Using learning analytics with temporal modeling to uncover the interplay of before-class video viewing engagement, motivation, and performance in an active learning context

IF 8.9 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Education Pub Date : 2023-12-14 DOI:10.1016/j.compedu.2023.104975
Jiun-Yu Wu , Chen-Hsuan Liao , Chin-Chung Tsai , Oi-Man Kwok
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Abstract

Video learning is a crucial component in flipped learning related to learners' readiness to attend more complex face-to-face activities. However, instructors may not know students' preparedness and motivation for each class. This study intended to examine the underlying mechanism between students' before-class video-viewing engagement and performance. We investigated learners' motivation (i.e., autonomous and controlled motivation) based on self-determination theory and controlled for their gender, age, and prior performance. An integrated temporal model was built under the dynamic structural equation modeling framework. Participants were 118 Taiwanese graduate students (78 females and 40 males) with a mean age of 26.14 years old. Study findings showed that students engage with the video content in a consistent viewing pattern, which has a significant relationship with their motivation and academic performance from week to week. The frequency of skipping backward was negatively associated with controlled motivation, while the frequency of pauses was positively associated with autonomous and controlled motivation. A high frequency of skipping forward predicted lower learning performance. Besides, a higher ratio of skipping backward predicted higher controlled motivation. Male students, older students, and students with higher prior performance exhibited higher autonomous motivation, which was related to better learning performance. Understanding these relationships can help educators and instructional designers develop more effective strategies for fostering students’ preparedness, engagement and success in active learning contexts. The study findings were discussed considering theoretical advancement and practical implications for enhancing personalized and active learning in higher education to achieve educational equity.

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利用学习分析和时间建模揭示主动学习环境中课前观看视频的参与度、学习动机和学习成绩之间的相互作用
视频学习是翻转学习的一个重要组成部分,它关系到学习者是否准备好参加更复杂的面授活动。然而,教师可能并不了解学生对每堂课的准备情况和学习动机。本研究旨在探讨学生课前观看视频的参与度与学习成绩之间的内在机制。我们以自我决定理论为基础,调查了学习者的学习动机(即自主动机和受控动机),并控制了他们的性别、年龄和先前成绩。我们在结构方程建模框架下建立了一个综合时间模型。参与者为 118 名台湾研究生(78 名女生和 40 名男生),平均年龄为 26.14 岁。研究结果表明,学生以一致的观看模式参与视频内容,这与他们每周的学习动机和学习成绩有显著关系。后跳的频率与受控动机呈负相关,而停顿的频率与自主动机和受控动机呈正相关。向前跳的频率越高,学习成绩越差。此外,向后跳的比例越高,预示着受控动机越高。男生、年龄较大的学生和以前成绩较好的学生表现出较高的自主动机,这与较好的学习成绩有关。了解这些关系有助于教育者和教学设计者制定更有效的策略,促进学生在主动学习情境中做好准备、参与并取得成功。研究结果的讨论考虑到了理论上的进步和在高等教育中加强个性化主动学习的实际意义。
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来源期刊
Computers & Education
Computers & Education 工程技术-计算机:跨学科应用
CiteScore
27.10
自引率
5.80%
发文量
204
审稿时长
42 days
期刊介绍: Computers & Education seeks to advance understanding of how digital technology can improve education by publishing high-quality research that expands both theory and practice. The journal welcomes research papers exploring the pedagogical applications of digital technology, with a focus broad enough to appeal to the wider education community.
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Editorial Board From experience to empathy: An empathetic VR-based learning approach to improving EFL learners’ empathy and writing performance Bridging computer and education sciences: A systematic review of automated emotion recognition in online learning environments Navigating elementary EFL speaking skills with generative AI chatbots: Exploring individual and paired interactions Investigating behavioral and cognitive patterns among high-performers and low-performers in Co-viewing video lectures
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