The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Making sense of video analytics: Lessons learned from clickstream interactions, attitudes, and learning outcome in a video-assisted course
Open accessAbstract
Online video lectures have been considered an instructional media for various pedagogic approaches, such as the flipped classroom and open online courses. In comparison to other instructional media, online video affords the opportunity for recording student clickstream patterns within a video lecture. Video analytics within lecture videos may provide insights into student learning performance and inform the improvement of video-assisted teaching tactics. Nevertheless, video analytics are not accessible to learning stakeholders, such as researchers and educators, mainly because online video platforms do not broadly share the interactions of the users with their systems. For this purpose, we have designed an open-access video analytics system for use in a video-assisted course. In this paper, we present a longitudinal study, which provides valuable insights through the lens of the collected video analytics. In particular, we found that there is a relationship between video navigation (repeated views) and the level of cognition/thinking required for a specific video segment. Our results indicated that learning performance progress was slightly improved and stabilized after the third week of the video-assisted course. We also found that attitudes regarding easiness, usability, usefulness, and acceptance of this type of course remained at the same levels throughout the course. Finally, we triangulate analytics from diverse sources, discuss them, and provide the lessons learned for further development and refinement of video-assisted courses and practices.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Empirical examination of the adoption of WebCT using TAM
Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2007) · Computers & Education · doi:10.1016/j.compedu.2004.11.007 · Cited by 813
Cited by
Online Environments for Supporting Learning Analytics in the Flipped Classroom: A Scoping Review
(2019) · Proceedings of the 18th European Conference on e-Learning · doi:10.34190/eel.19.063 · Cited by 3
Metadata
- Authors
- Giannakos, M. N., Chorianopoulos, K., & Chrisochoides, N.
- Venue
- The International Review of Research in Open and Distributed Learning
- Publisher
- Athabasca University Press
- Published
- 2015-01-20
- Volume
- 16
- Issue
- 1
- ISSN
- 1492-3831 (ISSN-L)
- OpenAlex
- W1511916964
- Cited by
- 163
- References
- 1
- FWCI
- 22.62
- Type
- journal article
- Language
- en
- Open access
- diamond
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Making sense of video analytics: Lessons learned from clickstream interactions, attitudes, and learning outcome in a video-assisted course. https://app.cochat.ai/papers/10.19173/irrodl.v16i1.1976
- The paper
- Giannakos, M. N., Chorianopoulos, K., & Chrisochoides, N. (2015). Making sense of video analytics: Lessons learned from clickstream interactions, attitudes, and learning outcome in a video-assisted course. The International Review of Research in Open and Distributed Learning. https://doi.org/10.19173/irrodl.v16i1.1976
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.
