From Data to Pathways: Personalizing Learning Experiences
summary
A lightweight recommendation engine maps 10-question submissions to three learner clusters using KMeans clustering, scikit-learn pipelines, pandas cleaning and PCA visualization. The dataset has 63 respondents and yields a silhouette score 0.09, so clusters are soft but interpretable; mode-based profiling produces three personas (49.2% beginners with clear goals, 36.5% misguided newcomers, 14.3% advanced needing guidance). The system returns curated course paths from a course library; deployed live it generated two direct responses, one enthusiastic and one uncomfortable, so recommendations are delivered without exposing persona labels.
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chapters (8)
- 0:00Introduction and background
- 1:19Personalized learning challenges
- 4:40Building the recommendation engine
- 10:05Creating learner personas
- 16:40Testing and refining recommendations
- 18:32Python's role in implementation
- 19:18Live demo of the code
- 25:49Conclusion and Q&A
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