Propose a Recommender System to Dynamically Align Higher Education Curriculums With 4IR Market Needs

The Fourth Industrial Revolution (4IR) era leads to significant economic shifts due to technological advancement. The changes introduced by 4IR raise concern about its impact on employment due to jobs automation, or lack of workforce equipped with the required skills. It is essential to reshape the curriculums according to 4IR requirements to mitigate unemployment risks. The objective of this paper is to automatically identify the rapidly changing 4IR jobs skills and identify the gap between curriculums and modern industry jobs as well. In addition, the proposed solution has the capability of delivering efficient gap analysis recommendations that could be used in the curriculum enhancement decision-making process. The proposed recommender system clusters jobs based on K-Means and TF-IDF algorithms and then identifies the similarity and dissimilarity by utilizing the Cosine Similarity algorithm. The solution utilizes the algorithm's result to construct gap analysis recommendations that curriculum developers can use to align curriculums with 4IR requirements. The results of the classification report indicated that the system was effectively capable of clustering jobs based on skills similarity and identifying the 4IR gap.