Beyond Self-Reports: Using text analysis to explore first-year students’ subjective experiences during their first examination session

Abstract

This study investigated first-yea r psychology students’ exam experiences through na rra tive a na lysis with a mixed-methods approach that combines manual thematic coding and Latent Dirichlet Allocation (LDA) with psychometric self -report assessments. Higher education marks a vital developmental phase during which students face increased academic stress especially when they take their first set of examinations. Although standardized self -report measures are the primary instrument in traditional research about coping and learning strategies, they do not effectively capture the full ra nge of students’ rea l-life experiences, because they are limited by the theoretical frameworks on which they were based. The research conducted interviews with 175 first -year students to identify eight categories of coping and learning strategies (Smarandache et al., 2021), including deep learning and problem-solving stra tegies (Spă ta ru et a l., 2024) a s well a s procra stina tion a nd a voidance (Kim et al., 2020). Our findings showed that linguistic patterns showed consistent relationships with motivational and affective variables, which proves the worth of textual data in supplementing traditional assessments methods. The present study demonstrates how natural language processing (NLP) methods can deliver advanced understanding of student adaptation to academic pressure and provides useful recommendations for implementing digital well-being programs and time management training and learning communities.