Since the first study on computer-mediated communication tools in support of language learning was published in 1992, asynchronous and synchronous tools have been widely adopted; however, few reviews have been conducted to explore the research status in this field. As COVID-19 has increased the use of online tools in education, the need to understand how asynchronous and synchronous tools are being used in language education has grown. In this bibliometric analysis, we reviewed asynchronous and synchronous online language learning (ASOLL) by analyzing the trends, topics, and findings of 319 articles on ASOLL. The results indicate that interest in ASOLL has increased over the past three decades with ASOLL for oral proficiency development and collaborative ASOLL being the two main research issues. Interest in three topics – collaborative ASOLL, emotions, and corrective feedback – was especially apparent. The review contributes to the understanding of ASOLL while providing practical implications for using information communication technologies to enhance language learning. Copyright © 2023 Laboratory for Knowledge Management & E-Learning, The University of Hong Kong.

The availability of educational data in novel ways and formats brings new opportunities to students with special education needs (SEN), whose behaviour and learning are highly sensitive to their body conditions and surrounding environments. Multimodal learning analytics (MMLA) captures learner and learning environment data in various modalities and analyses them to explain the underlying educational insights. In this work, we applied MMLA to predict SEN students’ behaviour change upon their participation in applied behaviour analysis (ABA) therapies, where ABA therapy is an intervention in special education that aims at treating behavioural problems and fostering positive behaviour changes. Here we show that by inputting multimodal educational data, our machine learning models and deep neural network can predict SEN students’ behaviour change with optimum performance of 98% accuracy and 97% precision. We also demonstrate how environmental, psychological, and motion sensor data can significantly improve the statistical performance of predictive models with only traditional educational data. Our work has been applied to the Integrated Intelligent Intervention Learning (3I Learning) System, enhancing intensive ABA therapies for over 500 SEN students in Hong Kong and Singapore since 2020. Copyright © 2023 IEEE.

Museums provide direct learning experiences. In museums visitors can encounter objects and exhibits from specific times and places, related to concepts and disciplinary subjects in the arts, history, science, and social sciences. A museum is an educational fair. The Hong Kong Special Administrative Region Government’s recent primary school General Studies curriculum guide recommends learning through museums as an informal learning pathway for all students. This project is an up-to-date exploratory study of why and how Hong Kong primary school teachers and students visit museums educationally and how they perceive those visits and the accompanying learning experiences. A survey study from a sample of five local primary schools, 90 teachers and 415 senior form students, shows that although teachers and students may not visit museums very frequently for academic purposes, they do have a strong interest and motivation to visit more often, attracted by different elements. Students’ learning and interaction with technology, adults and peers, and the museum environment during a visit, as well as the attractiveness of museum content, tend to correlate with their motivations. This has a range of implications which we discuss. This study will be useful for museum experts, educators and the wider public to map meaningful museum education journeys for students. Copyright © 2023 The Author(s). All Rights Reserved.

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.

In Hong Kong higher education, students' learning outcomes are increasingly treated as evidence to inform course and teaching improvement. Therefore, outcome-based teaching and learning (OBTL) has been encouraged by the University Grants Committee (UGC) since 2007. OBTL has gradually been implemented by Hong Kong higher education institutions (HEIs) to enhance student learning outcomes. Relating OBTL to the social cohesion/regulation matrix, this chapter aims at analyzing how OBTL is being implemented by the HEIs in Hong Kong. Given the high institutional autonomy and academic freedom afforded to individual HEIs, each university has established its own systematic framework for integrating outcome-based approaches into its teaching, learning, and assessment. Unlike other higher education systems in Asia with strong government supervision, the government in Hong Kong acts as an enabler and facilitator, leaving the UGC to invite international experts as an independent audit body to assure the quality of student learning. As a result, this chapter chooses the eight UGC-funded universities to investigate how they engage their faculty members in OBTL, and what the enabling and hindering factors are. Based upon the social cohesion/regulation matrix, the Hong Kong higher education system is featured by the individualist way of promoting OBTL. Nonetheless, while universities are empowered with institutional autonomy to decide upon teaching, and student learning matters, their strong orientation with OBTL means they cannot simply do whatever they like. Adopting a robust quality assurance mechanism in evaluating university performance through University Accountability Agreements, the institutional autonomy that universities enjoy rests heavily upon their performance in teaching and student learning, which is assessed through rigorous international benchmarking via the Quality Assurance Audit conducted by the UGC and research performance through the Research Assessment Exercise.

This chapter discusses the development and role of graduate education within a liberal arts college context by examining the case of Lingnan University, Hong Kong. With a student-centered focus, Lingnan University applies evidence-informed teaching and experiential learning to integrate liberal arts education, graduate programs, and research to enhance students’ learning experiences and maintain the core of whole-person education. Lingnan University’s efforts demonstrate the potential of developing graduate education in the liberal arts context, which can serve as implications for other liberal arts higher education institutions. Copyright © 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.