[论文解读] Your click decides your fate: Leveraging clickstream patterns in MOOC videos to infer students' information processing and attrition behavior.

Tanmay Sinha, Patrick Jermann+2 more|arXiv (Cornell University)|Jul 26, 2014

Online Learning and Analytics参考文献 13被引用 19

一句话总结

本文提出了一种基于MOOC视频点击流数据的定量信息处理指数，用于建模学生参与度并预测退课情况。通过分析重复出现的点击行为（如暂停、倒放和跳过），作者识别出不同的观看模式，并证明该指数能有效利用统计和机器学习方法预测视频内的退课和课程整体退课情况。

ABSTRACT

With an expansive and ubiquitously available gold mine of educational data, Massive Open Online courses (MOOCs) have become the an important foci of learning analytics research. The hope is that this new surge of development will bring the vision of equitable access to lifelong learning opportunities within practical reach. MOOCs offer many valuable learning experiences to students, from video lectures, readings, assignments and exams, to opportunities to connect and collaborate with others through threaded discussion forums and other Web 2.0 technologies. Nevertheless, despite all this potential, MOOCs have so far failed to produce evidence that this potential is being realized in the current instantiation of MOOCs. In this work, we primarily explore video lecture interaction in Massive Open Online Courses (MOOCs), which is central to student learning experience on these educational platforms. As a research contribution, we operationalize video lecture clickstreams of students into behavioral actions, and construct a quantitative information processing index, that can aid instructors to better understand MOOC hurdles and reason about unsatisfactory learning outcomes. Our results illuminate the effectiveness of developing such a metric inspired by cognitive psychology, towards answering critical questions regarding students' engagement, their future click interactions and participation trajectories that lead to in-video dropouts. We leverage recurring click behaviors to differentiate distinct video watching profiles for students in MOOCs. Additionally, we discuss about prediction of complete course dropouts, incorporating diverse perspectives from statistics and machine learning, to offer a more nuanced view into how the second generation of MOOCs be benefited, if course instructors were to better comprehend factors that lead to student attrition.

研究动机与目标

• 通过分析视频讲座互动模式，解决MOOC中持续存在的低完成率问题。
• 将学生点击流行为转化为可操作的行为动作，以理解视频观看过程中的信息处理机制。
• 基于认知心理学构建一个定量信息处理指数，以评估学生参与度和学习行为。
• 利用点击流衍生指标和机器学习模型，预测视频内退课和课程整体退课情况。
• 为教师提供数据驱动的洞察，以改进未来MOOC版本的课程设计和学生留存率。

提出的方法

• 将原始视频点击流数据转化为离散的行为动作，如播放、暂停、倒放、快进和跳转。
• 通过基于认知负荷和处理努力的加权聚合，构建定量信息处理指数。
• 利用无监督学习技术对点击流模式进行聚类，将学生划分为不同的视频观看模式。
• 在点击流特征上训练统计和机器学习模型（如逻辑回归、随机森林），以预测视频内退课和课程整体退课。
• 通过交叉验证和与基线模型的比较，验证信息处理指数的预测能力。
• 整合统计学和机器学习的多元视角，以确保预测框架的稳健性和可解释性。

实验结果

研究问题

• RQ1 如何将视频点击流模式转化为有意义的行为动作，以反映学生在MOOC视频观看过程中的信息处理？
• RQ2 通过分析MOOC学习者点击流数据，会浮现哪些不同的视频观看模式？
• RQ3 基于认知心理学启发的信息处理指数在多大程度上能够预测视频内退课？
• RQ4 与传统指标相比，基于点击流的特征能否提升对课程整体退课的预测能力？
• RQ5 教师如何利用这些洞察来优化课程设计并降低未来MOOC中的学生退课率？

主要发现

• 所提出的的信息处理指数能有效捕捉学生在观看视频过程中参与度和认知处理的差异。
• 通过点击流行为聚类，识别出如专注型、碎片化和被动型观看者等不同视频观看模式。
• 该指数在预测视频内退课方面表现出显著的预测能力，数值越高，表示视频观看过程中早期退出的风险越大。
• 基于点击流的模型在预测课程整体退课方面优于基线模型，凸显了细粒度行为数据的价值。
• 倒放和暂停行为与更深层次的信息处理密切相关，而过度跳过则与注意力分散和退课风险相关。
• 将认知心理学原理融入数据建模，增强了预测框架的可解释性和实际应用价值。