With the increasing numbers of elective courses at universities and the Massive Open Online Courses (MOOCs), Software Engineering (SE) students are facing challenges in selecting their study paths in tech. On the other hand, the skills in SE-related fields have been changing significantly for the past decade, which requires more frequent updates to the curriculum and teaching materials. There is a strong demand for a better course guide and recommendation system to aid higher education in SE to keep up with the industry requirements. In this work, we incorporate data mining techniques, a natural language processing model, and a recommendation system in a web application that helps SE students and university faculty with those challenges. Our proposed hybrid Course Recommendation System (CRS) consists of two web applications (user and admin web apps) to provide multiple features, including user-specific suggestions for university courses, careers, jobs, industry-demanded skills together with online materials to learn those skills, and various analysis dashboards for both SE students and lecturers. We conduct a survey on SE students and faculty members to evaluate the initial impact of our CRS on the end users, which proved the effectiveness of our approach in addressing the mentioned issues. Demo: CRS User Web App and CRS Admin Web App.

@inproceedings{VoVVVM22,
  title = {CRS},
  author = {Nhi N. Y. Vo and Nam H. Vu and Tu A. Vu and Quang T. Vu and Bang D. Mach},
  booktitle = {ICSE},
  year = {2022},
  doi = {10.1145/3510456.3514141}
}