Despite the wide adoption of emergency remote learning (ERL) in higher education during the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting student satisfaction for this novel learning environment in crisis. The present study investigated important predictors in determining the satisfaction of undergraduate students (N = 425) from multiple departments in using ERL at a self-funded university in Hong Kong while Moodle and Microsoft Team are the key learning tools. By comparing the predictive accuracy between multiple regression and machine learning models before and after the use of random forest recursive feature elimination, all multiple regression, and machine learning models showed improved accuracy while the most accurate model was the elastic net regression with 65.2% explained variance. The results show only neutral (4.11 on a 7-point Likert scale) regarding the overall satisfaction score on ERL. Even majority of students are competent in technology and have no obvious issue in accessing learning devices or Wi-Fi, face-to-face learning is more preferable compared to ERL and this is found to be the most important predictor. Besides, the level of efforts made by instructors, the agreement on the appropriateness of the adjusted assessment methods, and the perception of online learning being well delivered are shown to be highly important in determining the satisfaction scores. The results suggest that the need of reviewing the quality and quantity of modified assessment accommodated for ERL and structured class delivery with the suitable amount of interactive learning according to the learning culture and program nature. Copyright © 2021 Public Library of Science.

This paper is a report of a cross-cultural online collaboration between two cohorts of pre-service teachers in Hong Kong, China and an American university in St. Paul, Minnesota. It explicates the pedagogical design and implementation of online tools for group collaboration and students' perceptions of the benefits and challenges. Multiple web-based tools (e.g. Slack, Zoom) were selected and recommended to the students to facilitate resource sharing, communication, and artefact construction. Overall, students valued the experience of collaborating in a global virtual team in spite of some challenges encountered. Findings from this study indicated that the merits and perils of cross-cultural online collaboration coexisted and centered on three aspects: cross-cultural communication, group collaboration, and technological tools. The students greatly appreciated the values of online tools and manifested the ability to appropriate the tools to fulfill the needs of group work. The implications for pedagogical design are also discussed and technological tools supporting cross-cultural online collaboration are recommended. Copyright © 2021 Springer.

The emergence of English as a lingua franca (ELF) has focused scholarly attention on non-native speakers' use of communication strategies (CSs) in international communication, which places emphasis on ensuring mutual understanding. With reference to previous ELF pragmatics research, this study examined the extent to which L2 learners' use of CSs reflects that in ELF interactions in a group interaction task in Hong Kong's high-stakes public examination. Our analysis of the students' CSs use was accompanied by semi-structured focus group interviews, allowing them to reflect on their own performance and prior English learning/use experience. The findings revealed an unnatural discursive pattern resulting from the assessment task design, although some CSs were found that have been identified in previous ELF studies. Students with higher academic attainment were more capable of using some higher-level CSs and diversifying their language forms, but this was mainly because of their higher English proficiency level/motivation, exposure to English and awareness of effective communication rather than their prior English-learning experience in the classroom. The paper concludes by offering suggestions for the design of communicative tasks and language learning/teaching practices to address the misalignment between L2 students' pragmatic production and naturalistic ELF situations. Copyright © 2021 Elsevier.

To study what ways a gardening programme being implemented within the context of a Hong Kong pre-school support experiential learning. Dewey is one of the most significant figures in experiential learning. Kolb extended and modified some aspects of Dewey's work, with influence by Piaget and Lewin. Fenwick pinpointed some strengths and limitations of experiential learning. Experiential learning is the conceptual framework of this study (Dewey, 1938; Kolb, 1984; Fenwick, 2001). I was obliged to reconsider my own ideology both in relation to gardening and experiential learning and to recognise how ideology predisposed me to 'see' in some ways and not others. This study is a qualitative single case study using interpretivist paradigm. Multiple data collection methods were used, including unstructured, participant and non-participant observations, semi-structured individual and group interviews, research journal and documentation. The participants were duly informed of the purpose, procedures, and instruments that would be used, including any potential risks. Signed consent was secured. The findings indicated that the children learned through experiential learning. The teachers lacked the necessary subject knowledge - particularly science - for supporting children's learning. The study also highlighted how Chinese cultural beliefs and practices create significant tensions when implementing experiential learning, especially in terms of adult-child relationship. The implications to my teaching included (1) an integral component of my teaching; (2) students can try theories in order to develop practices; (3) enabling students to connect theories to practice; and (4) offering opportunities that students can reflect on their own learning, with engagement. Copyright © 2019 EECERA.

Objectives: This study had two aims. The first was to delineate the boundary between effective and inspiring teaching practices with cluster analysis again with a widely-researched instrument, International Comparative Analysis of Learning and Teaching (ICALT) (van de Grift, 2007; 2014) after initial success with Comparative Analysis of Effective and Inspiring Teaching (CETIT), a newly-developed classroom observation instrument that combined features of both effective and inspiring teaching (First Author et al., 2019) (Figure 1). Then, models hypothesizing some theoretical relationships of inspiring teaching behaviors were tested with structural equation modelling (SEM).Theoretical framework: Behavioral characteristics of inspiring teaching were initially characterized with a small sample by Sammons and colleagues (2014; 2016), but were found somewhat overlapped with those characteristics often associated with effective teaching.Methods: The hierarchical cluster analysis were conducted with SPSS 25 with five CETIT factors and seven ICALT factors. For SEM performed in LISREL 8.8, only the CETIT factors were used to explore the second aim. An inter-rater reliability over 0.7 (Gwet, 2014) was established using two different training videos before viewing the lesson samples.Data sources: The lesson sample was based on 260 videotaped lessons from both primary and secondary schools in Hong Kong, Shenzhen, and Guangzhou, selected from a pool of 500+ lessons in an earlier study by First Author and colleagues (2015). Table 1 summarizes the descriptive and correlation results.Results and conclusions: The hierarchical cluster analysis results showed two clusters identified (Figure 2). Average linkage to estimate the distance among factors was employed to overcome the shortcoming of single and complete linkage (Yim & Ramdeen, 2015). Together with the Agglomeration Coefficients, the results suggested location should stay at stage 10 with an elbow effect (Figure 3), where the factors

The global crisis of COVID-19 has severely impacted the entire higher education sector (Marinoni et al., 2020) and spurred timely, innovative, and radical responses from educational institutions across the sector and the world. The focus of much of this attention has been on flagship universities (University World News, 2020). However, the impact of COVID-19 has been even more severe on other institutional types, such as community colleges. About two-thirds of the 20,000 universities worldwide reported that online classes had substituted face to face teaching while the altered practices of teaching and learning through digital means call for new pedagogies, access to the Internet, digital devices and technical infrastructure, as well as digital literacies of instructors and students from diverse backgrounds (Marinoni et al., 2020). This study focuses on an exploration of the effects and implications of COVID-19 on the case study of Hong Kong community colleges. The higher education that community colleges deliver traditionally depends on the concept of dynamic learning community, featuring liberal teacher-student interactions and pedagogic environment, learner-centred formative assessment, effective student services for strategic university articulation, and dynamic student activities and engagement (Tang and Dang, 2019). In the case of Hong Kong, a small community college campus in Hong Kong that serves as a hub of geographic location where the above-mentioned activities take place in real time. In late 2019, it was unprecedented that COVID-19 made Hong Kong’s community colleges close their campus and moved teaching, learning and assessment completely online. This study engages in the case study of Hong Kong’s community colleges in view of how it mitigated COVID-19 through organisational adaptation and sustained the delivery of higher education, especially when global pandemic may persist in upcoming times. Copyright © 2020 CHER-Hong Kong.

BACKGROUND: In recent years, there has been considerable interest in taking up the opportunities of Artificial Intelligence (AI) in education. As an emerging concept and a driving technological force, AI is changing the kind of knowledge and skills students need for success in the forthcoming Industrial Revolution 4.0, and at the same time, enabling novel, powerful methods of teaching and learning. Although many school leaders, policy-makers and frontline teachers understand the need for embracing AI, they may lack clarity on what to teach students in the era of AI; how to harness the power of AI tools; and where to start given their own school contexts. This paper describes an ongoing study that uses Communities of Practice (CoPs) as a knowledge management strategy to support Hong Kong K-12 schools in embracing AI. CoPs comprise members who genuinely care about a mutual interest and seek to solve a common problem. The premise underlying this study is that the cultivation of CoPs may present enabling conditions and pave the way for AI adoptions. In these CoPs, members would engage in reflective discussions on the opportunities and challenges of embracing AI in their schools and collaboratively support each other through meaningful knowledge exchange. METHODS: A collective case study is being conducted with members from 20 K-12 schools in Hong Kong to substantiate and illustrate the power of CoPs that aim to support schools in embracing AI. Qualitative data are gathered via semi-structured interviews and focus-group meetings with participants. Thematic analysis is adopted for analysing the data. RESULTS: The preliminary results of this ongoing study have shown that CoPs, if successfully launched, can play an uplifting role for Hong Kong K-12 schools embracing AI. More specifically, the collaborative learning process in CoPs has helped members make sense of AI core concepts and the pedagogical implications as they access authentic, relevant and updated information. The

BACKGROUND: Research on Artificial Intelligence in Education (AIEd) has become increasingly active during the past 20 years, with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in the application scope of AIEd research. A comprehensive overview of this field is necessary to understand what had been concerned in AIEd research, what is going on in the field of AIEd, and what might be the future of AIEd research. METHODS: This paper combined the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale AIEd literature in the past two decades. Specifically, by using STM, we uncovered important research topics concerned within the AIEd community. The evolution of these topics was explored by using a nonparametric Mann-Kendall (MK) trend test. In addition, the correlation between these topics was further explored and visualized based on a semi-parametric Gaussian procedure. RESULTS: The annual trend of publications concerning AIEd had grown consistently across the past 20 years, indicating that research on AIEd had received a growing interest from academia Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics and promising research orientations. Specifically, the top six topics having the highest proportions in the dataset were cognition and perception, programming education, prediction, MOOCs, semantic Web and recommender systems, assessment and feedback, and emotion detection. Results of the MK test highlighted five topics receiving an increase in research interest, including prediction, assessment and feedback, flipped classroom and biosignal data, STEM education, and English language learning. In addition, several inter-topic research directions were identified, including 1) flipped classroom and biosignal data, emotion detection, and