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 selfexplanation. 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.

We describe an approach to dealing with interpretation errors in a tutorial dialogue system. Allowing students to provide explanations and generate contentful talk can be helpful for learning, but the language that can be understood by a computer system is limited by the current technology. Techniques for dealing with understanding problems have been developed primarily for spoken dialogue systems in informationseeking domains, and are not always appropriate for tutorial dialogue. We present a classification of interpretation errors and our approach for dealing with them within an implemented tutorial dialogue system.

The current review examined theoretical and empirical adaptive training systems research. Through our review of the literature, we identified different models of adaptive training, such as the macro, micro, aptitude-treatment interaction, and two-step approaches. Additionally, we identified different learner characteristics, such as personality and prior knowledge, that can be used as the basis for tailoring instructional content or difficulty. Finally, our review revealed that several empirical questions and considerations remain, such as adaptive training system evaluation, the relative effectiveness of different approaches, and interactions between individual difference variables and those chosen for adaptation.Recently, there has been an increased demand for advanced training techniques and technologies. One potential way to improve the effectiveness of a training system is to adapt it to better suit the learner. A review of the literature reveals that there are many definitions of Adaptive Training (AT), such as Park and Lee's (2003) explanation, which states that AT methods are "educational inter-

We present Beetle II, a tutorial dialogue system designed to accept unrestricted language input and support experimentation with different tutorial planning and dialogue strategies. Our first system evaluation used two different tutoring policies and demonstrated that Beetle II can be successfully used as a platform to study the impact of different approaches to tutoring. In the future, the system can also be used to experiment with a variety of parameters that may affect learning in intelligent tutoring systems.

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