Help4Mood is acceptable to some patients receiving treatment for depression although none used it as regularly as intended. Changes in depression symptoms in individuals who used the system regularly reached potentially meaningful levels.
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.
This paper describes work in progress to answer the question of how we can identify and model the depth and quality of student participation in class discussion forums using the content of the discussion forum messages. We look at two widely-studied frameworks for assessing critical discourse and cognitive engagement: the ICAP and Community of Inquiry (CoI) frameworks. Our goal is to discover where they agree and where they offer complementary perspectives on learning.In this study, we train predictive classifiers for both frameworks on the same data set in order to discover which attributes are most predictive and how those correlate with the framework labels. We find that greater depth and quality of participation is associated with longer and more complex messages in both frameworks, and that the threaded reply structure matters more than temporal order. We find some important differences as well, particularly in the treatment of messages of affirmation.
CCS CONCEPTS• Computing methodologies → Model development and analysis; Supervised learning by classification; • Applied computing → Education.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.