A Hybrid Text Summarization Technique of Student Open-Ended Responses to Online Educational Surveys

This study introduces a hybrid text summarization technique designed to enhance the analysis of qualitative feedback from online educational surveys. The technique was implemented at the Hellenic Open University (HOU) to tackle the challenges of processing large volumes of student feedback. The TextRank and Walktrap algorithms along with GPT-4o mini were used to analyze student comments regarding positive experiences, study challenges, and suggestions for improvement. The results indicate that students are satisfied with tutor–student interactions but concerns were raised about educational content and scheduling issues. To evaluate the proposed summarization approach, the G-Eval and DeepEval summarization metrics were employed, assessing the relevance, coherence, consistency, fluency, alignment, and coverage of the summaries. This research addresses the increasing demand for effective qualitative data analysis in higher education and contributes to ongoing discussions on student feedback in distance learning environments. By effectively summarizing open-ended responses, universities can better understand student experiences and make informed decisions to improve the educational process.