International Journal of Science, Engineering and TechnologyAuthors: G. Vedavyas, G. Hareesh, M. Ajith Kumar Reddy
Abstract: The proliferation of online educational content has deepened learner disorientation rather than alleviating it. Despite millions of instructional resources available across platforms such as YouTube, Coursera, and Khan Academy, the absence of a unified, domain-aware guidance mechanism forces learners into fragmented self-directed journeys. This paper presents the DomainBased Career Roadmap Management System (DCRMS), a webbased educational technology platform that integrates structured roadmap generation, AI-assisted personalized recommendation, NLP-based skill extraction, and learning progress tracking into a single coherent interface. The proposed architecture combines a rule-based domain engine with a hybrid recommendation pipeline that uses collaborative filtering, content-based similarity scoring, and a Directed Acyclic Graph (DAG) traversal model to construct prerequisite-aware learning paths. A Skill-Gap Computation Module employs cosine similarity between user-skill vectors and domain competency matrices to identify and prioritize learning deficiencies from uploaded resumes. The prototype recommendation engine achieves a Precision@10 of 0.847, Recall@10 of 0.791, and an F1-score of 0.818 across seven IT career domains in experimental evaluation. Under simulated load of 50 concurrent users, roadmap generation latency averages 318 ms. A structured usability study with 50 undergraduate participants yields a System Usability Scale (SUS) score of 82.4 (Grade B+), confirming strong user acceptance. DCRMS constitutes a reproducible proof-of-concept contribution to educational data mining, career guidance, and intelligent recommender systems for the informal learning context.
