Student achievement and French sentence repetition test scores

Abstract Sentence repetition (SR) tests are one way of probing a language learner's oral proficiency. Test-takers listen to a set of carefully engineered sentences of varying complexity one-by-one, and then try to repeat them back as exactly as possible. In this paper we explore how well an SR test that we have developed for French corresponds with the test-taker's achievement levels, represented by proficiency interview scores and by college class enrollment. We describe how we developed our SR test items using various language resources, and present pertinent facts about the test administration. The responses were scored by humans and also by a specially designed automatic speech recognition (ASR) engine; we sketch both scoring approaches. Results are evaluated in several ways: correlations between human and ASR scores, item response analysis to quantify the relative difficulty of the items, and criterion-referenced analysis setting thresholds of consistency across proficiency levels. We discuss several observations and conclusions prompted by the analyses, and suggestions for future work

英作文支援のための冠詞誤り訂正及びその根拠の提示

Tohoku University乾健太

Computational Models of Problems with Writing of English as a Second Language Learners

Learning a new language is a challenging endeavor. As a student attempts to master the grammar usage and mechanics of the new language, they make many mistakes. Detailed feedback and corrections from language tutors are invaluable to student learning, but it is time consuming to provide such feedback. In this thesis, I investigate the feasibility of building computer programs to help to reduce the efforts of English as a Second Language (ESL) tutors. Specifically, I consider three problems: (1) whether a program can identify areas that may need the tutor’s attention, such as places where the learners have used redundant words; (2) whether a program can auto-complete a tutor’s corrections by inferring the location and reason for the correction; (3) for detecting misusages of prepositions, a common ESL error type, whether a program can automatically construct a set of potential corrections by finding words that are more likely to be confused with each other (known as a confusion set). The viability of these programs depends on whether aspects of the English language and common ESL mistakes can be described by computational models. For each task, building computational models faces unique challenges: (1) In highlighting redundant areas, it is difficult to precisely define “redundancy” in a computer’s language. (2) In auto-completing tutors’ annotations, it is difficult for computers to correctly interpret how many writing problems were addressed during revision. (3) In confusion set construction, it is difficult to infer which words are more likely confused with the given word. To address these challenges, this thesis presents different model alternatives for each task. Empirical experiments demonstrate the degrees of success to which computational models can help with detecting and correcting ESL writing problem

A Statistical Approach to Grammatical Error Correction

Ph.DDOCTOR OF PHILOSOPH