Two Steps Forward, One Step Back: A Computer-aided Error Analysis of Grammar Errors in EAP Writing

This study consists of a computer-aided error analysis of grammar errors in 70 university placement essays, scores on which resulted in students being either placed in EAP (English for Academic Purposes) Level 1, placed in EAP Level 2, or exempted from the EAP program. Essay scoring happened prior to the study, using the department process whereby each essay was scored by at least two raters using an analytic rubric. An error taxonomy of 16 categories based on Lane and Lange (1999) was used to code the essay data. Data was assembled into a corpus and tagged using the text analysis program UAM (Universidad Autónoma de Madrid) CorpusTool. Results were exported and analyzed with statistical tests. The results of the study validate the EAP placement process. Scores in the language use section of the rubric were highly correlated with total scores, and inter-rater reliability was also found. Errors rates were also found to correlate with language use score, suggesting that raters were responding to grammatical errors in making their assessments. Comparisons between the three placement groups revealed significant differences in error rates between Level 2 and Exempt. Based on the correlations, between-group comparisons, and overall frequency of errors, six error categories were chosen for closer analysis: sentence structure, articles, prepositions, singular/plural, subordinate clauses, and other. The findings suggest that local errors, though often given low priority in textbooks, do significantly impact rater assessment. Results also suggest that error rates do not necessarily decrease with advancing level—some error rates may increase. Though this finding was surprising, it might be attributed in part to the fact that some errors can be evidence of interlanguage development as new forms are acquired. The study concludes with suggestions for teaching and future research

Annotating an Arabic Learner Corpus for Error

This paper describes an ongoing project in which we are collecting a learner corpus of Arabic, developing a tagset for error annotation and performing Computer-aided Error Analysis (CEA) on the data. We adapted the French Interlanguage Database FRIDA tagset (Granger, 2003a) to the data. We chose FRIDA in order to follow a known standard and to see whether the changes needed to move from a French to an Arabic tagset would give us a measure of the distance between the two languages with respect to learner difficulty. The current collection of texts, which is constantly growing, contains intermediate and advanced-level student writings. We describe the need for such corpora, the learner data we have collected and the tagset we have developed. We also describe the error frequency distribution of both proficiency levels and the ongoing work

A Corpus-Based Analysis of although Errors in Chinese EFL Learners’ Written Output

The subordinating conjunction although is frequently used in English and is considered easy for students to master by many Chinese English teachers. However, errors are often found in Chinese EFL learners although output during pedagogical practice. This paper aims to explore and analyze common errors of although in Chinese EFL learners writing. The study is a corpus-based analysis launched under the computer-aided error analysis framework which is a new practice developed from the error analysis hypothesis. Errors of although found in texts from the Chinese Learner English Corpus (CLEC) are extracted and analyzed. Qualitative and quantitative analyses are conducted in the study. According to the findings, there are four major types of although errors found in Chinese EFL learners writing but/yet addition, punctuation errors, although misuse, and omissions and blends. Factors such as interlingual difference between English and Mandarin Chinese, intralingual interference within the English language system, pedagogical neglect in English classrooms and different cognitive styles are potential causes of Chinese EFL learners although errors

Agreement Errors in Learner Corpora across CEFR: A Computer-Aided Error Analysis of Greek and Turkish EFL Learners Written Productions

This paper illustrates the use of learner corpus data (extracted from Cambridge Learner Corpus – CLC) to carry out an error analysis to investigate authentic learner errors and their respective frequencies in terms of types and tokens as well as contexts in which they regularly occur across four distinct proficiency levels, B1-B2; C1-C2, as defined by Common European Framework of Reference for Languages (henceforth CEFR) (Council of Europe, 2001). As a variety of learner corpora compiled by researchers become relatively accessible, it is possible to explore interlanguage errors and conduct error analysis (EA) on learner-generated texts. The necessity to cogitate over these authentic learner errors in designing foreign language learning programs and remedial teaching materials has been widely emphasized by many researchers (see e.g., Juozulynas, 1994; Mitton, 1996; Cowan, Choi, & Kim, 2003; Ndiaye & Vandeventer Faltin, 2003; Allerton et al., 2004). This study aims at conducting a corpus-based error analysis of agreement errors to reveal the related error categories between Greek and Turkish EFL learners, the distribution of agreement errors along the B1 - C2 proficiency range according to CEFR, and the distribution of agreement error types in respect of the L1 of the learners. The data analyzed in this study is extracted from the Cambridge Learner Corpus (CLC), the largest annotated test performance corpus which enables the investigation of the linguistic and rhetorical features of the learner performances in the above stated proficiency bands. The findings from this study reveal that, across B1-C2 proficiency levels and across different registers and genres, the most common agreement error categories by the frequency in which they occur are Verb Agreement (AGV), Noun Agreement (AGN), Anaphor Agreement (AGA), Determiner Agreement (AGD), Agreement Error (AG), and Quantifier Agreement (AGQ) errors. This study’s approach uses the techniques of computer corpus linguistics and follows the steps of the Error Analysis framework proposed by Corder (1971): identification, description, classification and explanation of errors

Writing/thinking in real time: digital video and corpus query analysis

The advance of digital video technology in the past two decades facilitates empirical investigation of learning in real time. The focus of this paper is the combined use of real-time digital video and a networked linguistic corpus for exploring the ways in which these technologies enhance our capability to investigate the cognitive process of learning. A perennial challenge to research using digital video (e.g., screen recordings) has been the method for interfacing the captured behavior with the learners’ cognition. An exploratory proposal in this paper is that with an additional layer of data (i.e., corpus search queries), analyses of real-time data can be extended to provide an explicit representation of learner’s cognitive processes. This paper describes the method and applies it to an area of SLA, specifically writing, and presents an in-depth, moment-by-moment analysis of an L2 writer’s composing process. The findings show that the writer’s composing process is fundamentally developmental, and that it is facilitated in her dialogue-like interaction with an artifact (i.e., the corpus). The analysis illustrates the effectiveness of the method for capturing learners’ cognition, suggesting that L2 learning can be more fully explicated by interpreting real-time data in concert with investigation of corpus search queries

Detección de trastornos del aprendizaje en la producción escrita de los alumnos en lengua extranjera: ¿Pueden ser útiles los corpus de estudiantes?

This paper reports on the possibility to detect learning disorders when conducting Computer-aided Error Analysis (CEA). The study of a longitudinal learner corpus compiled at the Universidad de Jaén (Spain) and error-tagged with the Université Louvain Error Editor (Hutchinson, 1996; Dagneaux, Denness, Granger and Meunier, 1996) reveals that the disorder of written expression, i.e. dysorthographia, may be detected in the students’ production in the foreign language. Failure in recognising symptoms of this disorder may lead to incorrect interpretations of the CEAs, as the errors found in a learner corpus may be misleadingly attributed to the student’s language acquisition process rather than to his or her learning disorders.Este artículo muestra la posibilidad de detectar trastornos de aprendizaje a la hora de realizar un análisis de errores informatizado. El estudio de un corpus de estudiantes longitudinal, compilado en la Univesidad de Jaén (España) y anotado con el anotador Université Louvain Error Editor (Hutchinson, 1996; Dagneaux, Denness, Granger and Meunier, 1996) muestra que es posible detectar un trastorno de la expresión escrita, disortografía, en la producción de estudiantes de inglés como lengua extranjera. Si los síntomas de este trastorno no se detectan, las interpretaciones de los resultados del análisis de errores informatizado pueden ser incorrectas, ya que los errores encontrados en un corpus de estudiantes se pueden atribuir de forma errónea al proceso de aprendizaje de idiomas del sujeto y no a su trastorno de aprendizaje

On the creation of a learner corpus for the purpose of error analysis

Learners with similar backgrounds have a tendency to make the same types of errors in L2 production. Such errors can be viewed as having the potential to inform pedagogical methodologies, in that they shed light onto which features of the L2 are the most problematic for particular learners. Analyzing such errors also provides insight as to why these learners tend to make these errors, thus furthering our understanding of how second languages are acquired.This study aimed to create a learner corpus for the purpose of error analysis to discover which errors occurred most frequently, and to examine why such errors occurred. Various CALL (computer aided language learning) methodologies were utilized to create an approximately 85,000 word learner corpus. Errors were corrected and classified, and error analysis was conducted on the most frequent errors found. This analysis revealed that interference from the learners\u27 L1 was the source for the majority of errors, while cultural and metalinguistic knowledge also proved to be at fault for some particular errors.The results of this study should prove to be valuable for English language teachers and researchers in Japan, in that the most frequent English errors that Japanese learners produce were quantified and also discussed. Thus, teachers and researchers can be cognizant of which errors prove to be the most troublesome, and can better understand why they occur to help Japanese learners to avoid them

Investigating language corpora as a grammar development resource

The digital era has brought new concepts and transformations into language development and has given rise to technology-based approaches to learner autonomy. It has shifted the focus from deductive to inductive learning, where the concept of ‘noticing’ (Schmidt, 1990) language forms is promoted. Literature suggests that this type of student-centered self-discovery of lexico-grammatical patterns can be greatly aided by corpus linguistics methods, specifically ‘Data-Driven Learning’ (DDL) (Johns, 1986; Braun, 2005; O’Keeffe et al, 2007). It reports on the valuable potential of DDL for developing learners’ multi-literacies and cognitive strategies, particularly raising their awareness of lexico-grammatical patterning (O’Keeffe and Farr, 2003). However, insights from corpus-based studies have not been widely applied in teaching practices (Reppen, 2022; Zareva, 2017). It has also been proposed that DDL enhances accurate representation of language, raises cultural understanding, provides learners with the freedom to explore and discover the language, and fosters learner autonomy, thus making them more effective language learners (Flowerdew, 2015). This affordance led to the design of a longitudinal experimental study which aimed to provide useful skills and processes in the use of language corpora as a grammar development resource in the pre-intermediate EFL classroom in an Armenain context outside of higher education. The evaluation data included pre-, post-, progress-, delayed post-test data, and Learner Autonomy Profile (LAP) form, the statistical analysis of which revealed the beneficial impact of the computer-based inductive approach of DDL on the learners’ grammar competency, independent learning skills, as well as the contribution of cognitive strategies to proceduralization of knowledge. It also included semi-structured interview data, which uncovered the learners’ increased engagement in the learning process, the positive change in their attitudes towards their own learning, and the ways of demonstrating autonomous abilities in working with concordances. These data also brought to light some of the fears and challenges of using DDL, as well discussing its theoretical and pedagogical underpinnings aligned with psychological processes of learning. The findings will serve all the participants of this hugely important ELT sector - researchers, language educators and learners. They will gain insights as to what is necessary to tap learners’ implicit long-term knowledge, to prepare them both psychologically and practically for independence so that they can be armed with confidence, interest in discovering the language, knowledge about their own learning, and understanding of how to make use of their learning styles and strategies. Keywords: conventional/technology-enhanced EFL classroom, corpus linguistics, data-driven learning (DDL), inductive/deductive grammar learning, direct/indirect written feedback, explicit/implicit knowledge, language awareness, learner autonomy.N