Unsupervised extraction of students navigation patterns on an EPFL MOOC

How do students learn in MOOCs? This project aims at answering this question by analyzing the activities of thousands of students registered on EPFL Scalaa MOOC hosted by Coursera. With the rapid growth of MOOCs, Education Science has entered the Big Data bubble, bringing new opportunities to study and improve learning technologies. We are interested in studying students navigation patterns which are the short sequences of learning activities that a students perform on the MOOC platform. In our case, the learning activities are one of watching a video lecture, reading or posting on the forum and submitting assignments. In this project we use unsupervised machine learning techniques to extract the main navigation patterns of students and gain insights on their behavior. We produce a simple and efficient visualization tool in order to provide feedback to teachers to help them understand the potential difficulties encountered by their students during the course and, if necessary, take actions accordingly

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Thibault Lucien Christian Asselborn, Victor Faramond, Louis Pierre Faucon
2017
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https://infoscience.epfl.ch/record/230131?ln=fr

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A Bayesian Model of Individual Differences and Flexibility in Inductive Reasoning for Categorization of Examples

Pierre Dillenbourg, Louis Pierre Faucon, Jennifer Kaitlyn Olsen

Inductive reasoning is an important educational practice but can be difficult for teachers to support in the classroom due to the high level of preparation and classroom time needed to choose the teaching materials that challenge students' current views. Intelligent tutoring systems can potentially facilitate this work for teachers by supporting the automatic adaptation of examples based on a student model of the induction process. However, current models of inductive reasoning usually lack two main characteristics helpful to adaptive learning environments, individual differences of students and tracing of students' learning as they receive feedback. In this paper, we describe a model to predict and simulate inductive reasoning of students for a categorization task. Our approach uses a Bayesian model for describing the reasoning processes of students. This model allows us to predict students' choices in categorization questions by accounting for their feature biases. Using data gathered from 222 students categorizing three topics, we find that our model has a 75% accuracy, which is 10% greater than a baseline model. Our model is a contribution to learning analytics by enabling us to assign different bias profiles to individual students and tracking these profile changes over time through which we can gain a better understanding of students' learning processes. This model may be relevant for systematically analysing students' differences and evolution in inductive reasoning strategies while supporting the design of adaptive inductive learning environments.

ASSOC COMPUTING MACHINERY2020

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Designing for the co-Orchestration of Social Transitions between Individual, Small-Group and Whole-Class Learning in the Classroom

Jennifer Kaitlyn Olsen

Educational technologies are often developed such that students work on specific social levels (e.g., individual, small group, whole class) at specific times. However, in the reality of the classroom, learning activities are not so cleanly divided, with transitions occurring between social levels for students at different times. To support these social transitions in a way that can promote student learning, we need to lower the teacher’s orchestration load around managing fluid social transitions. Co-orchestration, in which the orchestration decisions are shared between different parties, can help to lower the orchestration load when it is designed according to the teacher’s values and classroom culture. In this paper, we present a taxonomy of social transitions and investigate how the responsibilities of orchestration can be divided between primary school teachers and a co-orchestration system in order to support the extension from rigid social transitions to fluid transitions in technology-enhanced classrooms. Across two studies, we used a design process involving co-design and prototyping with teachers. We uncovered and refined co-orchestration design desires that balance teachers’ orchestration loads while providing them with a sense of control. We present six design desires for maintaining a balance between teacher and system responsibilities regarding the orchestration of social transitions that can be implemented, such as in our mid-fidelity prototype, to support the range of social transitions. The list of desires contributes to co-orchestration research and more broadly technology design for classrooms by highlighting the changing balance of teacher control depending on what is the focus of the orchestration support.

2020

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Étudiant

Un étudiant ou une étudiante est un mot dérivé du latin studere qui signifie « s'appliquer à apprendre quelque chose ». Cependant, le terme ne s'applique pas à toute personne qui apprend. On le ré

Apprentissage non supervisé

Dans le domaine informatique et de l'intelligence artificielle, l'apprentissage non supervisé désigne la situation d'apprentissage automatique où les données ne sont pas étiquetées (par exemple étiq

Apprentissage

L’apprentissage est un ensemble de mécanismes menant à l'acquisition de savoir-faire, de savoirs ou de connaissances. L'acteur de l'apprentissage est appelé apprenant. On peut opposer l'apprentissag