Students' digital reading literacy has attracted increasing attention in the current digital era; however, few studies have been conducted to explore how factors at different levels influence students' digital reading achievement. Grounded by socio-ecological theory, the current comprehensively explored the relative importance of 24 individual, microsystem, and mesosystem variables in predicting English digital reading achievement through the machine learning approach (i.e. random forest regression). The secondary data were retrieved from the Program for International Student Assessment (PISA) 2018, including 7,703 15-year-old students from Macao, Hong Kong, and Singapore. Our study identified 12 key factors that best predicted East Asian students' English digital reading achievement. Among them, students' socioeconomic status, subject-related ICT use during lessons, and interest in ICT ranked as the top three factors. The disparities in the roles played by disciplinary climate, gender, being bullied, immigrant status, and home language among the three economies were discussed. Copyright © 2024 Routledge.

The utilisation of the RIASEC Theory, based on the Holland Code, has gained substantial popularity in the realm of career planning. Nevertheless, only a limited number of studies have explored the potential influence of the six personality types identified in the Realistic-Investigative-Artistic-Social-Enterprising-Conventional Theory (RIASEC Theory) on students’ academic performance in their chosen subjects. In light of the subject suitability recommendations for different personality types provided by the career mapping outlined in the Hong Kong Association of Careers Masters and Careers Guidance Handbook, the present study aimed to investigate whether the six personality types of N = 1104 undergraduate students enrolled in a university in Hong Kong had an impact on their performance in four self-selected disciplines (Supply Chain Management, Applied Computing, Psychology and Culture and Creative Industry). Nevertheless, the present study hypothesises that six distinct personality types in the RIASEC Theory influence students’ performance in their chosen disciplines. Concurrently, an extensive evaluation was conducted employing a Machine Learning approach to identify the personality types that significantly impact students’ selection of academic disciplines However, the findings revealed that the present study did not build a Linear regression model that established a clear association between these six different personality types and academic performance in the chosen disciplines. Nonetheless, employing the Mean Decrease Impurity (MDI) method of Random Forest in Machine Learning, the current study successfully predicted the personality types that were most likely to influence students’ future subject choices. Specifically, the results indicated that the Artistic personality trait exerted the strongest influence on students’ selection of the four subjects. Consequently, the outcomes of this study offer valuable insights for informing future research endeavours pertaining

In recent years, schools started to incorporate artificial intelligence (AI) into computer science/STEAM curricula. However, few validated measurements have been designed to examine how secondary students develop AI literacy and perceive their learning outcomes. AI literacy has been measured from students' knowledge and skill acquisition, and behavior and attitudes. This research aims to develop and validate an instrument to assess AI literacy for secondary students. A questionnaire with 25 items measured in a 5-point Likert scale was created. In a pilot study, the questionnaire was administered to a sample of 363 secondary school students from two different schools in Hong Kong. Confirmatory factor analysis was conducted and grouped into six factors: (1) intrinsic motivation, (2) self-efficacy, (3) behavioral intention, (4) behavioral engagement, (5) know and understand, and (6) use and apply AI. The questionnaire showed a good fit model to support internal consistency reliability in most of the factors, with Cronbach's Alpha levels ranging from.58 to.88. A less-parsimonious model was proposed that help educators measure a wider AI literacy skill set with an acceptable model fit, with Cronbach's Alpha levels ranging from.91 to.94 based on affective, behavorial, cognitive and ethical (ABCE) learning framework. Further studies are needed to confirm the factor structure. Copyright © 2023 IEEE.

The United Nations Sustainable Development Goals (SDGs) Target 4.7 stated that by 2030, all learners should acquire the knowledge and skills needed to promote sustainable development. However, only scattered studies outline how SDG knowledge has been taught in public K12 curricula. This study aims to identify SDG topics in 13 subjects in the Hong Kong Diploma of Secondary Education curriculum and the perceptions of teachers in relation to SDG education. Data were collected through a machine learning approach and semi-structured interviews. Analysis of the module, subject, and curriculum level was conducted. By examining how teachers perceive in relation to the results of SDG classification and implementation of SDG education, the findings showed that teachers and administrators have adopted positive attitudes towards SDG classification, and supportive government can positively influence teachers and administrators in implementing SDG education. Such findings can be useful for policymakers to develop strategies to facilitate SDG education. © The Author(s) 2023.