Computer-assisted detection of 90% of EFL student errors

Harvey‐Scholes, Calum

doi:10.1080/09588221.2017.1392322

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Final sampling (11,475 words) settled on 60 compositions (20 for each proficiency level, representing all topic/prompts, and executed without supporting materials). This quadruples Lawley’s (2016) sample size (2,648 words) and falls within the range of successful ones from similar studies: Hernández García’s (2017) contained 7,184 words, San Mateo’s (2016) 9,500, Chacón-Beltrán’s (2017) 12,063, and Harvey-Scholes’s (2018) 13,644.…”

Section: Testing the Efficacy Of The Psc Detecting And Providing Wcfsupporting

confidence: 74%

“…Responding to these issues and limitations and drawing from related literature (Blázquez-Carretero, 2019;Blázquez-Carretero & Fan, 2019;Blázquez-Carretero & Woore, 2021;Harvey-Scholes, 2018;Lawley, 2015Lawley, , 2016, a team of researchers from the Universidad Nacional de Educación a Distancia decided to build a prototype spellchecker to help SFL students in selfcorrecting their written outputs. This tool is specially designed to tackle common SFL misspellings through WCF.…”

Section: Limitations Of Existing Spellcheckers For Sfl Learnersmentioning

confidence: 99%

“…A second sample of 30 compositions (5,954 words) by 30 different SFL learners was then selected, revealing 642 errors. As the second sample has a similar error-per-word rate with no new categories of error, “the research would have covered a critical mass of data” (Harvey-Scholes, 2018: 149).…”

Section: Testing the Efficacy Of The Psc Detecting And Providing Wcfmentioning

confidence: 99%

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Building a pedagogic spellchecker for L2 learners of Spanish

Blázquez-Carretero¹

2023

ReCALL

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In 2016, Lawley proposed an easy-to-build spellchecker specifically designed to help second language (L2) learners in their writing process by facilitating self-correction. The aim was to overcome the disadvantages to L2 learners posed by generic spellcheckers (GSC), such as that embedded in Microsoft Word. Drawbacks include autocorrection, misdiagnoses, and overlooked errors. With the aim of imparting explicit L2 spelling knowledge, this correcting tool does not merely suggest possible alternatives to the detected error but also provides explanations of any relevant spelling patterns. Following Lawley’s (2016) recommendations, the present study developed a prototype computer-based pedagogic spellchecker (PSC) to aid L2 learners in self-correcting their written production in Spanish. First, a corpus was used to identify frequent spelling errors of Spanish as a foreign language (SFL) learners. Handcrafted feedback was then designed to tackle the commonest misspellings. To subsequently evaluate this PSC’s efficacy in error detection and correction, another learner Spanish corpus was used. Sixty compositions were analysed to determine the PSC’s capacity for error recognition and feedback provision in comparison with that of a GSC. Results indicate that the PSC detected over 90% of the misspellings, significantly outperforming the GSC in error detection. Both provided adequate feedback on two out of three detected errors, but the pedagogic nature of the former has the added advantage of facilitating self-learning (Blázquez-Carretero & Woore, 2021). These findings suggest that it is feasible to develop spellcheckers that provide synchronous feedback, allowing SFL learners to confidently self-correct their writing while saving time and effort on the teacher’s part.

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Section: Testing the Efficacy Of The Psc Detecting And Providing Wcfsupporting

confidence: 74%

Section: Limitations Of Existing Spellcheckers For Sfl Learnersmentioning

confidence: 99%

Section: Testing the Efficacy Of The Psc Detecting And Providing Wcfmentioning

confidence: 99%

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Building a pedagogic spellchecker for L2 learners of Spanish

Blázquez-Carretero¹

2023

ReCALL

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“…Findings from a study on CEA (N-gram) software indicated that errors that were homophones were overlooked, accounting for 16% of spelling errors [54]. CBEA failed to identify incorrect multiword units or collocations [55,56].…”

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confidence: 99%

Synthesizing the Attributes of Computer-Based Error Analysis for ESL and EFL Learning: A Scoping Review

et al. 2022

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An error analysis (EA) is the process of determining the incidence, nature, causes, and consequences of unsuccessful language acquisition. Traditional EA for English as a second language/English as a foreign language technique lacks an orderly investigation due to human errors. Consequently, computer-based error analysis (CBEA) was introduced into EA in linguistics to achieve accuracy and instant analysis. Although many studies have concluded that CBEA holds numerous strengths, other studies have found that CBEA has certain limitations. However, the strengths and limitations of the CBEA were not clearly synthesized and outlined. Accordingly, this review aims to explore the strengths and limitations of CBEA to provide areas for improvement of computer applications toward an efficient EA procedure. This work also aims to synthesize the strengths and limitations of CBEA mentioned in a variety of articles into a single review to sustain its efficiency and serve as a guide for teachers to benefit from the strengths and gain awareness of CBEA’s limitations. Stakeholders can access broader perspectives on developing application software capable of addressing the deficiencies in EA. By doing so, we can sustain CBEA’s efficiency for the benefit of all. For this purpose, Arksey and O’Malley’s procedure of a scoping review and the PRISMA framework were adopted to guide the filtering and selection of relevant previous studies. Sixty-two articles were selected through the processes of identification, screening, eligibility, and inclusion. Although the findings showed six strengths and seven limitations of CBEA, CBEA can only perform the diagnostic part of EA. Human intervention is still required to perform the prognostic part to accomplish an efficient EA.

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Exploring the effects of corpus-based business English writing instruction on EFL learners’ writing proficiency and perception

Tsai

2021

J Comput High Educ

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Computer-assisted detection of 90% of EFL student errors

Cited by 5 publications

References 8 publications

Building a pedagogic spellchecker for L2 learners of Spanish

Building a pedagogic spellchecker for L2 learners of Spanish

Synthesizing the Attributes of Computer-Based Error Analysis for ESL and EFL Learning: A Scoping Review

Exploring the effects of corpus-based business English writing instruction on EFL learners’ writing proficiency and perception

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