Within STEM domains, physics is considered to be one of the most difficult topics to master, in part because many of the underlying principles are counter-intuitive. Effective teaching methods rely on engaging the student in active experimentation and encouraging deep reasoning, often through the use of self-explanation. Supporting such instructional approaches poses a challenge for developers of Intelligent Tutoring Systems. We describe a system that addresses this challenge by teaching conceptual knowledge about basic electronics and electricity through guided experimentation with a circuit simulator and reflective dialogue to encourage effective self-explanation. The Basic Electricity and Electronics Tutorial Learning Environment (BEETLE II) advances the state of the art in dynamic adaptive feedback generation and natural language processing (NLP) by extending symbolic NLP techniques to support unrestricted student natural language input in the context of a dynamically changing simulation environment in a moderately complex domain. This allows contextually-appropriate feedback to be generated “on the fly” without requiring curriculum designers to anticipate possible student answers and manually author multiple feedback messages. We present the results of a system evaluation. Our curriculum is highly effective, achieving effect sizes of 1.72 when comparing pre- to post-test learning gains from our system to those of a no-training control group. However, we are unable to demonstrate that dynamically generated feedback is superior to a non-NLP feedback condition. Evaluation of interpretation quality demonstrates its link with instructional effectiveness, and provides directions for future research and development.
['Myroslava O. Dzikovska', 'Natalie B. Steinhauser', 'Elaine Farrow', 'Johanna Moore', 'Gwendolyn E. Campbell']
BEETLE II: Deep natural language understanding and automatic feedback generation for intelligent tutoring in basic electricity and electronics