The use of a personalized learning approach to implementing self-regulated online learning

Ingkavara, Thanyaluck; Panjaburee, Patcharin; Srisawasdi, Niwat; Sajjapanroj, Suthiporn

doi:10.1016/j.caeai.2022.100086

Cited by 47 publications

(37 citation statements)

References 61 publications

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“…In our study, we evaluated the adaptive SRL support within and beyond learning-related activities based on learners' behaviour in naturalistic settings and self-report SRL measures. This study further advances the promising areas in AIED research, including: personalisation and feedback (Cavalcanti et al, 2021;Chen, Zou, et al, 2022;Hwang et al, 2020;Ingkavara et al, 2022), EDM for assessment and performance prediction (Chen et al, 2020;Molenaar et al, 2023;Zhang & Aslan, 2021), mitigating difficulties in attaining learners' skills (Kabudi et al, 2021), utilising AI to provide personalised scaffolding (Lim et al, 2023) and metacognitive prompts to change learners' behaviour (Raković et al, 2022), and contributes to the development of the hybrid human and artificial cognitive regulatory systems (Molenaar, 2022;Siemens et al, 2022).…”

Section: Discussionmentioning

confidence: 78%

From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning

Pogorskiy

Beckmann

2023

Computers and Education: Artificial Intelligence

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Section: Discussionmentioning

confidence: 78%

From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning

Pogorskiy

Beckmann

2023

Computers and Education: Artificial Intelligence

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“…The main theme of Cluster #2 is adaptive learning systems. The key citing publications in this cluster focus on the use of adaptive learning systems to support student learning, such as classroom learning (Wang et al , 2023) and self-regulated online learning (Ingkavara et al , 2022). These studies concern the design and architecture of learning systems, as well as the effectiveness of adaptive learning approaches.…”

Section: Resultsmentioning

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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“…Scholars prove through empirical research that individualized learning design enhances the process of online learning (ibid). A group of scholars came to a similar conclusion regarding the personalized learning methods in online environments claiming that the student's perceptions about the usefulness of learning suggestions, ease of use, goal setting, learning environmental structuring, task strategies, time management, selfevaluation, impact on learning, and attitude toward the learning environment are important predictors of behavioural intention to learn with the self-regulated online learning that integrated with the personalized learning approach (Ingkavara et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 95%

Peculiarities of Bulgarian universities' digitalization during the Covid-19 pandemic

Stoyanova¹,

Angelova²,

Parushev³

2022

JESI

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The COVID-19 pandemic forced the education system to transform quickly from a straightforward traditional process to an online one. Both schools and universities, including their management and administration) were challenged to maintain the quality of the educational service they provided, but this time under completely different conditions and in a new and unfamiliar online environment. In Bulgaria, due to the rules of the systems and the academic autonomy, each university had the opportunity to decide how to deal with these force major circumstances. Along with the technical challenges, professors have to overcome the difficulties related to the motivation and engagement of the students in such a mediated environment. The main objective of the current article is to evaluate the results of the online learning process during the COVID-19 pandemic in Bulgaria. The results from empirical research (N = 140) clearly show that most Bulgarian lecturers prefer face-to-face learning. At the same time, they admit the necessity of specific training for technical skills improvement and different approaches for students' attention and engagement in online learning. According to the professors, the overall assessment for the level of satisfaction from online learning during the COVID-19 pandemic is in the golden mean -3 on a scale of 1 -"not at all effective" to 5 -"it is effective".

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The use of a personalized learning approach to implementing self-regulated online learning

Cited by 47 publications

References 61 publications

From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning

From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning

Artificial intelligence in personalised learning: a bibliometric analysis

Peculiarities of Bulgarian universities' digitalization during the Covid-19 pandemic

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