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Fake-learner detection algorithms for CUMA and unauthorized collaboration in MOOCs

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 associational), q3 · 0 of 1 report an effect size · 1 claim rests on one study

Description

The article uses dedicated algorithms to identify two types of fake learners: "Copying Using Multiple Accounts (CUMA)", where a master account receives credit while harvesting accounts collect correct answers, and unauthorized "Collaborators" who submit assignments together, detected using dissimilarity metrics on submission timing. In the 8.MReVx course, the CUMA algorithm detects 65 master accounts out of 478 certificate earners (a lower bound), and the collaborator algorithm identifies 20 (~4%) of certificate earners.

Accounts

  • Fake-learner detection algorithms for CUMA and unauthorized collaboration in MOOCs.: The article uses dedicated algorithms to identify two types of fake learners: "Copying Using Multiple Accounts (CUMA)", where a master account receives credit while harvesting accounts collect correct answers, and unauthorized "Collaborators" who submit assignments together, detected using dissimilarity metrics on submission timing. In the 8.MReVx course, the CUMA algorithm detects 65 master accounts out of 478 certificate earners (a lower bound), and the collaborator algorithm identifies 20 (~4%) of certificate earners. (Alexandron et al. (2019))

Claims

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Key Sources

  • Alexandron, G., Yoo, L. Y., Ruipérez-Valiente, J. A., Lee, S., & Pritchard, D. E. (2019). Are MOOC Learning Analytics Results Trustworthy? With Fake Learners, They Might Not Be! International Journal of Artificial Intelligence in Education, 29, 484–506. https://doi.org/10.1007/s40593-019-00183-1
Page metadata
Field Value
type research-method
id fake-learner-detection-algorithms-for-cuma-and-unauthorized-collaboration-in-moocs
title Fake-learner detection algorithms for CUMA and unauthorized collaboration in MOOCs
description The article uses dedicated algorithms to identify two types of fake learners: "Copying Using Multiple Accounts (CUMA)", where a master account receives credit while harvesting accounts collect correct answers, and unauthorized "Collaborators" who submit assignments together, detected using dissimila
status review
generated by process:settle-candidates on 2026-10-28
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