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E-learning has been widely adopted due to its numerous advantages: (1) enabling self-directed learning without time and location constraints, (2) supporting personalized learning with ease, (3) reducing educational costs, and (4) facilitating adaptive learning through data analysis. However, existing e-learning systems are often criticized for insufficiently addressing knowledge retention. This study focuses on this limitation and aims to develop a system that enhances knowledge retention. To achieve this, we analyzed widely available e-learning systems and trialed services and content from commercial providers deemed beneficial. The proposed system incorporates mechanisms to reinforce knowledge retention, such as optimizing quiz timing based on Ebbinghaus’s forgetting curve. Additionally, the system was implemented among students in the authors’ academic department, and its learning effectiveness was evaluated through empirical analysis. The findings provide clear insights for developing more effective e-learning systems.

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Toward the Development of a Practical E-learning System to Promote Knowledge Retention

  • 会议论文

  • 发表日期：

    2025年

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  • Tetsuya Nakatoh

摘要

E-learning has been widely adopted due to its numerous advantages: (1) enabling self-directed learning without time and location constraints, (2) supporting personalized learning with ease, (3) reducing educational costs, and (4) facilitating adaptive learning through data analysis. However, existing e-learning systems are often criticized for insufficiently addressing knowledge retention. This study focuses on this limitation and aims to develop a system that enhances knowledge retention. To achieve this, we analyzed widely available e-learning systems and trialed services and content from commercial providers deemed beneficial. The proposed system incorporates mechanisms to reinforce knowledge retention, such as optimizing quiz timing based on Ebbinghaus’s forgetting curve. Additionally, the system was implemented among students in the authors’ academic department, and its learning effectiveness was evaluated through empirical analysis. The findings provide clear insights for developing more effective e-learning systems.