论文标题
在线学习平台中的学术问题的问题类型的识别
Question-type Identification for Academic Questions in Online Learning Platform
论文作者
论文摘要
在线学习平台为专家，同行或系统的学生提供学习材料和答案。本文探讨了问题类型的识别，这是在线学习平台内容理解的一步。问题类型标识符的目的是使用问题文本，主题和结构特征根据问题类型的结构和复杂性对问题类型进行分类。我们已经定义了十二个问题类型类，包括多项选择问题（MCQ），论文等。我们编制了一个内部数据集的学生问题，并结合了弱点技术和手动注释。然后，我们在此数据集上训练了基于BERT的集合模型，并在单独的人体标记的测试集上评估了该模型。我们的实验得出的MCQ二进制分类为0.94，对于12级多标记分类的有希望的结果。我们将模型部署在我们的在线学习平台中，以此作为内容理解的关键推动者，以增强学生的学习体验。
Online learning platforms provide learning materials and answers to students' academic questions by experts, peers, or systems. This paper explores question-type identification as a step in content understanding for an online learning platform. The aim of the question-type identifier is to categorize question types based on their structure and complexity, using the question text, subject, and structural features. We have defined twelve question-type classes, including Multiple-Choice Question (MCQ), essay, and others. We have compiled an internal dataset of students' questions and used a combination of weak-supervision techniques and manual annotation. We then trained a BERT-based ensemble model on this dataset and evaluated this model on a separate human-labeled test set. Our experiments yielded an F1-score of 0.94 for MCQ binary classification and promising results for 12-class multilabel classification. We deployed the model in our online learning platform as a crucial enabler for content understanding to enhance the student learning experience.
