文章基本信息

标题：Virtual Pre-Service Teacher Assessment and Feedback via Conversational Agents
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作者：Debajyoti Datta ; Maria Phillips ; James P. Bywater 等
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2021
卷号：2021
页码：185-198
语种：English
出版社：ACL Anthology
摘要：Conversational agents and assistants have been used for decades to facilitate learning. There are many examples of conversational agents used for educational and training purposes in K-12, higher education, healthcare, the military, and private industry settings. The most common forms of conversational agents in education are teaching agents that directly teach and support learning, peer agents that serve as knowledgeable learning companions to guide learners in the learning process, and teachable agents that function as a novice or less-knowledgeable student trained and taught by a learner who learns by teaching. The Instructional Quality Assessment (IQA) provides a robust framework to evaluate reading comprehension and mathematics instruction. We developed a system for pre-service teachers, individuals in a teacher preparation program, to evaluate teaching instruction quality based on a modified interpretation of IQA metrics. Our demonstration and approach take advantage of recent advances in Natural Language Processing (NLP) and deep learning for each dialogue system component. We built an open-source conversational agent system to engage pre-service teachers in a specific mathematical scenario focused on scale factor with the aim to provide feedback on pre-service teachers’ questioning strategies. We believe our system is not only practical for teacher education programs but can also enable other researchers to build new educational scenarios with minimal effort.