In search for the most informative data for feedback generation: Learning analytics in a data-rich context
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Examples of popular LMS include Moodle, WebCT, Blackboard, and Sakai.
“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
Demographic data is one of the most commonly employed data types in LA studies.
“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
Assessment data mostly serve educators with evidence-based formative and summative assessment.
“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
Methods used to support feedback include predictive modelling, classification, clustering, Bayesian network analysis, natural language processing, process mining, relationship mining, support vector machine, discovery model, machine learning, and neural network.
“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
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- 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 · 2014: 1
- 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
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