Head-Driven Generation and Indexing in ALE

We present a method for compiling gram- mars into efficient code for head-driven generation in ALE. Like other compilation techniques already used in ALE, this method integrates ALE's compiled code for logical operations with control-specific information from (SNMP90)'s algorithm along with user-defined directives to identify semantics-related substructures. This combination provides far better performance than typical bi-directional feature- based parser/generators, while requiring a minimum of adjustment to the grammar signature itself, and a minimum of extra compilation

K.: Pilot-testing a tutorial dialogue system that supports self-explanation

Abstract. Previous studies have shown that self-explanation is an effective metacognitive strategy and can be supported effectively by intelligent tutoring systems. It is plausible however that students may learn even more effectively when stating explanations in their own words and when receiving tutoring focused on their explanations. We are developing the Geometry Explanation Tutor in order to test this hypothesis. This system helps students, through a restricted form of dialogue, to construct general explanations of problem-solving steps in their own words. We conducted a pilot study in which the tutor was used for two class periods in a junior high school. The data from this study suggest that the techniques that we chose to implement the dialogue system, namely a knowledge-based approach to natural language understanding and classification of student explanations, are up to the task. There are a number of ways in which the system could be improved within the current architecture.

Logic-Based Natural Language Understanding for Cognitive Tutors

High-precision Natural Language Understanding is needed in Geometry Tutoring to accurately determine the semantic content of students ’ explanations. The paper presents an NLU system developed in the context of the Geometry Cognitive Tutor. The system combines unification-based syntactic processing with description logics based semantics to achieve the necessary accuracy level. The paper describes the compositional process of building the syntactic structure and the semantic interpretation of NLU explanations. It also discusses results of an evaluation of classification performance on data collected during a classroom study. 1 Explanations in Geometry Tutoring The Geometry Cognitive Tutor assists students in learning by doing as they work on geometry problems on the computer. Currently the Geometry Cognitive Tutor is in regular use (two days per week) in about 500 schools around the US. In previous evaluation studies Koedinger et al. (1997) have shown that the tutors are successful in raising high school students ’ test scores in both algebra and geometry. However, there is still a considerable gap between the effectiveness of current cognitive tutor programs and the best human tutors (Bloom, 1984). Cognitive Tutors pose problems to students and check their solutions to these problems step by step. They can also provide context-sensitive hints at each step in solving the problem, as needed. However, prior Cognitive Tutors do not ask students to explain or justify their answers in their words. On the other hand human tutors often engage students in thinking about the reasons behind the solution steps. Such “selfexplanation” has the potential to improve students ’ understanding of the domain, resulting in knowledge that generalizes better to new situations. This difference might also be the main explanation beneath the gap mentioned above. To verify this hypothesis, the next generation of intelligent cognitive tutors needs to be able to carry tutoring dialogs with students at the explanation level. Some of the current intelligent tutoring systems, like Autotutor (Wiemer-Hastings e