Frontiers in Educational Research, 2026, 9(5); doi: 10.25236/FER.2026.090525.
Xin Wang1, Juxiang Wang2
1School of Economics and Management, Anhui Jianzhu University, Hefei, 230601, China
2School of Mathematics and Physics, Anhui Jianzhu University, Hefei, 230601, China
Smart teaching environments provide online learning records, classroom interaction, assignments, quizzes, laboratory code, and learning feedback for course evaluation. However, end-to-end audio-video inference requires high computing power, bandwidth, annotation, privacy protection, and governance capacity, making it difficult to sustain in ordinary classrooms. This paper proposes a lightweight personalized teaching evaluation framework for the Data Structures course. The framework transforms multi-source process records into structured pedagogical indicators and builds a closed loop of data collection, learning diagnosis, personalized intervention, and effect feedback through indicator integration, student profiling, risk identification, and resource recommendation. The study argues that rule-guided, interpretable evaluation can lower the application threshold of smart teaching and provide an operable path for improving Data Structures teaching practice.
Smart teaching; Personalized teaching evaluation; Learning analytics; Student profile; Data structures
Xin Wang, Juxiang Wang. Research on a Personalized Teaching Evaluation Framework for Data Structures in Smart Teaching Environments. Frontiers in Educational Research (2026), Vol. 9, Issue 5: 181-186. https://doi.org/10.25236/FER.2026.090525.
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