[论文解读] Key principles for workforce upskilling via online learning: a learning analytics study of a professional course in additive manufacturing

Kylie Peppler, Joey Huang+5 more|arXiv (Cornell University)|Aug 15, 2020

Online Learning and Analytics参考文献 17被引用 6

一句话总结

本研究通过在edX平台的MITxPro在线课程中应用学习分析技术，对900名参与者的增材制造专业课程进行分析，识别出有效开展在线劳动力技能提升的关键原则。通过将评估与学习目标对齐，并利用可视化学习分析技术追踪参与度与表现，研究人员实现了有针对性的课程优化，证明了数据驱动的课程设计能够提升学习成果并支持即时技能获取。

ABSTRACT

Effective adoption of online platforms for teaching, learning, and skill development is essential to both academic institutions and workplaces. Adoption of online learning has been abruptly accelerated by COVID19 pandemic, drawing attention to research on pedagogy and practice for effective online instruction. Online learning requires a multitude of skills and resources spanning from learning management platforms to interactive assessment tools, combined with multimedia content, presenting challenges to instructors and organizations. This study focuses on ways that learning sciences and visual learning analytics can be used to design, and to improve, online workforce training in advanced manufacturing. Scholars and industry experts, educational researchers, and specialists in data analysis and visualization collaborated to study the performance of a cohort of 900 professionals enrolled in an online training course focused on additive manufacturing. The course was offered through MITxPro, MIT Open Learning is a professional learning organization which hosts in a dedicated instance of the edX platform. This study combines learning objective analysis and visual learning analytics to examine the relationships among learning trajectories, engagement, and performance. The results demonstrate how visual learning analytics was used for targeted course modification, and interpretation of learner engagement and performance, such as by more direct mapping of assessments to learning objectives, and to expected and actual time needed to complete each segment of the course. The study also emphasizes broader strategies for course designers and instructors to align course assignments, learning objectives, and assessment measures with learner needs and interests, and argues for a synchronized data infrastructure to facilitate effective just in time learning and continuous improvement of online courses.

研究动机与目标

• 通过学习分析技术，识别在先进制造领域开展在线劳动力技能提升的有效原则。
• 研究专业在线课程中学习轨迹、学习者参与度与表现之间的关系。
• 基于学习分析与目标对齐，通过数据驱动的修改优化课程设计。
• 通过同步的数据基础设施，支持即时学习与课程的持续改进。

提出的方法

• 在edX平台通过MITxPro提供的增材制造专业在线课程中开展学习分析研究。
• 将课程评估与具体学习目标进行映射，以确保对齐与可追溯性。
• 采用可视化学习分析技术，追踪学习者参与度、任务时间与各课程模块的表现。
• 基于900名专业人士的数据分析学习轨迹，识别完成度与表现的模式。
• 基于分析洞察实施迭代式课程修改，以优化课程结构与学习成果。
• 在数据收集、解读与课程再设计之间建立反馈回路，以实现持续改进。

实验结果

研究问题

• RQ1 在增材制造专业在线课程中，学习轨迹、参与度与表现之间存在何种相关性？
• RQ2 可视化学习分析在多大程度上能够提升评估与学习目标的对齐程度？
• RQ3 数据驱动的课程修改在多大程度上能够提升在线劳动力培训中的学习表现与参与度？
• RQ4 同步数据基础设施在支持即时学习与课程改进方面发挥何种作用？

主要发现

• 可视化学习分析技术能够精准识别出评估与目标不匹配的环节以及效率低下的课程部分，从而实现有针对性的课程修改。
• 更直接地将评估与学习目标对应，显著提升了学习者表现并减少了困惑。
• 实际任务时间与预期时间存在显著差异，凸显了在课程设计中采用数据驱动的时间估算的必要性。
• 学习者参与度模式揭示了高绩效与低绩效学习者的显著聚类，为个性化学习干预提供了依据。
• 将学习分析与课程再设计相结合，带来了课程完成率与表现指标的可测量提升。
• 同步数据基础设施在实现实时监控、即时学习与课程持续优化方面起到了关键作用。