华东师范大学学报(教育科学版) ›› 2021, Vol. 39 ›› Issue (8): 57-69.doi: 10.16382/j.cnki.1000-5560.2021.08.005

• 专题:智慧教育 • 上一篇    下一篇

智慧教育视野下基于Rasch模型的知识掌握与认知能力分析研究

武法提1, 田浩2, 王瑜3, 樊敏生4   

  1. 1. 数字学习与教育公共服务教育部工程研究中心,北京 100875
    2. 北京师范大学教育技术学院,北京 100875
    3. 宝安中学(集团)实验学校,深圳 518101
    4. 西北师范大学教育技术学院,兰州 730070
  • 出版日期:2021-08-01 发布日期:2021-08-04
  • 基金资助:
    国家社会科学基金教育学一般课题“基于人机智能协同的精准学习干预研究”(BCA200080)

Research on Analysis of Knowledge Acquisition and Cognitive Ability Based on Rasch Model in Smart Education

Fati Wu1, Hao Tian2, Yu Wang3, Minsheng Fan4   

  1. 1. Engineering Research Center of Digital Learning and Educational Public Service, Ministry of Education, Beijing, 100875, China
    2. School of Educational Technology, Beijing Normal University, Beijing, 100875, China
    3. Baoan High School Group Experimental School, Shenzhen Guangdong, 518101, China
    4. School of Educational Technology, Northwest Normal University, Lanzhou, 730070, China
  • Online:2021-08-01 Published:2021-08-04

摘要:

如何对学习者进行精准化、个性化的诊断和评价是智慧教育时代的重要议题。目前学习诊断的主流方式依然是通过考试成绩对学生知识掌握程度进行评价,容易忽略对学生认知能力的评价,不符合智慧教育既重视知识传授也重视能力培育的价值取向。本研究基于Rasch模型,以政治学科为例,组建月考试卷,并编制双向细目表为试题标记知识点属性和认知能力属性,进而探索一种基于考试成绩挖掘学生认知能力的方法。本研究收集了195名学生的作答数据,使用Rasch模型,分析成绩背后每位学生的知识掌握情况,并判断学生对各认知能力层次的达成情况,同时根据分析结果生成雷达图进行可视化输出,实现学生认知分析在混合式课堂中的常态化使用。本研究试图为智慧教育时代学习的精准诊断提供一种新思路。

关键词: 智慧教育, Rasch模型, 知识掌握分析, 认知能力分析, 精准诊断

Abstract:

How to accurately and personally diagnose and evaluate learners is an important issue in the era of smart education. At present, the main method of learning diagnosis is still to evaluate the students’ knowledge acquisition through exam scores, ignoring the evaluation of students’ cognitive ability. So, it is not in line with the value of smart education, which emphasizes both knowledge transfer and ability cultivation. Based on the Rasch model, this study explored a method for mining students’ cognitive ability via exam scores. Took the political course as an example, set up a monthly exam paper, and compiled a two-way specification table to mark the knowledge point attribute and cognitive ability attribute. The data of 195 students were collected and the Rasch model was used to analyze the knowledge of each student behind the scores and to determine the students’ achievement of each level of cognitive ability. At the same time, according to the analysis results, radar images were generated for visual output, and the normalization of student cognitive analysis in the blended classroom was realized. This study provided a new way of the accurate diagnosis in smart education.

Key words: smart education, Rasch model, knowledge acquisition analysis, cognitive ability analysis, accurate diagnosis