Fifty-five Chinese kindergarten teachers from Shenzhen (n = 38) and Hong Kong (n = 17) were surveyed to discern their beliefs and reported practices about multiple literacies related to e-learning and knowledge management. Results indicated that: (1) Shenzhen teachers had a better knowledge about multiple literacies and facilitated the development of multiple literacies in their students more than their counterparts from Hong Kong; (2) Teachers' educational attainment and their beliefs did not predict the multiple literacies practice; and (3) after controlling for these variables, child-PC ratio significantly contributed to the variation in multiple literacies practice. Educational implications and challenges associated with teacher education are discussed. Copyright © 2009 Laboratory for Knowledge Management & E-Learning, The University of Hong Kong.

This study investigated how English learners complete multimodal formative quizzes. Participants included 17,950 students enrolled in a mandatory English for Academic Purposes course at a university in Hong Kong. We retrieved data from Blackboard, a learning management system, and conducted a two-step cluster analysis to examine student self-regulated learning (SRL) profiles with the quizzes. We first identified five clusters of learners with distinctively different self-regulated learning patterns. Then, we performed a multivariate analysis of variance (MANOVA) to further explore their differences in SRL, in terms of start day, days started before deadline, differences in scores between first and last attempt, and scores in language learning activities. Our findings echoed those of previous studies on the relationship between self-regulated learning and academic success. This research enables us to better understand the needs of EAP students in Hong Kong. Copyright © 2023 International Forum of Educational Technology & Society.

The integration of generative artificial intelligence (AI) in English language teaching presents opportunities and challenges for instructors. This study explores the attitudes of higher education English language instructors towards generative AI tools, their intentions to use them and the institutional support and professional development necessary to teach and learn with them. As the field continues to evolve rapidly, it is essential to comprehend the readiness of front-line language instructors. This qualitative interpretive study seeks to identify the digital competencies and pedagogical knowledge required to implement generative AI in education and provide guidance for the design of professional development programmes that address the challenges and concerns associated with adopting AI. Drawing on semi-structured interviews with twelve instructors at a higher education institution in Hong Kong, the findings reveal the significance of familiarity and confidence with using AI-driven teaching tools, the challenges and concerns language instructors face and the need for tailored support and professional development. The study offers ten practical implications to cultivate language instructors’ digital competencies, pedagogical knowledge and positive attitudes towards integrating AI to enhance their students’ learning experiences. Copyright © 2023 The Authors. Published by Elsevier Ltd.

Reading is a multidimensional process that has evolved through history to adapt to accommodate environmental changes. Today, reading continues to evolve to meet the demands of globalization. The study examined the complex connection between curriculum, teachers, and students. To explore and research the relationship between stakeholders in the English reading curriculum, primarily policymakers, teachers, and students. The author selected English reading education for Hong Kong secondary students as a case study. Current and prior literature suggests that English reading to English Second Language Learners (ESL) focuses on the general perceptions and motivations of ESL learning and teaching. However, minimal research focuses on Chinese learners' self-perception of ESL reading, teachers’ beliefs and practices, curriculum interpretation, implementation, and intent. Therefore, examining, understanding, and identifying the relationship between Curriculum Intentions and Classroom Realities is essential.The objective of the study was threefold (1) to find the relations between learning and teaching and the perceptions of students, (2) to identify teachers' interpretations of the English reading curriculum and how their beliefs and classroom practices could affect the implementation of curriculum, and (3) the alignment of ESL reading lessons prescribed by the Education Bureau of Hong Kong policymakers and the teachers’ implementation in class.There were 250 junior form students participants and 24 English teachers. Questionnaires, semi-formal interviews, lesson observations, and focus group discussions were employed for data collection.The results highlighted four themes around the area of (i) teachers' efficacy of curriculum interpretation, (ii) challenges affecting beliefs and classroom practice for the implementation of curriculum, (iii) students' understanding of the intent of curriculum, and (iv) the misalignments of curriculum intentions and classroom realities. The points

A corpus provides extraordinarily rich and flexible resources for self-learning and can guide students in finding answers to their language queries. Although corpus linguistics has become mainstream in language research, a corpus-based language teaching approach remains largely unknown to the frontline teachers due to various reasons. In the past few years, our EdUHK corpus team have been working towards bridging the gap between corpus linguistic research and teachers’ teaching practice by introducing a corpus-based language pedagogy (CBLP) to English teachers in Hong Kong, China’s Mainland and elsewhere. In this presentation, we will focus on an awardwinning website, The Corpus-based Platform for Language Teachers (CAP) (https://corpus.eduhk.hk/cap/), and demonstrate how we theorised CBLP and enacted CBLP training for in-service and pre-service English teachers through the learning and teaching resources we created on the CAP website. The CAP website serves as the ideal pathway to transfer our expertise in corpus linguistics to the broad teacher community in Hong Kong, China’s Mainland, and other regions/countries. We will first provide the theoretical framework for CBLP and then use concrete examples from the CAP website to show how we implemented the CBLP training for our target participants to help them develop competence to learn and teach with corpora. Copyright © 2022 ICELEAL.

The linguistic backgrounds of English learners in Hong Kong are highly complex and diverse. There are local Hong Kong learners who speak Cantonese as their native language, new immigrants and students from different dialectal regions in mainland China and non-Chinese learners from the Philippines and Pakistan, among others. To optimise the effectiveness of English pronunciation teaching with such a diverse student body, it is of paramount importance to provide Hong Kong teachers with a spoken learner corpus. This paper introduces a self-developed spoken learner corpus and shows how it can be used in the design of pedagogic applications. The establishment of a learner corpus can help teachers understand the recurrent pronunciation difficulties encountered by learners of particular language backgrounds so that they can customise their teaching materials. To promote pronunciation teaching using this corpus, a teacher training programme was designed and conducted. Pre-service and in-service teachers participated in three face-to-face and real-time streaming workshop sessions, two online sessions, and a lesson plan design competition. Most of the participants appreciated the authenticity of the learner speech data, the diversity of speaker language backgrounds and the effectiveness of using the corpus as a teaching tool. Nonetheless, some suggested that the diversity of reading materials for eliciting speech data could be improved and that more dialect subgroups could be included in the corpus. Copyright © 2023 UKM Press, National University of Malaysia (UKM).

Background: Since the outbreak of COVID-19, universities in Hong Kong have implemented online and hybrid teaching modes, making computer-assisted language learning (CALL) a primary way for English learning. Research on English learning motivation and self-regulation has seldom considered learners' emotions (satisfaction and preparedness) and the synchronic online courses in a CALL context. Objectives: This study analyses the relationship between Hong Kong university students' motivation and self-regulation by considering their emotions in computer-assisted synchronic online English courses. Methods: 311 Hong Kong university students were invited to complete two questionnaires that investigated students' motivation and self-regulation in computer-assisted online English courses. SPSS 26 was employed for exploratory factor analysis (EFA) to extract factors and exclude unsuitable items, and confirmatory factor analysis (CFA) was performed to verify the results of EFA. One-way ANOVA was conducted to detect the impact of personal emotion on motivation and self-regulation. AMOS 26 was adopted to construct a structural equation model (SEM) to explore the relationship between motivation and self-regulation in the CALL context. Results and Conclusions: The results show that satisfaction and preparedness positively correlate with online English learning motivation and self-regulation. Instrumentality-attitude significantly predicts all factors of self-regulation via SEM and goal setting significantly predicts all factors of motivation. With feasible English learning goals, Hong Kong university students are more proactive in learning English online where they actively interact with teachers and peers. Implications: Based on the L2 self-system (Dörnyei, The psychology of the language learner: Individual differences in second language acquisition, 2005) and self-determination theory (Barnard et al., Internet and Higher Education, 2009, 12, 1–6), this study clarifies the relationship

This study investigates how the online behaviors of students can be associated with student outcomes via a learning analytics approach. The objective of this study is to examine whether online behaviors of students can predict student success in an English course. Also, this study aims to examine whether some machine learning techniques performed better than others. Learners in this study were students in an English for Academic Purposes course at a university in Hong Kong. Other than face-to-face teaching, this course included a mandatory multimodal language learning package first introduced in 2012. The research team retrieved a mega-dataset from all learners who took the course from 2012 to 2019 (n = 17,968). Using the Classification Tree technique, Logistical Regression, and Artificial Neural Networks, the research team derived three predictive algorithms to associate various online engagement variables (e.g., start day, first attempt scores, and number of attempts) with course success. Results suggest that Classification Tree performed better in predictive accuracy and F1 ratio. The chapter provides useful insights for adaptive design. The study also discusses several useful suggestions on how practitioners can adopt these algorithms for better adaptive design. Copyright © 2024 selection and editorial matter, Patsy D. Moskal, Charles D. Dziuban, and Anthony G. Picciano; individual chapters, the contributors.

Despite the rapid growth of English Medium Instruction (EMI) programmes around the globe, the language goals in such programmes have not been clearly defined, making it difficult to evaluate their success. Accordingly, our paper reports on a study that attempted to identify the language goals, operationalised as the vocabulary expected of learners, by investigating the under-explored area of EMI assessment. We sampled a corpus of 16 sets of examination papers in two subject areas, namely, Biology and Geography, spanning eight years in Hong Kong. The examination papers, analysed for their vocabulary profiles, indicated that students were expected to learn beyond the K3 level (the most frequent 3,000 words in English) and master mid-frequency words (K4−K8) as well as academic words. Our findings suggest that the less frequent words were not often shared by the two subjects. Analysing academic words also enabled us to showcase how some words were common across the two subjects while others were more subject-specific. Pedagogical implications are discussed, highlighting the need for teachers and students to attend to target language goals in EMI, and to consider the subject-specific nature of vocabulary items in EMI. Copyright © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Natural language generation (NLG) is a process within artificial intelligence where computer systems produce human-comprehensible language texts from information. English as a foreign language (EFL) students’ use of NLG tools might facilitate their idea generation, which is fundamental to creative writing. However, little is known about how EFL students interact with NLG tools to generate ideas. This study explores strategies adopted by EFL students when searching for ideas using NLG tools, evaluating ideas generated by NLG tools and selecting NLG tools for idea generation. Four Hong Kong secondary school students attended workshops where they learned to write stories comprising their own words and words generated by NLG tools. After the workshops, they answered questions to reflect on their writing experience with NLG tools. In a thematic analysis of the written reflections, we found students may have existing ideas when searching for ideas and evaluating ideas with NLG tools. Students showed some aversion to ideas generated by NLG tools and selected NLG tools that generated a greater quantity of ideas. The findings inform our understanding of EFL students’ concerns when using NLG tools for idea generation and can inform educators’ instruction to implement NLG tools for classroom creative writing. Copyright © 2023 The Author(s).