Harnessing linguistics to transform student satisfaction with assessment feedback

Assessment feedback is crucial for student learning and satisfaction in higher education. However, many students find it vague or unhelpful, leading to dissatisfaction. Workload pressures on educators limit opportunities to provide detailed, personalised feedback. The present study explores a data-driven approach that integrates linguistic analysis into assessment feedback, aiming to enhance its clarity and increase student satisfaction. Using computational methods, we analysed a 235,118-word corpus from 115 postgraduate essays, examining lexical diversity, readability, and word frequency. Individualised Linguistic Feedback Reports (LFRs) were created and shared alongside traditional feedback. Survey responses (N = 59) showed students found LFRs clearer and more satisfying. Although perceived usefulness and actionability were not significantly different, qualitative feedback highlighted the value of objective, specific writing insights. These findings suggest that linguistically informed feedback can improve clarity and satisfaction without increasing staff workload, offering a promising step towards more engaging and effective feedback models.