International audience

International audience

The high failure rate is a common issue among online institutions. Early Warning Systems (EWSs) are widely adopted as a solution to deal with this issue. However, these systems do not go beyond the early identification of failing learners. In this paper, we propose a new alert algorithm of an educational EWS for generating risk alerts at the earliest. This algorithm is based on a weekly prediction model that aims to generate early alerts. The regular tracking of prediction results enabled to propose measures for the right prediction earliness and the model’s temporal stability. These measures prepare the last step of the algorithm which is the alerts generation according to a predefined rule. The objective of this rule is to target at-risk learners to improve their learning. For this aim, we used data of k-12 learners enrolled in an online physics-chemistry module.

Because deep neural networks (DNNs) rely on a large number of parameters and computations, their implementation in energy-constrained systems is challenging. In this paper, we investigate the solution of reducing the supply voltage of the memories used in the system, which results in bit-cell faults. We explore the robustness of state-of-the-art DNN architectures towards such defects and propose a regularizer meant to mitigate their effects on accuracy. Our experiments clearly demonstrate the interest of operating the system in a faulty regime to save energy without reducing accuracy.