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This paper reports a comprehensive review of literature on personalised learning in STEM and STEAM (or STE(A)M) education, which involves the disciplinary integration of Science, Technology, Engineering, and Mathematics, as well as Arts. The review covered the contexts of STE(A)M education where personalised learning was adopted, the objectives of personalised learning and research issues, and various aspects of practising personalisation. A total of 72 publications from 2011 to 2020 were collected from Scopus for review. The results reveal the widespread studies in this area across various countries/territories, levels of education, subject disciplines, and modes of education. The most common objective of personalised learning lied in catering for learning styles. The issue most frequently addressed focused on evaluating the effectiveness of technologies for personalised learning. Blended learning and learning analytics are the most popular means to achieve personalised learning. Among various aspects of learning, learning material is the one most frequently addressed. Also, the factors and criteria for personalising learning were summarised, which reveal the heterogeneous nature of learners who have their own learning ability, interest, style and progress. The results suggest more research on interdisciplinary and integrative approaches for STE(A)M learning to examine how personalisation can be applied effectively, as well as more investigation on integrating personalisation with the pedagogies and elements commonly introduced to STE(A)M education.

This paper presents an analysis of learning analytics practices which aimed to achieve personalised learning. It addresses the need for a systematic analysis of the increasing amount of practices of learning analytics which are targeted at personalised learning. The paper summarises and highlights the characteristics and trends in relevant learning analytics practices, and illustrates their relationship with personalised learning. The analysis covers 144 related articles published between 2012 and 2019 collected from Scopus. The learning analytics practices were analysed from the dimensions of what (learning context, learning environment, and data collected), who (stakeholder), why (objective of learning analytics, and personalised learning goal), and how (learning analytics method), as well as their outcomes and limitations. The results show the diversified contexts of learning analytics, with the major ones being tertiary education and online learning. The types of data for learning analytics, which have been increasingly collected from online and emerging learning environments, are mainly related to the learning activities, academic performance, educational background and learning outcomes. The most frequent types of learning analytics objectives and personalised learning goals are enhancing learning experience, providing personal recommendations and satisfying personal learning needs. The learning analytics methods have commonly involved the use of statistical tests, classification, clustering and visualisation techniques. The findings also suggest the areas for future work to address the limitations revealed in the practices, such as investigating more cost-effective ways of offering personalised support, and the transforming role of teachers in personalised learning practices.

Purpose: This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices. Design/methodology/approach: A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed. Findings: Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning. Originality/value: This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers' roles and teaching strategies.

Student persistence has long been a major challenge for open universities. Despite the evolution of open education, an overall high student attrition rate remains. This paper examines the changes and trends in factors related to student persistence in open universities. It reviews the empirical studies from the 1970s to the 2010s which reported factors influencing student persistence. The relevant studies were searched from databases, including Scopus, Web of Science, and Google Scholar. Among the 108 studies collected, a total of 284 factors influencing student persistence were identified. The factors were categorised into student factors, institutional factors, and environmental factors. Their changes and trends over the years were examined. The results show that student factors were the most frequently studied over the years examined, with the major categories being students' psychological attributes and outcomes. Institutional factors have been increasingly studied in recent decades, with the design and delivery of programmes and courses being the strongest category. Finally, environmental factors have been decreasingly examined, with factors related to students' family and work being the two main categories. Based on the results, the implications for developing intervention and retention strategies for student persistence in open universities are discussed.

Purpose: This paper aims to present a comprehensive review of the present state and trends of smart education research. It addresses the need to have a systematic review of smart education to depict its research landscape in view of the growing volume of related publications. Design/methodology/approach: A bibliometric analysis of publications on smart education published in 2011 to 2020 was conducted, covering their patterns and trends in terms of collaboration, key publications, major topics and trends. A total of 1,317 publications with 29,317 cited references were collected from the Web of Science and Scopus for the bibliometric analysis. Findings: Research on smart education has been widely published in various sources. The most frequently cited references are all theoretical or discussion articles. Researchers in the USA, China, South Korea, India and Russia have been most active in research collaborations. However, international collaborations have remained infrequent except for those involving the USA. The research on smart education broadly covered smart technologies as well as teaching and learning. The emerging topics have addressed areas such as the Internet of Things, big data, flipped learning and gamification. Originality/value: This study depicts the intellectual landscape of smart education research, and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and research needs, and suggest future work related to research collaborations on a larger scale and more studies on smart pedagogies.

This article presents a review of the literature on smart learning in order to provide a comprehensive overview of its latest developments in research and practice. The review covered 90 studies published from 2010 to 2019, which were collected from three publication databases, namely Web of Science, Scopus and ProQuest. They were analysed for the patterns and trends in terms of publication years, sources of publication, countries or regions of publication, research purposes, research methods, educational levels of the studies, application domains, research issues, research participants, learning devices or tools, learning environments and learning features. Overall, the findings show that increasing global attention was given to smart learning from diverse disciplines and contexts of application. The results also reveal the areas which should be addressed in future work: development of pedagogies which make effective use of smart learning technologies in different learning environments; examination of smart learning to cope with learners' needs in informal contexts; in-depth analysis of how smart learning could be tailored to fit the characteristics of various application domains; and investigation of the views and perceptions of teachers on smart learning technologies, their readiness to use these technologies and their challenges and needs for support.

This paper provides a comprehensive review of the features and trends of personalised learning. The review covers a total of 203 journal articles collected from Scopus, which were published from 2001 to 2018 and involved personalised learning practices. Comparing the practices between 2001-2009 and 2010-2018, there was a clear trend that they became more diversified in terms of the educational levels and modes of education, as well as the means to achieve personalised learning, their objectives and the devices used. In most practices, the use of technology was clearly emphasised, such as various intelligent learning/tutoring systems, mobile devices, learning analytics, and augmented/virtual reality applications. Learning methods, materials and the time and places for learning were the aspects most frequently addressed. The objectives of the practices focused mainly on increasing learning effectiveness, followed by enhancing learners' motivation, engagement and satisfaction. The factors leading to successful personalised learning were identified from the articles reviewed, among which most factors were related to learners' characteristics, learning progress and technology. Research issues which need further investigation are recommended, particularly on the challenges arising from the adoption of emerging technologies, and the changing role of teachers in the trend towards student-centred personalised learning.

Purpose: This paper examines the pedagogical features of massive open online courses (MOOCs) for language learning--known as language MOOCs. The mainstream pedagogy of MOOCs typically involves the provision of short videos and reading materials for self-study; discussion forums, mostly for peer-to-peer interaction on course content; and machine-graded quizzes for self-assessment. For language learning, which has been conventionally understood as skill development, the pedagogical features of relevant MOOCs have yet to be comprehensively surveyed. Design/methodology/approach: This study surveyed a total of 123 language MOOCs from the major MOOC platforms. The pedagogical features shown in these courses were identified and categorised according to the types of course materials and learning activities as well as the participation of learners and instructors. Findings: English was the most common language taught in the courses. Over 80% of the courses took not more than six hours to complete. Most of these courses followed the typical approach of xMOOC delivery, with video watching, reading and auto-graded assessment being the most common learning activities. Less than half of the courses included discussion as part of learning, and instructors were involved in less than 30% of the discussion. Originality/value: The findings show that, despite the technological advances in course delivery, current language MOOCs do not differ substantially from conventional distance language learning. Yet, the utilisation of computer-assisted language learning technology and the massive student base of MOOCs for creating a virtual social community are opportunities for developing learners' language proficiency on this learning environment.

Purpose: This paper aims to present a review of case studies on the use of learning analytics in Science, Technology, Engineering, (Arts), and Mathematics (or STE[A]M) education. It covers the features and trends of learning analytics practices as revealed in case studies. Design/methodology/approach: A total of 34 case studies published from 2013 to 2018 reporting relevant learning analytics practices were collected from Scopus and Google Scholar for analysis. The features and trends of practices were identified through a comparison of the first (2013-2015) and the second period (2016-2018). Findings: The results showed an increasing adoption of learning analytics in STE(A)M education, particularly in the USA and European countries and at the tertiary level. More specific types of data have been collected for the learning analytics practices, and the data related to students' learning processes has also been more frequently used. The types of STE(A)M learning practices have become more diversified, with technology enhancement features increasingly introduced. The outcomes of the case studies reflect the overall benefits of learning analytics and address the specific needs of STE(A)M education. There have also been fewer types of limitations encountered in the learning analytics practices over the years, with unknown correlation among variables, small sample size and limited data being the major types. Originality/value: This study reveals the implementation of learning analytics in relation to the contexts and needs of STE(A)M education. The findings also suggest future work for examining the adoption of learning analytics to cope with the development of STE(A)M and, in particular, how the successful experience of learning analytics in other disciplines could be transferred to STE(A)M.

This paper presents an evaluation of mobile learning practice for the clinical practicum in nursing education. Nursing students need to practise nursing skills and follow specific clinical procedures in wards. In this study, they were provided with a mobile device for learning purposes, with mobile apps preinstalled for watching nursing videos and conducting clinical assessments. The evaluation was conducted following the Framework for the Rational Analysis of Mobile Education (FRAME). It included a questionnaire survey involving 265 nursing students and focus group interviews with 20 nursing students, the course coordinator of the clinical practicum and the instructional designer of the mobile apps. The participants shared their views, perceptions and experiences of mobile learning for studying nursing skills and conducting clinical assessment in the practicum context. The results showed the participants' overall satisfaction with the mobile learning practice. They gave positive feedback on the use of the mobile apps in terms of enabling ubiquitous access to materials for situated learning in wards, and offering effective support for teachers to keep track of students' learning progress. They also suggested areas for improvements, which emphasised the hardware capacity of devices, training on the use of apps and institutional support for the maintenance of devices. The results of factor analysis showed a composition of underlying factors different from that of the original FRAME model, which suggests contextual variation in the application of the model.

Purpose: This study aimed to identify the professional development needs of academics in Hong Kong higher education for the use of educational technology. Design/methodology/approach An online survey was conducted in 2017, which involved 374 academics from Hong Kong higher education institutions. The survey covered their perception and use of educational technology for professional activities and their relevant professional development needs. Findings: The results showed an overall positive perception by the academics of the usefulness of online learning with the aid of technology, which was regarded as an effective complement to face-to-face learning. However, their use of educational technology focused only on general online applications, such as document and video-sharing tools, and e-learning platforms. Among various professional development needs, those related to the use of specific educational software with sharing of practical examples were considered to be most highly desired by the academics. In addition, the academics working in lecturer positions tended to use more online applications and had a higher need for professional development, than those with more teaching experience. Originality/value: The findings contribute to identifying the current status of the use of educational technology and the professional development needs among academics in Hong Kong, as well as the differences between academics in different job positions and with different amounts of teaching experience. The results help in designing suitable professional development activities which address the specific needs of academics.

This study examined the effects of mobile apps on the learning motivation, social interaction and study performance of nursing students. A total of 20 students participated in focus group interviews to collect feedback on their use of mobile apps for learning and communicative activities. Two consecutive cohorts of students in a nursing programme, each with about 200 students, were recruited for assessing their learning motivation through a survey and study performance by two tests. The results showed that the students actively used the mobile apps for studying supplementary materials as well as participating in in-class activities and clinical assessments. The students had a relatively high level of motivation for performing and learned well, but relatively low perceived satisfaction and self-efficacy with mobile learning. They showed better study performance after practising mobile learning. The results also suggest that students' perceived intrinsic value with mobile learning has to be enhanced for improving study performance.

Purpose: This paper aims to review and identify the major patterns and trends in learning analytics practices in higher education institutions. The review covers the characteristics of the institutions, as well as the characteristics and outcomes of the learning analytics practices. Design/methodology/approach: This research collected literature published in 2011-2016 which reported learning analytics practices from Scopus and Google Scholar, covering a total of 47 institutions, and categorised the information about the relevant institutions and practices. Findings: The results show that most of the institutions were public ones in the USA and the UK of various sizes and offering different levels of study. The learning analytics practices were mainly institution-wide, apart from a small number focusing on selected courses. The purposes of the practices were mainly to enhance the effectiveness of learning support and administration, followed by facilitating students' learning progress. The most common types of data collected for the practices were students' academic behaviours and their background information. Positive outcomes were reported for a majority of the practices, and the most frequent ones being an increase in cost-effectiveness and understanding of students' learning behaviours. Other outcomes included the improvement of student retention, timely feedback and intervention, support for informed decision-making and the provision of personalised assistance to students. Originality/value: The results provide an overview of the use of learning analytics in the higher education sector. They also reveal the trends in learning analytics practices, as well as future research directions.