Constructivisme (psychologie)

Résumé

Le constructivisme, théorie de l'apprentissage, a été développée, entre autres, par Piaget, dès 1923, face au béhaviorisme qui, d’après lui, limitait trop l’apprentissage à l’association stimulus-réponse et considérait le sujet comme boîte noire. L’approche constructiviste s'intéresse à l'activité du sujet pour se construire une représentation de la réalité qui l’entoure. Le constructivisme part de l'idée que les connaissances de chaque sujet ne sont pas spécialement une « copie » de la réalité, mais un modèle plus ou moins fidèle de celle-ci construit par lui au cours du temps. Le constructivisme s'attache à étudier les mécanismes et processus permettant la construction de ce modèle chez les sujets à partir d'éléments déjà intégrés. On est donc à l'opposé d'une simple approche boîte noire. La compréhension, constamment renouvelée, s’élabore à partir des représentations plus anciennes d’événements passés, que le sujet a d’ores et déjà « emmagasiné » dans son vécu. En fait, le sujet

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https://fr.wikipedia.org/wiki/Constructivisme_(psychologie)

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Intelligent Tutoring Systems (ITS) are required to intervene in a learning activity while it is unfolding, to support the learner. To do so, they often rely on performance of a learner, as an approximation for engagement in the learning process. However, in learning tasks that are exploratory by design, such as constructivist learning activities, performance in the task can be misleading and may not always hint at an en- gagement that is conducive to learning. Using the data from a robot mediated collaborative learning task in an out-of-lab setting, tested with around 70 children, we show that data- driven clustering approaches, applied on behavioral features including interaction with the activity, speech, emotional and gaze patterns, not only are capable of discriminating between high and low learners, but can do so better than classical approaches that rely on performance alone. First experiments reveal the existence of at least two distinct multi- modal behavioral patterns that are indicative of high learning in constructivist, collaborative activities.

2020

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Introducing Productive Engagement for Social Robots Supporting Learning

Jauwairia Nasir

We have all been one such student or seen such students who can maintain the 'good student' image while playing a video game under the table or those loyal backbenchers, seemingly always distracted, who then ace their exams. These intricacies of human behaviors are just a few examples of what makes it non-trivial and challenging even for expert teachers to know how students' visible behaviors relate with learning. As research investigates ways in which robots and AI can support teachers and students, it is faced with the same challenge of inferring students' engagement; thus, making the investigation of this topic increasingly popular in educational HRI. The state of the art usually explores the relationship between the robot behaviors and the engagement state of the learner while assuming a linear relationship between engagement and learning. However, is it correct to assume that to maximize learning, one needs to maximize engagement? Furthermore, conventional supervised engagement models require human annotators to get labels. This not only is laborious but can also introduce subjectivity. Can we have machine-learning engagement models where annotations do not rely on human annotators? Additionally, with the increase in open-ended learning activities which by design employ the 'learning by failing' paradigm, in-task performance can not be the best measure for learning. Can we instead rely on multi-modal behaviors? In an effort to cater for these challenges, this thesis dives deep to identify and quantify the relationship between learning and engagement, which we term as Productive Engagement (PE). In order to develop, design, and evaluate our PE framework, (1) we first designed and developed an open-ended collaborative learning activity that served as a platform for evaluating different robot variants over time. With 98 children interacting with the baseline version from 2 international Swiss schools, we showed that in-task performance and learning are indeed not correlated. Thus, this showed the importance of not being limited to robot interventions that affect only superficial measures of students' learning. (2) Then, with learner's multi-modal behaviors, we showed that indeed there is a hidden link between learner's behaviors and learning that can be quantified, i.e., validating the proposed concept of Productive Engagement. (3) This quantifiable link surfaced three collaborative multi-modal learner profiles, by using a forward and backward clustering and classification technique, two of which are linked to higher learning. This technique gave a possibility to surface data driven labels for engagement; thus, evading the process of human annotations. We then identified similarities and differences between these learner profiles both at an aggregate and at the temporal level. (4) Based on (3), we constructed a PE score that can either be directly used as an assessment metric by a social robot in real-time or as data driven labels for building more sophisticated regression models. (5) With the learner profiles and the PE score, we designed and evaluated more advanced robot variants for the final studies with ~160 students from 7 international Swiss schools. With the design of different robot variants that employ knowledge about the learner's skills conducive to learning, rather than domain knowledge, in order to provide interventions; we provided a complementary perspective on the role of social robots in educational settings.

EPFL2022

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Understanding how learners engage with learning technologies, and its relation with their learning, is crucial to the design of effective learning interventions. Assessing the learners’ state however, is non-trivial. Research suggests that performance is not always a good indicator of learning, especially with open-ended constructivist activities. In this paper, we describe a combined multi-modal learning analytics and interaction analysis method which uses video, audio and log data to identify multi-modal collaborative learning behavioral profiles of 32 dyads as they work on an open-ended task around interactive tabletops with a robot mediator. These profiles, that we name Expressive Explorers, Calm Tinkerers, and Silent Wanderers, confirm previous findings that in a collaborative setting, the amount of speech interaction and the overlap of speech between a pair of learners are highly discriminating behaviors between learning and non-learning pairs, signifying that overlapping speech while turn-taking can indicate engagement that is conducive to learning. However, additionally considering learner affect and actions during the task helps us identify that there exist multiple behavioural profiles exhibited even among those who learn. Specifically, we discover that those who learn vary in their behaviors along the two dimensions of problem solving strategy (actions) and emotional expressivity (affect), suggesting that there is a relation between problem solving strategy and emotional behaviour; one strategy leads to more frustration compared to another. These findings have implications for the design of real-time learning interventions that support productive collaborative learning in open-ended tasks.

2021

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Introducing Productive Engagement for Social Robots Supporting Learning

Jauwairia Nasir

EPFL2022