Teaching staff distrusted LA tools' data accuracy and prediction precision despite trusting their usefulness¶

Claim · All claims
Evidence · 1 study · qualitative r3 · q1
Currency · newest study 2023 · not yet checked against its sources · not reviewed by a person · recheck by 2026: tool and model accuracy changes quickly

Subclaims¶

q1 i? Interviews found six data-accuracy concerns (outdated data, different data channels, lack of data governance, power relationship, response bias, technical issues) and concerns about prediction precision due to inconsistent student data and absent psychological factors. → Asma Shannan Alzahrani 2023

Evidence¶

Asma Shannan Alzahrani 2023¶

Asma Shannan Alzahrani, Yi-Shan Tsai, Naif Aljohani, Emma Whitelock-wainwright, Dragan Gasevic. (2023). Do teaching staff trust stakeholders and tools in learning analytics? A mixed methods study. Education Tech Research Dev. https://doi.org/10.1007/s11423-023-10229-w

q1 · i? · qualitative · r3

Thematic analysis of 24 interviews. Although all teaching staff viewed LA as useful, they raised accuracy concerns (e.g., surveys completed with random answers, Blackboard attendance misrecording) and precision concerns about generalising predictive models across cohorts.

"Six codes were developed to capture concerns about data accuracy, including outdated data, different data channels, lack of data governance, power relationship, response bias and technical issues."

Discussion¶

Related Claims¶

• Teaching staff trusted HEIs' competence to adopt LA based on existing e-learning infrastructure and data-analysis expertise — related
• Teaching staff distrusted third parties in LA on privacy and ethics, centring on data sharing and ownership — related
• The largest trust gap toward LA tools concerned promoting students' academic and professional skill development — related
• Institutions adopting LA were particularly wrestling with social issues rather than technical ones — related