Claims

Survey respondents rated analytics expertise as leaning towards a large or critical barrier to LA success

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2020 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2025: baselines and conditions change

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? Among 13 barrier items rated on a 5-point scale, views of analytics expertise leaned towards large or critical barrier, while institutional strategy and staff and student capabilities leaned towards small or not a barrier. → Tsai 2020

Evidence

Tsai 2020

Tsai, Yi-Shan, et al. (2020). Learning Analytics in European Higher Education – Trends and Barriers. Computers & Education. https://doi.org/10.1016/j.compedu.2020.103933

q2 · i? · associational · r1

Survey of 45 respondents rating 13 barrier items on a 5-Likert scale found responses polarised, with "views of analytics expertise (Item 7) were leaning towards large or critical barrier."

"views of analytics expertise (Item 7) were leaning towards large or critical barrier. In contrast, views of institutional strategy (Item 4) and staff and student capabilities’ (Item 5) were leaning towards small or not a barrier."

Discussion

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