Pilot study on optimal task scheduling in learning

Abstract

Living in an information era where various online learning contents are rapidly available, students often learn with a combination of multiple learning tasks. In this work we explore the possibilities of using optimization theory to find the optimal trade-off between the time invested in two different completing learning tasks for each individual student. We show that the problem can be formulated as a linear programming problem, which can be efficiently solved to determine the optimal amount of time for each task. We also report our ongoing attempts to apply this theory to our Facebook Messenger chatbot software that can optimize the trade-off between learning and self-assessing in form of MCQs on the chatbot platform.