Claims

LA deployments at higher education institutions report positive results, with some interventions reporting improvements of up to 20%

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q2 i? A survey of 16 HEIs that deployed LA projects found positive results, with institutions mostly using LA for student retention and some reporting intervention improvements of up to 20%. → Teo Susnjak 2024

Evidence

Teo Susnjak 2024

Teo Susnjak. (2024). Beyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT. Int J Artif Intell Educ 34:452–482. https://doi.org/10.1007/s40593-023-00336-3

q2 · i? · design · r2

The article reports, citing Hernández-de Menéndez et al. (2022), a survey of the practices of 16 HEIs with deployed LA projects. The review attributes to these institutions positive results from retention-focused LA, while noting that implementation requires consideration of benefits, challenges, money, time, and experience, and that understandability of the systems remains a critical challenge.

"The authors found that these institutions have mostly used LA technologies for student retention. These tools largely supported strategies for identifying at-risk students through predictive analytics, which served as a springboard for being proactive and initiating various types of interventions, which some reported as resulting in improvements of up to 20%."

Discussion

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