Review on A big data-based innovative knowledge teaching evaluation system in universities
Review on A big data-based innovative knowledge teaching evaluation system in universities
复制标题
基于大数据的高校创新知识教学评价体系综述
DOI：
10.1016/j.jik.2022.100197
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Liao Dan
中科院分区：
文献类型：
--
作者：
Xu Xin;Shu Yu;Pang Nan;Chenxu Dou;Liao Dan
随着大数据、云计算、人工智能等数字技术在高等教育中的广泛运用，如何建立科学、系统的评价体系，将单向传播知识的传统课堂转变为交流思想、启发智慧的互动空间，已成为高校人力资源管理的一项重要任务，也是提高教学质量的关键。然而，由于教学评价中科学主义与人文主义的争论，教学绩效相关的研究与人力资源管理相分离，导致此类研究缺乏系统的视野和框架。相关研究仍局限于不同评价主体的评价内容。评价也往往只关注教学过程，忽视人才培养目标，评价难以发挥目标导向作用，阻碍了相关研究的进一步发展。因此，本文借鉴人力资源管理方法论，分析大数据背景下高校知识教学评价体系特点，构建“多元评价、三位一体、四步闭环”的基于大数据的知识教学评价体系。 “三位一体”是指从教学效果、教学行为、教学能力三个绩效维度进行评价。 “多元评价”是指根据教学数据设计教学绩效指标，打破不同评价主体之间的壁垒。 “四步闭环”借鉴绩效管理理论，从规划、实施、评估、反馈四个方面规范教学绩效管理流程。该评价体系为大数据背景下高校创新知识教学评价体系的理论与实践相统一提供了系统的方法论。
With the widespread use of digital technologies such as big data, cloud computing and artificial intelligence in higher education, how to establish a scientific and systematic evaluation system to turn the traditional classroom with the one-way transmission of knowledge into an interactive space for exchanging ideas and inspiring wisdom has become an essential task for human resource management in universities, and a key to improving teaching quality. However, due to the debate between scientism and humanism in teaching evaluation, studies related to teaching performance have been isolated from human resource management, resulting in the lack of a systematic vision and framework for such studies. Relevant studies are still limited to the evaluation contents of different evaluation subjects. Evaluations also tend to focus only on the teaching process, ignoring the objectives of talent training, making it difficult for evaluations to play a goal-oriented role and hindering the further development of relevant studies. Therefore, this paper draws on human resource management methodologies and analyzes knowledge teaching evaluation system characteristics in colleges and universities in a big data context to construct a “multiple evaluations, trinity and four-step closed-loop” big data-based knowledge teaching evaluation system. “Trinity” represents evaluation from three performance dimensions: teaching effect, teaching behavior and teaching ability. “Multiple evaluations” represents the design of teaching performance indicators based on teaching data, breaking the barriers between different evaluation subjects. “Four-step closed-loop” draws on performance management theory to standardize the teaching performance management process from four aspects: planning, implementation, evaluation, and feedback. This evaluation system provides a systematic methodology for unifying the theory and practice of innovative knowledge teaching evaluation system in universities in a big data context.