Research on the “Efficiency—Ethics” Tension of Digital Transformation in Educational Evaluation: A Double Helix Mechanism Based on Teacher Data Privacy Protection and Machine Learning Model Optimization
Abstract
The digital transformation of educational evaluation has improved efficiency and accuracy, but it also brings ethical risks between privacy protection and model optimization. The study analyzes the conflict between algorithm interpretability, privacy protection and data transparency, and fi nds that although privacy technology safeguards data security, it may reduce model transparency and increase ethical risks. For this reason, this paper proposes a double helix mechanism to optimize privacy protection and enhance interpretability to promote the educational evaluation system towards intelligence and fairness.
Keywords
Educational evaluation; Digital transformation; Machine learning; Privacy Protection
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[1] OECD.(2021).OECD digital education outlook 2021:Pushing the frontiers with artificial intelligence, blockchain and robots[EB/OL].[2021-06-08].
[2] Victor Mayer Sch ö nberger, Kenneth Kukye Walking with Big Data - The Future of Learning and Education [M]. Zhao Zhongjian, Zhang Yannan Shanghai: East China Normal University Press 2015:1121
DOI: http://dx.doi.org/10.18686/ahe.v8i10.13976
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