Publication

On the generalization ability of distributed online learners

Publications associées (33)

Variations in Pedagogical Design of Massive Open Online Courses (MOOCs) Across Disciplines

Given that few studies have formally examined pedagogical design considerations of Massive Online Open Courses (MOOCs), this study explored variations in the pedagogical design of six MOOCs offered at the University of Toronto, while considering disciplina ...

Indiana Univ Press2017

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network

Wulfram Gerstner, Aditya Gilra

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedfo ...

Elife Sciences Publications Ltd2017

Supervisory teleoperation with online learning and optimal control

Sylvain Calinon

We present a general approach for online learning and optimal control of manipulation tasks in a supervisory teleoperation context, targeted to underwater remotely operated vehicles (ROVs). We use an online Bayesian nonparametric learning algorithm to buil ...

IEEE2017

Traffic Pattern Analysis and Anomaly Detection via Probabilistic Inference Model

Kwang Moo Yi

In this chapter, we introduce a method for trajectory pattern analysis through the probabilistic inference model with both regional and velocity observations. By embedding Gaussian models into the discrete topic model framework, our method uses continuous ...

Springer Netherlands2016

On the influence of momentum acceleration on online learning

Ali H. Sayed, Bicheng Ying, Kun Yuan

This paper examines the convergence rate and mean-square-error performance of momentum stochastic gradient methods in the constant step-size and slow adaptation regime. The results establish that momentum methods are equivalent to the standard stochastic g ...

IEEE2016

A Tutorial on Machine Learning in Educational Science

Pierre Dillenbourg, Lukasz Kidzinski

Popularity of massive online open courses (MOOCs) allowed educational researchers to address problems which were not accessible few years ago. Although classical statistical techniques still apply, large datasets allow us to discover deeper patterns and to ...

Springer-Verlag Singapore Pte Ltd2016

Using Learning Analytics at School: a Go-Lab Study

Denis Gillet, Maria Jesus Rodriguez Triana, Andrii Vozniuk

Go-Lab is an European project devoted to promote STEM education by means of pedagogical guidelines based on blended Inquiry-Based Learning (IBL), authoring tools for rich open educational resources, and online labs. In such a framework, we have analysed th ...

2016

MOOC Video Interaction Patterns: What Do They Tell Us?

Pierre Dillenbourg, Patrick Jermann, Lukasz Kidzinski, Nan Li

For MOOC learners, lecture video viewing is the central learning activity. This paper reports a large-scale analysis of in-video interactions. We categorize the video behaviors into patterns by employ- ing a clustering methodology, based on the available t ...

2015

Looking AT versus Looking THROUGH: A Dual Eye-tracking study in MOOC Context

Pierre Dillenbourg, Patrick Jermann, Kshitij Sharma, Himanshu Verma, Daniela Caballero Díaz

We report the results from an eye-tracking study to show the differences in gaze patterns across the MOOC learners, while they watch a lecture individually as well as when they collaborate on an add-on activity. 98 university students took part in a study ...

ISLS2015

A tutorial on machine learning in educational science

Pierre Dillenbourg, Lukasz Kidzinski

2015

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