Tutorial Dialog in an Equation Solving Intelligent Tutoring System

This thesis makes a contribution to Intelligent Tutoring Systems (ITS) architectures. A new intelligent tutoring system is presented for the domain of solving linear equations. This system is novel, because it is the first intelligent equation-solving tutor that combines a cognitive model of the domain with a model of dialog-based tutoring. The tutorial model is novel because it is based on the observation of an experienced human tutor and captures tutorial strategies specific to the domain of equation-solving. In this context, a tutorial dialog is the equivalent of breaking down problems into simpler steps and then asking new questions to the student before proceeding to the next navigational step. The resulting system, named E-tutor, was compared, via a randomized controlled experiment, to an algebra ITS similar to the“Cognitive Tutor by Carnegie Learning, Inc®. The Cognitive Tutor can provide traditional model-tracing feedback and buggy messages to students, but does not engage students in dialog. Preliminary results using a very small sample size, i.e., teaching equation solving to 15 high school students, showed that E-Tutor with dialog capabilities performed better than E-tutor without dialog. This result showed an effect size of 0.4 standard deviations for overall learning by condition. This set of preliminary results, though not statistically significant, shows promising opportunities to improve learning performance by adding tutorial dialog capabilities to ITSs. However, significant further validation is required, specifically, adding greater numbers and variations of the work to our sample size, before this approach can be deemed successful. The system is available at www.wpi.edu/~leenar/E-tutor

The intuitive advantage and lead indicators of the MCAS.

An analysis of 1129 cases of fourth grade Massachusetts Comprehensive Assessment System (MCAS) test results from 9 schools in 3 districts by learning style, grades and other achievement tests revealed several useful and some disquieting patterns in the results. The learning style indicator (MMTIC) identified a fairly consistent rank ordering of scores by type of learners regardless of subject. In short, the MCAS is cognitively biased in a pattern reminiscent of the SAT at the high school level. However, using information available at the time the students took the test (3rd grade test scores and 4th grade report cards), it is possible to predict who is most likely to have difficulty with the test. Some of the MMTIC types are more predictable than others based on this kind of information. Those in the districts that had the lowest average scores are the most predictable. Hence, considerable progress has been made in coming to understand the MCAS and who it serves most and least well, given its cognitive bias

Tutorial dialog in an equation solving intelligent tutoring system

mathematical education, artificial intelligence

A Distance learning system for Tea programming.

The goal of this project was to enhance the user-interface of a Tea programming language interpreter and to create a web-based learning system to teach Tea. The system, originally designed for PC\u27s, was extended to include the Unix platform. The user-interface was improved using techniques in the field of user-interface design. Tea lessons and quizzes were written using techniques in the field of distance education. The Tea interpreter, lessons, and quizzes were designed to be easily accessible on the Internet for anyone who wants to learn computer programming