DOI: 10.5061/dryad.vmcvdnd59
title: Data from: Exam-level analysis of lecture capture viewing and student exam performance

description: 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 viewed) was the strongest predictor of exam score. A post-hoc
 median-split of average LC view duration per lecture indicated an 8.02%
 higher score for students above the median. Decomposition of total LC view
 time revealed a between-student effect on exam grade (+2.52% per hour) and
 a within-student effect (–0.84% per hour), showing that spikes above a
 student’s own average view time is associated with a lower exam grade.
 These findings align with self-regulated learning theory, demonstrating
 that while greater LC viewing time generally benefits performance, its
 impact depends on strategic, habitual engagement rather than episodic
 cramming.
description: # 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
 identifier; the same student retains the **same ID** across courses/exams.
 * **Exam** *(integer)* — Exam block (**1**, **2**, or **3**), where exams
 are **non-cumulative**. * **Exam %** *(percent, 0–100)* — Exam score in
 percentage points. * **LecturesViewed** *(count)* — Number of **distinct
 recorded lectures** accessed (≥1 minute of viewing) for this exam’s
 lecture set. * **UniqueViews** *(count)* — Number of **unique access
 events** to recorded lectures for this exam (i.e., distinct
 plays/sessions). * **TotalViewDuration** *(minutes)* — Total **minutes**
 of recorded-lecture viewing accumulated for this exam’s lecture set. *
 **DaysBeforeExam** *(days)* — Integer **days between the last
 recorded-lecture access and the exam date**; **0 = access on exam day**,
 **1 = one day before**, etc. * **Abbreviations used** * **LC** — *Lecture
 capture* (recorded lecture). *(Not used as a column name but used in
 documentation.)* ## Missing data * **None** ## Notes * IDs are anonymized
 and sequential; no direct identifiers are present. * Viewing variables
 (**LecturesViewed**, **UniqueViews**, **TotalViewDuration**,
 **DaysBeforeExam**) are **exam-scoped** (they refer only to the lectures
 assessed by that exam). * Percentages are expressed on a 0–100 scale (not
 proportions). ## Code/software * **LibreOffice Calc**
 (Windows/macOS/Linux; open-source) * **OnlyOffice Desktop Editors**
 (free/community edition) * **Apache OpenOffice Calc** (open-source) *
 **Google Sheets** (free, web) or **Microsoft Excel Online** (free, web; MS
 account) ## Human subjects data 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