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#human-computer interaction Preprint Open access

Measuring Student Self-Assessment against Viva-Demonstrated Mastery in a Large First-Year Programming Course

Sakshi Sharma Pavani Ayinampudi Aditya B. M. V. Jinal Gupta Prakash Hegade Rohit Sharma Meenakshi V S. R. S. Iyengar
Oct 2026
Human-computer Interaction

Abstract

Mastery-based education increasingly places the reporting of learning progress in students' hands, who record task completion on learning dashboards. The usefulness of such self-reports depends on how closely reported mastery corresponds to demonstrated competence. Most evidence on student self-assessment compares an overall self-rating with an overall examination score and therefore provides limited evidence about which tasks or which students account for the mismatch. This study examines first-year students' self-assessment against viva-demonstrated mastery at the level of individual tasks across a ladder of sixty programming tasks. The study draws on a large first-year programming course taught in 2023, involving 203 students and 12 examiners, in which every reported task was verified through an oral viva. Because a task entered the Viva only after it was reported, the design is one-sided and captures over-estimation but not under-estimation. Of 11,093 reported tasks, 10,885 (98.1%) were demonstrated, indicating a high degree of correspondence between self-report and demonstrated mastery. The remaining 208 overestimations were not evenly distributed. A small number of students accounted for most of the errors, and they occurred mainly on difficult tasks near the end of the task ladder rather than on higher-point tasks. This task-level analysis shows that high overall self-assessment accuracy can coexist with specific areas where reported and demonstrated mastery diverge. It also provides a practical basis for directing additional verification and formative feedback toward students and tasks where such divergence is more likely.

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