摘要
新时代教师队伍建设是教育现代化的重要内容和前提。考虑到开放教育教师的丰富内涵,基于大数据分析对其进行适应性的、多元的、发展的教师数字画像时必须结合职责定位。研究以探索开放教育教师多维度画像实现路径为目的,从画像目标确立、数据采集与预处理、模型构建、标签体系构建、画像输出与评估、精准应用七个环节系统探讨了开放教育教师画像的必要性、可行性与实施构想,构建了双维个体标签体系,并提出教师画像在职业生涯规划、考核评价、循证管理、专业发展规划等场景下的应用设想。研究旨在帮助构建符合高质量开放教育教师成长规律的多维度教师画像,为新时代教师评价和发展支持体系的建立提供参考。
The construction of teachers is an important content and prerequisite of educational modernization in the new era.Considering the rich connotation of open education teachers,the adaptive,diverse and developed digital portrait of teachers must be combined with their duties based on big data analysis.The purpose of the study is to explore the implementation path of multi-dimensional portraits of open education teachers.This paper systematically discusses the necessity,feasibility and implementation concept of the portraits of open education teachers from seven aspects of portrait goal establishment,data collection and preprocessing,model construction,label system construction,portrait output and evaluation,and precise application.Based on this,a dual-dimensional individual label system is constructed,and the idea of application scenarios such as career planning,assessment and evaluation,evidence-based management and professional development planning is proposed.The research aims to help build a multi-dimensional teacher portrait that meets the growth law of high-quality open education teachers,in order to provide reference for the establishment of the support system of teacher evaluation and development in the new era.
作者
李雨朦
隋皓辰
LI Yu-meng;SUI Hao-chen(Department of Student Affairs and Teacher Development,The Open University of China,Beijing 100039,China;Department of Organization/Personnel,The Open University of China,Beijing 100039,China)
出处
《成人教育》
北大核心
2024年第7期50-60,共11页
Adult Education
基金
中国成人教育协会“十四五”成人继续教育科研规划2023年度重点课题“新时代开放大学创新发展研究——以高层次人才评价为例”,课题编号为2023-765ZA
2023年国家开放大学青年科研项目“人工智能视域下高校人力资源管理模式转型研究”,项目编号为Q23B0029。
关键词
教师画像
开放教育
多维度画像
应用场景
数据抓取
portrait of teachers
open education
multi-dimensional portrait
application scenario
data scraping
