Framework for personalized learning with smart E-learning system using macro and micro adaptive approach

Nurcahyo, Widyat; Agustina, Yumniati

doi:10.1063/5.0122540

Cited by 2 publications

(2 citation statements)

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“…For example, Shemshack & Spector (2020) identified the use of different terms like adaptive learning and individualized instruction to refer to this concept. In fact, Villegas-Ch and García-Ortiz (2023) and Nurcahyo and Agustina (2023) are some examples of an entire line of research that associates personalized learning with adaptation. In addition to the above, Schmid & Petko (2019) highlight the need for a precise definition due to the multifaceted and complex nature of personalized learning.…”

Section: Introductionmentioning

confidence: 99%

“…In the context of the use of digital technologies in education and more specifically related to e-learning, the personalization of learning has been addressed mainly associated with the adaptivity of learning management systems (LMS) (Ghallabi et al, 2015;Aplugi and Santos, 2022;Nurcahyo and Agustina, 2023), to the adaptation of content through learning objects (Luna-Urquizo, 2019; Gan and Zhang, 2020) and to gamification processes and use of serious games (Kickmeier-Rust and Dietrich, 2009;Makarenya, Stash and Nikashina, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Harnessing AI for Education 4.0: Drivers of Personalized Learning

Barrera Castro,

Chiappe,

Becerra Rodriguez

et al. 2024

EJEL

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Personalized learning, a pedagogical approach tailored to individual needs and capacities, has garnered considerable attention in the era of artificial intelligence (AI) and the fourth industrial revolution. This systematic literature review aims to identify key drivers of personalized learning and critically assess the role of AI in reinforcing these drivers. Following PRISMA guidelines, a thorough search was conducted across major peer-reviewed journal databases, resulting in the inclusion of 102 relevant studies published between 2013 and 2022. A combination of qualitative and quantitative analyses, employing categorization and frequency analysis techniques, was performed to discern patterns and insights from the literature. The findings of this review highlight several critical drivers that contribute to the effectiveness of personalized learning, both from a broad view of education and in the specific context of e-learning. Firstly, recognizing and accounting for individual student characteristics is foundational to tailoring educational experiences. Secondly, personalizing content delivery and instructional methods ensures that learning materials resonate with learners' preferences and aptitudes. Thirdly, customizing assessment and feedback mechanisms enables educators to provide timely and relevant guidance to learners. Additionally, tailoring user interfaces and learning environments fosters engagement and accessibility, catering to diverse learning styles and needs. Moreover, the integration of AI presents significant opportunities to enhance personalized learning. AI-driven solutions offer capabilities such as automated learner profiling, adaptive content recommendation, real-time assessment, and the development of intelligent user interfaces, thereby augmenting the personalization of learning experiences. However, the successful adoption of AI in personalized learning requires addressing various challenges, including the need to develop educators' competencies, refine theoretical frameworks, and navigate ethical considerations surrounding data privacy and bias. By providing a comprehensive understanding of the drivers and implications of AI-driven personalized learning, this review offers valuable insights for educators, researchers, and policymakers in the Education 4.0 era. Leveraging the transformative potential of AI while upholding robust pedagogical principles, personalized learning holds the promise of unlocking tailored educational experiences that maximize individual potential and relevance in the digital economy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Harnessing AI for Education 4.0: Drivers of Personalized Learning

Barrera Castro,

Chiappe,

Becerra Rodriguez

et al. 2024

EJEL

View full text Add to dashboard Cite

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Optimizing cognitive load and learning adaptability with adaptive microlearning for in-service personnel

Zhu,

Chau,

Mokmin

2024

Sci Rep

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Adaptive microlearning has emerged as a crucial approach for enhancing the working skills of in-service personnel. This study introduces the design and development of an innovative adaptive microlearning (AML) system and investigates its effectiveness compared to a conventional microlearning (CML) system. The main distinguishing feature of an AML system from a CML system is its adaptive features that tailor the learning experience to individual needs, including personalized content delivery, real-time feedback, and adaptive learning paths. A quasi-experimental study involving 111 in-service personnel (N AML = 56, N CML = 55) was conducted. ANCOVA results confirmed that the AML system significantly reduced unnecessary cognitive load due to inappropriate instructional design (mean difference of -20.02, p < 0.05) and significantly improved learning adaptability (mean difference of 40.72, p < 0.05). These findings highlight the potential of adaptive microlearning systems to overcome barriers to effective learning, thereby supporting lifelong learning and professional development in various working contexts.

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Framework for personalized learning with smart E-learning system using macro and micro adaptive approach

Cited by 2 publications

References 11 publications

Harnessing AI for Education 4.0: Drivers of Personalized Learning

Harnessing AI for Education 4.0: Drivers of Personalized Learning

Optimizing cognitive load and learning adaptability with adaptive microlearning for in-service personnel

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