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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.

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.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to evaluate the implementation of mobile learning in a nursing course at The Open University of Hong Kong, and identify the potentials of, and constraints on, introducing mobile technologies in the instructional design of nursing education. The paper also considers the pedagogical implications of the expansion of mobile learning in the field of nursing.

The research adopts a qualitative approach to obtain the students’ and teacher’s experiences, opinions, and expectations on mobile learning. Two focus groups with 20 student participants were conducted and an in-depth interview with the course teacher was arranged. The Framework for the Rational Analysis of Mobile Education (FRAME) model was used as the research framework to support data collection and analysis.

The aspects of device usability, interaction learning, and social technology as suggested in the FRAME model were partly fulfilled in the study. Mobile technology enhanced the portability and accessibility of learning information, and networking tools facilitated interaction among students and between students and the teacher. However, the readability of text was limited due to constraints on the user interface and screen size, and concerns over the reliability of learning content were also raised, given the abundance of unfiltered online information. The difficulty in updating the content of multimedia materials and sourcing videos of an appropriate level, together with the problem of device networking, also limited the usefulness of mobile learning. Attention should also be paid to the perceptual differences between students and the teacher on the nature and functions of mobile learning.

This empirical study provides a detailed evaluation of the delivery of mobile learning in a nursing course. The findings reveal the strengths and limitations of using mobile technologies to support the nursing education.

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.

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).

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.

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 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.

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.

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.

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.

The purpose of this paper is to survey the factors which facilitate effective teaching through massive open online courses (MOOCs).

A descriptive meta-analysis was conducted to first examine the literature covering the characteristics of teaching in MOOCs, the profile of participants, the instructional design of course materials and/or the course assessment methods – and then to summarise the factors which are conducive to the teaching effectiveness of MOOCs. A random sample of MOOCs was then reviewed to sort out the extent to which the factors can be identified in these courses.

The factors leading to effective teaching of MOOCs revolve around six areas according to the stages of course delivery, namely, preparation, attraction, participation, interaction, consolidation and post-course support. They address the application of technology to achieve educational purposes, while coping with the potentials and constraints of the MOOC environment. In practice, however, existing MOOCs show varying degrees of the implementation of the factors.

As this is an exploratory study summarising and categorising the factors, further work should be done, in particular on the proper adoption of these factors in teaching, their effectiveness and ways of assessing such effectiveness.

The factors identified will help institutions and academics who plan to offer MOOCs to be aware of how teaching can be best delivered to promote effective student learning.