The treatment of error in learners of Russian as a foreign language: Visual analytics

Abstract

There are well-known challenges in the assessment of learning in general, and especially in foreign language learning. The treatment of error in the classroom is a recent topic of research and one that has given rise to multiple approaches to pinpoint, identify and classify the errors made by learners of foreign and second languages. This article presents a methodological model based on visual analytics and education data mining to optimise teacher intervention in the face of individual and collective errors in the Russian language classroom. The methodology has been tested on learners of Russian as a foreign language at the University of Granada. It comprised an online questionnaire for skills assessment, with 75 questions that were classified by grammatical category and sub-category. It was filled out by the learners of the 2021/2022 academic year, yielding 31 responses. The responses were then analysed through visual analytics and education data mining techniques. Clustering questions and learners allowed the identification of different error patterns and groups of learners with common errors. This serves to demonstrate the usefulness of these techniques for classroom assessment.