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The tech sector has been growing at a rapid speed, demanding a higher level of expertise from its labor force. New skills and programming languages are introduced and required by the industry every day, while the university courses are not updated adequately. Finding the high-demand skills and relevant courses to study has become essential to both students and faculty members at tech universities, which leads to a growing research interest in building an intelligence system to support decision making. Leveraging recent development in Natural Language Processing, we built an NLP-based course recommendation system specifically for the computer science (CS) and information technology (IT) fields. In particular, we built (1) a Named Entity Recognition (CSIT-NER) model to extract tech-related skills and entities, then used these skills to build (2) a personalized multi-level course recommendation system using a hybrid model (hybrid CSIT-CRS). Our CSIT-NER model, trained and fine-tuned on a large corpus of text extracted from StackOverflow and GitHub, can accurately extract the relevant skills and entities, outperforming state-of-the-art models across all evaluation metrics. Our hybrid CSIT-CRS can provide recommendations on multiple individualized levels of university courses, career paths with job listings, and industry-required with suitable online courses. The whole system received good ratings and feedback from users from our survey with 201 volunteers who are students and faculty members of tech universities in Australia and Vietnam. This research is beneficial to students, faculty members, universities in CS/IT higher education sector, and stakeholders in tech-related industries.
Vo et al. (Fri,) studied this question.