Pokémon GO as a Cognitive and Societal Development Tool for Personalised Learning

The present research develops and tests a theoretical gamification model (GM) that explores the use of mobile learning (ML) and massively multiplayer online (MMO) games to strengthen group prospection of teams and improve retention. The GM used Pokémon GO to enable higher education students to engage in activities and challenges with a view to observe the impacts on health and wellbeing through collection of quantitative and qualitative data. The data that was collected involved a sample set (N = 50) of participants within the general educational sector. The model constructs were measured throughout the first academic semester, from September 2018 to February 2019. There is significant evidence to show that the use of ML in the classroom is beneficial depending on the influences from and engagement with participants. The contributions from these findings should provide the basics for further research into different studies involving MMOs and ML or gamification studies.

Pokémon GO as a Cognitive and Societal Development Tool for Personalised Learning

The present research develops and tests a theoretical gamification model (GM) that explores the use of mobile learning (ML) and massively multiplayer online (MMO) games to strengthen group prospection of teams and improve retention. The GM used Pokémon GO to enable higher education students to engage in activities and challenges with a view to observe the impacts on health and wellbeing through collection of quantitative and qualitative data. The data that was collected involved a sample set (N = 50) of participants within the general educational sector. The model constructs were measured throughout the first academic semester, from September 2018 to February 2019. There is significant evidence to show that the use of ML in the classroom is beneficial depending on the influences from and engagement with participants. The contributions from these findings should provide the basics for further research into different studies involving MMOs and ML or gamification studies.

Cloud Computing Big Data Adoption Impacts on Teaching and Learning in Higher Education

There is a rapid evolution in the purpose and value of higher education brought about by technological advancement and data ubiquity. Data mining and advanced predictive analytics are increasingly being used in higher education institutions around the world to perform tasks, ranging from student recruitment, enrolment, predicting student behaviour, and developing personalised learning schemes. This chapter evaluates and assesses the impact of big data and cloud computing in higher education. The authors adopt systematic literature research approach that employs qualitative content analysis to establish their position with regards to the impact, benefits, challenges, and opportunities of integrating big data and cloud computing to facilitate teaching and learning.

Researching Into the Use of Real-Life Materials in Language Learning Apps

The study reported in this chapter investigates how mobile language apps facilitate the learning of L2 Chinese, focusing on the use of real-life materials in the design of HANZI – a mobile app specifically developed for this study. Guided by the pedagogy of using authentic materials in language learning and some key design principles of language mobile apps, this chapter explores beginner Chinese learner experiences of mobile-assisted Chinese language learning. It is a qualitative-led case study with multiple datasets including descriptive statistics from 111 survey responses, student focus group discussions, and teacher interviews. The analysis reveals that real-life materials can promote learners' motivation, personalised learning, and social interaction, which improve language proficiency. Such materials should be included in language activities in both digital and print media because they are central to overcoming the challenges of learning Chinese characters.

Artificial Intelligence and E-Learning

The Edtech sector has grown with the emergence of new ventures at all levels of education and training. A number of experiments are carried out in this domain across the globe. Artificial intelligence is instrumental in helping institutions to engage students, provide personalised learning, and stay profitable. The chapter provides information about prevalent models being used for instruction design. It also includes examples of best practices of using technology in learning across the globe. Case studies of Yuanfudao (China), Embibe (India), Ruangguru (Indonesia), Tonies (Germany), Virti (UK), Examity (USA), and Packback (USA) are part of this chapter.

Proposing the Introduction of Personalised Learning and Differentiated Learning into Saudi Arabian Education: مقترح إدخال التعلّم الفردي والتعلّم المتمايز في التعليم السعودي

Personalised learning and the differentiated curriculum are ideal strategies to explore the needs of students and take full advantage of students’ abilities so they can achieve better outcomes in the learning process. This study focuses on the differentiated curriculum and explores its suitability for Saudi Arabia considering the country’s current education policies and vision. The study consists of three stages: reviewing the global personalised learning and differentiation literature, investigating the current education policies in Saudi Arabia, and reviewing the available differentiation literature conducted in Saudi Arabia. The findings suggest that despite the obstacles found in the application including low family participation and resistance from teachers this teaching strategy is suitable for Saudi Arabia. The Saudi vision for education and the country’s current policies do not conflict with the principles of differentiation, although success may require patience and building teacher capacity. With only a limited number of studies about differentiation in Saudi Arabia, more research about the impact of school management on the implementation of the differentiated curriculum is necessary.

Perezhivanie and Its Application within Early Childhood Science Education Research

Perezhivanie is a concept that was originally defined by Vygotsky, but it did not become a part of educational theory until recently. Today the concept has been revived, and it is now used as a way to include emotional aspects into education and educational research. The concept also provides a rationale for describing and forming personalised learning. The present study provides a literature review with the aim of covering the variety in definitions of the concept, as well as the different perspectives that the concept lends to research in general, and to research with focus on early years education in particular. Results show that the concept has been applied within the most common theoretical perspectives in use today (such as social, cultural and subjective perspectives) with an interesting array of outcomes, such as design of educational methods, analysis of different modes of experiencing and development of self-awareness. The use of this concept becomes a shift toward more emotional perspectives of learning and development that may not be altogether positive, as perezhivanie holds the risk of blurring the border between psychotherapy and education, which is something that would provide new challenges for education in general and especially for teacher education.

Providing equitable education through personalised adaptive learning

Educators around the globe are striving to promote equity in their classrooms by adopting adaptive learning systems to provide customised learning resources and tools to help the learners achieve mastery at their own pace. This personalised approach goes a long way in closing the divide between students' socioeconomic status and special needs. Over the years, the advancement in technology offers more sophisticated adaptive technologies. It aggregates data such as students' prior knowledge and academic performances to predict and better adapt the learning paths. This paper presents the evaluation of adaptive technologies for personalised learning and the vision of a Personalised Adaptive Learning and Assessment (PALAS) system for Higher Education. This vision could be an imperative piece supporting Singapore's ‘National AI Strategy’, set to focus on personalised education through adaptive learning and assessment.

Automatic e-content sequencing system for personalised learning environments by using fuzzy AHP based on multiple intelligences

The use of personalised learning environments (PLEs) and adaptive learning environments (ALEs) in education has increased during the COVID-2019 pandemic. Thus, the need of effective and high-quality PLEs and ALEs has emerged but developing such learning environments has some difficulties such as the need for a huge number of qualified e-contents. Generation of e-contents for these systems is a time-consuming and costly process. Moreover, presenting the same e-content for each student is not effective because each student can have different intelligence type. To overcome these problems, low cost, reusable and dynamic e-contents can be used for PLE and ALE systems. For this purpose, fuzzy analytic hierarchy process (FAHP) was used to provide sequenced e-contents for PLE and ALE systems. This proposed system was used in a case study to investigate its impact on learning process. The performance of the proposed study was compared with analytical hierarchy process (AHP), which is another multi-criteria decision-making (MCDM) method, also further statistical analyses were investigated. The results showed that 84.6% of students showed interest in the proposed system, so the FAHP method can be used for presenting personalised e-content effectively.