The DOI matches its Crossref record; 57 of the 59 references it cites were found in a registry or an index.
In search for the most informative data for feedback generation: Learning analytics in a data-rich context
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In the citing paper
“Examples of popular LMS include Moodle, WebCT, Blackboard, and Sakai (Tempelaar et al., 2015).”
From Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023) (2024) · cites this paper as Tempelaar et al., 2015
In the citing paper
“This data type is one of the most commonly employed in LA studies (Azcona et al., 2019; Tempelaar et al., 2015).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Tempelaar et al., 2015
In the citing paper
“Our review suggests that assessment data mostly serve educators with evidence-based formative and summative assessment (e.g. Choi & Cho, 2020; Tempelaar et al., 2015).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Tempelaar et al., 2015
In the citing paper
“Methods used to support feedback include predictive modelling (Azcona et al., 2019; Tempelaar et al., 2015), classifi cation (Baneres et al., 2019), clustering (Lim, Gasevic, et al., 2021; Tempelaar et al., 2018; Tsai et al., 2021), Bayesian network analysis (Choi & Cho, 2020), natural language processing (Perikos et al., 2017), process mining (Sedrakyan et al., 2016), relationship mining (T”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Tempelaar et al., 2015
Citations
Cites
Visible learning: a synthesis of over 800 meta‐analyses relating to achievement
Carter, M. (2009) · Educational Psychology · doi:10.1080/01443410903415150 · Cited by 7,696
(2008) · Cited by 6,939
Handbook of educational psychology
(1997) · Choice Reviews Online · doi:10.5860/choice.34-3084 · Cited by 4,850
Handbook of Research on Educational Communications and Technology
(2013) · Routledge eBooks · doi:10.4324/9781410609519 · Cited by 2,744
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2010.10.002 · Cited by 2,248
Handbook of Research on Educational Communications and Technology
Jonassen, D. H., Spector, M. J., Driscoll, M., Merrill, M. D., van Merriënboer, J. J. G., & Driscoll, M. P. (2008) · Routledge eBooks · doi:10.4324/9780203880869 · Cited by 1,692
Aligning assessment with long‐term learning
Boud, D. J., & Falchikov, N. (2006) · Assessment & Evaluation in Higher Education · doi:10.1080/02602930600679050 · Cited by 1,516
Learning Analytics: The Emergence of a Discipline
Siemens, G. (2013) · American Behavioral Scientist · doi:10.1177/0002764213498851 · Cited by 1,284
Mining LMS data to develop an “early warning system” for educators: A proof of concept
Macfadyen, L. P., & Dawson, S. (2010) · Computers & Education · doi:10.1016/j.compedu.2009.09.008 · Cited by 1,045
Micheletta, S. (2013) · Form re - Open Journal per la formazione in rete · doi:10.13128/formare-13251 · Cited by 1,016
Research works printed but not linked: 3
- Rienties, 2012, A dynamic analysis of social interaction in Computer Mediated Communication; a preference for autonomous learning, Interactive Learning Environments
- Rienties, B., Slade, S., Clow, D., Cooper, M., & Ferguson, R. (submitted for publication). Risks and ethical concerns of Learning Analytics: a state-of-the-art review.
- Siemens, 2013, Improving the quality of productivity of the higher education sector: Policy and strategy for systems-level deployment of learning analytics, Solar Research
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Dias, S. B., Hadjileontiadou, S. J., Diniz, J., & Hadjileontiadis, L. J. (2020) · Scientific Reports · doi:10.1038/s41598-020-76740-9 · Cited by 115
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Self-citationIglesías-Pradas, S., Ruiz-de-Azcárate, C., & Agudo-Peregrina, Á. F. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.09.065 · Cited by 81
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Rienties, B., Nguyen, Q., Holmes, W., & Reedy, K. (2017) · Interaction design & architecture(s)/ID&A Interaction design & architecture(s) · doi:10.55612/s-5002-033-007 · Cited by 64
Self-citationLearning analytics: A glance of evolution, status, and trends according to a proposed taxonomy
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Gelan, A., Fastré, G., Verjans, M., Martin, N., Janssenswillen, G., Creemers, M., Lieben, J., Depaire, B., & Thomas, M. (2018) · Computer Assisted Language Learning · doi:10.1080/09588221.2017.1418382 · Cited by 60
Ortigosa, Á., Carro, R. M., Bravo, J., Lizcano, D., Alcolea, J. J., & Blanco, O. J. (2019) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2019.2911608 · Cited by 59
Tan, J. P.-L., Koh, E., Jonathan, C. R., & Yang, S. (2017) · Journal of Learning Analytics · doi:10.18608/jla.2017.41.7 · Cited by 58
Hellings, J., & Haelermans, C. (2020) · Higher Education · doi:10.1007/s10734-020-00560-z · Cited by 56
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Self-citationZamecnik, A., Kovanović, V., Joksimovíc, S., & Liu, L. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100051 · Cited by 55
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Bulut, O., Gorgun, G., Yildirim‐Erbasli, S. N., Wongvorachan, T., Daniels, L. M., Gao, Y., Lai, K. W., & Shin, J. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13276 · Cited by 58
Verstege, S., Pijeira‐Díaz, H. J., Noroozi, O., Biemans, H. J. A., & Diederen, J. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.02.020 · Cited by 56
Umer, R., Sušnjak, T., Mathrani, A., & Lim, S. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2021.1933542 · Cited by 50
A Review of Recent Advances in Adaptive Assessment
Vie, J.-J., Popineau, F., Bruillard, É., & Bourda, Y. (2017) · Studies in systems, decision and control · doi:10.1007/978-3-319-52977-6_4 · Cited by 62
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Herodotou, C., Rienties, B., Boroowa, A., Zdráhal, Z., Hlosta, M., & Naydenova, G. (2017) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3027385.3027397 · Cited by 50
Self-citationBlended learning in higher education: the integrated and distributed model and a thematic analysis
Starčič, A. I. (2024) · Discover Education · doi:10.1007/s44217-024-00239-y · Cited by 87
Gomaa, Y., Moussa, S. M., & Khalifa, M. E. (2020) · IEEE Access · doi:10.1109/access.2020.3034284 · Cited by 48
Assessing collaborative learning: big data, analytics and university futures
Williams, P. (2016) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2016.1216084 · Cited by 51
Nguyen, Q., Rienties, B., & Richardson, J. T. E. (2019) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2019.1679088 · Cited by 49
Self-citationMittelmeier, J., Rienties, B., Tempelaar, D., Hillaire, G., & Whitelock, D. (2018) · Computers & Education · doi:10.1016/j.compedu.2017.11.003 · Cited by 47
Self-citationWhitelock, D. M., Twiner, A., Richardson, J. T. E., Field, D., & Pulman, S. (2015) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2723576.2723599 · Cited by 45
Seufert, S., Meier, C., Söllner, M., & Rietsche, R. (2019) · Technology Knowledge and Learning · doi:10.1007/s10758-019-09399-5 · Cited by 45
Chen, C.-H., & Su, C. (2019) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 45
Wei, X., & Taecharungroj, V. (2022) · The International Journal of Management Education · doi:10.1016/j.ijme.2022.100675 · Cited by 54
Quadir, B., Chen, N.-S., & Isaias, P. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2020.1712427 · Cited by 46
The temporal dynamics of online problem-based learning: Why and when sequence matters
Saqr, M., & López‐Pernas, S. (2023) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-023-09385-1 · Cited by 56
Data-driven problem based learning: enhancing problem based learning with learning analytics
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Nikolaidis, P., Ismail, M. A., Shuib, L. M., Khan, S., & Dhiman, G. (2022) · Sustainability · doi:10.3390/su142013584 · Cited by 56
MOOC Quality: the need for new measures
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Laer, S. V., & Elen, J. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.02.016 · Cited by 45
A Learning Analytics Conceptual Framework for Augmented Reality-Supported Educational Case Studies
Kazanidis, I., Πέλλας, Ν., & Christopoulos, A. (2021) · Multimodal Technologies and Interaction · doi:10.3390/mti5030009 · Cited by 40
Metadata
- Authors
- Tempelaar, D. T., Rienties, B., & Giesbers, B.
- Affiliations
- Maastricht University (NL)
- The Open University (GB)
- Erasmus University Rotterdam (NL)
- Corresponding author
- Tempelaar, Dirk T.
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2015-06
- Volume
- 47
- Pages
- 157-167
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W1994211543
- Cited by
- 449
- References
- 59
- Citations by year
- 2026: 33 · 2025: 22 · 2024: 25 · 2023: 31 · 2022: 40 · 2021: 51 · 2020: 44 · 2019: 53 · 2018: 60 · 2017: 43 · 2016: 36 · 2015: 10
- FWCI
- 44.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers in Human Behavior (publishedVersion)
- Open Research Online - ORO (The Open University) (acceptedVersion)
- Research Publications (Maastricht University) (submittedVersion)
- Funders
- Dutch SURF
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Online and Blended Learning
- Keywords
- learning analytics, learning dispositions, formative assessment, virtual learning environments, student performance prediction, underachieving students, blended learning, problem-based learning, e-tutorials, feedback generation
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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