The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success
Open accessAbstract
Abstract This study examined the extent to which instructional conditions influence the prediction of academic success in nine undergraduate courses offered in a blended learning model (n = 4134). The study illustrates the differences in predictive power and significant predictors between course-specific models and generalized predictive models. The results suggest that it is imperative for learning analytics research to account for the diverse ways technology is adopted and applied in course-specific contexts. The differences in technology use, especially those related to whether and how learners use the learning management system, require consideration before the log-data can be merged to create a generalized model for predicting academic success. A lack of attention to instructional conditions can lead to an over or under estimation of the effects of LMS features on students' academic success. These findings have broader implications for institutions seeking generalized and portable models for identifying students at risk of academic failure.
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Cited by
Let’s not forget: Learning analytics are about learning
Gašević, D., Dawson, S., & Siemens, G. (2015) · TechTrends · doi:10.1007/s11528-014-0822-x · Cited by 1,049
Self-citationPractical and ethical challenges of large language models in education: A systematic scoping review
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13370 · Cited by 1,435
“Researchers need to carefully examine the extent of generalisability of their innovations and inform the limitations to stakeholders (Gašević et al., 2016).”
BackgroundSelf-citationUtilising learning analytics to support study success in higher education: a systematic review
Ifenthaler, D., & Yau, J. Y.-K. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-020-09788-z · Cited by 659
Learning analytics to unveil learning strategies in a flipped classroom
Jovanović, J., Gašević, D., Dawson, S., Pardo, A., & Mirriahi, N. (2017) · The Internet and Higher Education · doi:10.1016/j.iheduc.2017.02.001 · Cited by 376
Self-citationPredicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS
Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2017) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2016.2616312 · Cited by 371
Metadata
- Authors
- Gašević, D., Dawson, S., Rogers, T., & Gasevic, D.
- Venue
- The Internet and Higher Education
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 28
- Pages
- 68-84
- ISSN
- 1096-7516 (ISSN-L)
- OpenAlex
- W2238759989
- Cited by
- 683
- References
- 10
- FWCI
- 64.84
- Type
- journal article
- Language
- en
- Open access
- green
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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.
