Data from: Exam-level analysis of lecture capture viewing and student exam performance
Data files
Feb 13, 2026 version files 49.93 KB
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Exam_Level_Lecture_Capture_Grade_Raw_Data_.xlsx
47.08 KB
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README.md
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Abstract
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.
Dataset DOI: 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