Learning from errors? The impact of erroneous example elaboration on learning outcomes of medical statistics in Chinese medical students

BACKGROUND: Constructivism theory has suggested that constructing students’ own meaning is essential to successful learning. The erroneous example can easily trigger learners’ confusion and metacognition, which may “force” students to process the learning material and construct meaning deeply. However, some learners exhibit a low level of elaboration activity and spend little time on each example. Providing instructional scaffolding and elaboration training may be an efficient method for addressing this issue. The current study conducted a randomized controlled trial to examine the effectiveness of erroneous example elaboration training on learning outcomes and the mediating effects of metacognitive load for Chinese students in medical statistics during the COVID-19 pandemic. METHODS: Ninety-one third-year undergraduate medical students were randomly assigned to the training group (n = 47) and the control group (n = 44). Prerequisite course performance and learning motivation were collected as covariates. The mid-term exam and final exam were viewed as posttest and delayed-test to make sure the robustness of the training effect. The metacognitive load was measured as a mediating variable to explain the relationship between the training and academic performance. RESULTS: The training significantly improved both posttest and delayed-test performance compared with no training (F(posttest) = 26.65, p < 0.001, Partial η(2) = 0.23; F(delayed test) = 38.03, p < 0.001, Partial η(2) = 0.30). The variation trend in metacognitive load in the two groups was significantly different (F = 2.24, p < 0.05, partial η(2) = 0.20), but metacognitive load could not explain the positive association between the treatment and academic performance (β = − 0.06, se = 0.24, 95% CI − 0.57 to 0.43). CONCLUSIONS: Erroneous example learning and metacognitive demonstrations are effective for academic performance in the domain of medical statistics, but their underlying mechanism merits further study.