Students' perceptions of the fairness of MMLA visualizations in an authentic nursing simulation deployment were mixed, hinging on accuracy and completeness of data representation¶

Claim · All claims
Evidence · 1 study (2 entries) · qualitative r? · q2
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person · recheck by 2027: tool and model accuracy changes quickly

Subclaims¶

q2 i? Undergraduate nursing students were divided on whether MMLA visualizations fairly represented their individual and team performance, with accuracy of reflection the dominant lens. → Jin 2024 q2 i? Students attributed perceived unfairness to flawed or incomplete data, including complete omission of a student from a visualization. → Jin 2024 (2)

Evidence¶

Jin 2024¶

Jin, Y., Echeverria, V., Yan, L., Zhao, L., Alfredo, R., Tsai, Y.-S., Gašević, D., & Martinez-Maldonado, R. (2024). FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics. Journal of Learning Analytics, 11(3), 6–23. https://doi.org/10.18608/jla.2024.8351

q2 · i? · qualitative · r?

Semi-structured interviews with 14 nursing students after using MMLA visualizations in debriefs; deductive-inductive thematic analysis. Perceptions were "mixed": six agreed the visualizations were fair, four disagreed, four said somewhat fair.

"Students' perspectives on the fairness of the MMLA visualizations at reflecting their individual and team performance were mixed: six participants (P1, P3, P4, P9, P10, and P12) agreed, four (P2, P7, P8, and P11) disagreed, and four (P5, P6, P13, and P14) found the visualizations to be "somewhat fair.""

Jin 2024 (2)¶

Jin, Y., Echeverria, V., Yan, L., Zhao, L., Alfredo, R., Tsai, Y.-S., Gašević, D., & Martinez-Maldonado, R. (2024). FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics. Journal of Learning Analytics, 11(3), 6–23. https://doi.org/10.18608/jla.2024.8351

q2 · i? · qualitative · r?

Interview theme on flawed or incomplete data: missing data and technology flaws, such as a mic problem or an uncaptured interaction with a patient's relative, led students to see the visualizations as misrepresenting events.

"P2 highlighted the unfairness of evaluating based on incomplete data, pointing out the complete omission of Secondary Nurse 1 from the ward map: "She doesn't even exist there, so I feel like it's not fair in that aspect.""

Discussion¶

Related Claims¶

• Maritime students supported MMLA but preferred on-demand post-simulation access via mobile application over real-time in-simulation dashboards — related
• Students supported tiered data access: broad access for self-reflection, educator access for teaching, and restricted access to raw video and audio — related
• Students raised four categories of concern about potential misuse of MMLA data: judgment, privacy, career impact, and academic integrity — related