On the Design of Unstructured Student Feedback Summarization Model using Transformer Architecture for Quality Education

In the past few years, the Universities across the world has increased their focus on teaching-learning process improvisation. In this regards, the Universities periodically collect the students’ feedback through pre-defined questionnaire using online tools such as Google forms. However, such pre-defined questionnaire does not allow the students to express their opinion outside the questions. Therefore, students unable to convey their true feedback and the overall purpose of the feedback mechanism collapses. The alternative mechanism is to allow students to write a free style feedback within certain word limits. However, it is highly challenging to summarize such freestyle feedback manually to know the overall conclusion of the entire class. In this paper, we have engaged a Bidirectional Encoder Representations from Transformers to design an extractive summarization model for unstructured student feedbacks to improve the quality of educations. The proposed model allows the students to provide feedback about the course and also allow the course faculties to be able to summarize received student feedbacks in its true spirit by extracting the key ideas from students’ feedback. The experimental results show that the if produced summaries are too small then it unable to include all the important aspects. However, when produced summaries are sufficient in size then it successfully captures all the important aspects with Precision BERT , Recall BERT , and Fscore BERT of 78%, 61%, and 0.61%, respectively. The proposed model is compared with three existing schemes and it provides the improved results for Precision BERT , Recall BERT , and Fscore BERT .