Self-Assessment of Competencies in Project-Based Learning

This study investigates the innovative application of New Pedagogies for Deep Learning (NPDL) Learning Progressions as a novel method for competency-based self-assessment within the context of Digital Education in the São Paulo Municipal Teaching Network (RME). We examined this assessment approach in a nine-month project involving 56 students (aged 11-12), titled “Projeto Marajoara”, which integrated digital fabrication, museum studies and programming. Based on the NPDL’s six competencies and their respective dimensions, students performed a self-assessment using a Likert scale after project completion. Data was analyzed using descriptive statistics, Spearman correlations, network analysis, and structural equation modeling (SEM), utilizing a mixed-methods approach. Results showed students’ self-assessments concentrated in the mid-range, with meaningful correlations between key competencies. Network analysis identified “global perspective,” “teamwork” and “evaluation of arguments” as central and influential dimensions. SEM revealed that models for Character, Collaboration, Communication and Critical Thinking had acceptable fit. Dominance analysis confirmed key dimensions for their respective competencies. This study contributes to the field by providing new insights into the implementation of the NPDL framework in a constructionist educational context, demonstrating its potential for self-assessment and highlighting the complex interrelations of competencies. These findings also reveal the need to utilize diverse methodological techniques to fully understand the complexities of competency-based evaluation. Future research should extend this analysis to include teacher perspectives and explore other contexts to verify the stability of these results, as our results suggest how such methods can contribute to a more nuanced and meaningful practice of assessment in education.