基于在线评论意见挖掘的数字教育资源质量评价研究——以国家高等教育智慧教育平台为例  

Research on Quality Evaluation of Digital Educationat Resources Based on Online Review Opinion Mining:Taking the National Higher Education Smart Education Platform as an Example

作　　者：张琳 姜强 赵蔚 赵艳 ZHANG Lin;JIANG Qiang;ZHAO Wei;ZHAO Yan(School of Information Science and Technology,Northeast Normal University,Changchun 130117,China;School of Education,Changchun Normal University,Changchun 130032,China)

机构地区：[1]东北师范大学信息科学与技术学院,吉林长春130117 [2]长春师范大学教育学院教育学院,吉林长春130032

出　　处：《开放学习研究》2025年第1期42-51,共10页Journal of Open Learning

基　　金：2023年度吉林省社会科学基金项目“吉林省高等教育数字化转型路径及对策研究”(项目编号:2023B88);2024年度吉林省高等教育教学改革研究课题“数智时代混合式教学模式下教育技术学专业课程建设与教学实践研究”(课题编号:2024L5L2IY6001U)的研究成果。

摘　　要：如何科学有效地评价数字教育资源质量以促进其优化建设和有效供给,成为当前教育领域亟待解决的关键问题。文章采集国家高等教育智慧教育平台中音乐艺术、文史哲法、教育教学、医学保健、计算机和经济管理类资源学习者的在线评论数据,采用主题聚类方法确定数字教育资源质量评价维度,然后应用融合主题特征向量的意见挖掘模型得到各维度的质量评分。研究结果表明:影响数字教育资源质量最重要的维度是内容组织和语言表达,其次分别是知识讲解、教学材料、学习评价、资源适配、教学媒体、教学策略、教学交互、扩展资源、学习体验、学习成效、资源更新和教师特质;在各类数字教育资源中,音乐艺术类资源质量评价最好,计算机类和经济管理类资源质量评价整体较低。本研究为数字教育资源质量评价提供有效方法。How to scientifically and effectively evaluate the quality of digital educational resources to promote their optimized construction and effective supply has become a critical issue urgently needing resolution in the current educationalfield.This article collected learner online review data in six categories:Music&Art,Humanities&Social Sciences(History,Philosophy,and Law),Education&Teaching,Medical&Health Sciences,Computer Science,and Economics&Management from the National Higher Education Smart Education Platform.A topic clustering method was adopted to identify evaluation dimension for digital educational resources quality,followed by an opinion mining model integrating topic feature vectors to calculate quality scores across these dimensions.Results show that the most critical dimensions influencing resource quality are content organization and linguistic expression,followed sequentially by knowledge explanation,instructional materials,learning assessment,resource adaptability,teaching media,instructional strategies,teaching interaction,supplementary resources,learning experience,learning outcomes,resource updating,and instructor characteristics.Among all the categories,Music&Arts resources received the highest quality evaluations,while Computer Science and Economics&Management resources were rated comparatively lower.By employing opinion mining to analyze textual reviews of National Higher Education Smart Education Platform resources,this article provides an effective methodology for evaluating digital educational resource quality.

关 键 词：数字教育 LSTM模型 BERTopic模型 意见挖掘 质量评价 

分 类 号：G434[文化科学—教育学]