基于机器学习的高职生学习绩效影响因素研究
Research on Factors Influencing the Learning Performance of Higher Vocational College Students Based on Machine Learning
刘笑言 1刘创 1周建军 1杨进2
作者信息
- 1. 成都工业职业技术学院
- 2. 成都工业职业技术学院财经商贸学院
- 折叠
摘要
学习绩效是衡量高职教学质量的重要指标.以混合学习模式下的高职生为研究对象,基于"学习预备—学习过程—学习结果"的3P分析框架,收集学生个体特征、学习行为和阶段性学习结果数据,对比多种机器预测模型,结合聚类分析探究高职生学习绩效的影响因素.结果发现:高职生学习绩效影响程度排序为学习行为特征＞阶段性学习结果特征＞学生个体特征,课外学习行为＞课内学习行为,学习次数＞学习时长,小阶段学习结果＞大阶段学习结果;群体特征呈现优秀学生学习时间投入多但学习效率不理想,普通学生技能和素养不强的特征.基于此,从教学对象、教学资源、教学过程和教学评价四方面提出干预,以提高学习绩效.
Abstract
Learning performance is an important indicator for measuring the quality of higher voca-tional education.Taking higher vocational college students in blended learning mode as the research object,based on"learning presage-learning process-learning product"analysis framework(3P),individual charac-teristics,learning behavior,and stage learning results data of students were collected.Multiple machine pre-diction models were compared,and cluster analysis was used to explore the influencing factors of higher vo-cational college students'learning performance.The results show that the order of the degree of influence on the learning performance of higher vocational college students is learning behavior characteristics＞phased learning outcome characteristics＞individual student characteristics,extracurricular learning behav-ior＞in-class learning behavior,learning frequency＞study duration,small-stage learning outcomes＞large-stage learning outcomes;The group characteristics show that outstanding students invest a lot of time in learning but their learning efficiency is not ideal,and ordinary students have weak skills and literacy.Based on this,interventions are proposed from four aspects:teaching objects,teaching resources,teaching process,and teaching evaluation to improve learning performance.
关键词
职业教育/高职生/机器学习/学习绩效/学习行为Key words
vocational education/higher vocational college student/machine learning/learning perfor-mance/learning behavior引用本文复制引用
基金项目
中国物流学会-中国物流与采购联合会-教育部高等学校物流管理与工程类专业教指委项目(JZW2023195)
四川省教育厅项目(GZJG2022-054)
四川省教育厅人文社会科学重点研究基地四川省教育信息化应用与发展研究中心项目(JYXX21-005)
出版年
2024
NETL
NSTL
万方数据