Data-driven recommendation system for calculus learning using Funk-SVD: Evidence from a mid-scale case study

Chih-Cheng Hsu; Che-Yu Hsu

doi:10.29333/ejmste/16604

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Chih-Cheng Hsu ¹ , Che-Yu Hsu ¹ ^*

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¹ Research Center for Science and Technology for Learning, National Central University, Taoyuan, TAIWAN^* Corresponding Author

Abstract

This study leverages student performance data and the Funk-singular value decomposition (Funk-SVD) model to identify conceptual weaknesses in first-year calculus learning and generate targeted practice recommendations. Rather than relying on error counts or instructor judgment, the model infers individual learning gaps based on predicted success probabilities. Using data from six exams administered to 850 students, the model achieved strong predictive performance, with an F1-score of 0.794. Simulated intervention analysis revealed that the most substantial learning gains occurred among lower-achieving students. Frequently recommended items indicated persistent difficulties with volume integration, curvature, and Riemann sums. These findings underscore the potential of advanced recommendation models to support scalable, personalized learning–grounded in precise, data-informed diagnosis of conceptual weaknesses–thereby enabling more effective instructional support and promoting long-term academic continuity.

Keywords

calculus
Funk-SVD
recommendation systems
data-driven instruction

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, Volume 21, Issue 7, July 2025, Article No: em2666

https://doi.org/10.29333/ejmste/16604

Publication date: 15 Jul 2025

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Article Downloads: 23

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How to cite this article

APA

Hsu, C.-C., & Hsu, C.-Y. (2025). Data-driven recommendation system for calculus learning using Funk-SVD: Evidence from a mid-scale case study. Eurasia Journal of Mathematics, Science and Technology Education, 21(7), em2666. https://doi.org/10.29333/ejmste/16604

Vancouver

Hsu CC, Hsu CY. Data-driven recommendation system for calculus learning using Funk-SVD: Evidence from a mid-scale case study. EURASIA J Math Sci Tech Ed. 2025;21(7):em2666. https://doi.org/10.29333/ejmste/16604

AMA

Hsu CC, Hsu CY. Data-driven recommendation system for calculus learning using Funk-SVD: Evidence from a mid-scale case study. EURASIA J Math Sci Tech Ed. 2025;21(7), em2666. https://doi.org/10.29333/ejmste/16604

Chicago

Hsu, Chih-Cheng, and Che-Yu Hsu. "Data-driven recommendation system for calculus learning using Funk-SVD: Evidence from a mid-scale case study". Eurasia Journal of Mathematics, Science and Technology Education 2025 21 no. 7 (2025): em2666. https://doi.org/10.29333/ejmste/16604

Harvard

Hsu, C.-C., and Hsu, C.-Y. (2025). Data-driven recommendation system for calculus learning using Funk-SVD: Evidence from a mid-scale case study. Eurasia Journal of Mathematics, Science and Technology Education, 21(7), em2666. https://doi.org/10.29333/ejmste/16604

MLA

Hsu, Chih-Cheng et al. "Data-driven recommendation system for calculus learning using Funk-SVD: Evidence from a mid-scale case study". Eurasia Journal of Mathematics, Science and Technology Education, vol. 21, no. 7, 2025, em2666. https://doi.org/10.29333/ejmste/16604