大学生在线编程学习行为投入与学习效果分析研究

Analysis of College Students’Online Programming Learning Behavioral Engagement and Learning Outcomes

基于EduCoder平台的C语言程序设计实践项目学习者行为投入和测试成绩数据,构建在线编程学习行为投入测量框架。采用系统聚类和K-Means聚类方法将学习者划分为出色突破型、努力完成型、边缘落后型三个类别,并依据不同类别学习者特征提出指导策略。使用逐步回归方法测量在线编程学习行为投入对学习效果的影响程度,结果表明:“参与”维度的出勤次数、“专注”维度的关卡完成数和“学术挑战”维度的附加训练对学习效果具有显著性正向影响,“专注”维度的实训总耗时对学习效果具有负向影响,“坚持”维度对学习效果未表现出显著影响。根据研究结果,提出开展小组协作学习、加强学习互动、合理划分学习难度、提升编程学习效能感等相关建议。

By analyzing the behavioral engagement and test scores of C programming learners using the EduCoder platform,devised a framework that measures behavioral input within online programming education.Systematic clustering and K-Means clustering methods were used to classify learners into three categories:outstanding breakthrough,effort completion,and marginal lagging,and instructional strategies were proposed based on the characteristics of learners in different categories.Using a stepwise regression method to measure the effect of behavioral inputs on the learning effect of online programming,the results showed that the number of attendance in the“engagement”dimension,the number of practical training passes in the“focus”dimension,and the additional practice in the“academic challenge”dimension had a significant positive effect on the learning effect,the total time spent on practical training in the“focus”dimension had a negative effect on the learning effect,and the“persistence”dimension did not show a significant effect on the learning effect.Based on the results of the study,recommendations related to conducting collaborative group learning,strengthening learning interaction,reasonably dividing learning difficulty,and improving programming learning efficacy are proposed.