um a um, de casa em casa nas Vilas. Como considerações assinala-se a satisfação dos moradores das Vilas e o acesso à água proveniente da transposição a qual os retirou do seu local de residência anterior

Pre-stimulus antero-posterior EEG connectivity predicts performance in a UAV monitoring task

Long monitoring tasks without regular actions, are becoming increasingly common from aircraft pilots to train conductors as these systems grow more automated. These task contexts are challenging for the human operator because they require inputs at irregular and highly interspaced moments even though these actions are often critical. It has been shown that such conditions lead to divided and distracted attentional states which in turn reduce the processing of external stimuli (e.g. alarms) and may lead to miss critical events. In this study we explored to which extent it is possible to predict an operator’s behavioural performance in a Unmanned Aerial Vehicle (UAV) monitoring task using electroencephalographic (EEG) activity. More specifically we investigated the relevance of large-scale EEG connectivity for performance prediction by correlating relative coherence with reaction times (RT). We show that long-range EEG relative coherence, i.e. between occipital and frontal electrodes, is significantly correlated with RT and that different frequency bands exhibit opposite effects. More specifically we observed that coherence between occipital and frontal electrodes was: negatively correlated with RT at 6Hz (theta band), more coherence leading to better performance, and positively correlated with RT at 8Hz (lower alpha band), more coherence leading to worse performance. Our results suggest that EEG connectivity measures could be useful in predicting an operator’s attentional state and her/his performances in ecological settings. Hence these features could potentially be used in a neuro-adaptive interface to improve operator-system interaction and safety in critical systems

A Loewner-based Approach for the Approximation of Engagement-related Neurophysiological Features

Currently, in order to increase both safety and performance of human-machine systems, researchers from various domains gather together to work towards the use of operators' mental state estimation in the systems control-loop. Mental state estimation is performed using neurophysiological data recorded, for instance, using electroencephalography (EEG). Features such as power spectral densities in specific frequency bands are extracted from these data and used as indices or metrics. Another interesting approach could be to identify the dynamic model of such features. Hence, this article discusses the potential use of tools derived from the linear algebra and control communities to perform an approximation of the neurophysiological features model that could be explored to monitor the engagement of an operator. The method provides a smooth interpolation of all the data points allowing to extract frequential features that reveal fluctuations in engagement with growing time-on-task