The Importance of Student Feedback in Higher Education 

In today’s competitive educational landscape, Higher Education Institutions (HEIs) are increasingly recognizing that feedback insights are about more than just the student experience. It’s also crucial for making informed decisions that drive continuous improvement and increased enrollment rates. During a recent webinar hosted by HESPA and Explorance, experts from the University of Westminster, Liverpool John Moores University (LJMU), and the University of Manchester discussed how AI tools like Explorance MLY are empowering their institutions to revolutionize the way they collect and implement student feedback. 

1. Automating Qualitative Analysis for Efficiency

Qualitative analysis is essential for understanding the nuanced feedback provided by students. Unlike quantitative data, which offers numerical insights, qualitative feedback dives deeper into student experiences, emotions, and suggestions. This type of analysis allows HEIs to capture rich, detailed comments and sentiments, which are pivotal in improving the student experience. 

Matthew Abley, Institutional Research Analyst at the University of Westminster, stated, “Using AI to aid our analysis of open-ended comments has created space for a culture shift within the institution towards good data and robust evaluative practices.” This shift has pushed institutions to move beyond surface-level data, uncovering underlying themes in student feedback. 

The University of Westminster’s adoption of MLY highlights a significant transformation in its approach to feedback. Previously, the manual thematic analysis of open comments was a labor-intensive process, often limiting the scope of analysis due to time constraints. This lack of time led to incomplete insights, missing trends, and an overall less effective understanding of student feedback, hindering the university’s ability to make informed improvements. 

Matthew emphasized that integrating AI has not only expedited this process but also enhanced the quality and depth of insights drawn from student comments. This technological advancement has allowed the university to analyze a significantly higher volume of feedback, ensuring that more student voices are not just heard, but also considered in decision-making processes.

2. Gaining Deeper Insight into Student Sentiments

With MLY’s advanced sentiment analysis capabilities, universities can better understand student experiences and sentiments. Those insights allow them to respond more effectively to student needs and optimize the educational environment.  

Fenna Boerkamp, Institutional Research and Evaluation Officer at LJMU, emphasized the effectiveness of MLY in capturing sentiments, particularly in questions that invite students to comment on their feelings.  

“For the National Student Survey (NSS), which specifically asks students to comment on what they feel positive or negative about, MLY has been extremely useful. We first conducted a rapid manual analysis because we wanted to see if MLY was as helpful as we hoped it would be. We found that the themes we identified manually were very much in line with what MLY identified.”   

MLY’s ability to identify and categorize sentiments has proven beneficial for LJMU in developing action plans based on the NSS results. 

3. Enhancing Collaboration and Efficiency in Feedback Analysis 

The capability of MLY to facilitate sharing and collaboration on projects is particularly noteworthy. This feature enables multiple users to work on the same dataset simultaneously, enhancing the efficiency and coherence of the analysis process. 

“We’ve found the ability to share and collaborate on projects incredibly useful, especially in recent weeks,” Fenna highlighted. “Being able to simultaneously review the same dataset and collaboratively interpret findings has streamlined our workflow and improved the accuracy of our analyses.” 

The alert function offered by MLY has also been a significant asset for managing student feedback. Fenna explains:  

“The alert function has been particularly useful for our institution. According to our policy, we must redact all names from negative comments, which means we read all the comments, especially for our module evaluation surveys. The alert function helps us quickly identify and address any concerning comments, ensuring we adhere to our policies while efficiently managing feedback.” 

By enabling collaborative analysis and providing powerful alert functions, MLY enhances the quality and efficiency of feedback analysis and ensures that institutions can address critical issues promptly. These features contribute to a more responsive and adaptive educational environment, promoting inclusivity and equity within the institution. 

4. Future-Proofing Student Survey Strategies

AI tools like MLY are also revolutionizing how universities process large volumes of qualitative data, uncovering patterns and sentiments that might be missed through manual analysis. “MLY derives not only topics but also specific recommendations from comments,” Fenna noted. “It allows us to break down the data by demographics using filters. You can also create widgets for specific demographic groups.” 

This capability enables universities to swiftly identify key issues and areas for improvement, fostering a more responsive and adaptive educational environment. The University of Manchester has also seen significant improvements.  

“This is a complete overhaul for us. We often collect 50,000 comments from one survey, and MLY quickly does the analysis, allowing us to start getting top-level reporting within a couple of hours. It saves me weeks of work,” said Daniel Bayes, Teaching and Learning Officer. 

The ability to handle vast amounts of data quickly and accurately means institutions can keep up with the constant influx of feedback, continually refining their approaches and strategies. “My small but mighty team of three used to recruit student researchers, taking about five to six months to train and deploy them,” Matthew explained. But with MLY, we’ve gone from analyzing around 7,000 comments to about 35,000 comments across multiple datasets in this academic year alone.” 

Acting on student feedback is crucial for creating a responsive culture built on trust. MLY helps HEIs quickly identify actionable insights and implement necessary changes, ensuring that student input leads to tangible improvements. “Our commitment to responsiveness fosters a culture of trust and collaboration between students and the institution,” Fenna said. It’s one thing to open a feedback loop, but it’s another greater challenge to close it with evidence of impact.” 

The scalability and efficiency of MLY are game-changers for institutions managing large volumes of feedback. This rapid turnaround is vital for timely decision-making and demonstrates the potential of AI tools like MLY to revolutionize feedback management in higher education.

5. Enhancing Institutional Responsiveness through AI

Creating an environment where student feedback is valued involves making students feel heard and ensuring their opinions matter. Utilizing AI to analyze feedback rapidly allows for timely and actionable insights, that can lead to meaningful changes. Building this culture requires consistent efforts from the entire university community, including faculty, staff, and administrators, to prioritize and act upon student feedback. 

Institutions can build a stronger, more collaborative community by demonstrating that student feedback leads to real improvements. When students see that their feedback results in tangible changes, they are more likely to continue providing valuable input, further enhancing the quality of education and campus life. 

6. Leveraging AI for a Better Student Experience

As more institutions adopt AI tools like MLY, the landscape of student feedback analysis is evolving. These tools allow universities to analyze feedback quickly and effectively, leading to a more responsive and student-centered educational environment. AI enables institutions to not only gather feedback more efficiently but also understand it in depth, ensuring that every student’s voice is heard and valued. 

Integrating AI in feedback analysis marks a significant step towards achieving a higher standard of education and student satisfaction. By leveraging MLY, institutions can stay ahead of the curve, continuously improving their strategies and fostering a culture of responsiveness and trust. 

In conclusion, AI tools are transforming the way HEIs approach student feedback. Accurately analyzing large volumes of qualitative data empowers institutions to make informed decisions that enhance the student experience.  

As universities continue to embrace these technological advancements, they will be better equipped to meet the evolving needs of their students and create a more inclusive, responsive, and adaptive educational environment. 

For More Detailed Insights and to Hear Directly From the Speakers, Access the Full Webinar Recording Here