Inverted Grading in Project-Based Learning: A Learning Path Approach for Sustainable Performance Evaluation

Abstract

Higher education assessment is traditionally based on cumulative grading models, where students progressively build their final classification over time. However, such approaches often promote risk-averse behaviour and limit continuous improvement.
This paper proposes an inverted grading strategy integrated with learning paths within the TechTeach paradigm. In this model, students are evaluated according to the maximum expected grade of their selected path and assessed on their ability to maintain or recover their performance throughout the course. The strategy combines group and individual evaluation, enabling effective differentiation of student contributions in project-based learning environments.
The approach was validated through a case study conducted in a Web Programming course with 153 students. The results show a dynamic evolution, with an average decrease from 16.31 at the first control point to 15.34 at the second, followed by a recovery to 16.05 in the final evaluation. The overall variation (-0.26, or -1.6%) indicates that outcomes remain close to initial performance despite intermediate fluctuations. Additionally, 62% of students experienced a decrease in grade, 28% improved their performance, and 10% maintained stable results, confirming the model’s ability to capture performance dynamics and support recovery.
Student feedback further reinforces the approach’s applicability, with more than 80% of responses indicating acceptance of the model. 
Overall, the results suggest that inverted grading offers a robust alternative to traditional cumulative models, promoting sustainable performance, accountability, and continuous engagement in higher education.