A Phraseology Approach in Developmental Education Placement

Abstract

. This study focuses on an automatic classification task aiming at placing community college students into the appropriate level (Level 1 and 2) of Developmental Education (DevEd) courses, according to their English L1 proficiency. DevEd courses are designed to remediate and support students’ communication skills in reading and writing be - fore they can fully participate in college-level or college-bearing courses. This paper uses machine-learning methods to investigate the impact of considering multiword expressions (MWE) as entire tokens on the automatic classification task. Since many MWE are often non-compositional in meaning and constitute a large percentage of the textual units of many texts, they are likely to have a relevant role in the data representation of texts and, hence, improve subsequent classification task. Information is scarce regarding the tokenization of MWE and how this affects automatic placement. To this end, a random, balanced corpus of 186 sample texts (93 from each level) was used. Experiments compared the performance of a set of classifiers on the plain text corpus and on a version of the same corpus annotated for MWE. Results showed that using MWE as lexical features improved the classification accuracy by 8.1% above the baseline.