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 (Fposttest = 26.65, p < 0.001, Partial η2 = 0.23; Fdelayed test = 38.03, p < 0.001, Partial η2 = 0.30). The variation trend in metacognitive load in the two groups was significantly different (F = 2.24, p < 0.05, partial η2 = 0.20), but metacognitive load could not explain the positive association between the treatment and academic performance (ß = - 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.

Interdisciplinary Surgical Management of Multiple Facial Fractures With Image-Guided Navigation.

PURPOSE: To evaluate the effectiveness of interdisciplinary surgical management of multiple facial fractures with image-guided surgical navigation. PATIENTS AND METHODS: From 2011 through 2014, 36 patients with multiple facial fractures were enrolled in the study. With individual virtual 3-dimensional (3D) modeling, interdisciplinary planning and surgical simulation were carried out on an Accu-Navi software platform. Through an interactive collaboration among specialists, all patients underwent 1-stage open reduction under guidance of the navigation system. The outcome was assessed by superimposing the postoperative 3D computed tomographic (CT) model on the preoperative plan and clinical examination. RESULTS: Through the registration procedure, an accurate match between the actual intraoperative position and the CT images was achieved within a systematic error of 1 mm. The fractured bone segments were released and repositioned according to the preoperative plan and simulation with the aid of instrument- and probe-based navigation. All patients underwent uneventful healing without serious complications. Postoperative assessment of surgical intervention showed a quantitative discrepancy less than 2 mm (1.49 ± 0.27), showing a satisfactory concordance. CONCLUSION: In the interdisciplinary surgical management of multiple facial fractures, image-guided surgical navigation, including preoperative planning, surgical simulation, postoperative assessment, and computer-assisted navigation, proved an optimal strategy and valuable option for this potentially complicated procedure.