Improving Graduate Outcomes by Identifying Skills Gaps and Recommending Courses Based on Career Interests

• URL: http://arxiv.org/abs/2511.09819v1
• Date: Fri, 14 Nov 2025 01:11:05 GMT
• Title: Improving Graduate Outcomes by Identifying Skills Gaps and Recommending Courses Based on Career Interests
• Authors: Rahul Soni, Basem Suleiman, Sonit Singh,
• Abstract summary: This paper proposes the design and development of a course recommendation system.<n>It uses data analytics techniques and machine learning algorithms to recommend courses that align with current industry trends and requirements.<n>The proposed system could be a useful tool for students, instructors, and career advisers to use in promoting lifelong learning and professional progression.
• Score: 0.09558392439655013
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper aims to address the challenge of selecting relevant courses for students by proposing the design and development of a course recommendation system. The course recommendation system utilises a combination of data analytics techniques and machine learning algorithms to recommend courses that align with current industry trends and requirements. In order to provide customised suggestions, the study entails the design and implementation of an extensive algorithmic framework that combines machine learning methods, user preferences, and academic criteria. The system employs data mining and collaborative filtering techniques to examine past courses and individual career goals in order to provide course recommendations. Moreover, to improve the accessibility and usefulness of the recommendation system, special attention is given to the development of an easy-to-use front-end interface. The front-end design prioritises visual clarity, interaction, and simplicity through iterative prototyping and user input revisions, guaranteeing a smooth and captivating user experience. We refined and optimised the proposed system by incorporating user feedback, ensuring that it effectively meets the needs and preferences of its target users. The proposed course recommendation system could be a useful tool for students, instructors, and career advisers to use in promoting lifelong learning and professional progression as it fills the gap between university learning and industry expectations. We hope that the proposed course recommendation system will help university students in making data-drive and industry-informed course decisions, in turn, improving graduate outcomes for the university sector.

Related papers

• A Survey of Real-World Recommender Systems: Challenges, Constraints, and Industrial Perspectives [29.526174878343742]
  We provide a systematic review of industrial recommender systems and contrast them with their academic counterparts.<n>We highlight key differences in data scale, real-time requirements, and evaluation methodologies.<n>We then examine how industry practitioners address these challenges in Transaction-Oriented Recommender Systems and Content-Oriented Recommender Systems.
  arXiv  Detail & Related papers  (2025-09-07T10:29:41Z)
• Towards a Real-World Aligned Benchmark for Unlearning in Recommender Systems [49.766845975588275]
  We propose a set of design desiderata and research questions to guide the development of a more realistic benchmark for unlearning in recommender systems.<n>We argue for an unlearning setup that reflects the sequential, time-sensitive nature of real-world deletion requests.<n>We present a preliminary experiment in a next-basket recommendation setting based on our proposed desiderata and find that unlearning also works for sequential recommendation models.
  arXiv  Detail & Related papers  (2025-08-23T16:05:40Z)
• Continual Recommender Systems [47.467562063027195]
  Current tutorials on machine learning do not address recommendation-specific demands.<n>We begin by reviewing the background and problem settings, followed by a comprehensive overview of existing approaches.<n>We then highlight recent efforts to apply continual learning to practical deployment environments.
  arXiv  Detail & Related papers  (2025-07-05T02:20:15Z)
• Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation [85.52251362906418]
  This tutorial explores two primary approaches for integrating large language models (LLMs)<n>It provides a comprehensive overview of generative large recommendation models, including their recent advancements, challenges, and potential research directions.<n>Key topics include data quality, scaling laws, user behavior mining, and efficiency in training and inference.
  arXiv  Detail & Related papers  (2025-02-19T14:48:25Z)
• A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions [16.00188808166725]
  recommender systems have become increasingly influential in shaping user behavior and decision-making.<n>Widespread adoption of machine learning models in recommender systems has raised significant concerns regarding user privacy and security.<n>Traditional machine unlearning methods are ill-suited for recommendation unlearning due to the unique challenges posed by collaborative interactions and model parameters.
  arXiv  Detail & Related papers  (2024-12-17T11:58:55Z)
• Deep Adaptive Interest Network: Personalized Recommendation with Context-Aware Learning [0.3495246564946556]
  This paper proposes a novel model called the Deep Adaptive Interest Network (DAIN)<n>DAIN dynamically models users' interests while incorporating context-aware learning mechanisms to achieve precise and adaptive personalized recommendations.<n> Experiments conducted on several public datasets demonstrate that DAIN excels in both recommendation performance and computational efficiency.
  arXiv  Detail & Related papers  (2024-09-04T04:12:22Z)
• Course Recommender Systems Need to Consider the Job Market [16.88792726960708]
  This paper focuses on the perspective of academic researchers, working in collaboration with the industry, aiming to develop a course recommender system that incorporates job market skill demands. We outline essential properties for course recommender systems to address these demands effectively, including explainable, sequential, unsupervised, and aligned with the job market and user's goals. We introduce an initial system that addresses some existing limitations of course recommender systems using large Language Models (LLMs) for skill extraction and Reinforcement Learning (RL) for alignment with the job market.
  arXiv  Detail & Related papers  (2024-04-16T19:52:57Z)
• Embedding in Recommender Systems: A Survey [54.55152033023537]
  This survey presents a comprehensive analysis of advances in recommender system embedding techniques.<n>In matrix-based scenarios, collaborative filtering generates embeddings that effectively model user-item preferences.<n>We introduce emerging approaches, including AutoML, hashing techniques, and quantization methods, to enhance performance.
  arXiv  Detail & Related papers  (2023-10-28T06:31:06Z)
• Recommender Systems in the Era of Large Language Models (LLMs) [62.0129013439038]
  Large Language Models (LLMs) have revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI) We conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting.
  arXiv  Detail & Related papers  (2023-07-05T06:03:40Z)
• Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation [49.85548436111153]
  We propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC) SRC formulates the recommendation task under a set-to-sequence paradigm. We conduct extensive experiments on two real-world public datasets and one industrial dataset.
  arXiv  Detail & Related papers  (2023-06-07T08:24:44Z)
• KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting [52.623349754076024]
  We provide an overview of the recommendation approaches integrated in KnowledgeCheckR. Examples thereof are utility-based recommendation that helps to identify learning contents to be repeated in the future, collaborative filtering approaches that help to implement session-based recommendation, and content-based recommendation that supports intelligent question answering.
  arXiv  Detail & Related papers  (2021-02-15T20:06:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.