A deep learning-based career recommendation system using program learning outcomes and TPQI standards in the digital industry - Nongnuch, Krommavut; Piriyasurawong, Pallop (Edelweiss Applied Science and Technology, 2025)
This research aims to (1) assess current and future digital industry demands to align academic learning with industry-required competencies, (2) classify and analyze computer science curriculum components based on Thailand’s Professional Qualification Standards, (3) evaluate digital software-related professional competencies and their alignment with academic courses, (4) develop a decision-support model for career selection based on program learning outcomes (PLOs), (5) implement a web-based platform to support decision-making, and (6) evaluate the predictive accuracy of the model. Findings reveal seven key components of higher education improvement in computer science (mean = 4.92), five key course learning outcomes (mean = 4.87), and seventeen PLO elements (mean = 4.81), all rated at excellent levels. The study integrated the Thai Professional Qualification Institute (TPQI) framework into curriculum development. Data from 200 graduates were analyzed and mapped with current job roles and industry expectations. A predictive model using deep learning was developed and deployed on a browser-based platform. Accuracy evaluation shows that the system aids students in aligning career paths with qualifications and helps industries identify suitable talent. User satisfaction among students was high, indicating practical value in academic and industrial contexts.