Exam-level analysis of lecture capture viewing and student exam performance

Lecture capture (LC) systems offer students flexible review of lecture content, but their impact on learning outcomes remains mixed. LC engagement and exam performance were analyzed in three in-person courses with LC videos posted for review, each with 3 lecture blocks and 3 independent non-cumulative exams. Zoom analytics and exam grade data were collected for 299 students across 982 non-cumulative exam observations. Four LC metrics were derived per exam: total view duration, number of lectures viewed, number of unique views, and days between access and exam. Average exam scores were compared between LC viewers (n = 216) and non-viewers (n=83): LC viewers scored significantly higher than non-viewers (66.1% vs. 59.4%). A linear mixed-effects model with student-level random intercepts showed opposing effects of total viewing time (+1.74% per hour) and number of lectures viewed (–1.92% per lecture), implying that average LC view duration per lecture (total minutes watched ÷ lectures viewe..., , # Data from: Exam-level analysis of lecture capture viewing and student exam performance Dataset DOI: [10.5061/dryad.vmcvdnd59](10.5061/dryad.vmcvdnd59) ## Description of the data and file structure This study data looks at exam grades of students in 3 different undergraduate courses with in-person lectures, which were recorded on Zoom, and the video was posted for subsequent student review. In each course, there are 3 independent, non-cumulative exams. Zoom analytics data was used to determine if there was any association between student exam grades and their usage of lecture capture recordings ### Files and variables #### File: Exam_Level_Lecture_Capture_Grade_Raw_Data_.xlsx **Description:** ##### Variables * **Course** *(integer code)* — Coded identifier for the course (**1–3**). * **Year** *(integer code)* — Course level code (**1** or **3**) corresponding to first- and third-year courses, respectively. * **StudentSeqID** *(integer)* — Anonymized **sequential** student iden..., This study involved the secondary use of fully anonymized educational data and was exempt from Research Ethics Board review, in accordance with Article 2.4 of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans,课堂录播（Lecture Capture, LC）系统可为学生提供灵活的课程内容复习途径，但其对学习成果的影响仍存在分歧。本研究针对三门开设了可供复习的课堂录播视频的线下课程，分析了学生的录播使用行为与考试表现之间的关联；每门课程包含3个授课单元与3门独立的非累积性考试。 研究收集了299名学生的Zoom平台分析数据与考试成绩数据，共计982条非累积性考试观测样本。 每门考试衍生出4项课堂录播使用指标：总观看时长、观看的讲座数量、独特观看次数，以及首次访问录播视频至考试的间隔天数。将使用录播的学生（n = 216）与未使用者（n=83）的平均考试成绩进行对比：使用录播的学生平均得分显著高于未使用者（66.1% vs. 59.4%）。采用包含学生水平随机截距的线性混合效应模型分析发现，总观看时长（每小时提升1.74%）与观看的讲座数量（每多观看1场讲座降低1.92%）存在相反的影响效应，这意味着单场讲座的平均录播观看时长（总观看分钟数 ÷ 观看讲座数，…… # 数据集来源：Exam-level analysis of lecture capture viewing and student exam performance 数据集DOI：[10.5061/dryad.vmcvdnd59](10.5061/dryad.vmcvdnd59) ## 数据与文件结构说明 本研究数据聚焦于三门线下授课的本科课程的学生考试成绩，这些课程的讲座由Zoom录制并发布供学生后续复习。每门课程设置3门独立的非累积性考试。本研究借助Zoom平台分析数据，探究学生考试成绩与其课堂录播使用行为之间的关联。 ### 文件与变量 #### 文件：Exam_Level_Lecture_Capture_Grade_Raw_Data_.xlsx **文件说明：** ##### 变量说明 * **Course（整数型编码）** —— 课程的编码标识符，取值范围为1至3。 * **Year（整数型编码）** —— 课程学段编码，分别对应一年级（取值为1）与三年级（取值为3）课程。 * **StudentSeqID（整数型）** —— 匿名化的连续学生标识符…… 本研究对完全匿名化的教育数据进行二次使用，符合《三理事会政策声明：涉及人类研究的伦理行为》第2.4条规定，无需伦理审查委员会审查。