Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints
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Academic history data is often combined with other types of data to provide reflective and predictive feedback, and formative assessment.
“in LA feedback studies, it is often combined with other types of data to provide reflective (Lim, Gentili, et al., 2021) and predictive feedback (Azcona et al., 2019), and formative assessment (Tempelaar et al., 2018).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Azcona et al., 2019
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 Azcona et al., 2019
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 Azcona et al., 2019
LA-based feedback helps educators to receive recommendations through automatic feedback for better pedagogical interventions.
“the use of LA for feedback helps educators to receive recommendations through automatic feedback for better pedagogical interventions (e.g. Azcona et al., 2019; Sedrakyan et al., 2016).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Azcona et al., 2019
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“From a student perspective, work can be developed to improve the design and delivery of the automatic feedback to improve the experience, including finding a way to personalize the feedback [79].”
BackgroundDai, W., Tsai, Y.-S., Lin, J., Aldino, A., Jin, H., Li, T., Gašević, D., & Chen, G. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100299 · Cited by 93 · see citing works
- Authors
- Azcona, D., Hsiao, I.-H., & Smeaton, A. F.
- Affiliations
- Dublin City University (IE)
- Arizona State University (US)
- Corresponding author
- Azcona, David
- Venue
- User Modeling and User-Adapted Interaction
- Published
- 2019-04-25
- Volume
- 29
- Issue
- 4
- Pages
- 759-788
- ISSN
- 0924-1868 (ISSN-L)
- 1573-1391
- OpenAlex
- W2940725132
- Cited by
- 114
- References
- 58
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 20 · 2023: 23 · 2022: 17 · 2021: 17 · 2020: 6 · 2019: 2
- FWCI
- 11.40
- Type
- journal article
- Language
- en
- Open access
- green
- doras.dcu.ie
- Licence
- cc-by-nc-sa
- www.springer.com/tdm (tdm)
- Locations
- User Modeling and User-Adapted Interaction (publishedVersion)
- Dublin City University Open Access Institutional Repository (Dublin City University) (submittedVersion, cc-by-nc-sa)
- Arrow@dit (Dublin Institute of Technology) (submittedVersion)
- Funders
- Irish Research Council (GOIPG/2015/3497)
- Science Foundation Ireland (12/RC/2289)
- Fulbright Ireland
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- at-risk students, student failure prediction, programming education, learning analytics, digital footprint, adaptive feedback, personalized feedback, student demographics, achievement gap, higher education
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