The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Features students really expect from learning analytics
Open accessAbstract
More and more learning in higher education settings is being facilitated through online learning environments. Students' ability to self-regulate their learning is considered a key factor for success in higher education. Learning analytics offer a promising approach to better support and understand students' learning processes. The purpose of this study is to investigate students' expectations towards features of learning analytics systems and their willingness to use these features for learning. A total of 20 university students participated in an initial qualitative exploratory study. They were interviewed about their expectations of learning analytics features. The findings of the qualitative study were complemented by a quantitative study with 216 participating students. The findings show that students expect learning analytics features to support their planning and organization of learning processes, provide self-assessments, deliver adaptive recommendations, and produce personalized analyses of their learning activities. Investigating students' expectations toward features of learning analytics systems.Illustrating 15 learning analytics features which support learning processes.Relating learning analytics features to phases of self-regulation.
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Cited by
The current landscape of learning analytics in higher education
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.07.027 · Cited by 766
Utilising 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
Self-citationChang, D. H., Lin, M. P.-C., Hajian, S., & Wang, Q. Q. (2023) · Sustainability · doi:10.3390/su151712921 · Cited by 370
“This type of reflection can help learners become more aware of their learning processes and develop more effective strategies for learning [74,75].”
BackgroundMethodLearning analytics in European higher education—Trends and barriers
Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Delgado Kloos, C., & Gašević, D. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103933 · Cited by 192
Delcker, J., & Ifenthaler, D. (2021) · Technology, Pedagogy and Education · doi:10.1080/1475939x.2020.1857826 · Cited by 88
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Metadata
- Authors
- Schumacher, C., & Ifenthaler, D.
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 78
- Pages
- 397-407
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2627033315
- Cited by
- 319
- References
- 53
- FWCI
- 34.52
- Type
- journal article
- Language
- en
- Open access
- green
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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.
