MOOC Renewal Dataset and Code for VIKOR Optimization

his dataset and code archive supports the research article titled "Optimizing MOOC Renewal Decisions with VIKOR for Evidence-Based and Sustainable Course Design." It contains real learner feedback from a SWAYAM-based MOOC (N=437), used to evaluate and rank 26 candidate course updates. The study applies the VIKOR multi-criteria decision-making (MCDM) method, comparing it with a greedy value-effort heuristic and an instructor-defined plan, under varying budget constraints. Included are CSV files representing raw learner-derived inputs, normalized decision matrices, baseline comparisons, and ranked selections. Additionally, the code for computing VIKOR scores, generating rankings, and visualizing outcomes is provided in Python/Jupyter format. This open dataset is intended to support reproducibility, promote transparent instructional design decisions in MOOC platforms, and facilitate further research in learner-centric course improvement frameworks. Keywords: MOOC renewal, VIKOR, learner analytics, decision-making, MCDM, instructional design, SWAYAM, educational technology.