At the end of each semester, once the students finish their courses, they are asked about the overall feedback related to teacher performance and course suitability. This activity is carried out in almost all the academic institutions worldwide. Teachers get feedback from students about their teaching, behaviour, assessment, course content and its usefulness for the relevant degree program. Anyone who has taken part in a university or college course is familiar with the procedure. But have you ever wondered what happens to the feedback?
The feedback is manually analysed by the quality assessment department of a university to understand what aspects of the teacher and course students are talking about along with their opinions, so that necessary improvements can be made. Particularly, the department is always interested in extracting the below list of information for each single feedback from students and aggregate it into new knowledge.
- Teacher Aspects → Assessment, Behaviour, Experience, General, Knowledge, Teaching Skills
- Course Aspects → Content, General, Learning Material, Pace, Relevancy
- Opinions → Positive, Negative, Neutral
Most of the time, the feedback is processed manually to extract the above information which is quite time consuming and requires a lot of human effort.
LITHME STSM grantee Sarang Shaikh is a PhD student at the Norwegian University of Science and Technology (NTNU), Gjøvik, Norway. The aim of this short-term scientific mission at the Sukkur IBA University, Pakistan, was to meet and work with Prof. Dr. Sher Muhammed Daudpotta, Director, Dept. of Quality Enhancement Cell (QEC) along with his team. During this short term scientific mission, Sarang Shaikh learned how the dept. of QEC processes students’ feedback manually to evaluate faculty teaching performance and course content quality. With a background in artificial intelligence, natural language processing, machine learning, deep learning, and learning technologies, he hoped to build a system that makes processing student feedback forms automated. Particularly, his expectation was to build an automated system which will be able to classify the feedback based on the above defined categories: teacher, course aspects and opinion.
Shaikh says that he hopes to ease the QEC department’s burden with his solution. The department should have more time for other relevant tasks, instead of going through all the feedback manually. This project fits well into LITHME’s goals to bring together and further dialogue between experts from the fields of language and technology.
Sarang Shaikh worked on his STSM for the duration of more than one month at the host institution. He was able to accomplish all the goals defined at the start of the STSM. In Shaikh’s application, the list of student’s feedback is provided as an input; the developed application extracted all the information and presented it in the aggregated way for the department to understand student’s thoughts for specific teacher/course. To see more detailed information of the developed application, please look into Shaikh’s scientific report. A link to all scientific reports can be found below.
Sarang Shaikh visited Sukkur IBA University, Pakistan, from 10/09/2022 to 25/10/2022 , his scientific report has been published on the LITHME website. To see all short term scientific mission reports click here. Sarang Shaikh was interviewed by LITHME assistant Enni Kuusela.
