Abstract—In strengthening educational policy, it is crucial to understand recipients’ feedback and experiences. This paper presents an innovative method for generating contextual analysis of qualitative responses from the beneficiaries of a free education program. We employed a two-tier clustering approach using the K-means algorithm and Louvain community detection, based on enhanced word embeddings and a bi-gram network. Our methodology extends beyond traditional text analysis by combining Word2vec and Glove embeddings, which captured the semantic meaning of words within the dataset. Through the application of the K-means algorithm, we identified five distinct clusters with a notable silhouette score of 0.3477, corresponding to themes of “Support and Educational Opportunity”, “Accessibility and Financial Relief”, “Gratitude and Satisfaction”, “Positive Evaluation with Suggestions for Improvement”, and “Program Effectiveness”. Further refinement of the clusters with the Louvain method, coupled with the use of a bi-gram text network instead of a uni-gram, achieved a higher modularity score of 0.637. While the transition from K-means clusters to Louvain communities resulted in slight thematic changes, it provided a more comprehensive view of the relationships among different response aspects. The two-tier clustering method highlights the methodology’s strengths and effectiveness in revealing hidden patterns and themes in the text responses. The findings not only highlight the strengths of the free education program in providing support to the beneficiaries but also reveal certain areas needing attention and improvement, which are crucial in policy development and enhancement.
 
Keywords—enhanced word embedding, clustering, community detection, text analysis, quality tertiary education, program evaluation


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