ICT Adoption in Education: Unveiling Emergency Remote Teaching Challenges for Students with Functional Diversity Through Topic Identification in Modern Greek Data

This study explores topic identification using text analysis techniques in Modern Greek interviews with parents of students with functional diversity during Emergency Remote Teaching. The analysis focused on identifying key educational themes and addressing challenges in processing Greek educational data. Machine learning models, combined with Natural Language Processing techniques, were applied for topic identification, utilizing cross-validation and data balancing methods to enhance reliability. The findings revealed the impact of linguistic complexity on topic modeling and highlighted the educational implications of analyzing qualitative data in this context. Among the models tested, the Naïve Bayes (Kernel) algorithm performed best when combined with lemmatization-based preprocessing, confirming that text normalization significantly enhances classification accuracy in Greek educational data. The proposed framework contributes to the analysis of qualitative educational data by identifying key parental concerns related to Emergency Remote Teaching. It demonstrates how text analysis techniques could support data-driven decision-making and help guide policy development for the inclusive and effective integration of Information and Communication Technology in education.