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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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Flagged first, then by citations
2 of the 59 references couldn’t be matched to a known paper.
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
Cited by
Sorted by citations
van Dijck, J., Poell, T., & de Waal, M. (2018) · doi:10.1093/oso/9780190889760.001.0001 · Cited by 2,951
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
Predicting academic performance of students from VLE big data using deep learning models
Waheed, H., Hassan, S., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.106189 · Cited by 676
Designing educational technologies in the age of AI: A learning sciences‐driven approach
Luckin, R., & Cukurova, M. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12861 · Cited by 694
Rajabalee, Y. B., & Santally, M. I. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10375-1 · Cited by 484
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.
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