In this article, we use the family resemblance approach as a framework to contribute to the debate about the similarities and differences between the constituent disciplines of STEAM (science, technology, engineering, arts and mathematics) and to explore the implications for education. The family resemblance approach has been used in science education in various ways, for instance, in teacher education and undergraduate teaching and as an analytical tool for examining science curricula and assessments. The relevant sense of application of the family resemblance approach for our purposes in this article is that it is a framework that has the potential to differentiate the disciplines underpinning STEAM. We explore the utility of the family resemblance approach for clarifying what is meant by the nature of STEAM and, subsequently, we elaborate on some practical examples drawn from a project conducted in Hong Kong with Year 7 (12–13-year-old) students to illustrate how the use of the family resemblance approach can help articulate a contrast of nature of science and the arts in school activities. Copyright © 2024 Sibel Erduran and Kason Ka Ching Cheung.

Students’ development of multilingual competence has attracted increasing attention from language researchers and educators. However, research on students’ interaction strategy use in group discussions across different language settings remains scarce. In this study, therefore, we examined interaction strategy use in Cantonese as a first language (L1) and Putonghua as a second language (L2) during group discussion tasks among 42 primary school students in Hong Kong. We also investigated the effects of interaction strategy use on performance in respective tasks. We discovered that students employed significantly more interaction strategies in L1 than in L2, with a higher contribution to L1 task performance. Specifically, three of the five strategies identified—Strategy 2 (S2) asking for opinions, S3 expressing attitude, and S5 non-verbal language—were employed more frequently in L1 than in L2. Furthermore, we found that strategy use had various effects on oral performance between the two languages. In the L1 task, S1 expressing actively, S3 expressing attitude, and S4 giving clarification significantly predicted students’ group discussion performance, whereas this effect was only observed in S1 expressing actively in the L2 task. Pedagogical implications for primary students’ learning of interaction strategies for group discussions in both L1 and L2 are discussed. Copyright © 2024 John Wiley & Sons Ltd.

Background: Different instructional factors are related to students’ achievement in different cognitive domains. Most research studies on large-scale assessment focus on science achievement as an entire variable, without considering science achievement in different cognitive domains. Purpose: This study investigates the predictive effects of the frequency of teachers’ using practical work and clarity of instruction on students’ achievement in different cognitive domains. Sample: A regional representative sample of 3265 Hong Kong students who took part in Trends in International Mathematics and Science Study (TIMSS) 2019 was used in this study. Design and Method: In order to disentangle the interrelationships between science instructional factors, attitudes toward science and students’ achievement in different cognitive domains, we carried out structural equation modelling to explore the responses by 3265 Hong Kong students to TIMSS 2019 surveys. Results: Frequency of teachers’ use of practical work had a significant positive impact on students’ achievement in the domains of knowing, applying, and reasoning, while clarity of instruction had a significantly negative impact on students’ achievement in the domains of knowing and applying. Enjoyment of science mediates the relationship between the relationships between frequency of practical work and achievement in three cognitive domains. Conclusion: This informs how teachers use practical work to enhance students’ enjoyment of science, intrinsic motivation and self-concept, in order to improve their achievement in knowing, applying and reasoning science. Implications contribute to future research on how best to improve students’ achievement in all three cognitive domains are discussed. Copyright © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

The rapid development of generative artificial intelligence (GenAI) tools (e.g. ChatGPT) has elicited mixed reactions among English language instructors and learners. This study explores how first-year students in an English for Academic Purposes (EAP) course at a Hong Kong university perceive GenAI and traditional grammar-checking tools (e.g. Grammarly, MS Word). We employed a qualitative methodology grounded in the interpretivist paradigm, conducting semi-structured interviews with 14 students. The findings revealed the students perceived GenAI tools to be more comprehensive and authoritative, as they provide detailed explanations and contextual insights that enhance language proficiency. However, they also noted concerns about overreliance, data privacy and equitable access to premium features. The study examines the ethical and pedagogical implications of integrating GenAI tools into higher education, highlighting their potential and the necessity of institutional guidance. It contributes to the ongoing discourse on the role of GenAI in academic writing instruction. Copyright © 2024 The Author(s).

Entrepreneurship is crucial for fostering innovation and opportunity in a rapidly evolving global economy. Introducing it in secondary schools is challenging due to potential cognitive overload and the complexity of the skills required. This study evaluates the effectiveness of a Learning Management System (LMS) using Cognitive Load Theory for improving entrepreneurial skills among secondary students in Hong Kong. In the study, 280 students from 16 schools completed the LMS intervention. Through a series of t-tests, ANCOVAs, and multilevel models, we examined the effects of an LMS on students' entrepreneurial attributes and aspirations. The results indicated that the LMS enhances students' perceptions of entrepreneurial concepts. However, the effectiveness of the LMS was influenced by two key factors: the level of student engagement with the system and their previous entrepreneurial experiences. Higher engagement with the LMS and more extensive prior entrepreneurial experiences were found to significantly boost students' entrepreneurial attributes and aspirations. The outcomes indicate that by organizing content to minimize cognitive overload, the LMS facilitated the development of sophisticated cognitive schemas essential for entrepreneurial thinking. Copyright © 2024 The Author(s).

Purpose: Using Hong Kong as the context of study, this paper examines the role of education policy, particularly professional standards for teachers (PST) and principals (PSP), in enabling leadership for action on contemporary challenges and crises such as climate change.Design/methodology/approach: This paper utilises critical policy analysis (CPA) and an associated analytical framework to examine key policy texts that are designed with the intent to frame the professional roles, practice and learning of educators and leaders in Hong Kong. The data analysis process involved rounds of descriptive coding, which was then collated into broader themes for discussion.Findings: Policy texts emphasised the scope for educators to exercise leadership and decision-making that supports innovation, improvement and collaboration in the pursuit of a broad social mission for education, emphasising the skills students do and will need to respond to dynamic contemporary challenges. However, how this relates to broader policy goals, and limited articulation of the means through which this is possible, questions remain about the extent to which teachers and leaders are able to exercise leadership for action on climate change.Originality/value: This study has important implications for how those in policymaking, research and practice spaces understand the framing of the role of teachers and school leaders in policy and the influence of this on their capacity to lead action on contemporary challenges and crises, such as climate change. Copyright © 2024, Emerald Publishing Limited.

Recent studies identify Self-Regulated Learning (SRL) as a key factor in enhancing cognitive development, particularly within Computational Thinking (CT) literature. However, research gaps remain in understanding how SRL and CT interact from a developmental perspective. Our study designed a program with tailored animations and worksheets to specifically foster students' learning and cognitive development. Using a mixed-methods approach, we administered surveys among 1235 students from 29 Hong Kong primary schools to investigate a dynamic learning system comprising SRL and CT in a large-scale CT program. Additionally, semi-structured interviews were conducted with 15 students to further probe insights. Quantitative findings revealed a mutually reinforcing relationship in the dynamic system, suggesting that higher SRL skills enhance cognitive CT abilities, and vice versa. Complementing these results, qualitative findings showed that integrating animations and worksheets into the CT program significantly boosted students' SRL and cognitive development. These findings have significant implications for educational practices and curriculum design. Schools should consider implementing SRL strategies, such as proactive goal setting and reflective evaluation, within their curricula to promote students’ cognitive development. Furthermore, strategic investments in educational technology, including collaborations with EdTech developers, are essential to create effective, pedagogically sound tools that enhance learning outcomes. These insights provide valuable guidance for creating a supportive SRL environment that fosters cognitive development in primary school education. Copyright © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.