Research Article
Enhancing Flashcard Learning through Spaced Repetition Optimization
@INPROCEEDINGS{10.4108/eai.28-4-2025.2358003, author={M. Misba and Kandrathi Deekshitha and Srikanth Reddy Upputuri and Venkata Sai Kumar Mullamuri and R. RoselinKiruba and J. Kavitha}, title={Enhancing Flashcard Learning through Spaced Repetition Optimization}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part II}, publisher={EAI}, proceedings_a={ICITSM PART II}, year={2025}, month={10}, keywords={spaced repetition leitner system sm2 algorithm learning efficiency memory retention adaptive learning}, doi={10.4108/eai.28-4-2025.2358003} }- M. Misba
Kandrathi Deekshitha
Srikanth Reddy Upputuri
Venkata Sai Kumar Mullamuri
R. RoselinKiruba
J. Kavitha
Year: 2025
Enhancing Flashcard Learning through Spaced Repetition Optimization
ICITSM PART II
EAI
DOI: 10.4108/eai.28-4-2025.2358003
Abstract
Spaced repetition algorithms improve flashcard learning by spacing out reviews at the right time to increase long-term retention of information. In this paper two popular ones are discussed: the Leitner System and the SM2 Algorithm, analysing their designs and pointing out strengths as well as weaknesses. Leitner System is based on the simple mechanical concept of linearly declining memory repetition, while SM2 Algorithm uses a exponentially declining model of repetition together with the ease factor that allows to adjust the model parameters individually for each user and therefore, create a personal learning experience with an improved cost/quality ratio. The presentation emphasizes the trade-off between flexibility and parsimony, urging the choice of an algorithm consistent with one's learning objectives. The results of the study indicate that a spaced repetition approach substantially increase the efficiency of learning and potential future integration of spaced repetition algorithms as part of an AI-driven personalized learning can further enhance efficacy in education.
