IJIET 2026 Vol.16(8): 2254-2264
doi: 10.18178/ijiet.2026.16.8.2684
doi: 10.18178/ijiet.2026.16.8.2684
Evaluation of Learning Efficiency in MOOC Teaching Systems Based on Kirkpatrick’s Model and AHP-fuzzy Comprehensive Evaluation Method
Yangbo Wu 1,*, Ng Giap Weng 1, Beng Yu Leau 1, and Ying Lin 2
1. Faculty of Computing and Informatics, University Malaysia Sabah, Sabah, Malaysia
2. Faculty of Psychology and Education, University Malaysia Sabah, Sabah, Malaysia
Email: wuyangbo@126.com (Y.W.); nggiapweng@ums.edu.my (N.G.W.); lybeng@ums.edu.my (B.Y.L.); linying0813@126.com (Y.L.)
*Corresponding author
2. Faculty of Psychology and Education, University Malaysia Sabah, Sabah, Malaysia
Email: wuyangbo@126.com (Y.W.); nggiapweng@ums.edu.my (N.G.W.); lybeng@ums.edu.my (B.Y.L.); linying0813@126.com (Y.L.)
*Corresponding author
Manuscript received March 13, 2026; revised April 9, 2026; accepted May 20, 2026; published August 21, 2026
Abstract—Conventional single-dimensional scoring methods for Massive Open Online Course (MOOC) teaching systems often fail to capture holistic learning efficiency and suffer from low evaluation accuracy. To address these limitations, this paper proposes a novel learning efficiency evaluation method integrating Kirkpatrick’s four-level model with the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation. First, a multidimensional indicator system is constructed across Reaction, Learning, Behavior, and Results levels. The AHP is then employed to calculate the weight vectors of each indicator, ensuring rational weight distribution through consistency testing. Subsequently, a fuzzy relation matrix is established based on membership degrees derived from expert assessments. By synthesizing the AHP-derived weights with the fuzzy matrix via weighted average operators, the method defuzzifies the results to yield a comprehensive efficiency score. Experimental validation involving 200 learners demonstrates that the proposed method outperforms existing approaches, achieving a Root Mean Squared Error (RMSE) of 0.45, Mean Absolute Error (MAE) of 0.35, and an accuracy rate of 89.5%. The results indicate that this method maintains stable, high-confidence evaluation performance across all levels, effectively handling the fuzziness and uncertainty inherent in learning efficiency assessment, thereby providing a reliable basis for optimizing MOOC teaching systems.
Keywords—Kirkpatrick’s model, Analytic Hierarchy Process (AHP) method, fuzzy comprehensive evaluation method, Massive Open Online Course (MOOC) teaching system, learning efficiency evaluation
Copyright © 2026 by the authors. 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 (CC BY 4.0).
Keywords—Kirkpatrick’s model, Analytic Hierarchy Process (AHP) method, fuzzy comprehensive evaluation method, Massive Open Online Course (MOOC) teaching system, learning efficiency evaluation
Cite: Yangbo Wu, Ng Giap Weng, Beng Yu Leau, and Ying Lin, "Evaluation of Learning Efficiency in MOOC Teaching Systems Based on Kirkpatrick’s Model and AHP-fuzzy Comprehensive Evaluation Method," International Journal of Information and Education Technology, vol. 16, no. 8, pp. 2254-2264, 2026.
Copyright © 2026 by the authors. 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 (CC BY 4.0).