TO ERR IS HUMAN: COMPARING HUMAN AND AUTOMATED CORRECTIVE FEEDBACK

Authors

DOI:

https://doi.org/10.33407/itlt.v90i4.4980

Keywords:

Grammarly, writing correction, feedback, automated learning, automated written evaluation

Abstract

The importance of Corrective Feedback (CF) to language learners has been a controversial topic for a long time. While some studies recognised CF's importance for accurate language use, others considered it deterrent to the meaningful acquisition of a second language. Recently, modern types of corrective feedback that utilise the vast advance in IT and Artificial Intelligence (AI) have emerged. This advancement has opened new investigation areas. Up to now, researchers have acknowledged the role of Automated Written Evaluation (AWE) in enhancing students’ writing and motivating them. Other studies have focused on students’ and teachers’ perceptions of such tools. However, the particular variance between this type of CF and the traditional one is still an area to explore. Accordingly, the present study aimed to compare CF provided by teachers to that offered by a well-known writing assistant Grammarly. The descriptive design was used to analyse the CF instances provided by five college professors to the Grammarly suggestions on a corpus of 115 texts, 23700 words, written by college students. The descriptive statistics method was adopted to summarise the findings. The study's main results indicated no significant difference in the number of errors detected by the two techniques. However, human raters outperformed Grammarly in detecting grammatical errors and were more accurate in identifying structure-related mistakes. On the other hand, Grammarly was found more effective in detecting errors related to spelling and punctuation. These findings imply using focused CF to exploit both methods. Teachers can implement their regular CF approach to develop structural aspects of language. Further, they can encourage students to adopt sophisticated writing assistants to develop their writing mechanics. To account for the potential limitations of the current study, further research that employs a larger sample size and is conducted on longitudinal and experimental bases is required.

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Author Biography

Abdulaziz Sanosi, Prince Sattam Bin Abdulaziz University

PhD Applied Linguistics, Lecturer, Department of English language & Literature

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Published

2022-09-29

How to Cite

[1]
A. Sanosi, “TO ERR IS HUMAN: COMPARING HUMAN AND AUTOMATED CORRECTIVE FEEDBACK”, ITLT, vol. 90, no. 4, pp. 149–161, Sep. 2022.

Issue

Section

ICT and learning tools in the higher education establishments