Evidence map›Paper›PMID 40476309›Full record

ArticleThe British journal of mathematical and statistical psychology2026

Keeping Elo alive: Evaluating and improving measurement properties of learning systems based on Elo ratings.

Maria Bolsinova, Bence Gergely, Matthieu J S Brinkhuis

Abstract read
In one paragraph

Article in The British journal of mathematical and statistical psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Maria BolsinovaDepartment of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands.ORCID 0000-0002-8376-5951
Bence GergelyDepartment of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands.
Matthieu J S BrinkhuisDepartment of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands.

Funding

Nederlandse Organisatie voor Wetenschappelijk Onderzoek VI.Veni.221G.056
6 · The paper itself

Abstract

The Elo Rating System which originates from competitive chess has been widely utilised in large-scale online educational applications where it is used for on-the-fly estimation of ability, item calibration, and adaptivity. In this paper, we aim to critically analyse the shortcomings of the Elo rating system in an educational context, shedding light on its measurement properties and when these may fall short in accurately capturing student abilities and item difficulties. In a simulation study, we look at the asymptotic properties of the Elo rating system. Our results show that the Elo ratings are generally not unbiased and their variances are context-dependent. Furthermore, in scenarios where items are selected adaptively based on the current ratings and the item difficulties are updated alongside the student abilities, the variance of the ratings across items and students artificially increases over time and as a result the ratings do not converge. We propose a solution to this problem which entails using two parallel chains of ratings which remove the dependence of item selection on the current errors in the ratings.

Indexed as

Educational MeasurementLearningPsychometricsComputer SimulationHumansModels, StatisticalReproducibility of ResultsStudentsadaptive learning systemsElo rating systemmeasurement

Identifiers

PMID40476309
PMCPMC12784335

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Registered trials

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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.