Past Seminars

Knowledge and use of academic vocabulary is important for learners preparing for or participating in English medium university education. Academic vocabulary knowledge contributes to academic success. Corpus-based research has shed light on the nature of written and spoken academic lexis across and within subjects which is useful for identifying vocabulary worth focusing on in EAP classrooms, textbooks, materials and websites such as the EAP Foundation. The focus of this talk is issues related to acquiring academic vocabulary, using examples from research in secondary and tertiary contexts in English as a foreign and second language countries. The talk begins with a model of vocabulary growth from Nation and Coxhead (2021), before turning to the following issues: (1) challenges EAP learners can face when learning and using academic vocabulary; (2) reports of low levels of vocabulary of EAP learners in a range of contexts; (3) the need for principled approaches and understanding to support vocabulary acquisition and use in EAP. The final part of the talk includes recommendations for practice based on the three issues as well as some suggestions for future research. There will be time for questions.

In recent years, AI technology has advanced significantly, leading to its increasing integration into various fields, including second language (L2) education. These developments offer exciting opportunities for language teachers to enhance their teaching methods and student engagement. Given such developments, this presentation aims to provide an accessible introduction to a range of AI technologies relevant to L2 education, with a focus on the latest research findings. It will explore several tools, starting with large language model (LLM)-based chatbots as speaking practice partners, and ChatGPT as automated writing evaluation tools. Additionally, the presentation will cover how ChatGPT can be used to build test items, offering practical insights for L2 educators. By reviewing previous studies on these AI applications in L2 education, it will provide a clear overview of their benefits and limitations.

Current Research in Multilingual and Multimodal Literacy across Content Areas

The role of text in literacy development across content areas

Talk 1: A Universal Infrastructure for Learning Languages through Cultural Activities: The ENACT Web App

ENACT is a free web app https://enacteuropa.com/ developed at Newcastle University UK, co-funded by the European Union. The app enables people to learn aspects of foreign languages while performing a meaningful real-world task which enables them to experience the culture of the target language. In this presentation, we will introduce the principles underlying the app design, and briefly demonstrate the key features of the ENACT app: the interactive player, the author, and the community. We will then present our research on vocabulary development outcomes and processes, and locate the findings in a universal infrastructure for language development (SCIERRA) and make recommendations for designers of digital environments and language learners. We will end with an update on the latest materials we have produced for learning Chinese https://enacteuropa.com/?q=node/437, on the impact of the project and our in-press Bloomsbury book.

Talk 2: Investment in Ethnocultural Maintenance: Multimodal Cultural Artifact Creation as a pedagogical tool

Ethnocultural maintenance is important to both the cultural diversity of society and to the well-being of marginalized ethnics. In this presentation, we will present a research study on a group of university marginalized ethnic students’ experience of creating multimodal cultural artifacts, and how this experience shaped their investment in ethnocultural maintenance. Following that, we will also present a study on the impact of multimodal cultural artifact creation on secondary ethnic minority students’ linguistic and cultural collaboration. We argue for multimodal cultural artifact composing as a culturally sustaining pedagogy to enhance ethnocultural maintenance.

This presentation explores the fine-grained processes of adult-child language interactions by leveraging advanced machine learning and natural language processing algorithms. Previous research has largely focused on the frequency of children’s exposure to language features, such as lexical diversity and syntactic complexity, using audio and video samples to predict language development. However, these approaches often overlook the complex semantic relationships and the influence of contextual and individual variability. By employing techniques such as semantic network analysis, probabilistic topic modeling, and sentiment analysis, this study aims to provide a more nuanced understanding of these interactions. The findings indicate that AI-driven methods can significantly enhance our comprehension of children’s language learning environments. They support the development of targeted interventions that adapt strategies to specific activity contexts and individual child characteristics, ultimately advocating for the effective fostering of high-quality language input to meet the diverse needs of young learners.