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Six emerging evaluation approaches for intelligent tutoring and adaptive learning environments

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 review), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

The article organizes its survey around six approaches that "hold great promise for evaluating educational environments": using the open web and crowd sourcing, comparing decisions of adaptive environments with human experts, using simulated learners, washing out selection bias, examining learning curves, and performing evaluations derived from mega quantities of micro measurements. Each is presented as an expansion of the 1993 survey, enabled by increases in computer speed, storage, and connectivity. The framework spans formative and summative evaluation of smart, intelligent, or adaptive educational environments.

Accounts

  • Six emerging evaluation approaches for intelligent tutoring and adaptive learning environments: The article organizes its survey around six approaches that "hold great promise for evaluating educational environments": using the open web and crowd sourcing, comparing decisions of adaptive environments with human experts, using simulated learners, washing out selection bias, examining learning curves, and performing evaluations derived from mega quantities of micro measurements. Each is presented as an expansion of the 1993 survey, enabled by increases in computer speed, storage, and connectivity. The framework spans formative and summative evaluation of smart, intelligent, or adaptive educational environments. (Jim Greer et al. (2015))

Claims

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Key Sources

  • Jim Greer & Mary Mark. (2015). Evaluation Methods for Intelligent Tutoring Systems Revisited. Int J Artif Intell Educ 26:387–392. https://doi.org/10.1007/s40593-015-0043-2
Page metadata
Field Value
type research-method
id six-emerging-evaluation-approaches-for-intelligent-tutoring-and-adaptive-learning-environm
title Six emerging evaluation approaches for intelligent tutoring and adaptive learning environments
description The article organizes its survey around six approaches that "hold great promise for evaluating educational environments": using the open web and crowd sourcing, comparing decisions of adaptive environments with human experts, using simulated learners, washing out selection bias, examining learning c
status review
generated by process:settle-candidates on 2026-10-28
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