Student behaviours and performance in the online learning environment are becoming more apparent through online learning activities and student data. Often, however, these data points are not fully utilized for use in career or academic advising. To connect assessment of students’ academic progress to the employability of students, an Intelligent Student Performance Tracker and Job Recommendation System is proposed in this paper using educational data mining, machine learning, and natural language processing (NLP) capabilities. The performance tracker registers and examines student activity within a learning management system (LMS), as well as student academic records, to identify students’ skill strengths and weaknesses. The performance tracker then cross-analyzes students’ skill qualities against key job roles in the current job market to identify fit scores and missing competencies. With this skill set comparison in mind, the system further recommends relevant online courses to bolster students’ employability. Furthermore, the performance tracker generates a student report card summary utilizing a BART transformer model. Results indicate that the performance tracker helped improve visibility into student performance in addition to providing actionable growth opportunities.