This research uses a three-phase method to evaluate and forecast the academic efficiency of engineering programs. In the first phase, university profiles are created through cluster analysis. In the second phase, the academic efficiency of these profiles is evaluated through Data Envelopment Analysis. Finally, a machine learning model is trained…

Clustering, a prevalent class of machine learning (ML) algorithms used in data mining and pattern-finding--has increasingly helped engineering education researchers and educators see and understand assessment patterns at scale. However, a challenge remains to make ML-enabled educational inferences that are useful and reliable for research or…

This paper investigates the viewpoints and perspectives of 179 undergraduate students engaged in a co-creation project regarding their anticipated progress, exploring, simultaneously, the affecting factors. The students attended the physics course in the Department of Industrial Design and Production Engineering at the University of West Attica,…