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The issue of the "research-practice gap" has been a long-standing concern in the education community. To tackle this issue, there is a growing interest in research–practice partnerships (RPPs) to connect research with practice by providing solutions for improving practice in schools. Although studies have reported that constructive relationship would produce positive outcomes, establishing long-term and mutually beneficial RPPs requires further investigation. In Hong Kong, the Learning Study (LS) approach with Variation Theory derived from phenomenology as the theoretical framework for pedagogical design has been adopted as a RPP model and has sustained for two decades. Like many RPPs models, LS approach has been facing different challenges when teachers are increasingly held accountable for meeting new demands from education reform. In this respect, this paper explores the challenges and strategies of the LS approach as a RPP model and its conditions of success. It reports a case study of how a group of English Language teachers adopting a LS approach while integrating education initiatives in their instructional design. The findings have identified trust building, teacher's capacity building and visible outcomes as possible key factors of effective RPP model. This paper may shed light on the implementation of research–practice partnerships. [ABSTRACT FROM AUTHOR]

Background: Artificial Intelligence (AI) literacy is a crucial part of digital literacy that all individuals should possess in today's technologically advanced world. Despite the potential benefits that AI education offers, little research has been done on how to teach AI literacy to children. Objectives: This study aimed to fill that gap by investigating how children were engaged in AI literacy activities that are supported by intelligent agents. These activities were implemented in a Hong Kong kindergarten with a class of six 5‐year‐olds (Mage = 62.83 months, SD = 2.91) over a 6‐week period. Methods: The study gathered data from multiple sources, including classroom observations, teacher interviews, and documents/artefacts. Results and Conclusions: The results showed that children could learn about AI through interaction with intelligent agents in embodied learning contexts. The findings of this study have implications for the broader field of digital technology education, particularly in the context of early childhood education. Lay Description: What is already known about this topic: Artificial Intelligence (AI) literacy is essential in today's world.Little research exists on engaging young children in learning AI literacy. What this paper adds: Technology‐enhanced embodied learning approach affords multimodality in enabling young children's learning with AI‐related agents.Project‐based learning and real‐life examples can enhance children's learning. Implications for practice and/or policy: Early exposure to AI can help children navigate the increasingly digital world.Practical approaches, such as embodied and multimodal learning, seem to be effective.Teachers can enhance children's digital learning by using pedagogical strategies like project‐based learning and real‐life examples. [ABSTRACT FROM AUTHOR]

Similar to other developed economies, Hong Kong also faces a severe aging-related problems. Considering the aging-in-place initiative to enhance the safe living of the elderly population in their familiar living environments, the use of smart home technologies (SHTs) has been proposed and employed. Meanwhile, there are challenges that may impede the purpose of developing SHTs by the elderly. Therefore, this paper investigated the barriers of SHTs to the elderly in Hong Kong, determined the critical ones and further categorized into manageable size. A total of 201 face-to-face questionnaire survey was conducted in Hong Kong. Based on the data collected, various descriptive and inferential statistics namely, mean score, standard deviation, factor analysis, and reliability analysis were conducted. Based on the various analyses conducted, the characterization of the critical barriers of SHTs to the elderly in Hong Kong are cost and knowledge barrier, personal concern, and uncertainty barrier. The study provides recommendations to manufacturers of SHTs, the organizations where the senior adults worked before retirements, families of the elderly, the government and the seniors that could help to improve and enhance the aging-in-place (AIP) in Hong Kong. [ABSTRACT FROM AUTHOR]

Background: The number of artificial intelligence (AI) literacy studies in K‐12 education has recently increased, with most research focusing on primary and secondary education contexts. Little research focuses on AI literacy programs in early childhood education. Objectives: The aim of this mixed‐methods study is to examine the feasibility of an AI literacy program called "AI4KG" and explore how it might affect kindergarteners' perceptions of robots and attitudes towards engineering and science. Methods: A total of 26 child–parent dyads recruited from a Hong Kong kindergarten were involved in this study, consisting of 26 children (Mage = 4 years, SD = 0.28) and their parents. Quantitative and qualitative data were collected through surveys and interviews designed to explore children's perceptions of robots and attitudes towards engineering and science, and parents' perceptions of the AI4KG intervention. Results: It is found that children have increased their perceptions of robots after the AI literacy program, but the AI4KG curriculum had no significant effects on kindergarten children's engineering and science attitudes. Most parents (22 out of 26) agreed that their children's AI knowledge, AI skills, and AI attitudes have been enhanced after learning through the AI4KG curriculum. Conclusions: This study suggests that the AI4KG curriculum is potentially effective in promoting early AI literacy and favourable attitudes towards the technology, but further research is needed to develop age‐appropriate measures and assess its long‐term impact on children's education and career paths. Lay Description: What is currently known about this topic?: AI literacy is becoming important as AI transforms society, but few studies have explored AI literacy in early childhood education.Prior research found AI activities can positively influence older children's views of robots and engineering. What does this paper add?: It evaluates an 8‐week AI literacy curriculum called AI4KG for kindergarteners in Hong Kong.The curriculum used tools like Teachable Machine and taught concepts through play and storytelling.It improved children's perceptions of robots but did not significantly impact engineering/science attitudes.Parents perceived improvements in children's AI knowledge, skills, and attitudes after the curriculum. Implications for practice/policy: The curriculum provides an example for developing age‐appropriate AI literacy activities.More rigorous research on impacts over time is needed, but results suggest potential benefits of AI literacy in early childhood.Policymakers could consider incorporating AI literacy into kindergarten curricula to prepare children for the future. [ABSTRACT FROM AUTHOR]

Digital literacy competence (DL) is an important capacity for students' learning in a rapidly changing world. However, little is known about the empirical structure of DL. In this paper, we review major DL assessment frameworks and explore the dimensionality of DL from an empirical perspective using assessment data collected using authentic software applications, rather than simulated assessment environments. Secondary analysis on representative data collected from primary and secondary school students in Hong Kong using unidimensional and multidimensional item response theory reveals a general dimension of digital literacy performance and four specific, tool-dependent dimensions. These specific DL dimensions are defined by the software applications that students use and capture commonality among students' performance that is due to their familiarity with the assessment tools and contexts. The design of DL assessment is discussed in light of these findings, with particular emphasis on the influence of the nature of digital applications and environments used in assessment on the DL achievement scores measured. [ABSTRACT FROM AUTHOR]

Self-directed learning (SDL) is acknowledged as an effective language learning paradigm because learning a language is time-consuming. As language and literacy education is now embracing a more multimodal approach in writing instruction, teachers' multimodal technological, pedagogical, and content knowledge (TPACK) receives growing attention in language education. Based on a VR-supported L1 Chinese writing program, the present study collected and analyzed the VR courseware, lesson plans and student worksheets developed by a group of Chinese language teachers in Hong Kong. A coding scheme regarding multimodal TPACK for SDL was employed to assess the learning activities and to understand teachers' multimodal TPACK literacies in L1 Chinese writing instruction. The findings revealed that teachers were able to use VR to immerse students in an authentic environment to develop students' SDL ability and comprehension of nuanced socioemotional aspects of L1 Chinese writing. Nonetheless, there existed some limitations in the utilization of VR for teaching L1 Chinese writing which might be compensated for by pedagogical design. Moreover, teachers' pedagogical conception revealed their teaching was driven by syllabus requirements and focused on students' completion of writing, which might disregard the process of SDL in pedagogical implementation, and thus the process of collaborative knowledge building might be less developed. This study may contribute to the development of VR-based multimodal TPACK, which enhances multimodal lesson design for self-directed L1 writing. [ABSTRACT FROM AUTHOR]

• Algorithms affect practices of reading and writing as well as broader patterns of language use, communication and consumption. • This paper describes a participatory project, in which university students explored their 'folk beliefs' about algorithms. • The participants articulated six metaphors through which they and their classmates understand how algorithms work • Engaging learners in 'folk beliefs' about algorithms can contribute to critical online reading practices. Most accounts of the way digital technologies have changed practices of reading and writing have focused on surface aspects of digital texts (such as hypertextuality, multimodality and the development of new registers). There are, however, less visible aspects of digital communication environments that have had an equally profound effect on reading and writing – namely the algorithms that lie behind texts that monitor the actions of readers and writers and alter the form and content of the texts they are exposed to. Algorithms have the potential to affect not just local communication practices, but also broader social practices, as they work to encourage and reinforce patterns of language use, communication and consumption. This paper describes the results of a two-year long participatory project, in which university students in Hong Kong and the United Kingdom explored the communication and inference forming practices they engage in when interacting with algorithms. The participants articulated six primary metaphors through which they and their classmates understand how algorithms work: (1) Algorithm as agent; (2) Algorithm as authority; (3) Algorithm as adversary; (4) Algorithm as communicative resource; (5) Algorithm as audience; and (6) Algorithm as oracle. Engaging learners in articulating the 'folk beliefs' that govern people's interaction with algorithms, it is argued, can contribute to the development of the kinds of digital literacies they will need to better understand the ways algorithms affect the kinds of information they are exposed to, the kinds of inferences they form about this information, and the ways their own acts of reading and writing can be used by algorithms to manipulate them. [ABSTRACT FROM AUTHOR]

Hong Kong was one of the first places hit with the COVID‐19 outbreak in January 2020 and has since responded with several periods of full or partial school suspension, during which teaching and learning often occurred remotely using digital means. During this time, collecting research data and testing students have become more difficult than before. In this article, we report on a large‐scale digital literacy (DL) performance assessment in Hong Kong that had to be conducted in a mix of modes—onsite, online supported, and online self‐directed—during the pandemic. We discuss the administrative and methodological challenges of assessing DL, and explore preliminary data on the associations between student performance and the assessment mode adopted. We further discuss potential implications, including for high‐stakes testing, and make recommendations for assessing DL remotely. [ABSTRACT FROM AUTHOR]