An analysis of learning analytics in personalised learning

Wong, Billy; Li, Kam Cheong; Cheung, Siu Tim

doi:10.1007/s12528-022-09324-3

Cited by 23 publications

(6 citation statements)

References 35 publications

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“…In the meantime, these data can be utilized to offer specific assistance and solutions to these students. The customization of learning experiences can be facilitated by the utilization of learning analytics, allowing for customized instruction to be provided to individual students [26]. Learning analytics can be employed to determine a student's areas of competence and areas for improvement, as well as to suggest suitable learning activities and resources tailored to their specific requirements.…”

Section: How Can Learning Analytics Be Used To Improve Student Learni...mentioning

confidence: 99%

The Evolving Classroom: How Learning Analytics Is Shaping the Future of Education and Feedback Mechanisms

Sharif,

Atif

2024

Education Sciences

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In the dynamic world of higher education, technological advancements are continually reshaping teaching and learning approaches, with learning analytics (LA) playing a crucial role in this transformation. This systematic literature review (SLR) explores the significant impact of LA in higher education, specifically its transformative role in personalizing and enhancing educational feedback mechanisms. Utilizing a wide range of educational data, LA facilitates a shift from generic to individualized feedback, leading to improved learning outcomes and equity. However, incorporating LA into higher education is not without challenges, ranging from data privacy concerns to the possibility of algorithmic errors. Addressing these challenges is vital for unlocking the full potential of LA. This paper also examines emerging LA trends, such as augmented reality, emotion-sensing technology, and predictive analytics, which promise to further personalize learning experiences in higher education settings. By anchoring these advancements within core educational principles, we foresee a future of education marked by innovation and diversity. This SLR provides an overview of LA’s evolution in higher education, highlighting its transformative power, acknowledging its challenges, and anticipating its future role in shaping a dynamic, responsive educational environment.

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Section: How Can Learning Analytics Be Used To Improve Student Learni...mentioning

confidence: 99%

The Evolving Classroom: How Learning Analytics Is Shaping the Future of Education and Feedback Mechanisms

Sharif,

Atif

2024

Education Sciences

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“…Extensive review studies over the years have been undertaken to examine AI applications in various educational contexts (Chen et al , 2020b, 2020c; Chen et al , 2022d). However, systematic review studies are rare that examine the applications of AI-based techniques within the contexts of personalised learning, which have become increasingly prevalent with a broad range of relevant technologies being applied in the learning of various subject disciplines (Choi et al , 2018; Li and Wong, 2021, 2022a; Wong et al , 2022). Review studies in relation to AI applications in education have addressed their overall development as well as specific focus, such as AI technologies, disciplinary and regional practices.…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligence in personalised learning: a bibliometric analysis

Li¹,

Wong²

2023

ITSE

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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.

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“…These systems can assess a student's performance, strengths, and weaknesses, and then tailor their instruction accordingly [61]. By analyzing student responses and engagement, virtual tutors can provide personalized exercises, explanations, and recommendations, ensuring that the learning experience is both effective and efficient [62].…”

Section: Adapt To the Specific Needs Of Each Studentmentioning

confidence: 99%

AI in Educational Technology

Zhao

2023

Preprint

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This article focuses on artificial intelligence in educational technology, starting with an introduction to educational technology, an interdisciplinary field of study that covers the design, development, utilization, and evaluation of technology and digital tools in educational Settings. A detailed description of its definition in an academic context - a multidisciplinary field of computer science and cognitive science that deals with the development of computational systems that exhibit intelligent behaviour, describing its areas of coverage and scope of application. It then introduces the benefits of AI in education technology, specifically addressing personalized learning, adaptive learning systems, automated scoring and feedback, virtual tutors and chatbots, data analytics, as well as content recommendations and natural language processing, accessibility and inclusion. Then it introduces the main concepts of AI personalized learning: AI personalized learning uses the power of artificial intelligence to meet the unique needs and preferences of individual learners, as well as its key principles and main characteristics. Adaptive learning systems harness the power of artificial intelligence and data analytics to tailor the learning experience to each student's needs and abilities, as well as its core principles and strengths. It also introduces the main operating principles of automated grading and feedback: it uses artificial intelligence and computer algorithms to evaluate and evaluate student assignments, tests and exams without the direct involvement of human graders, and its associated benefits. Secondly, the main concepts of virtual tutor are introduced: Virtual tutor is a computer-based system that uses artificial intelligence and machine learning algorithms to provide students with personalized and interactive educational support and its key characteristics and main advantages, and then the nature and benefits of chatbots and help in the field of education. The final conclusion summarizes the benefits and future challenges of integrating AI into educational technology.

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An analysis of learning analytics in personalised learning

Cited by 23 publications

References 35 publications

The Evolving Classroom: How Learning Analytics Is Shaping the Future of Education and Feedback Mechanisms

The Evolving Classroom: How Learning Analytics Is Shaping the Future of Education and Feedback Mechanisms

Artificial intelligence in personalised learning: a bibliometric analysis

AI in Educational Technology

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