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

This paper proposes using Natural Language Processing word embeddings to automatically score Cloze test responses by measuring semantic similarity between student answers and correct answers, validated with data from Brazilian K-12 students. The approach aims to make large-scale reading comprehension assessment more efficient by moving beyond exact-match scoring to accept semantically similar responses.

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,

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