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Improving the effectiveness of video-based flipped classrooms with question-embedding

Deng, Ruiqi, Feng, Siqi, Shen, Suqin·2024·Education and Information Technologies
DOI 10.1007/s10639-023-12303-5
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Abstract

Although video-based flipped learning is a widely accepted pedagogical strategy, few attempts have been made to explore the design and integration of pre-class instructional videos into in-class activities to improve the effectiveness of flipped classrooms. This study investigated whether question-embedded pre-class videos, together with the opportunity to review these questions at the beginning of in-class sessions, affected student learning processes and outcomes. Seventy university students from two naturally constituted classes participated in the quasi-experimental study. The experiment adopted a pre-test/post-test, between-subjects design and lasted for six weeks, with the same instructional content for the experimental and control groups. Students’ age, sex, pre-experiment motivation, prior knowledge, and perceived knowledge were controlled for in the study. The results indicate that the educational intervention significantly strengthened learning performance, likely due to the increased regularity of engagement with pre-class materials. The intervention did not influence student satisfaction with the pre-class videos or the video viewing duration. Overall, the findings suggest that instructors should consider embedding questions in pre-class videos and reviewing them at the beginning of in-class sessions to facilitate student learning in video-based flipped classrooms.

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Metadata

Authors
Deng, R., Feng, S., & Shen, S.
Venue
Education and Information Technologies
Publisher
Springer Science and Business Media LLC
Published
2023-12-07
Volume
29
Issue
10
Pages
12677-12702
ISSN
  • 1360-2357
  • 1573-7608
DOI
10.1007/s10639-023-12303-5
Cited by
21
References
3
Language
en
Update notices
correction (2023-12-23)
Licence
Funders
National Natural Science Foundation of China (72204072)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.