International audience

International audience

The ability to self-regulate one's learning (SRL) is considered to have a significant impact on educational outcomes. We present here a research work aiming first at detecting self-evaluation and self-efficacy deficits for young (5-7 years old) students, in the context of a literacy web application. From SRL answers we were able to characterize some answer patterns associated to SRL deficits, showing that around 30% of students seem to suffer from at least one of the four deficits considered in this study. We also surveyed close to 300 teachers to find out how they would like to be informed about their students' SRL deficits and how they would address them, so that the remediation of deficits in the application could be co-designed with them.

Students' questions are essential to help teachers in assessing their understanding and adapting their pedagogy. However, in a flipped classroom context where many questions are asked online to be addressed in class, selecting questions can be difficult for teachers. To help them in this task, we present here three alternative ways of organizing questions: one based on pedagogical needs, one based on estimated students' profiles and one mixing both approaches. Results of a survey filled by 37 teachers in a flipped classroom pedagogy show no consensus over a single organization. A cluster analysis based on teachers' flipped classroom experience allowed us to distinguish two profiles, but they were not associated with any particular question organization preference. Qualitative results suggest the need for different organizations may rely more on a pedagogical philosophy and advocates for differentiated dashboards.

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' 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.

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 categorizing those questions, and to provide students with feedback on the quality of their questions. To do so, first we manually categorized students' questions, which led to a taxonomy then used for an automatic annotation of the whole corpus. We identified students' characteristics from the typology of questions they asked using K-Means algorithm over four courses. The students were clustered by the proportion of each question asked in each dimension of the taxonomy. Then, we characterized the clusters by attributes not used for clustering such as the students' grade, the attendance, the number and popularity of questions asked. Two similar clusters always appeared: a cluster (A), made of students with grades lower than average, attending less to classes, asking a low number of questions but which are popular; and a cluster (D), made of students with higher grades, high attendance, asking more questions which are less popular. This work demonstrates the validity and the usefulness of our taxonomy, and shows the relevance of this classification to identify different students' profiles.

In self-regulated learning concept, Intelligent Tutoring Systems (ITS) can be designed to foster learning behaviors through pedagogical agents (PAs) that are used for interactions and exchange information with the human learner. These agents are intelligent and follow rational behaviors, but in the case of multi-agent environments they need to be systematically and specifically designed, however in order to follow a common goal, different self-regulatory systems have been designed that use pedagogical agents, but they fail to constrain the decision making of the agents and maintain a sequential decision making process during learning interactions with human learners. In this paper, we provide a new theoretical model for agent-learner interactions in MetaTutor, a multi-agent hypermedia learning environment, using Markovdecision processes. We theoretically define the agents' Markovdecisions and their influence on MetaTutor's performance as a whole. First, we formally define the Markov architecture and its parameters. We then link these characteristics to the pedagogical agents we use in MetaTutor and define different versions of MetaTutor agents equipped with Markov decision mechanism. Furthermore, we explore additional details about agents' sequential decision making and how reward functions influence their acting strategies with learners in the learning environment. We introduce the optimization problem in which we aim to maximize the expected return of the overall agents' acts in a self-regulatory system. What specifically distinguishes this work from the previous proposals in the same domain is its novelty in continuous decision making mechanism investigation and performance analysis that improve the applicability of the proposed adaptive model in a multi-agent ITS like MetaTutor.

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 P

We describe here a framework dedicated to studies and experimentations upon the nature of the relationships between the rational reasoning process of an artificial agent and its psychological counterpart, namely its behavioral reasoning process. This study is focused on the domain of Assistant Conversational Agents which are software tools providing various kinds of assistance to people of the general public interacting with computer based applications or services. In this context, we show on some examples how the agents must exhibit both rational reasoning about the system functioning and a human-like believable dialogical interaction with the users.