Address ethics and privacy considerations — transparency, security, student control, accountability, and informed consent — at the initial stages of MMLA design in simulation-based training¶

Strategy · All strategies
Evidence · 3 claims (2 for, 1 mixed) · 2 studies (1 review, 1 qualitative), q2 · 0 of 2 report an effect size · 3 claims rest on one study

Description¶

The article states that any LA system can have considerable socioeconomic impact, and that ethics and privacy, noted by Lemay et al. (2021) as insufficiently discussed in the EDM and LA fields, "can be the crucial enabler in implementation of learning analytics." It recommends attending to data access, storage location, anonymization, privacy, and informed consent, citing Pardo and Siemens's practical principles.

Design Implications¶

Context¶

Requirements¶

• Consider data collection and management issues including access to data, storage location, anonymization, privacy, and the principle of informed consent

Constraints¶

• The considerations are stated as relevant and important but beyond the scope of the current study

Target Learners¶

• master mariner students whose simulator training data are collected

Target Learning Goals¶

• ethical and privacy-respecting deployment of learning analytics

Claims¶

• Students raised four categories of concern about potential misuse of MMLA data: judgment, privacy, career impact, and academic integrity [+W]
• In one MMLA nursing simulation study, 11 of 14 interviewed students preferred opt-out over opt-in consent, but most did not fully read the explanatory statement [~W]
• Most reviews reporting neurophysiological AI use stressed the need for further ethical considerations, including participant consent and transparent development [+W]

Related Strategies¶

• Address core ethical challenges—data privacy, algorithmic fairness, and transparency—when deploying AI-driven personalized learning
• Apply an affective data governance framework: minimization, purpose limitation, consent, human oversight, and separation from evaluation
• Combine system logs, eye-tracking, and speech recognition to capture professional performance, communication, and collaboration for automated feedback in full-mission simulator training

Examples¶

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Key Sources¶

• Charlott Sellberg · Amit Sharma. (2025). Toward multimodal learning analytics in simulation-based collaborative learning: A design ethnography of maritime training. Intern. J. Comput.-Support. Collab. Learn. https://doi.org/10.1007/s11412-024-09435-2