Authors - Wannakorn Phornprasert, Waraporn Phothirin, Thapanapong Sararat, Wongpanya S. Nuankaew, Pratya Nuankaew Abstract - This study uses Learning Analytics to assess university students’ eye health risks based on social media usage data, focusing on descriptive and diagnostic analyses. Data collected from 44 undergraduates via a self-reported questionnaire with 82 key questions covered general details, social media habits, device and screen environments, symptoms of Computer Vision Syndrome, and Felder–Silverman learning styles. The descriptive analysis revealed Instagram as the most popular platform, frequent nighttime use after 20:00, and many students spend over six hours daily on social media. While most respondents were categorized as low risk, symptoms such as watery eyes, eye pain, light sensitivity, and neck pain were commonly reported. The diagnostic analysis linked risky sitting postures, looking below eye level, prolonged daily usage, and nighttime social media activity to increased eye health risks. These findings support initiatives for digital well-being and learning support in higher education.
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