The Why2-Atlas system teaches qualitative physics by having students write paragraph-long explanations of simple mechanical phenomena.

This article presents the tutoring systems that we have been developing. AUTOTUTOR is a conversational agent, with a talking head, that helps college students learn about computer literacy. ANDES, ATLAS, AND WHY2 help adults learn about physics. Instead of being mere information-delivery systems, our systems help students actively construct knowledge through conversations

Abstract. In this paper, we explore the problem of selecting appropriate interventions for students based on an analysis of their interactions with a tutoring system. In the context of the WHY2 conceptual physics tutoring system, we describe CarmelTC, a hybrid symbolic/statistical approach for analysing conceptual physics explanations in order to determine which Knowledge Construction Dialogues (KCDs) students need for the purpose of encouraging them to include important points that are missing. We briefly describe our tutoring approach. We then present a model that demonstrates a general problem with selecting interventions based on an analysis of student performance in circumstances where there is uncertainty with the interpretation, such as with speech or text based natural language input, complex and error prone mathematical or other formal language input, graphical input (i.e., diagrams, etc.), or gestures. In particular, when student performance completeness is high, intervention selection accuracy is more sensitive to analysis accuracy, and increasingly so as performance completeness increases. In light of this model, we have evaluated our CarmelTC approach and have demonstrated that it performs favourably in comparison with the widely used LSA approach, a Naive Bayes approach, and finally a purely symbolic approach.

We describe an annotation scheme for syntactic information in the CHILDES database (MacWhinney, 2000), which contains several megabytes of transcribed dialogs between parents and children. The annotation scheme is based on grammatical relations (GRs) that are composed of bilexical dependencies (between a head and a dependent) labeled with the name of the relation involving the two words (such as subject, object and adjunct). We also discuss automatic annotation using our syntactic annotation scheme.

It is currently difficult and time-consuming to validate and manipulate data in web applications, so we have developed an editor and a parser to simplify these tasks. Our editor enables end-user programmers to create and debug reusable, flexible data formats without learning a complex new language. Our parser uses these formats to turn strings into structured objects and to report its level of confidence that each string is a valid instance of the format. End-user programmers can use our system to create validation code that takes a graduated response to slightly invalid data. We evaluate our system’s expressiveness by defining formats for commonly-occurring web data.

This paper discusses the process of parsing adult utterances directed to a child, in an effort to produce a syntactically annotated corpus of the verbal input to a human language learner. In parsing the Eve corpus of the CHILDES database, we encountered several challenges relating to parser coverage and ambiguity, for which we describe solutions that result in a system capable of analyzing almost 80% of the adult utterances in the corpus correctly. We describe characteristics of the language in the corpus that make this task unique, and present specific ways to deal with the analysis of this type of language. We discuss each step of the corpus analysis in detail, focusing on how selected techniques, such as part-of-speech tagging, rule-based robust parsing and statistical disambiguation, affect the trade-off between coverage and accuracy. Finally, we present a detailed evaluation of the performance of our system. A parsed corpus resulting from the research described in this paper is available to the research community.

Ambiguity packing is a well known technique for enhancing the efficiency of context-free parsers. However, in the case of unification-augmented context-free parsers where parsing is interleaved with feature unification, the propagation of feature structures imposes difficulties on the ability of the parser to effectively perform ambiguity packing. We demonstrate that a clever heuristic for prioritizing the execution order of grammar rules and parsing actions can achieve a high level of ambiguity packing that is provably optimal. We present empirical evaluations of the proposed technique, performed with both a Generalized LR parser and a chart parser, that demonstrate its effectiveness.

We investigate an aspect of the relationship between parsing and corpus-based methods in NLP that has received relatively little attention: coverage augmentation in rule-based parsers. In the specific task of determining grammatical relations (such as subjects and objects) in transcribed spoken language, we show that a combination of rule-based and corpus-based approaches, where a rule-based system is used as the teacher (or an automatic data annotator) to a corpus-based system, outperforms either system in isolation.

Abstract. 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 recent 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 150 schools around the US. In previous evaluation studies Koedinger et al. [1] 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

This paper presents a method for speeding up a deep parser through backbone extraction and pruning based on CFG ambiguity packing