The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Current state and future trends: a citation network analysis of the learning analytics field
Abstract
This paper provides an evaluation of the current state of the field of learning analytics through analysis of articles and citations occurring in the LAK conferences and identified special issue journals. The emerging field of learning analytics is at the intersection of numerous academic disciplines, and therefore draws on a diversity of methodologies, theories and underpinning scientific assumptions. Through citation analysis and structured mapping we aimed to identify the emergence of trends and disciplinary hierarchies that are influencing the development of the field to date. The results suggest that there is some fragmentation in the major disciplines (computer science and education) regarding conference and journal representation. The analyses also indicate that the commonly cited papers are of a more conceptual nature than empirical research reflecting the need for authors to define the learning analytics space. An evaluation of the current state of learning analytics provides numerous benefits for the development of the field, such as a guide for under-represented areas of research and to identify the disciplines that may require more strategic and targeted support and funding opportunities.
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Citations
Cites
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
Centrality in social networks conceptual clarification
Freeman, L. C. (1978) · Social Networks · doi:10.1016/0378-8733(78)90021-7 · Cited by 17,062
Gephi: An Open Source Software for Exploring and Manipulating Networks
Bastian, M., Heymann, S., & Jacomy, M. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13937 · Cited by 11,675
Systematic Mapping Studies in Software Engineering
Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008) · Electronic Workshops in Computing · doi:10.14236/ewic/ease2008.8 · Cited by 3,239 · cited 1×
“For the analysis of the papers based on their contribution type, we followed the classification scheme commonly adopted in the areas of information systems and software technology [10; 17; 24].”
MethodSome Unique Properties of Eigenvector Centrality
Bonacich, P. (2007) · Social Networks · doi:10.1016/j.socnet.2007.04.002 · Cited by 1,572 · cited 1×
“The three networks were analyzed using the following well-established measures in social network analysis [3; 4; 7; 22]:”
MethodSocio-Bibliometric Mapping of Intra-Departmental Networks
Mählck, P., & Persson, O. (2000) · Scientometrics · doi:10.1023/a:1005661208810 · Cited by 73
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-citationThe 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
Using learning analytics to scale the provision of personalised feedback
Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., & Mirriahi, N. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12592 · Cited by 425
Self-citationIdentifying significant indicators using LMS data to predict course achievement in online learning
You, J. W. (2016) · The Internet and Higher Education · doi:10.1016/j.iheduc.2015.11.003 · Cited by 388
Ferreira‐Mello, R., André, M., Pinheiro, A., Costa, E., & Romero, C. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1332 · Cited by 193
Metadata
- Authors
- Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S.
- Venue
- Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
- Publisher
- ACM
- Published
- 2014-03-24
- Pages
- 231-240
- OpenAlex
- W2017403239
- Cited by
- 328
- References
- 6
- FWCI
- 21.65
- Type
- conference paper
- Language
- en
- Open access
- closed
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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.
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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.
