This repository contains the replication code, data structures, and the final research paper evaluating the social dynamics and structural evolutions between two consecutive editions of the MOOC-Ed course Digital Learning and Teaching (DLT1 and DLT2).
Given the evolution of the MOOC-Ed DLT course design between DLT1 and DLT2, to what extent do the participants' profile characteristics (Professional Role, Experience, and Gender) associate with their network dynamics (Participation and Centrality), and how does this association differ between the two editions?
The repository is organized as follows:
/notebooks: Contains the Google Colab Python notebook (ENG_Giacometti_Analisi_SNA_MOOC.ipynb) covering the entire computational pipeline:- Exploratory Data Analysis (EDA)
- Network Construction and Centrality Metric Calculations
- Ordinary Least Squares (OLS) Regressions
- Statistical Visualizations
/paper: Contains the complete research paper (Giacometti_2026_SNA_MOOC.pdf) detailing the theoretical background, methodology, structural findings, and socio-educational implications.
The analysis was performed using Python 3 and the following core data science libraries:
- NetworkX – Network creation, topology analysis, and centrality metrics.
- Statsmodels – OLS Regression modeling and statistical inference.
- Pandas & NumPy – Data ingestion, cleaning, and structural alignment.
- Matplotlib & Seaborn – Graph visualizations and regression plots.
- Open the notebook located in the
/notebooksfolder. - Click on the "Open in Colab" badge (or upload the
.ipynbdirectly to Google Colab). - Ensure the initial cells properly pull the four essential source datasets (Edgelists and Nodelists) hosted on Google Drive.
- Run all cells sequentially (
Runtime > Run all) to reproduce the statistical summaries, regressions, and plots.