Strengthen IVR eye-tracking validity through theory-driven metric selection, transparent data-quality documentation, and triangulation with complementary methods¶

Strategy · All strategies
Evidence · 3 claims (2 for, 1 mixed) · 1 study (1 quant-synthesis), q2 · 1 of 1 report an effect size · 3 claims rest on one study

Description¶

The review recommends that future IVR eye-tracking studies select metrics based on established theoretical rationales rather than scanning vendor-provided indices, document data-quality control procedures transparently, and avoid relying solely on manufacturer default settings. It states: "We recommend transparent documentation of data quality control procedures and advise researchers not to rely solely on manufacturer default settings." Triangulation with self-reports and interviews is encouraged to enhance convergent validity, and the proposed classification and scoring systems can serve as a reference for validating future measures.

Design Implications¶

Context¶

Requirements¶

• Researchers must document experimental configurations adapted to specific research questions rather than default settings

Constraints¶

• Machine-learning-based multi-index approaches were excluded from the review and require distinct validity evaluation procedures

Target Learners¶

• IVR learners in professional, medical, and educational training contexts

Target Learning Goals¶

• Valid measurement of cognitive, affective, social, and metacognitive learning processes in IVR

Claims¶

• Overall validity of eye-tracking measures of learning in IVR is limited, averaging 1.93 of 4, with data quality the weakest component [+W]
• IVR eye-tracking studies commonly use small samples, adult participants, professional-domain tasks, and default vendor hardware and software configurations [+W]
• Eye-tracking measures of cognitive load show significantly higher validity than measures of other learning constructs in IVR [~W]

Related Strategies¶

• Pair convenient platform analytics with transparent, education-specific evaluation criteria rather than trusting either alone

Examples¶

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Key Sources¶

• Lee, J. Y., Moon, J., Paas, F., Cho, M., Darejeh, A., Holmer, S., & Ayres, P. (2026). Validity of Eye-Tracking Measures in Immersive Virtual Reality: Using Cognitive Theories of Learning. Educational Psychology Review. https://doi.org/10.1007/s10648-026-10186-9