More than Words: Unlocking Value in Text-Based Student Feedback

There is an untapped source of insight sitting within higher education institutions and many are starting to realize its potential. Text analytics is an emerging field of institutional research that maximizes qualitative data – captured through open-ended student comments – to unlock key themes that are important to every level of an institution. By applying analytics techniques to textual data, institutional leaders can now have a more complete picture of student feedback. The results can be used to make smarter, more informed decisions that target educational goals. This article explores the potential of text analytics in key areas of higher education institutions.

Application and enrollment

Admission forms and essays create increasing amounts of unstructured data that, if unlocked, can be used to drive important decisions in different departments at an institution. For example, information gathered from a personal essay can be analyzed to help identify students who come from low income environments. The admission and financial aid offices can use this data to target these prospective students by offering scholarships and bursaries. This will help increase the students’ chance of enrolling and the institution’s enrollment rates.

Text analytics can also help predict which factors contribute to academic success. By analyzing comments on an application or essay, students at risk of dropping out can be identified early and timely. Determinants like undiagnosed learning disabilities (e.g. dyslexia, ADHD) cannot be picked up on an answer key, but comments on a personal essay might reveal them. Appropriate support such as tutorials, assistive technology, or transition/bridge programs can be offered to help them succeed.

Retention and progression

Not only do colleges and universities have pressure to attract students, they also need to drive student success. Interaction with students during the life of their academic career is crucial in ensuring that they complete their studies. Many institutions turn to real-time feedback tools to proactively monitor and address the key causes of student attrition. Instructors can strategically collect and analyze student comments about course material on a daily basis and use it to modify their course design. At the course level the manual reading of texts and the withdrawal of insight is simple enough. However, at an institutional level the high volumes of textual data is not as straightforward to analyze and interpret.

Text analytics software like Blue Text Analytics (BTA) allows institutions to sift through unlimited open-ended responses to uncover countless themes and opportunities that would otherwise be buried. The BTA engine uses a combination of custom dictionaries and powerful algorithms to identify themes in student comments rather than key words. Leveraging text analytics empowers institutions to learn what is relevant to each student by providing detail on the reasons behind student dissatisfaction. By transferring that data into structured and quantifiable results, institutions can begin to anticipate issues that may cause student departure and proactively address them.

Tenure and promotions

Qualitative responses on course evaluations can be a rich source of information on teaching quality and are routinely used in instructor reviews, tenure, and promotion deliberations. The major labor for the institution is to transcribe the text data into meaningful insights. BTA will automate the comment processing, saving time and resources. Decision-makers can use the results to look for trends in student opinions about individual instructors over time. They can also compare the aggregate rating of any instructor to the average for his/her department as a whole. With a complete picture of student feedback institutional leaders can confidently make decisions about which candidates will be promoted. Instructors can consider this feedback when preparing for their next course offering – ensuring a better review for the next year.