What Makes Human Explanations Effective
Year: 1993
Type: article
Abstract: If computer-based instructional systems are to reap the benefits of natural language interaction, they must be endowed with the properties that make human natural language interaction so effective. To identify these properties, we replaced the natural language component of an existing Intelligent Tutoring System (ITS) with a human tutor, and gathered protocols of students interacting with the human tutor. We then compared the human tutor's responses to those that would have been produced by the ITS. In this paper, I describe two critical features that distinguish human tutorial explanations from those of their computational counterparts. Introduction There is growing interest in teaching real world problem-solving tasks using computer-based intelligent apprenticeship environments in which students learn by doing (Gott, 1989). Such skills typically involve complex chains of hidden reasoning and one goal of an apprenticeship environment is to help externalize the cognitive processes th...
Author: Johanna D. Moore
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W1531538040