Pilot study on optimal task scheduling in learning

Lin Ling, Chee-Wei Tan
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引用次数: 2

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.
学习中最优任务调度的初步研究
生活在一个信息时代,各种在线学习内容迅速可用,学生经常学习多种学习任务的组合。在这项工作中,我们探索了使用优化理论在每个学生完成两种不同学习任务所投入的时间之间找到最佳权衡的可能性。我们表明,该问题可以被表述为一个线性规划问题,可以有效地解决,以确定每个任务的最佳时间量。我们还报告了我们正在尝试将这一理论应用于我们的Facebook Messenger聊天机器人软件,该软件可以在聊天机器人平台上以mcq的形式优化学习和自我评估之间的权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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