Aggregation improves learning: experiments in natural language generation for intelligent tutoring systems

Authors:
Barbara Di Eugenio;Davide Fossati;Dan Yu;Susan Haller;Michael Glass
Affiliations:
University of Illinois, Chicago, IL;University of Illinois, Chicago, IL;University of Illinois, Chicago, IL;University of Wisconsin - Parkside, Kenosha, WI;Valparaiso University, Valparaiso, IN
Venue:
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Year:
2005

Citing 12
Cited 9

Using Grice's maxim of quantity to select the content of plan descriptions

Artificial Intelligence
Building natural language generation systems

Building natural language generation systems
SNePS: a logic for natural language understanding and commonsense reasoning

Natural language processing and knowledge representation
Lessons from a failure: generating tailored smoking cessation letters

Artificial Intelligence
Developing and empirically evaluating robust explanation generators: the KNIGHT experiments

Computational Linguistics
An empirical study on the generation of anaphora in Chinese

Computational Linguistics
A fast and portable realizer for text generation systems

ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Integrating text plans for conciseness and coherence

COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Evaluating and comparing three text-production techniques

COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
A corpus-based analysis for the ordering of clause aggregation operators

COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
An empirical study of the influence of argument conciseness on argument effectiveness

ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Natural Language Generation for Intelligent Tutoring Systems: a case study

Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology

Introduction

Natural Language Engineering
Aggregation via set partitioning for natural language generation

HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Learning Linked Lists: Experiments with the iList System

ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Be Brief, And They Shall Learn: Generating Concise Language Feedback for a Computer Tutor

International Journal of Artificial Intelligence in Education
Natural Language Generation for Intelligent Tutoring Systems: a case study

Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Learning Tutorial Rules Using Classification Based On Associations

Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
The role of positive feedback in intelligent tutoring systems

HLT-SRWS '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Student Research Workshop
An intelligent tutoring system that generates a natural language dialogue using dynamic multi-level planning

Artificial Intelligence in Medicine
Task-based evaluation of NLG systems: control vs real-world context

UCNLG+EVAL '11 Proceedings of the UCNLG+Eval: Language Generation and Evaluation Workshop

Quantified Score

Hi-index	0.00

Visualization

Abstract

To improve the interaction between students and an intelligent tutoring system, we developed two Natural Language generators, that we systematically evaluated in a three way comparison that included the original system as well. We found that the generator which intuitively produces the best language does engender the most learning. Specifically, it appears that functional aggregation is responsible for the improvement.