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.

二十一世紀初，香港掀起教育改革的浪潮。隨着 2000 年的教育改革報告書《終身學習•全人發展：香港教育制度改革建議》[2] 及 2001 年的課程發展議會報告書《學會學習──課程發展路向》[3] 的發佈，中國語文教育學習領域課程也配合改革的步伐，進行全面改革。文章試圖透過回顧基礎教育中國語文教育學習領域課程評估標準、評估系統的轉變、實施和調整的過程，以及香港學生在主要大型低風險國際評估中的表現，梳理基本能力評估存在的問題，並進行相應的探討，為相關教育工作者提供參考。 Copyright © 2021 澳門語言學會.

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

The COVID-19 pandemic has posed challenges to education systems around the world, resulting in a ‘new normal’ for school music education in which teaching and learning can no longer rely only on conventional classroom practices. Concerns have also been raised over music teachers’ digital competencies, given the perceived importance of the use of technology within the new educational landscape. This article presents the results of a study that examined the challenges and experiences associated with the digital competencies of music teachers during the pandemic period. Semi-structured interviews were conducted with full-time music teachers (N = 18), all of whom taught music as a subject at mainstream primary or secondary schools in Hong Kong. The findings revealed challenges and upskilling of music teachers during the lockdown period, including the technological constraints imposed by the school and the online environment, how they made use of technology to teach music online, and the different ways they developed their digital competencies. Alongside with the catalyst effect of the pandemic that stimulated their upskilling, curriculum changes in teacher training programmes are necessary to better prepare music teachers for the digital transformation of music education and future challenges under the post-pandemic ‘new normal’ situation. Copyright © 2023 The Author(s).

Museums provide direct learning experiences. In museums visitors can encounter objects and exhibits from specific times and places, related to concepts and disciplinary subjects in the arts, history, science, and social sciences. A museum is an educational fair. The Hong Kong Special Administrative Region Government’s recent primary school General Studies curriculum guide recommends learning through museums as an informal learning pathway for all students. This project is an up-to-date exploratory study of why and how Hong Kong primary school teachers and students visit museums educationally and how they perceive those visits and the accompanying learning experiences. A survey study from a sample of five local primary schools, 90 teachers and 415 senior form students, shows that although teachers and students may not visit museums very frequently for academic purposes, they do have a strong interest and motivation to visit more often, attracted by different elements. Students’ learning and interaction with technology, adults and peers, and the museum environment during a visit, as well as the attractiveness of museum content, tend to correlate with their motivations. This has a range of implications which we discuss. This study will be useful for museum experts, educators and the wider public to map meaningful museum education journeys for students. Copyright © 2023 The Author(s). All Rights Reserved.

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.