Frontiers in Educational Research, 2024, 7(10); doi: 10.25236/FER.2024.071004.
Zhangliang Chen, Junwei Shi, Hongzhu Jin
School of Management Science and Engineering, Shandong Technology and Business University, Yantai, China
In the era of artificial intelligence, knowledge graphs have become pivotal for enhancing knowledge management in education. This paper explores the development of a teaching evaluation system for diversified curricula within hybrid teaching models, utilizing knowledge graphs. The study aims to improve the evaluation of teaching effectiveness by integrating knowledge graphs, which offer a structured representation of educational entities and their interrelationships. The proposed evaluation system leverages knowledge graphs to assess various dimensions of teaching quality, such as student engagement, instructional effectiveness, and alignment with educational objectives. By organizing and analyzing data through knowledge graphs, the system provides comprehensive and actionable insights for continuous improvement. The research identifies practical pathways for implementing this evaluation system, including AI-driven analytics for real-time monitoring, automated feedback mechanisms, and adaptive evaluation criteria. Empirical results from pilot implementations show that this system enhances evaluation precision and adaptability, leading to more effective and responsive teaching practices. In conclusion, the integration of knowledge graphs into teaching evaluation frameworks offers significant advantages, including improved precision, relevance, and the ability to tailor teaching strategies to individual needs, ultimately contributing to better learning outcomes and higher teaching quality.
teaching evaluation system; path of diversified courses; blended teaching; artificial intelligence
Zhangliang Chen, Junwei Shi, Hongzhu Jin. Research on the Teaching Evaluation System and Path of Diversified Courses in Blended Teaching Based on Knowledge Graph under the Background of Artificial Intelligence. Frontiers in Educational Research (2024) Vol. 7, Issue 10: 21-30. https://doi.org/10.25236/FER.2024.071004.
[1] Marcos C M . Teaching Innovation Experience for COVID-19 Times: A Case Study on Blended Learning of Television Journalism Courses with Moodle [J]. Asia Pacific Media Educator, 2021, 31 (2): 178-194.
[2] Pischetola , Magda . Teaching Novice Teachers to Enhance Learning in the Hybrid University [J]. Postdigital Science and Education, 2021, (prepublish): 1-23.
[3] Guoqin L , Wen Z ,Sai L . The exploration of PBL mixed teaching mode in secondary vocational classes [J]. Journal of Physics: Conference Series, 2021, 1976 (1):
[4] Zhaoxia D . Research on the Application Status and Countermeasures of College English Blended Teaching Model under Big Data [J]. Journal of Physics: Conference Series, 2021, 1955 (1):
[5] Sascha H , Sven D ,Lennart H . Urgent need hybrid production - what COVID-19 can teach us about dislocated production through 3d-printing and the maker scene [J]. 3D Printing in Medicine, 2020, 6 (1): 37-37.
[6] Firouzi F J , Maryam O . Application of a Hybrid Method for Performance Evaluation of Teaching Hospitals in Tehran [J]. QUALITY MANAGEMENT IN HEALTH CARE, 2020, 29 (4): 210-217.
[7] Manuel M A , Anabel A B ,Sebastian G M . From blended teaching to online teaching in the days of Covid19. Student visions [J]. CAMPUS VIRTUALES, 2020, 9 (2): 35-50.
[8] Singh G , Sharma N ,Sharma H . Shuffled teaching learning-based algorithm for solving robot path planning problem [J]. International Journal of Metaheuristics, 2020, 7 (3):
[9] Zhou B , Peng T . Scheduling just-in-time part replenishment of the automobile assembly line with unrelated parallel machines [J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2019, 233 (14): 5113-5130.
[10] Thomas M , Clack L , Plaspohl S . Blending Pedagogical Approaches in Public Health Education: The ADOPT Model [J]. Pedagogy in Health Promotion, 2018, 4 (3): 227-233.
[11] M. T M . A Hybrid Spiral Project Based Learning Model for Microprocessor Course Teaching [J]. Kurdistan Journal of Applied Research, 2017, 2 (3): 125-130.