Apex, un système d'aide à la préparation d'examens

National audienceLes avancées de l’intelligence artificielle parviennent parfois jusqu’au grand public, pour peu qu’une « mutation » visible, avec une dimension sociale, puisse être mise en avant. Par exemple, la disparition d’un corps de métier — ici les enseignants — au profit de machines. C’est ce qui s’est passé en 1998 avec la conception de l’Intelligent Essay Assessor (IEA) de Foltz, Laham et Landauer [FOL 99], logiciel qui permet de noter des dissertations par le biais d’un appariement sémantique de ces dernières avec des copies modèles sélectionnées au préalable par l’enseignant. Un débat médiatique s’est organisé, tout d’abord américain [HOL 98], [KAH 98], [PER 98], puis français [ZIL 98], sur le thème désormais classique : « Les ordinateurs mettront-ils un jour les enseignants à la porte ? » [LAM 98].Tout en essayant de ne pas verser de nouvelle polémique dans ce débat, nous allons ici présenter les fonctionnalités d’IEA ainsi que celles du logiciel que nous élaborons, APex, qui utilise le même moteur que le premier mais procède de manière sensiblement différente. Commençons par décrire brièvement le moteur commun de ces deux logiciels : LSA pour Latent Semantic Analysis (analyse sémantique latente)

Conception of an E-learning scheme at the University of Algarve

With the proliferation of the Internet use, a growth of e-learning courses has been verified. We arrived to the moment where it is not enough for Universities to have standard courses to offer to the students, because there is an increasing population which tends to choose his formation according to their objectives, styles, needs and learning preferences (the student profile). This way, the universities are faced with a new challenge, which is to offer, together with the standard courses, modules specially tailored to the user desires, based on the identification of the customers needs. In this paper, a model for the distance formation through Internet is discussed, that is being developed in the University of Algarve, which makes possible each individual to learn in agreement with his/her profile

Adapting Collaborative Chat for Massive Open Online Courses: Lessons Learned

Abstract. In this paper we explore how to import intelligent support for group learning that has been demonstrated as effective in classroom instruction into a Massive Open Online Course (MOOC) context. The Bazaar agent architecture paired with an innovative Lobby tool to enable coordination for synchronous reflection exercises provides a technical foundation for our work. We describe lessons learned, directions for future work, and offer pointers to resources for other researchers interested in computer supported collaborative learning in MOOCs

Effective Tutoring with Empathic Embodied Conversational Agents

This thesis examines the prospect of using empathy in an Embodied Tutoring System (ETS) that guides students through an online quiz (by providing feedback on student answers and responding to self-reported student emotion). The ETS seeks to imitate human behaviours successfully used in one-to-one human tutorial interactions. The main hypothesis is that the interaction with an empathic ETS results in greater learning gains than a neutral ETS, primarily by encouraging positive and reducing negative student emotions using empathic feedback. In a preparatory study we investigated different strategies for expressing emotion by the ETS. We established that a multimodal strategy achieves the best results regarding how accurately human participants can recognise the emotions. This approach was used in developing the feedback strategy for our empathic ETS. The preparatory study was followed by two studies in which we compared a neutral with an empathic ETS. The ETS in the second of these studies was developed using results from the first of these studies. In both studies, we found no statistically significant difference in learning gains between the neutral and empathic ETS. However, we did discover a number of interactions between the ETS system, learning gains and, in particular 1) student scores on an empathic tendency test and 2) student ability. We also analysed the subjective responses and the relation between self-reported emotions during the quiz and student learning gains. Based on our studies in a formal class room setting, we assess the prospects of using empathic agents in a classroom setting and describe a number of requirements for their effective use

Applying latent semantic analysis to computer assisted assessment in the Computer Science domain: a framework, a tool, and an evaluation

This dissertation argues that automated assessment systems can be useful for both students and educators provided that the results correspond well with human markers. Thus, evaluating such a system is crucial. I present an evaluation framework and show how and why it can be useful for both producers and consumers of automated assessment systems. The framework is a refinement of a research taxonomy that came out of the effort to analyse the literature review of systems based on Latent Semantic Analysis (LSA), a statistical natural language processing technique that has been used for automated assessment of essays. The evaluation framework can help developers publish their results in a format that is comprehensive, relatively compact, and useful to other researchers. The thesis claims that, in order to see a complete picture of an automated assessment system, certain pieces must be emphasised. It presents the framework as a jigsaw puzzle whose pieces join together to form the whole picture. The dissertation uses the framework to compare the accuracy of human markers and EMMA, the LSA-based assessment system I wrote as part of this dissertation. EMMA marks short, free text answers in the domain of computer science. I conducted a study of five human markers and then used the results as a benchmark against which to evaluate EMMA. An integral part of the evaluation was the success metric. The standard inter-rater reliability statistic was not useful; I located a new statistic and applied it to the domain of computer assisted assessment for the first time, as far as I know. Although EMMA exceeds human markers on a few questions, overall it does not achieve the same level of agreement with humans as humans do with each other. The last chapter maps out a plan for further research to improve EMMA

Modeling Learner Mood In Realtime Through Biosensors For Intelligent Tutoring Improvements

Computer-based instructors, just like their human counterparts, should monitor the emotional and cognitive states of their students in order to adapt instructional technique. Doing so requires a model of student state to be available at run time, but this has historically been difficult. Because people are different, generalized models have not been able to be validated. As a person’s cognitive and affective state vary over time of day and seasonally, individualized models have had differing difficulties. The simultaneous creation and execution of an individualized model, in real time, represents the last option for modeling such cognitive and affective states. This dissertation presents and evaluates four differing techniques for the creation of cognitive and affective models that are created on-line and in real time for each individual user as alternatives to generalized models. Each of these techniques involves making predictions and modifications to the model in real time, addressing the real time datastream problems of infinite length, detection of new concepts, and responding to how concepts change over time. Additionally, with the knowledge that a user is physically present, this work investigates the contribution that the occasional direct user query can add to the overall quality of such models. The research described in this dissertation finds that the creation of a reasonable quality affective model is possible with an infinitesimal amount of time and without “ground truth” knowledge of the user, which is shown across three different emotional states. Creation of a cognitive model in the same fashion, however, was not possible via direct AI modeling, even with all of the “ground truth” information available, which is shown across four different cognitive states

La técnica del Ánalisis de la Semántica Latente (LSA/LSI) como modelo informático de la comprensión del texto y el discurso: una aproximación distribuida al análisis semántico

Programa de doctorado de calidad: comprensión del texto y del discursoTesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Psicología. Departamento de Psicología Social y de Metodología. Fecha de lectura: 10 de diciembre de 2010El formato de esta tesis consiste en una colección de cuatro manuscritos individuales, que han sido recientemente aceptados o enviados para publicación a revistas internacionales de psicología experimental, lingüística o tecnología

The Foundations and Architecture of Autotutor

. The Tutoring Research Group at the University of Memphis is developing an intelligent tutoring system which takes advantages of recent technological advances in the areas of semantic processing of natural language, world knowledge representation, multimedia interfaces, and fuzzy descriptions. The tutoring interaction is based on in-depth studies of human tutors, both skilled and unskilled. Latent semantic analysis will be used to semantically process and provide a representation for the student's contributions. Fuzzy production rules select appropriate topics and tutor dialogue moves from a rich curriculum script. The production rules will implement a variety of different tutoring styles, from a basic untrained tutor to one which uses sophisticated pedagogical strategies. The tutor will be evaluated on the naturalness of its interaction, with Turing-style tests, by comparing different tutoring styles, and by judging learning outcomes. 1 Introduction At the University of Memphis, our..

The foundations and architecture of AutoTutor

is developing an intelligent tutoring system which takes advantages of recent technological advances in the areas of semantic processing of natural language, world knowledge representation, multimedia interfaces, and fuzzy descriptions. The tutoring interaction is based on in-depth studies of human tutors, both skilled and unskilled. Latent semantic analysis will be used to semantically process and provide a representation for the student's contributions. Fuzzy production rules select appropriate topics and tutor dialogue moves from a rich curriculum script. The production rules will implement avariety of di erent tutoring styles, from a basic untrained tutor to one which uses sophisticated pedagogical strategies. The tutor will be evaluated on the naturalness of its interaction, with Turing-style tests, by comparing di erent tutoring styles, and by judging learning outcomes.

The foundations and architecture of autotutor

The Tutoring Research Group at the University of Memphis is developing an intelligent tutoring system which takes advantages of recent technological advances in the areas of semantic processing of natural language, world knowledge representation, multimedia interfaces, and fuzzy descriptions. The tutoring interaction is based on in-depth studies of human tutors, both skilled and unskilled. Latent semantic analysis will be used to semantically process and provide a representation for the student’s contributions. Fuzzy production rules select appropriate topics and tutor dialogue moves from a rich curriculum script. The production rules will implement a variety of different tutoring styles, from a basic untrained tutor to one which uses sophisticated pedagogical strategies. The tutor will be evaluated on the naturalness of its interaction, with Turing-style tests, by comparing different tutoring styles, and by judging learning outcomes