Classification of Natural Language Descriptions for Bayesian Knowledge Tracing in Minecraft
Classification of Natural Language Descriptions for Bayesian Knowledge Tracing in Minecraft
复制标题
Minecraft 中贝叶斯知识追踪的自然语言描述分类
DOI：
10.1007/978-3-031-11647-6_45
复制
发表时间：
2022
影响因子：
4.9
通讯作者：
H. Lane
中科院分区：
文献类型：
--
作者：
Samuel Hum;Frank Stinar;HaeJin Lee;Jeff Ginger;H. Lane
贝叶斯知识追踪（Bayesian Knowledge Tracing，BKT）主要应用于正式的学习环境。本文提出了一个整合的BKT在非正式的学习背景下，评估学习者的科学观察的结构和技能水平。我们在Minecraft科学模拟中比较了不同的文本分类方法。我们的模型是根据从两所不同背景的中学收集的数据进行训练的。实验结果证明了几种机器学习模型自动标记观察结果的有效性。
Application of Bayesian Knowledge Tracing (BKT) has primarily occurred in formal learning settings. This paper presents an integration of BKT in an informal learning context to assess the structure and skill level of learner scientific observations. We compare different approaches to text classification in a Minecraft science simulation. Our models were trained on data collected from two separate middle schools with students of different backgrounds. Experimental results demonstrate the effectiveness of several machine learning models to automatically label observations.