A Random Forest Model for Prediction of Software Engineering Skill Set among Computer Science Students through Explainable AI - Jasmin Nizar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Student skill evaluation is an essential part of education as it gives information on each student's unique talents, strengths, abilities, and areas for development. The purpose of skill-based education in software engineering is to close the knowledge gap between courses of study and industry demands, so graduates can make valuable contributions in a professional software development setting. This method emphasises the value of actual skills in addition to academic knowledge which is in line with the dynamic and quick evolving nature of the software business. A well-rounded set of Soft skills, Life skills, and Technical skills is frequently cited as the reason for the success of individuals working on software development projects. In today's educational landscape, predicting students' skill sets is imperative, encompassing a spectrum of capabilities ranging from Soft and Life skills to Technical expertise. Achieving equilibrium among these proficiencies is crucial for excelling in the ever-changing and cooperative milieu of software development endeavors. This research introduces a novel predictive framework leveraging Random Forest (RF) Algorithm, Principal Component Analysis (PCA) and Explainable Artificial Intelligence (XAI) for software engineering students skillset prediction. The purpose of Random Forest in skillset prediction is to enhance predictive accuracy and robustness by aggregating the outputs of multiple decision trees. To further optimize the efficiency of the proposed model, this study incorporates Principal Component Analysis that ensures the extraction of high-quality and relevant features. Additionally, the study employs Explainable AI techniques using SHAP to identify key features crucial for accurate predictions. The performance of the proposed classification model is evaluated using metrics like accuracy, precision, recall, F1 score, and the Area Under Curve (AUC) value. The simulation results indicate that the recommended PCA-enhanced Random Forest using the XAI model exhibits superior predictive accuracy compared to the baseline machine learning models.