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Learning Analytics on Structured and Unstructured Heterogeneous Data Sources: Perspectives from Procrastination, Help-Seeking, and Machine-Learning Defined Cognitive Engagement
Computers & Education ( IF 12.0 ) Pub Date : 2021-04-01 , DOI: 10.1016/j.compedu.2020.104066
Jiun-Yu Wu

Abstract Statistics is one of the most challenging courses for university students. The personal learning environment (PLE) pedagogical design was introduced to assist students’ Statistics learning. With the PLE pedagogy, this study examined learners’ demographic backgrounds, motivational measures (i.e., help-seeking and academic procrastination due to the use of Information and Communication Technologies, ICT), and ill-structured data (i.e., Facebook posts and comments) to understand what student demographic information, how they feel, and what they do can impact their statistics learning performance. Seventy-eight people joined Facebook groups to form statistics learning communities. Using weakly supervised machine learning (ML), we categorized students’ Facebook messages into statistics-relevant and statistics-irrelevant. Results of the learning analytics on multimodal sources of student data showed that help-seeking positively predicted statistics achievement. In contrast, academic procrastination with ICT negatively predicted statistics achievement, controlling for students' demographics information, including age, gender, prior knowledge, and Internet/social media use. Moreover, the ensemble ML classified messages constructed by taking the sum of relevance coding (0 or 1) across three selected ML algorithms was highly aligned with the human coded message in terms of the degree of relevance to statistics. The ensemble ML classified messages were conceptualized as students’ cognitive engagement in statistics learning due to their high consistency with the human-labeled relevance coding and were positively associated with statistics achievement with a large effect size. The study contributed to developing an integrated learning analytics framework with the PLE pedagogical design encompassing learner backgrounds and unstructured learner artifacts.

中文翻译:

结构化和非结构化异构数据源的学习分析:从拖延、求助和机器学习定义的认知参与的观点

摘要 统计学是大学生最具挑战性的课程之一。引入个人学习环境(PLE)教学设计,以协助学生的统计学习。本研究使用 PLE 教学法检查了学习者的人口背景、动机措施(即,由于使用信息和通信技术、ICT 而导致的寻求帮助和学业拖延)以及结构不良的数据(即 Facebook 帖子和评论)了解哪些学生的人口统计信息、他们的感受以及他们的行为会影响他们的统计学习表现。78 人加入了 Facebook 群组,形成了统计学习社区。使用弱监督机器学习 (ML),我们将学生的 Facebook 消息分为统计相关和统计无关。对学生数据多模态来源的学习分析结果表明,寻求帮助可以积极预测统计成绩。相比之下,信息通信技术的学业拖延对统计成绩有负面预测,控制了学生的人口统计信息,包括年龄、性别、先验知识和互联网/社交媒体的使用。此外,通过在三个选定的 ML 算法中取相关编码(0 或 1)的总和构建的集成 ML 分类消息在与统计的相关程度方面与人类编码的消息高度一致。集成 ML 分类消息被概念化为学生在统计学习中的认知参与,因为它们与人类标记的相关性编码高度一致,并且与具有大效应量的统计成就正相关。该研究有助于开发具有 PLE 教学设计的集成学习分析框架,其中包括学习者背景和非结构化学习者工件。
更新日期:2021-04-01
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