Developing a Combined VARK and Social Condition Dominance Diagnostic to Optimise Student Learning Experiences
Traditional higher education models frequently rely on a “one size fits all” approach to instructional delivery, which can lead to significant students’ learning difficulties and diminished motivation. While sensory preference models like Neil Fleming’s VARK (Visual, Auditory, Reading, Kinaesthetic) have been widely adopted to personalise learning, they often fail to account for the social conditions and environmental preferences under which information processing occurs. This action research study investigates the design, development, and empirical deployment of SoVARK. This novel digital diagnostic tool integrates VARK sensory modalities with social condition learning dominance to map student learning preferences. Deployed digitally to a cohort of undergraduate students (N = 123, unique respondents = 97), the diagnostic revealed a critical baseline of student needs: 74.8% of the cohort preferred processing information under Solo (47.2%) or Reflective (27.6%) conditions, with Visual-Solo (28.5%) emerging as the single most dominant combined learning profile. By mapping these multidimensional learning preferences, SoVARK provides immediate, automated, and personalised feedback through digital flipbooks containing a custom study strategy known as The Success Blueprint. This study demonstrates how low-cost, scalable digital diagnostics can be successfully developed and implemented to optimise student learning experiences, enhance self-directed learning, and provide concrete, verifiable evidence of educational innovation for academic promotion audits.