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This study seeks to investigate whether training students in peer review improves their ability to identify both local and global level writing issues in texts. Undergraduate university students of English attending a one-semester writing course were asked to identify writing issues in sample texts at the beginning and end of the course. Results show that students improved in their ability to identify local or surface-level issues and had much lower rates of wrongly reporting text segments as incorrect. However, there was no improvement regarding identifying global or discourse-level issues. This points to the value of peer review for improving students’ language mechanics competence. However, it also shows that peer review is not a panacea: many students may still fail to develop their ability to assess content, organization, and cohesion in texts.
In recent years, there has been a push for digitalization in education, including foreign language teaching. This article shows how a switch to digital tools may prove particularly fruitful in the area of vocabulary development in EFL classes. It makes concrete suggestions for the integration of H5P, a tool offered on Moodle platforms, and ChatGPT (OpenAI, 2023), or similar large language model AI tools, into vocabulary teaching and testing in EFL classes. H5P offers those without website coding knowledge an accessible interface that allows them to code their Moodle webpages to present information in aesthetically pleasing and interactive ways. This paper presents a variety of H5P features that are particularly well-suited to presenting, practicing, and testing foreign language vocabulary, helping teachers create resources that are engaging for a digital generation of learners. A second section then discusses ChatGPT’s potential and weaknesses when supporting teachers in creating multiple-choice vocabulary gap-fill tasks for practice and tests. Based on a small sample of recorded student interactions with ChatGPT, we examine where the tool provides helpful and unhelpful feedback to learners looking to improve the language mechanics in their writing. This knowledge provides guidance to both teachers and learners on where large language models may currently be best used to support vocabulary learning generally and, more specifically, to gain feedback on learner writing.
