基于多源数据融合的师范生教学设计能力循证评价体系研究

• 영문명: Research on the Evidence-Based Evaluation System for Teaching Design Ability of Teacher Education Students Based on Multi-Source Data Fusion
• 발행기관: YIXIN 출판사
• 저자명: Lu Xu
• 간행물 정보: 『Journal of Education and Teaching』Vol.3 No.10, 1~8쪽, 전체 8쪽
• 주제분류: 사회과학 > 교육학
• 파일형태: PDF
• 발행일자: 2025.10.30

국문 초록

This study proposes a value-added evaluation model for teaching design ability of normal university students based on multi-source data fusion, addressing the issues of “emphasizing results over process”, “emphasizing static over development”, and “emphasizing single over comprehensive” in the current evaluation of teaching design ability. The model integrates multi-source heterogeneous data such as teaching behavior data, teaching evaluation data, and student learning data, and combines data science methods such as principal component analysis (PCA), random forest ensemble learning, and LSTM time series prediction to construct a value-added evaluation model centered on initial ability baseline and stage progress indicators, achieving dynamic tracking and precise evaluation of the development of teaching design ability of normal university students. This provides theoretical support for the precise, personalized, and scientific cultivation of teaching ability of normal university students.

영문 초록

本研究针对当前师范生教学设计能力评价中存在的“重结果、轻过程”“重静态、轻发展”“重单一、轻综合”等问题，提出了一种基于多源数据融合的师范生教学设计能力增值性评价模式。该模式通过整合教学行为数据、教学评价数据、学生学习数据等多源异构数据，结合主成分分析（PCA）、随机森林集成学习、LSTM 时间序列预测等数据科学方法，构建了以初始能力基线与阶段性进步指标为核心的增值评价模型，实现了对师范生教学设计能力发展的动态追踪与精准评估，为师范生教学能力培养的精准化、个性化和科学化提供了理论支持。

목차

Ⅰ. 引言

Ⅱ. 多源异构数据的系统化采集与汇聚

Ⅲ. 基于多元算法的数据融合与能力挖掘

Ⅳ. 基于基线与发展轨迹的增值模型构建

Ⅴ. 支持个性化诊断的可视化反馈与应用

Ⅵ. 结论与展望

参考文献

키워드

Instructional design ability Multi-source data fusion Evidence-based evaluation 教学设计能力 多源数据融合 循证评价