Fail-Safe Project-Based Learning for Computer Vision: A Reproducible, Failure-Informed, and Accountability-Centred Design for Robotics Education

Didik Iskandar

doi:10.55927/fjmr.v5i8.170

Authors

Didik Iskandar Universitas Cendekia Abditama

DOI:

https://doi.org/10.55927/fjmr.v5i8.170

Keywords:

Computer Vision, Project-Based Learning, Reproducibility, Authentic Assessment, Robotics Education

Abstract

Computer Vision courses often reward code that runs while under-assessing whether students can explain assumptions, reproduce experiments, diagnose failures, or manage risks when visual outputs drive robotic actions. This conceptual design study develops FAIL-SAFE PjBL, an eight-stage learning architecture: Frame the decision, Audit data, Isolate variables, Log failures, Stress-test conditions, Account for individual mastery, Formalize fail-safe behavior, and Explain reproducibly. The framework was produced through curriculum mapping, integrative literature synthesis, artifact prototyping, and an internal alignment audit. Its outputs include milestone-based projects, experiment logs, error portfolios, data and algorithm cards, latency/FPS profiling, safety gates, and individual viva. The design shifts assessment from polished demonstrations to traceable engineering judgment and provides a testable blueprint for future classroom action research.

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