Evaluating teachers’ perceptions and use of a portal for digital personalised learning: A multiple case study in Flanders

Vanbecelaere, Stefanie; Schoors, Rani Van; Bhatt, Sohum Mandar; Rajagopal, Kamakshi; Debeer, Dries; Depaepe, Fien

doi:10.1007/s10639-023-11931-1

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…What sets Khan Academy apart is its adaptive learning technology that personalizes educational content based on individual learner performance and progression. The platform utilizes instructional videos, practice exercises, and a personalized learning dashboard that empowers learners to study independently in and outside the classroom [50].…”

Section: Dynamic Adjustment Of Learning Paths and Difficulty Levelsmentioning

confidence: 99%

Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways

Song,

Shin,

Shin

2024

Applied Sciences

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This study introduces a novel approach named the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), aimed at personalizing educational pathways through machine learning technology. Our findings reveal that this framework significantly enhances student engagement and learning effectiveness by providing real-time feedback and personalized instructional content tailored to individual learning needs. This research demonstrates the potential of leveraging advanced technology to create more effective and individualized learning environments, offering educators a new tool to support each student’s learning journey. The study thus contributes to the field by showcasing how personalized education can be optimized using modern technological advancements.

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Section: Dynamic Adjustment Of Learning Paths and Difficulty Levelsmentioning

confidence: 99%

Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways

Song,

Shin,

Shin

2024

Applied Sciences

View full text Add to dashboard Cite

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Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways

Song,

Shin,

Shin

2024

Preprint

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This study introduces the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), an innovative machine learning-based approach to personalize educational pathways. DFDLOF leverages advanced algorithms for in-depth analysis of learner data, enabling the dynamic customization of educational content and methodologies. Its application in diverse online platforms, including Khan Academy and Coursera, underscores its adaptability and scalability. DFDLOF enhances learner engagement and outcomes by providing tailored learning experiences and real-time feedback. Key insights reveal its strengths in personalized education, particularly in dynamically adapting learning paths and the importance of real-time feedback. Challenges identified include integration complexities and data privacy concerns. Future research directions emphasize the need for longitudinal impact studies and broader demographic applications. This study contributes significantly to the evolving domain of adaptive learning technologies, demonstrating the transformative potential of machine learning in personalizing education.

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Evaluating teachers’ perceptions and use of a portal for digital personalised learning: A multiple case study in Flanders

Cited by 2 publications

References 37 publications

Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways

Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways

Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways

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