From Concurrent Self-Assessment to Postdiction: Grade-Judgment Calibration Across Two Assessments in a First-Year Computer Science Course

Abstract

First-year computer-science students judged their grade on two assessments differing in judgment type: a concurrent self-assessment embedded in an early quiz (Assessment 1, N=101) and a postdiction after a mid-course written-and-laboratory exam (Assessment 2, N=117), with 94 students linked across both. The concurrent judgment was optimistic (mean signed error +9.10 pp; 70.3% overestimators; Spearman ρ=0.533). The postdictions reversed the bias: students underestimated the written component by 10.08 pp and the laboratory by 4.21 pp, rank-order calibration being markedly better for the laboratory task (ρ=0.804). In the linked subsample, the reversal was large (paired dz=1.00) but absolute error did not improve (p=0.582): the error changed direction, not magnitude. A tertile split shows broad-based quiz optimism and, on the postdictions, a gradient compatible with regression to the mean. An exploratory post hoc re-banding of the laboratory scores, excluding the administrative 0–1 segment, yields a Dunning–Kruger-compatible between-band difference of 1.80 raw points (95% CI [0.94,2.66], p=0.0001), robust to alternative bandings, though the low-band overestimation is not. The Assessment 1 overestimation replicates in two further cohorts. Calibration therefore differed markedly across two contexts that differ simultaneously in judgment type, timing, format, content and diagnostic cues, which these data cannot disentangle.