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Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

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Published on: September 27, 2020

A model for programmatic assessment fit for purpose.

C P M van der Vleuten¹, L W T Schuwirth, E W Driessen

¹Department of Educational Development and Research, Faculty of Health, Medicine and Life Sciences, Maastricht University, The Netherlands. c.vandervleuten@maastrichtuniversity.nl

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|February 28, 2012

Summary

This summary is machine-generated.

This study introduces a programmatic assessment model optimizing learning and progress decisions. It uses cycles of training, assessment, and support, with expert judgment and bias reduction for reliable evaluations.

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Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

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Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Area of Science:

Educational Measurement
Programmatic Assessment
Learning Sciences

Background:

Current assessment models often separate learning and decision-making functions.
Psychometric approaches tend to focus on individual instruments rather than systemic design.

Purpose of the Study:

To propose a model for programmatic assessment that integrates assessment for learning and assessment for decision-making.
To provide a framework for designing assessment systems grounded in empirical research and theory.

Main Methods:

Development of a model based on empirically derived assessment principles.
Specification of cycles including training, assessment, learner support, and evaluation.
Incorporation of expert judgment with strategies for sampling and bias reduction.
Application of procedural assessment strategies inspired by qualitative research.

Main Results:

The proposed model optimizes individual data points for learning and feedback value.
High-stakes decisions are based on aggregated data, enhancing reliability.
Expert judgment is integrated with methods to mitigate subjectivity and bias.

Conclusions:

The model facilitates a systems approach to assessment design, moving beyond a narrow focus on individual instruments.
It offers a theoretically grounded and empirically supported framework for programmatic assessment.
This approach enhances the utility of assessment for both learner development and progress evaluation.