François Bouchet

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• Ph.D. in Computer Science
• Professor (Assistant) at Sorbonne University

Extended interactions with a pedagogical agent (PA) assisting students to enact cognitive and metacognitive self-regulated processes requires the system to adapt the types and frequency of scaffolding. We compared learners’ perception of PAs’ prompts with MetaTutor, a hypermedia adaptive learning environment, with 40 undergraduates randomly assigne...

In this paper we present a methodology dedicated to the computational implementation of personality traits in Conversational Agents. First, a significant set of personality-traits adjectives is registered from thesaurus sources. Then the lexical semantics related to personality-traits is extracted while using the WordNet database and it is given a...

Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). In this paper, we analyze trace data to identify distinguishing patterns of behavior in a study of 51 college students lear...

In this paper, we present an approach based on the principle that psychological capacities, especially personality traits, influence the decision making process of rational agents. Using a three-level (trait, facet, scheme) extension of the FFM/NEO PI-R taxonomy facilitating its computational implementation, we propose a model for the expression of...

In this paper, we present the results obtained using a clustering algorithm (Expectation-Maximization) on data collected from 106 college students learning about the circulatory system with MetaTutor, an agent-based Intelligent Tutoring System (ITS) designed to foster self-regulated learning (SRL). The three extracted clusters were validated and an...

Predictive student models are increasingly used in learning environments. However, due to the rising social impact of their usage, it is now all the more important for these models to be both sufficiently accurate and fair in their predictions. To evaluate algorithmic fairness, a new metric has been developed in education, namely the Model Absolute...

In both Massive Open Online Courses (MOOCs) and private courses, instructors face a large amount of queries in discussion forum posts that may merit a response. There has been ongoing research on how to employ machine learning to predict a post's urgency in order to focus instructors' a en on. However, it is unclear how large a course is needed to...

Self-regulated learning (SRL) skills are critical for students of all ages to maximize their learning. Two key processes of SRL are being aware of one's performance (self-evaluation) and believing in one's capabilities to produce given attainments (self-efficacy). To assess and improve these capabilities in young children (5 to 8), we use a literac...

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term implicatio...

Predictive models used in intelligent learning environments can suffer from biased and unfair representation. However, existing fairness metrics that are meant to capture these issues are only based on the models' predictive performances. In this paper, we propose a novel fairness metric that measures to what extent the models behave unfairly. In a...

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term implicatio...

In-person sessions of participative design are commonly used in the field of learning analytics, but to reach students not always available on-site (e.g., during a pandemic), they have to be adapted to online-only context. Card-based tools are a common co-design method to collect users’ needs, but this tangible format limits data collection and usa...

Learning Analytics Dashboards (LADs) are becoming a key element in enabling learners to monitor their learning, plan and actually learn. However, LADs are sometimes not completely adapted to students, who are rarely involved in their design. Moreover, even when they are, the implemented LADs are often the same for all students, whereas previous wor...

We focus on the predictors of persistence and achievement in online learning by studying the students’ learning intentions and their psychological states during learning activities. Flow/autotelic experience is a powerful predictor of engagement in MOOCs and online learning in general and relates to the deep involvement and sense of absorption duri...

In this work, we propose a learning analytics implementation based on a model-driven engineering approach. It aims at assessing the benefits that could arise from such an implementation, when pedagogical resources are produced via publishing chains, that already use the same approach to produce documents. Previously, we have discussed these potenti...

Self-regulated learning (SRL) is critical for learning across tasks, domains, and contexts. Despite its importance, research shows that not all learners are equally skilled at accurately and dynamically monitoring and regulating their self-regulatory processes. Therefore, learning technologies, such as intelligent tutoring systems (ITSs), have been...

Self-regulation skills are critical for students of all ages in order to maximize their learning. A key aspect of self-regulation is being aware of one’s performance and deficits in self-evaluation. Additionally, a clear consensus has not been reached regarding the age one can start learning these self-regulation processes. In order to investigate...

Digital transformation induces rapid and profound changes in higher education and research (HER). With this foresight, INRAE and Agreenium, two French public HER institutions centered on food, agriculture and the environment, explore the challenges they face in a world increasingly dependent on digital resources. Four scenarios generated via morpho...

To improve the learning process, the evolution of learner’s characteristics (cognitive, affective, prior knowledge, workflow, organization, …) must be taken into account during the personalization or adaptation. This requires generating several scenarios (a description of activities, their order and links in the learning sequence as well as the exp...

Student attrition is one of the most frequently stated problems with massive open online courses. Although there is a growing body of research that investigates the factors leading to withdrawing from a course, there is also a need for data-driven solutions for early detection as a means to take remedial action. In this case study, we examine the u...

Plateformes, réseaux sociaux, ressources en ligne, simulations, apprentissage à distance, données d’apprentissage, données massives, intelligence artificielle… la transition numérique bouleverse l’enseignement supérieur et la recherche publics. Elle modifie les contenus, les outils et méthodes pédagogiques, ainsi que le rôle des enseignants et des...

Digital transformation induces rapid and profound changes in higher education and research (HER). With this foresight, INRAE and Agreenium, two French public HER institutions centered on food, agriculture and the environment, explore the challenges they face in a world increasingly dependent on digital resources. Four scenarios generated via morpho...

Les questions des élèves sont utiles pour leur apprentissage et pour l'adaptation pédagogique des enseignants. Nous étudions ici la nature des questions posées en ligne par les étudiants et comment le vote sur ces questions peut être lié à l’apprentissage. Nous avons donc développé un schéma de codage, puis conçu un annotateur automatique que nous...

Human-centered project-based teaching methods have proved their efficiency and popularity in the last decade. Such practice emphasizes the existence of interdisciplinary skills that students manipulate and incrementally learn to master throughout their higher education curriculum. This paper addresses some questions around the integration and evalu...

Going 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...

MOOCs are becoming more and more integrated in the higher education landscape of learning, with many institutions now pushing their students towards MOOC as part of their curriculum. But what does it mean for other MOOC learners? Are these students socializing the same way when they have an easier possibility to interact with classmates offline? Is...

The analysis of students’ questions can be used to improve the learning experience for both students and teachers. We investigated questions (N = 6457) asked before the class by 1st year medicine/pharmacy students on an online platform, used by professors to prepare their on-site Q&A session. Our long-term objectives are to help professors in categ...

To improve the learning process, the evolution of learner’s characteristics (cognitive, affective, prior knowledge, workflow, organization, ...) must be taken into account during the personalization or adaptation. This requires generating several scenarios (a description of activities, their order and links in the learning sequence as well as the e...

Designing an educational scenario is a sensitive and challenging activity because it is the vector of learning. However, the designed scenario may not correspond to some learners’ characteristics (pace of work, cognitive styles, emotional factors, prerequisite knowledge, …). To personalize the learning task and adapt it gradually to each learner, s...

Adapting ITSs that promote the use of metacognitive strategies can sometimes lead to intense prompting, at least initially, to the point that there is a risk of it feeling counterproductive. In this paper, we examine the impact of different prompting strategies on self-reported agent-directed emotions in an ITS that scaffolds students’ use of self-...

Automatic analysis of learners' questions can be used to improve their level and help teachers in addressing them. We investigated questions (N=6457) asked before the class by 1st year medicine/pharmacy students on an online platform, used by professors to prepare their on-site Q&A session. Our long-term objectives are to help professors in categor...

MOOCs are part of the ecosystem of self-learning for which self-regulation is one of the pillars. Weakness of self-regulation skills is one of the key factors that contribute to dropout in a MOOC. We present a conceptual framework to promote self-regulated learning in a MOOC. This framework relies on the use of a virtual companion to provide metaco...

Research on collaborative learning between humans and virtual pedagogical agents represents a necessary extension to recent research on the conceptual, theoretical, methodological, analytical, and educational issues behind co- and socially-shared regulated learning between humans. This study presents a novel coding framework that was developed and...

Adaptation in learning environments can be performed according to various aspects, such as didactics, pedagogy or game mechanics. While most current approaches propose to adapt according to a single aspect, this paper proposes a Multi-Aspect Generic Adaptation Model (MAGAM). Based on the Q-matrix, this model aims at taking into account heterogeneou...

Lack of social relationship has been shown to be an important contribution factor for attrition in Massive Open Online Courses (MOOCs). Helping students to connect with other students is therefore a promising solution to alleviate this phenomenon. Following up on our previous research showing that embedding a peer recommender in a MOOC had a positi...

Peer recommender systems (PRS) in MOOCs have been shown to help reducing attrition and increase performance of those who use them. But who are the students using them and what are their motivations? And why are some students reluctant to use them? To answer these questions, we present a study where we implemented a chat-based PRS that has been used...

MOOCs are part of the ecosystem of self-learning for which self-regulation is one of the pillars. Weakness of self-regulation skills is one of the key factors that contribute to dropout in a MOOC. We present a conceptual framework to promote self-regulated learning in a MOOC. This framework relies on the use of a virtual companion to provide me...

Dans les EIAH, l'adaptation peut se faire suivant plusieurs aspects, notamment didactique, pédagogique, ludique, ou encore en fonction du contexte. Alors que les approches actuelles proposent d'adapter suivant un seul aspect, cet article propose le modèle d'adaptation générique M AGAM ayant la capacité à prendre en compte de multiples aspects dans...

People around the world use social network sites such as Twitter or Facebook to share messages on any topic, including emotionally charged and educationally relevant messages. As such, social network sites afford a novel methodological approach of potential benefit to researchers who examine the theoretical and empirical implications of the emotion...

This study examines whether an ITS that fosters the use of metacognitive strategies can benefit from variations in its prompts based on learners’ self-regulatory behaviors. We use log files and questionnaire data from 116 participants who interacted with MetaTutor, an advanced multi-agent learning environment that helps learners to develop their se...

The current study examined the relationships between learners’ (\(N = 123\)) personality traits, the emotions they typically experience while studying (trait studying emotions), and the emotions they reported experiencing as a result of interacting with four pedagogical agents (agent-directed emotions) in MetaTutor, an advanced multi-agent learning...

E-learning research shows students who interact with their peers are less likely to drop out from a course, but is this applicable to MOOCs? This paper examines MOOC attrition issues and how encouraging social interactions can address them: using data from 4 sessions of the GdP MOOC, a popular Project Management MOOC, we confirm that students displ...

The current study examined the relationships between learners’ (N=123) personality traits, the emotions they typically experience while studying (trait studying emotions), and the emotions they reported experiencing as a result of interacting with four pedagogical agents (agent-directed emotions) in MetaTutor, an advanced multi-agent learning envir...

Pedagogical agents (PAs) have the ability to scaffold and regulate students' learning about complex topics while using intelligent tutoring systems (ITSs). Research on ITSs predominantly focuses on the impact that these systems have on overall learning, while the specific components of human-ITS interaction, such as student-PA dialogue within the s...

This paper presents the evaluation of the synchronization of three emotional measurement methods (automatic facial expression recognition, self-report, electrodermal activity) and their agreement regarding learners’ emotions. Data were collected from 67 undergraduates enrolled at a North American University whom learned about a complex science topi...

The current study examined the relationships between learners’ (N = 124) personality traits, the emotions they experience while typically studying (trait studying emotions), and the emotions they reported experiencing as a result of interacting with two Pedagogical Agents (PAs - agent-directed emotions) in MetaTutor, an advanced multi-agent learnin...

Today, most students enrolling in a MOOC already have attended to another MOOC before. We study here a subcategory of these students, who register to the same MOOC several times, which we call Recurring Students (RS). Using data collected during three 5-week sessions of the GdP MOOC (N > 14,000 on average per session), we show there is a significan...

In this article we address coverage and comprehensiveness issues raised by the integration of a large class of psychological phenomena into rational dialogical agents. These two issues are handled through the definition of a generic framework based on the notion of personality engine, which makes it possible to reify in separate modules in one hand...

Research on Self-Regulated Learning (SRL) in hypermedia-learning environments revealed that students are unable to engage in effective use of SRL, which have important implications for designing hypermedia-learning environments. 60 undergraduate students interacted with MetaTutor, an intelligent, multi-agent hypermedia-learning environment, with th...

Though some research has focused on agent-direct affective processes, none has examined its impact on multi-agent learning environments and on the detection, modeling and fostering of self-regulated learning processes. 38 participants interacted with MetaTutor, an intelligent, multi-agent hypermedia-learning environment, to learn about the human ci...

In this paper we discuss the methodology and results of aligning three different emotional measurement methods (automatic facial expression recognition, self-report, electrodermal activation) and their agreement regarding learners’ emotions. Data was collected from 67 undergraduate students from a North American university who interacted with MetaT...

In this paper we discuss the ways in which assessments of learning were evaluated, designed, and implemented in MetaTutor (a multi-agent, hypermedia learning environment about several human body systems; Azevedo et al., 2012, 2013). We also share the lessons that we have learned from assessing learning with MetaTutor across three different universi...

Plusieurs études ont récemment été menées sur l’attribution de compétences cognitives et de caractéristiques psychologiques à des agents artificiels. Cependant ces études reposent sur des approches procédurales, difficiles à analyser, et elles se focalisent sur des phénomènes particuliers au lieu de couvrir une partie significative du domaine psycho...

100 undergraduate participants were randomly assigned to one of two experimental conditions (Prompt and Feedback [PF] and Control), and used MetaTutor (a multi-agent hypermedia intelligent tutoring system [ITS]) to learn about a challenging science topic (i.e., the human circulatory system) for two hours. During the session, we collected product (e...

This study investigated students’ regulation of learning processes across science multimedia. Within an adaptive, hypermedia learning environment, 81 university students were presented 38 pages of texts and images of the circulatory system. A subset of 4 pages were rated for high or low coherence between text and image. Participants’ learning strat...

In this paper, we explore the potential of gaze data as a source of in-formation to predict learning as students interact with MetaTutor, an ITS that scaffolds self-regulated learning. Using data from 47 college students, we show that a classifier using a variety of gaze features achieves considerable accuracy in predicting student learning after s...

Nous présentons une approche flexible et déclarative pour l’implémentation informatique de traits de personnalité issus de la taxonomie FFM / NEOPI-R . Elle repose sur le principe que les traits activent des opérateurs influençant le processus de décision rationnelle des agents. Notre approche est validée sur une étude de cas portant sur la sous-cl...

In this study we aligned and compared self-report and on-line emotions data on 67 college students’ emotions at five different points in time over the course of their interactions with MetaTutor. Self-reported emotion data as well as facial expression data were converged and analyzed. Results across channels revealed that neutral and positively-val...

The aim of the current study was to extend research on the effects and interactions between epistemic beliefs and learning task conditions by investigating students' on-line regulation of attentional allocation evidenced in eye-movement patterns during multimedia science learning.

Integrating text and diagrams during multimedia learning is extremely challenging for students and a major issue impeding metacomprehension of scientific material. The effect of three types of discrepancies (within-text [WT], between-text-and-graph [BTG], and no-discrepancy) on participants’ multimedia learning of science content, inspection time,...

We used Pekrun et al.’s (2002) AEQ measure to investigate the relationship between college students’ (N = 105) trait emotions and their emotional experiences with MetaTutor, an intelligent, multiagent, hypermedia environment for learning about the human circulatory system. We also examined the relationship between learners’ self-reported emotions a...

Examining co-regulated learning between human and artificial pedagogical agents is extremely important and has recently led to a surge in research on the conceptual, theoretical, methodological, analytical, and educational issues behind other-regulated learning. This study used a novel coding framework to examine Co-Regulated Learning (CoRL) betwee...

This chapter emphasizes the importance of using multi-channel trace data to examine the complex roles of cognitive, affective, and metacognitive (CAM) self-regulatory processes deployed by students during learning with multi-agent systems. We argue that tracing these processes as they unfold in real-time is key to understanding how they contribute...

MetaTutor is a hypermedia, multi-agent learning environment that teaches students about the human circulatory system and fosters the development of self-regulated learning skills. MetaTutor’s design is guided by contemporary models of SRL that emphasize the temporal deployment of cognitive, metacognitive, and affective (CAM) processes during learni...

We present in this article an architecture implementing personality traits from the FFM/NEO PI-R taxonomy as influence operators upon the rational decision making process of dialogical agents. The objective is to separate designer-dependent resources (traits taxonomies, influence operators, behaviors/operators links) from the core part of the compu...

Nous présentons une approche systématique de l’implémentation du principe stipulant que les traits de personnalité ont une influence potentielle et effective sur le processus de décision rationnelle d’agents cognitifs. L’apport de ce travail se situe au niveau de la couverture du domaine psychologique traité, de sa généricité par rapport aux modèle...

Co-adapted learning involves complex, dynamically unfolding interactions between human and artificial pedagogical agents (PAs) during learning with intelligent systems. In general, these interactions lead to effective learning when (1) learners correctly monitor and regulate their cognitive and metacognitive processes in response to internal (e.g.,...

Co-adapted learning involves complex, dynamically unfolding interactions between human and artificial pedagogical agents (PAs) during learning with intelligent systems. In general, these interactions lead to effective learning when (1) learners correctly monitor and regulate their cognitive and metacognitive processes in response to internal (e.g.,...

This paper extends upon traditional emotional measurement frameworks used by intelligent tutoring systems in which emotions are analyzed as single, discrete psychological experiences and examines co-occurring emotions (COEs) (e.g., Conati) through a novel methodological approach. COEs are emotional states that occur simultaneously, where their disc...

MetaTutor is both (1) a learning tool designed to teach and train stu-dents to self-regulate (e.g., by modeling and scaffolding metacognitive monitor-ing, facilitating the use of effective learning strategies, and setting and coordi-nating relevant learning goals), and (2) a research tool used to collect trace data on students’ cognitive, metacogni...