Un modelo propuesto a partir de la inteligencia artificial y la didáctica. El ejemplo de Cabri-Euclide

En el presente artículo expondremos un modelo inspirado en la teoría Semi-empírica y en la teoría de situaciones. La teoría semi-empírica ha sido propuesta dentro del área de la inteligencia artificial y la teoría de situaciones pertenece al área de la didáctica. En la primera parte presentaremos estas dos teorías, para luego introducir nuestro modelo. Finalmente, ilustraremos este modelo con la presentación de un programa de aprendizaje de la prueba en geometría: Cabri- Euclide

Contribution des traces de nature différente à la sensibilité de la modélisation des connaissances en situation d'apprentissage

National audienceDans le cadre du projet TELEOS, qui vise à développer une plateforme d'apprentissage en chirurgie orthopédique via l'usage d'un simulateur, nous cherchons à respecter ces deux niveaux de diagnostic par une démarche centrée sur les traces. L'architecture logicielle de notre plateforme distingue les traces comportementales des traces dont le résultat est une inférence vis-à-vis d'un modèle épistémique. Les traces comportementales, issues directement du simulateur, sont de plusieurs natures. Ce sont les évènements produits lors d'actions sur l'interface du simulateur ou les traces gestuelles produites lors de la manipulation du bras à retour d'effort (simulant un trocart). Ces traces, de bas niveau, sont utilisées pour le calcul du diagnostic comportemental afin d'informer sur l'état du monde, que nous appelons des variables de situation. Ainsi, par exemple, la position du trocart par rapport à des parties anatomiques d'intérêt (pédicule, etc.) sont des variables de situation. Ces variables de situation sont utilisées dans le diagnostic épistémique, qui est représenté par un réseau bayesien, lequel permet d'inférer sur l'état de connaissance

Contribution des traces de nature différente à la sensibilité de la modélisation des connaissances en situation d'apprentissage

National audienceDans le cadre du projet TELEOS, qui vise à développer une plateforme d'apprentissage en chirurgie orthopédique via l'usage d'un simulateur, nous cherchons à respecter ces deux niveaux de diagnostic par une démarche centrée sur les traces. L'architecture logicielle de notre plateforme distingue les traces comportementales des traces dont le résultat est une inférence vis-à-vis d'un modèle épistémique. Les traces comportementales, issues directement du simulateur, sont de plusieurs natures. Ce sont les évènements produits lors d'actions sur l'interface du simulateur ou les traces gestuelles produites lors de la manipulation du bras à retour d'effort (simulant un trocart). Ces traces, de bas niveau, sont utilisées pour le calcul du diagnostic comportemental afin d'informer sur l'état du monde, que nous appelons des variables de situation. Ainsi, par exemple, la position du trocart par rapport à des parties anatomiques d'intérêt (pédicule, etc.) sont des variables de situation. Ces variables de situation sont utilisées dans le diagnostic épistémique, qui est représenté par un réseau bayesien, lequel permet d'inférer sur l'état de connaissance

DOP8_Qpp: Model to pre-process educational data

This paper addresses problem of reuse of data to help data anlysis and enhace data quality. The article describes a process to take over data that come from educational context. This process to reuse TEL data contains: major tasks, identified data properties and a set of quality criteria to reach these data properties. The objective of the process is to evaluate if data are reusable to serve learning analytics. This process is integrated in an existing data life cycle DOP8. The model was elaborated from several works with set of data since 2012 and from interviews with data-scientists. Also, we have administrated an on-line survey with data-scientists to evaluate feasibility of our proposal

DOP8_Qpp: Model to pre-process educational data

This paper addresses problem of reuse of data to help data anlysis and enhace data quality. The article describes a process to take over data that come from educational context. This process to reuse TEL data contains: major tasks, identified data properties and a set of quality criteria to reach these data properties. The objective of the process is to evaluate if data are reusable to serve learning analytics. This process is integrated in an existing data life cycle DOP8. The model was elaborated from several works with set of data since 2012 and from interviews with data-scientists. Also, we have administrated an on-line survey with data-scientists to evaluate feasibility of our proposal

“Keep Your Eyes on ’em all!”: A Mobile Eye-Tracking Analysis of Teachers’ Sensitivity to Students

International audienceThis study aims at investigating which cues teachers detect and process from their students during instruction. This information capturing process depends on teachers' sensitivity, or awareness, to students' needs, which has been recognized as crucial for classroom management. We recorded the gaze behaviors of two pre-service teachers and two experienced teachers during a whole math lesson in primary classrooms. Thanks to a simple Learning Analyt-ics interface, the data analysis reports, firstly, which were the most often tracked students, in relation with their classroom behavior and performance; secondly, which relationships exist between teachers' attentional frequency distribution and lability, and the overall classroom climate they promote, measured by the Classroom Assessment Scoring System. Results show that participants' gaze patterns are mainly related to their experience. Learning Analytics use cases are eventually presented, enabling researchers or teacher trainers to further explore the eye-tracking data

From Student Questions to Student Profiles in a Blended Learning Environment

International audienceThe analysis of student questions can be used to improve the learning experience for both students and teachers. We investigated questions (N = 6457) asked before the class by first-year medicine/pharmacy students on an online platform, used by professors to prepare for Q&A sessions. Our long-term objectives are to help professors in categorizing those questions, and to provide students with feedback on the quality of their questions. To do so, we developed a coding scheme and then used it for automatic annotation of the whole corpus. We identified student characteristics from the typology of questions they asked using the k-means algorithm over four courses. Students were clustered based on question dimensions only. Then, we characterized the clusters by attributes not used for clustering, such as student grade, attendance, and number and popularity of questions asked. Two similar clusters always appeared (lower than average students with popular questions, and higher than average students with unpopular questions). We replicated these analyses on the same courses across different years to show the possibility of predicting student profiles online. This work shows the usefulness and validity of our coding scheme and the relevance of this approach to identify different student profiles. Notes for Practice • Questions provide important insights into students' level of knowledge, but coding schemes are lacking to study this phenomenon. • After providing a bottom-up coding scheme of student questions in a blended environment, we analyzed the relationship between the questions asked and the student profiles. • Profiling students based on their questions over a year allows us to predict the profiles of future students to help the teacher understand who asks what. • These results provide both a coding scheme that can be reused in various contexts involving questions, and a methodology that can be replicated in any context where students ask many questions, in particular to help the teacher in prioritizing them according to their own criteria. • Teachers need to focus on the nature of questions asked by their students, because they can reveal information about their profile (attendance, activity, etc.)

Conception of a simulator for a TEL system in orthopaedic surgery.

Within a research project whose aim is to promote the learning of percutaneous operation in orthopedic surgery, we investigate some representation models of empirical, deductive, and perceptivo-gestural knowledge. From these models, we design an TEL system (Tecnological Enhaced Learning) This project belongs to a multidisciplinary field including computer, orthopedic surgery, medical imaging, didactic and cognitive sciences. The article presents the design principles of TEL with a particular interest in the development of a simulator. This simulator allows a virtual exercise interacting with the learner in visual, temporal and haptic dimension

An Interaction Centred Approach to the teaching of Non-technical Skills in a Virtual Environment

In most domains involving expert knowledge, there is a number of cognitive and social factors influencing how efficient one human being is at correctly assessing and responding to certain situations. These factors, which contribute to the efficient and safe realization of a technical activity, are known as non-technical skills, and correspond to a wide range of cognitive proficiencies such as situation awareness, decision making, stress or fatigue management, but also social skills such as communication, leadership and team working. Different studies have shown the impact such skills can have in the successful resolving of a number of critical situations, even more so in our domains of interest which are medical surgery or driving. In this paper, we take a look at the difficulties raised by the teaching of the technical and non-technical skills mobilized during a critical situation, in the context of TEL within virtual environments. We present the advantages of using a combined enactive and situated learning approach to this problematic, and then take an ill-defined perspective to raise some important designing issues in this respect. We show that some aspects of this problem have not been encompassed yet in the ill-defined domains literature, and should be further studied in any attempt at teaching behaviours inducing technical and non-technical skills in a virtual world

Towards Improving Students’ Forum Posts Categorization in MOOCs and Impact on Performance Prediction

International audienceGoing beyond mere forum posts categorization is key to understand why some students struggle and eventually fail in MOOCs. We propose here an extension of a coding scheme and present the design of the associated automatic annotation tools to tag students’ questions in their forum posts. Working of four sessions of the same MOOC, we cluster students’ questions and show how the obtained clusters are consistent across all sessions and can be sometimes correlated with students’ success in the MOOC. Moreover, it helps us better understand the nature of questions asked by successful vs. unsuccessful students