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arxiv: 2602.17314 · v2 · submitted 2026-02-19 · 💻 cs.CY · cs.DB· cs.LG

Recognition: no theorem link

Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE

Valdemar \v{S}v\'abensk\'y , Brendan Flanagan , Erwin Daniel L\'opez Zapata , Atsushi Shimada
Authors on Pith no claims yet

Pith reviewed 2026-05-15 20:59 UTC · model grok-4.3

classification 💻 cs.CY cs.DBcs.LG
keywords open datasetslearning analyticseducational data miningAI in educationdata sharingreproducibilitybest practicessurvey
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The pith

A review of 1,125 learning analytics papers identifies 172 open datasets, 143 of them previously undocumented, and offers an 8-item PRACTICE checklist for better data publication.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper conducts a manual survey of papers from three major conferences in learning analytics, educational data mining, and AI in education over five years. It locates 172 open datasets used across 204 publications and shows that most had not been captured in earlier reviews of the field. The authors categorize the datasets by context, methods, and usage, identify gaps in sharing and documentation, and distill their findings into the PRACTICE guidelines, an eight-item checklist meant to help researchers release usable data. This matters because open datasets support reproducibility and collaboration, yet current practices leave many studies hard to verify or extend.

Core claim

By manually examining 1,125 papers from the LAK, EDM, and AIED conferences, the authors identified 172 open datasets appearing in 204 publications. Of these, 143 had not been recorded in any prior survey. The work supplies the most detailed categorization to date of dataset contexts, analytical methods, and properties, along with an analysis of current shortcomings. From this base the authors derive the PRACTICE guidelines, a concrete eight-item checklist, and release their own annotated inventory of the datasets and corresponding papers as a shared resource.

What carries the argument

The PRACTICE guidelines, an eight-item checklist that translates observed gaps into specific recommendations for publishing open educational datasets so they support reproducibility and reuse.

If this is right

  • Researchers can consult the released inventory to locate existing datasets instead of creating new ones.
  • Adopting the PRACTICE checklist should increase the proportion of reusable, well-documented datasets in future publications.
  • The identified gaps point to specific needs such as better metadata standards and longer-term data hosting.
  • Wider use of the guidelines would raise citation rates and visibility for papers that share data openly.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same survey approach could be applied to other education-related data-science venues to test whether the same gaps appear.
  • A live, searchable version of the inventory would let researchers query datasets by method or educational context rather than reading the static paper.
  • The PRACTICE items could be adapted into journal submission requirements to shift norms faster than voluntary guidelines alone.

Load-bearing premise

That papers from only three flagship conferences over five years capture the full range of open dataset practices in the field.

What would settle it

Finding dozens of additional open datasets from papers outside the three surveyed conferences or from years outside the five-year window whose sharing practices differ markedly from the reported trends and gaps.

Figures

Figures reproduced from arXiv: 2602.17314 by Atsushi Shimada, Brendan Flanagan, Erwin Daniel L\'opez Zapata, Valdemar \v{S}v\'abensk\'y.

Figure 1
Figure 1. Figure 1: PRISMA flow diagram. Generated using the tool by Haddaway et al. [ [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distributions of dataset frequency across educational topics and levels of students. [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Geographical contexts of the dataset collection distributed per continent. This map considers 163 datasets (172 minus the 9 [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of number of students into bins. [PITH_FULL_IMAGE:figures/full_fig_p019_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Distribution of number of data points into bins. [PITH_FULL_IMAGE:figures/full_fig_p019_5.png] view at source ↗
read the original abstract

Open datasets play a crucial role in three research domains that intersect data science and education: learning analytics, educational data mining, and artificial intelligence in education. Researchers in these domains apply computational methods to analyze data from educational contexts, aiming to better understand and improve teaching and learning. Providing open datasets alongside research papers supports reproducibility, collaboration, and trust in research findings. It also provides individual benefits for authors, such as greater visibility, credibility, and citation potential. Despite these advantages, the availability of open datasets and the associated practices within the learning analytics research communities, especially at their flagship conference venues, remain unclear. We surveyed available datasets published alongside research papers in learning analytics. We manually examined 1,125 papers from three flagship conferences (LAK, EDM, and AIED) over the past five years. We discovered, categorized, and analyzed 172 datasets used in 204 publications. Our study presents the most comprehensive collection and analysis of open educational datasets to date, along with the most detailed categorization. Of the 172 datasets identified, 143 were not captured in any prior survey of open data in learning analytics. We provide insights into the datasets' context, analytical methods, use, and other properties. Based on this survey, we summarize the current gaps in the field. Furthermore, we list practical recommendations, advice, and 8-item guidelines under the acronym PRACTICE with a checklist to help researchers publish their data. Lastly, we share our original dataset: an annotated inventory detailing the discovered datasets and the corresponding publications. We hope these findings will support further adoption of open data practices in learning analytics communities and beyond.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper conducts a manual survey of 1,125 research papers from the LAK, EDM, and AIED conferences over five years, identifying 172 open datasets used in 204 publications. Of these, 143 are claimed to be novel compared to prior surveys. The authors categorize and analyze the datasets' contexts, methods, and properties, identify gaps in open data practices, propose an 8-item PRACTICE guideline with checklist for data publication, and share their annotated inventory of datasets and publications.

Significance. If the findings hold, this survey provides significant value by offering the most detailed and comprehensive collection of open datasets in learning analytics to date, along with actionable guidelines to promote better open data practices. The explicit sharing of the original annotated dataset is a notable strength that enhances reproducibility and allows the community to build upon this work. It addresses a clear need for understanding current trends and challenges in data sharing within the field.

major comments (2)
  1. [Methods] The manual review process for the 1,125 papers lacks reported details on inter-rater reliability, precise criteria for identifying and categorizing datasets, and validation steps for decisions. This affects the reliability of the reported counts and the claim that 143 datasets are novel.
  2. [Survey Scope and Limitations] No justification is given for limiting the search exclusively to the three flagship conferences (LAK, EDM, AIED) over five years, and there is no estimate or discussion of potentially missed datasets from other venues such as journals or workshops. This makes the 'most comprehensive' claim and gap analysis vulnerable to scope bias.
minor comments (1)
  1. [Abstract] The abstract introduces the PRACTICE acronym and 8-item guidelines but does not provide the expansion or list the items, which would improve immediate clarity for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of the work's value. We address each major comment below and have revised the manuscript accordingly to improve methodological transparency and scope discussion.

read point-by-point responses

  1. Referee: [Methods] The manual review process for the 1,125 papers lacks reported details on inter-rater reliability, precise criteria for identifying and categorizing datasets, and validation steps for decisions. This affects the reliability of the reported counts and the claim that 143 datasets are novel.

    Authors: We agree that additional methodological details are needed. In the revised manuscript, we have added a new subsection in Methods that specifies: (1) the exact inclusion criteria for identifying open datasets (e.g., public repository links, licenses, and accessibility at time of review); (2) the categorization taxonomy with definitions and examples; and (3) the validation process, including independent review of a 20% random sample by two authors, discrepancy resolution via consensus meetings, and resulting inter-rater agreement (Cohen's kappa = 0.87). These additions directly support the reliability of the 172 datasets and the 143 novel count. revision: yes

  2. Referee: [Survey Scope and Limitations] No justification is given for limiting the search exclusively to the three flagship conferences (LAK, EDM, AIED) over five years, and there is no estimate or discussion of potentially missed datasets from other venues such as journals or workshops. This makes the 'most comprehensive' claim and gap analysis vulnerable to scope bias.

    Authors: We selected the three flagship conferences because they constitute the primary, peer-reviewed outlets for the LA/EDM/AIED communities and enable consistent, high-quality analysis of open-data practices within the field's core venues. In the revision we have added an explicit Limitations section that: (a) justifies the five-year window and venue choice by referencing prior surveys with similar scope; (b) acknowledges that datasets appearing only in journals, workshops, or other conferences are excluded; and (c) qualifies the 'most comprehensive' phrasing to 'most comprehensive survey focused on these flagship conferences.' We also note that extending coverage to additional venues remains valuable future work. revision: yes

Circularity Check

0 steps flagged

Empirical survey with no derivations or self-referential claims

full rationale

This paper performs a manual survey of 1,125 papers from three conferences to identify and categorize 172 datasets, with claims resting directly on that empirical count rather than any equations, fitted parameters, predictions, or derivations. No load-bearing steps reduce by construction to inputs, self-citations, or ansatzes; the methodology is self-contained as a descriptive inventory whose completeness depends on the stated sampling frame but does not create circular equivalence. The 'most comprehensive' assertion follows from the performed enumeration and is not forced by prior self-referential results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is a survey paper. No mathematical derivations, fitted parameters, or new postulated entities are introduced. The work relies on standard practices for literature review and manual data extraction.

axioms (1)
  • domain assumption Manual review of papers from three conferences accurately represents open dataset practices in the broader field
    The survey scope is limited to LAK, EDM, and AIED; this assumption is stated implicitly by the choice of venues.

pith-pipeline@v0.9.0 · 5616 in / 1232 out tokens · 16170 ms · 2026-05-15T20:59:56.674230+00:00 · methodology

discussion (0)

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Reference graph

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