NLP and Education: using semantic similarity to evaluate filled gaps in a large-scale Cloze test in the classroom

TL;DR

This paper explores using NLP, specifically word embeddings, to automatically evaluate student responses in large-scale Cloze tests, improving efficiency and accuracy in educational assessments.

Contribution

It introduces an automated semantic similarity assessment method using WE models for Brazilian Portuguese, validated against human judgments, enhancing large-scale Cloze test evaluation.

Findings

GloVe model showed highest correlation with human judges

WE models effectively assess semantic similarity in student responses

Proposed method improves efficiency in large-scale educational testing

Abstract

This study examines the applicability of the Cloze test, a widely used tool for assessing text comprehension proficiency, while highlighting its challenges in large-scale implementation. To address these limitations, an automated correction approach was proposed, utilizing Natural Language Processing (NLP) techniques, particularly word embeddings (WE) models, to assess semantic similarity between expected and provided answers. Using data from Cloze tests administered to students in Brazil, WE models for Brazilian Portuguese (PT-BR) were employed to measure the semantic similarity of the responses. The results were validated through an experimental setup involving twelve judges who classified the students' answers. A comparative analysis between the WE models' scores and the judges' evaluations revealed that GloVe was the most effective model, demonstrating the highest correlation with the judges' assessments. This study underscores the utility of WE models in evaluating semantic similarity and their potential to enhance large-scale Cloze test assessments. Furthermore, it contributes to educational assessment methodologies by offering a more efficient approach to evaluating reading proficiency.