MOOC Optimization Dataset: Learner Feedback and AI-Based Pareto Solutions

This dataset was collected as part of a research study on AI-based multi-objective optimization for MOOC update planning. It contains anonymized learner feedback from the Introduction to Cyber Security MOOC offered on the SWAYAM platform, along with structured candidate updates, optimization outputs, and reproducible code. The dataset includes: MOOC_Feedback_Anonymized.csv – 437 anonymized learner survey responses. Candidate_Updates_Table.csv – Structured update table with learner value, effort, and categories. nsga_outputs.zip – Pareto-optimal solutions (tables and plots) for budgets B = 8, 10, 12, 14, 16. Code.docx – Python implementation of NSGA-II optimization framework. README.txt – Dataset documentation, methodology, and citation. This dataset supports reproducibility of the results presented in the associated research paper and can be reused for further work in educational data mining, learner analytics, and AI-driven decision support.