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What’s missing from learning analytics? Challenging the assumption of neurotypicality

Susan Harrington

Interacting with Computers2026https://doi.org/10.1093/iwc/iwag004article
AJG 2ABDC B
Weight
0.50

Abstract

As the digital landscape of education continues to evolve, learning analytics has become an integral tool for understanding, measuring, and improving student outcomes. However, a significant gap remains in the design and implementation of these technologies, particularly in their ability to cater to the diverse cognitive and neurological profiles of students. This conceptual paper highlights the need for a neuroinclusive approach to learning analytics, arguing that current practices in educational technology risk excluding neurodivergent students by reinforcing a neurotypical-centric design. In doing so, the paper highlights the limitations of existing frameworks and offers suggestions for how learning analytics can become more inclusive to better serve all students, especially those with undiagnosed or undisclosed neurodivergence.

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https://doi.org/https://doi.org/10.1093/iwc/iwag004

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@article{susan2026,
  title        = {{What’s missing from learning analytics? Challenging the assumption of neurotypicality}},
  author       = {Susan Harrington},
  journal      = {Interacting with Computers},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/iwc/iwag004},
}

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What’s missing from learning analytics? Challenging the assumption of neurotypicality

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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