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Related Experiment Video

Updated: May 22, 2026

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

Published on: February 7, 2025

Computer-based assessment of student-constructed responses.

Joseph P Magliano¹, Arthur C Graesser

¹Northern Illinois University, DeKalb, IL 60115, USA. jmagliano@niu.edu

Behavior Research Methods

|May 15, 2012

Summary

Grading student writing using natural language processing (NLP) is challenging. This review suggests hybrid NLP systems analyzing diverse student responses improve assessment accuracy.

Area of Science:

Educational Technology
Natural Language Processing
Cognitive Science

Background:

Student-constructed responses (essays, think-alouds) are crucial for assessing learning.
Manual grading of these responses presents significant challenges for instructors.
Advances in Natural Language Processing (NLP) offer potential solutions for automated analysis.

Purpose of the Study:

To review the application of NLP in analyzing student-constructed responses.
To identify key dimensions for developing effective NLP assessment systems.
To guide researchers in selecting appropriate NLP methodologies for educational assessment.

Main Methods:

Literature review focusing on NLP applications for student responses.
Analysis of two critical dimensions: response type and NLP system application.

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Consideration of meaning-making responses versus comprehension products.

Main Results:

The effectiveness of NLP systems is influenced by the type of student response analyzed.
Different NLP algorithms are suited for different response types (e.g., essays vs. think-alouds).
Hybrid systems combining multiple NLP algorithms show promise for robust analysis.

Conclusions:

The choice of assessment protocol must align with the type of student response.
Hybrid NLP systems leveraging convergent algorithms are recommended for accurate evaluation.
Future NLP systems should consider both response characteristics and algorithmic approaches for educational assessment.