Teachers fostering future-ready graduates need to master updated pedagogical and technological knowledge, so teacher professional development (TPD) is essential. Conventional TPD activities such as seminars and workshops are limited as they require specific time blocks and lack flexibility. The current study investigated TPD through microlearning courses in online and blended learning modes as an innovative TPD approach in Hong Kong. We applied a qualitative approach and thirty-two preservice teachers in the English language education program participated. The data were collected using questionnaires, semi-structured interviews, and observations for data collection. The pre-service teachers’ digital competencies were evaluated against an observation protocol based on the SAMR framework and TPACK model. The effects of microlearning and the participants’ digital competence needs were also identified. The results revealed how preservice teachers perceived the integration of technology and the challenges they encountered (e.g., design of learning tasks and time management). Based on the findings, personalized and hands-on training is recommended to fulfill teachers’ diverse learning targets in applying specific technology and deepen their understanding of technology use. Furthermore, as more conceptual frameworks for assisting microlearning in TPD are necessary, this study can help enrich the underpinning theories for the microlearning design of TPD. Copyright © 2024 International Forum of Educational Technology & Society, National Taiwan Normal University, Taiwan.

English-medium instruction (EMI) is taking hold within higher education in non-Anglophone settings, but there is insufficient research into the challenges students encounter when taking EMI courses online. This exploratory quantitative study conducted in Hong Kong examines the language and studying challenges faced by undergraduate students when in-person classes were suspended due to COVID-19. One hundred thirteen first- and second-year students completed a questionnaire, rating their perceived challenges in the areas of writing, speaking, reading, listening, and study skills. The results showed that they faced particular challenges with reading and study skills (especially self-motivation), as well as vocabulary range, which affected more than one skill. Corroborating existing research, students with less secondary school EMI experience reported greater challenges. As providing English for Academic Purposes (EAP) courses is a primary way to support the language skills of students in EMI settings, we offer guidance to EAP practitioners who seek to help their students overcome the challenges identified in this article. As online technology continues to deliver content in tertiary education, EAP courses must be closely aligned with the language and study skills needs of students in digital EMI environments. Copyright © 2023 by the authors.

This paper proposes and describes a context- and subject-specific professional digital competence (PDC) framework for beginning English language teachers in Hong Kong. Taking a localised approach, the framework was developed through a four-stage data collection process, (1) Literature review of empirical studies conducted in Hong Kong of English language teachers’ technology use, (2) Review of local government curriculum documents; (3) An online survey of English language teachers’ technology use (n = 83); (4) follow-up individual interviews (n = 22). The study revealed that the local subject-specific PDC of teachers includes five aspects; Technological proficiency; Pedagogical compatibility; Preparing students for the digital world; Risk, well-being and ethical awareness and Professional work. Each aspect includes ability statements. The framework can be used to evaluate existing teacher education programmes, support the development of pre-service English-language teachers and facilitate self-, peer- and teacher assessment. The elements of PDC can be converted into programme- and course-level learning outcomes or incorporated into assessment rubrics. In addition, the methods used to develop the framework can be used by scholars in other contexts and subject areas to develop localised subject-specific PDC frameworks. Copyright © 2023 De La Salle University.

The shift from face-to-face teaching to emergency remote teaching has become the new normal due to the pandemic in 2020. However, little research has been conducted in Hong Kong to investigate pre-service teachers’ perceptions of the immediate adoption and delivery of online face-to-face teaching. In response to this call, the present research conducted a questionnaire survey with 48 pre-service teachers at a Hong Kong university to examine their perceptions of Zoom as a viable alternative to the suspended traditional face-to-face delivery mode of instruction. It also discussed key factors that lead to better online learning outcomes. The results showed that the participants had overall positive perceptions of Zoom-supported synchronous online education. The Zoom feature that most students considered useful was screen sharing on computers. Zoom-supported synchronous online learning benefited students by helping them to attend class meetings remotely, study course materials and content, communicate and collaborate with their instructors and classmates, and develop a sense of community and social presence. Four main factors may influence learners’ perceptions of synchronous online learning: the quality of online communication and collaboration, learning efficiency, learner autonomy and the usability of technology. Copyright © 2023 Laboratory for Knowledge Management & E-Learning, The University of Hong Kong.

This study explores the readiness of pre-service primary school teachers in Hong Kong to develop future-ready learners. Societal advances require the education sector to continuously update curricula to prepare young people for the future. Schools must equip students with the flexibility to use technologies that have not yet been invented and solve unanticipated problems. In this qualitative study, we analysed the perceptions of 15 pre-service primary school English language teachers at a university in Hong Kong. The study focused on the knowledge and learning environment necessary for developing future-ready learners. Via semi-structured interviews, the findings revealed that the participants believed it is essential to integrate technology into classrooms to address future digital literacy needs. They also noted that students must develop critical thinking skills, problem-solving skills, and soft skills to succeed in the future. The findings also suggest that teachers need to be innovative and adaptable in their pedagogical approaches as the existing primary school curriculum is not cultivating learners’ future-readiness. Copyright © 2023 National Institute of Education, Singapore.

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.

Despite the significant research on the effectiveness and challenges of emergency remote teaching (ERT) during the global COVID-19 pandemic, few studies have focused on the systematic facilitation of positive emotions by classroom teachers. This study aimed to identify the strategies that teachers of English for Academic Purposes (EAP) used during the ERT period, by interviewing 18 university English teachers in Hong Kong. Our results suggest that one traditional learning theory, the constructionist approach, played an unexpectedly important role in facilitating positive student emotions, as well as encouraging learning. Cognitively demanding tasks helped divert students’ attention away from the negative emotions they faced and towards their learning. Interactions also played an essential role in helping students learn and in reducing negative emotions. These results shed light on the significance of positive emotions in an online or ERT environment, with significant implications for university teachers who want their teaching to systematically promote positive emotions. Copyright © 2023 by the authors.

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.

This paper explored teacher professional development (TPD) for online and blended learning (OBL) via microlearning in the higher education English language teaching context in Hong Kong in 2021 and 2022. OBL requires teachers to integrate technology. This study drew on quantitative survey data (N = 67) and interviews (N = 12) that took place after the participants were exposed to microlearning. The findings illuminated that microlearning was perceived as flexible and stress-free and allowed teachers to focus on relevant and immediate tasks using bite-sized learning segments to achieve digital growth. Such activities created favourable conditions to equip them with the skills necessary to teach effectively in the context of the “new normal”. Moreover, the study shed light on how previous TPD activities were often considered too general to improve teachers’ digital competence. It concluded with a call for more research on microlearning and TPD for OBL to help teachers gain pedagogical, theoretical and practical knowledge to enhance their teaching. Copyright © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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.