Reinforcement Learning for Estimating Student Proficiency in Math Word Problems

J. Pérez, E. Dapena, J. Aguilar, Gilberto Carrillo
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Abstract

Math Word Problems (MWPs) allow preparing students to apply mathematical skills in everyday life. Teaching MWPs is challenging because the mathematics difficulties of individual students vary across students within a class. In this paper, it is proposed a reinforcement learning algorithm based on Q-learning for estimating individual student proficiency according to the basic arithmetic operations: addition, subtraction, multiplication, and division. The proposal is analyzed by simulating interactions with hand-coded users but tested by simulating interactions with data-driven users. In addition, results are compared with those in a common Cognitive Diagnosis Model (CDM). Results show that our approach allows estimating the individual student proficiency in around 5 episodes. Thus, since reinforcement learning usually is applied to induce instructional policies in tutoring systems, this paper shows that it can be used for estimating student proficiency as well.
强化学习评估学生对数学字题的熟练程度
数学应用题(mwp)让学生准备在日常生活中应用数学技能。教学mwp是具有挑战性的,因为每个学生的数学困难在一个班级的学生中是不同的。本文提出了一种基于q学习的强化学习算法,用于根据基本的算术运算:加、减、乘、除来估计个体学生的熟练程度。通过模拟与手工编码用户的交互来分析该建议,并通过模拟与数据驱动用户的交互来测试该建议。并将结果与常用的认知诊断模型(CDM)进行了比较。结果表明,我们的方法可以在大约5集的时间内估计单个学生的熟练程度。因此,由于强化学习通常用于诱导辅导系统中的教学政策,因此本文表明它也可以用于估计学生的熟练程度。
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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