多维度解析线上学习效果的影响机制与路径优化研究
Research on the Impact Mechanism and Path Optimization of Multi-Dimensional Analysis of Online Learning Effect
摘要: 伴随数智时代到来,线上学习成为全球教育抢占的新领域。线上教育模式打破了传统的教学模式,将时间和空间限制大幅度降低。而在线学生线上学习效果的影响因素研究成为教育技术领域的重要课题。本研究基于专注度监测,视觉追踪和效率结构方程,整合“学习者–课程–环境–技术”四维框架,结合文献分析与实证数据,系统探究大学生线上学习效果的关键影响因素。通过对760份有效问卷的数据分析发现,学习主动性、师生互动质量、课后练习质量和频率对学习效果具有显著正向影响;外界因素影响,课程互动性弱等对学习效果有一定的负面影响。通过剖析学习者个体差异、技术应用效能、学习资源丰富度和适配性、学习社区活跃度和互助性等多元要素,确定关键影响因子,并提出个性化学习路径规划、互动式教学模块强化、学习专注度智能监测与即时反馈等创新优化方案。全方位赋能学习者素养进阶与能力提升。
Abstract: With the advent of the digital age, online learning has become a new field of global education. The online education model has broken the traditional teaching mode and greatly reduced the time and space constraints. The research on the influencing factors of online students’ online learning effect has become an important topic in the field of educational technology. Based on concentration monitoring, visual tracking and efficiency structural equation, this study integrates the four-dimensional framework of “learner-curriculum-environment-technology”, and combines literature analysis and empirical data to systematically explore the key influencing factors of college students’ online learning effect. Through the data analysis of 760 valid questionnaires, it is found that learning initiative, quality of teacher-student interaction, quality and frequency of after-school exercises have a significant positive impact on learning effect. The influence of external factors and the weak interaction of courses have a certain negative impact on the learning effect. By analyzing the multiple factors such as individual differences of learners, technical application efficiency, richness and adaptability of learning resources, activity and mutual assistance of learning communities, the key influencing factors are determined, and innovative optimization schemes such as personalized learning path planning, interactive teaching module strengthening, intelligent monitoring of learning concentration and instant feedback are proposed. Comprehensively empower learners ' literacy advancement and ability improvement.
参考文献
|
[1]
|
李慧琪. 基于面部视觉特征的在线学习专注度评价研究[D]: [硕士学位论文]. 湖州: 湖州师范学院, 2023.
|
|
[2]
|
董伟, 张美, 高晨璐, 等. 基于用户体验的在线教育平台学习效果影响因素研究[J]. 中国远程教育, 2020(11): 68-75.
|
|
[3]
|
Fredricks, J.A., Blumenfeld, P.C. and Paris, A.H. (2004) School Engagement: Potential of the Concept, State of the Evidence. Review of Educational Research, 74, 59-109. https://doi.org/10.3102/00346543074001059
|
|
[4]
|
袁可一. 在线学习中的学业情绪的强度识别[D]: [硕士学位论文]. 上海: 华东师范大学, 2023.
|
|
[5]
|
薛耀锋, 李卓玮. 基于眼动追踪技术的在线学习认知负荷量化模型研究[J]. 现代教育技术, 2019, 29(7): 59-65.
|
|
[6]
|
戴立操, 张美慧, 马莉. 基于眼动数据的核电厂数字化主控室操纵员认知负荷量化模型研究[J]. 南华大学学报(社会科学版), 2022, 23(2): 1-7.
|
|
[7]
|
吴智丽. 在线学习环境下基于眼部状态识别的学生注意评估研究[D]: [硕士学位论文]. 南昌: 江西财经大学, 2019.
|
|
[8]
|
易佳玥. 基于眼动检测的在线学习状态实时评估系统的研究与应用[D]: [硕士学位论文]. 上海: 上海交通大学, 2016.
|
|
[9]
|
李艳. 基于在线学习数据的学业预测与干预研究[D]: [硕士学位论文]. 昆明: 云南师范大学, 2024.
|
|
[10]
|
谢佳敏. 视频播放速度对多媒体学习的影响: 知识类型和学习者经验水平的调节作用[D]: [硕士学位论文]. 武汉: 湖北大学, 2024.
|
|
[11]
|
谢晓宇, 范深, 宋金玉, 等. 基于MOOC学习平台的学习行为数据分析与预测[J]. 软件导刊, 2025, 24(2): 198-203.
|
|
[12]
|
齐晨阳. 基于数据挖掘的在线学习行为分析与学业预警研究[D]: [硕士学位论文]. 郑州: 河南工业大学, 2024.
|