Applying Bayesian neural network to evaluate the influence of specialized mini projects on final performance of engineering students: A case study

Abstract

In this article, deep learning probabilistic models are applied to a case study on evaluating the influence of specialized mini projects (SMPs) on the performance of engineering students on their final year project (FYP) and cumulative grade point average (CGPA). This approach also creates a basis to predict the final performance of undergraduate students based on their SMP scores, which is a vital characteristic of engineering training. The study is conducted in two steps: (i) establishing a database by collecting 2890 SMP and FYP scores and the associated CGPA of a group of engineering students that graduated in 2022 in Hanoi; and (ii) engineering two deep learning probabilistic models based on Bayesian neural networks (BNNs) with the corresponding architectures of 8/16/16/1 and 9/16/16/1 for FYP and CGPA, respectively. The significance of this study is that the proposed probabilistic models are capable of (i) providing reasonable analysis results such as the feature importance score of individual SMPs as well as an estimated FYP and CGPA; and (ii) predicting relatively close estimations with mean relative errors from 6.8% to 12.1%. Based on the obtained results, academic activities to support student progress can be proposed for engineering universities.