An AI-driven empirical analysis of educational perceptions from the Congolese academic ecosystem using NLP techniques

Abstract Education typically brings together three primary stakeholders: the government, teachers, and students. These actors collectively form the foundation of a country’s educational system, each contributing in their way to the enhancement of education quality. However, student voices are often underrepresented in decisions regarding teaching standards, a situation particularly evident in the academic ecosystem of the Democratic Republic of Congo (DRC). One major gap in the DRC’s educational landscape is the lack of a comprehensive report reflecting computer science students’ feedback on key aspects of teaching quality. This study seeks to fill that gap by leveraging Natural Language Processing (NLP) techniques to analyze student perceptions of computer science education, using responses to open-ended survey questions focused on infrastructure and teaching quality, faculty, and curriculum. Sentiment analysis was applied to gauge the emotional tone of responses from students across 18 leading universities and higher educational institutions in the DRC, revealing a plurality of positive sentiment, though accompanied by substantial neutral responses (29.3% to 44.4%) and notable concerns regarding curriculum (21.8% negative). Furthermore, topic modeling was used to identify recurring themes in the feedback, highlighting two dominant concerns: learning process and pedagogical conditions, and academic system and structural reform, with the latter reflecting student perceptions of challenges related to the recent LMD system implementation. The findings of this work offer valuable guidance for the Congolese government and academic institutions seeking to enhance the country’s educational system.