Automated, Unobtrusive, Action-by-Action Assessment of Self-Regulation During Learning With an Intelligent Tutoring System

被引：65

作者：

Aleven, Vincent ^{[1
]}

Roll, Ido ^{[2
]}

McLaren, Bruce M. ^{[1
]}

Koedinger, Kenneth R. ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Human Comp Interact Inst, Pittsburgh, PA 15217 USA

[2] Univ British Columbia, Carl Wieman Sci Educ Initiat, Vancouver, BC V5Z 1M9, Canada

来源：

EDUCATIONAL PSYCHOLOGIST | 2010年 / 45卷 / 04期

基金：

美国国家科学基金会;

关键词：

COGNITIVE TUTORS; HELP-SEEKING; TECHNOLOGY; INQUIRY; MOTIVATION;

D O I：

10.1080/00461520.2010.517740

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

Assessment of students' self-regulated learning (SRL) requires a method for evaluating whether observed actions are appropriate acts of self-regulation in the specific learning context in which they occur. We review research that has resulted in an automated method for context-sensitive assessment of a specific SRL strategy, help seeking while working with an intelligent tutoring system. The method relies on a computer-executable model of the targeted SRL strategy. The method was validated by showing that it converges with other measures of help seeking. Automated feedback on help seeking driven by this method led to a lasting improvement in students' help-seeking behavior, although not in domain-specific learning. The method is unobtrusive, is temporally fine-grained, and can be applied on a large scale and over extended periods. The approach could be applied to other SRL strategies besides help seeking.

引用

页码：224 / 233

页数：10

共 65 条

[1] Help seeking and help design in interactive learning environments [J].