Abstract

We introduce a dialogue-based explanation facility for Intelligent CALL (ICALL) Systems. Our prototype system, DiBEx, uses meta reasoning to build up an explanation (error) tree, given a faulty user input. It relies on correct grammatical subtheories, instead of explicit error taxonomies. DiBEx, thus, realizes anticipation free error diagnosis. The system enters in a tutorial dialogue with the student, where each explanation (dialogue) step is based on the principles of a single tutorial strategy and a dynamic user model.

Highlights

  • Computer Assisted Language Learning (CALL) has been one of the first incarnations of ELearning, which has become a trend nowadays and a commercial factor

  • These design standards do improve the quality of E-Learning systems, it is widely accepted that an optimal learning environment is one where the learner is guided by an intelligent personalized tutor, that adapts to the level of expertise of the learner, for example in tailoring explanations to the user's domain knowledge and selecting appropriate exercises

  • This is the area of Intelligent Tutorial Systems (ITS) and User Modelling (UM), so far a mainly academic discipline

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Summary

Introduction

Computer Assisted Language Learning (CALL) systems were developed as early as the 60s, when mainframe computers were installed at universities and large firms and experimentation with more personalized, non-mathematical and industrial uses of the computer began. Real life teachers have a variety of choices at their disposal to respond to a student They can respond both in writing and orally, make judgements about the students' expertise and learning progress and adapt in certain teaching situations they can overlook language errors to advance the communicative tasks and build confidence. This flexibility of the teacher, both consciously driven by the curriculum and the underlying pedagogy and partly driven by intuition, is very difficult to simulate. Building on such a diagnosis, a response can be generated, which can be varied according to input of the user model and the tutorial model

Examples of Explanatory Dialogues
The Problem of Error Diagnosis
NLP-based Intelligent Tutoring Systems
The DiBEx System
Grammatical Facts
Grammatical Rules
Error diagnosis
The Tutor
Summary and Outlook
Full Text
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