ArticleScientific data2026
Open data, private learners: a de-identified student activity and performance dataset for learning analytics.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Open data, private learners: a de-identified student activity and performance dataset for learning analytics.Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The data-driven study and optimization of learning processes has become possible through collecting the digital traces left by learners as they interact with digital study materials and learning tools - a field known as Learning Analytics (LA). The increased availability of such data has fueled the growth of the LA field, enabling the development of new frameworks and models, and supporting the transfer of findings across domains, and collaboration on joint educational initiatives. However, given the sensitive and personal nature of intermediary learning results and outcomes, such data availability also raises significant ethical and privacy concerns. To support and foster the open and transparent development and evaluation of LA solutions, we present a detailed clickstream dataset collected at KU Leuven across two first-year bachelor courses over three academic years. The public dataset is accompanied by transparent documentation of the de-identification process, and we report on privacy and utility validation results.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.