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This article investigates the potential of using error-annotated L2 utterances and their associated language profile to more accurately assess the language proficiency of an L2 learner. The main goal of this paper is to demonstrate the use of machine learning methods for uncovering features of the learner's interlanguage and their proficiency level...
Context in source publication
Context 1
... visual representation also emphasizes the interconnection between the values of the Error_Type and the Proficiency_Level variables. One possible interpretation of the biplot in figure 2 is that L2 Greek learners at the A2 proficiency level begin to encounter the intricacy of grammatical agreement in Greek, which is why the red dotted A2 is relatively close to the blue data point, Agreement. The biplot reveals that B1 learners present a higher proportion of both Agreement and Gender errors, whereas L2 Greek learners at the B2 proficiency level have moved away from Agreement errors and display a higher frequency of errors related to Aspect, Gender and Case. ...
