Authors - Wannakorn Phornprasert, Nisarat Onthong, Thapanapong Sararat, Wongpanya S. Nuankaew, Pratya Nuankaew Abstract - This study proposes an Educational Data Analytics approach to understanding students' digital behavior and academic achievement using Descriptive and Cognitive Analytics. Data were collected from 40 purposively selected students using questionnaires that covered general information, social media usage, sleep behavior, Kolb-based learning style, and GPA. Descriptive Analytics was applied to summarize frequencies, percentages, means, and key behavioral patterns, while Cognitive Analytics was used to interpret these patterns in relation to learning readiness, self-regulation, and academic achievement. The findings showed that students had an average GPA of 3.38, spent an average of 7.53 hours on social media per day, and most frequently used social media between 20:01 and 00:00. The most common bedtime was 01:00, and Concrete Experience was the dominant learning style. The results suggest that small-scale learner data can support understanding of digital behavior, sleep patterns, and academic achievement in Thai higher education.
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