Higher education values assessment. Pre-tests, post-tests, program evaluations, surveys, and everything in between.
These tools help administrators make informed decisions about their institution’s growth and direction at both micro and macro levels. The value of assessment is also demonstrated by the passion faculty and students pour into researching their areas of interest to advance learning and understanding. In Higher Ed, data is definitely king.
In the realm of tutoring programs and services, data collection is woefully behind. We know students need academic support and, often times, great support is provided, but what is being measured? Research shows the value of peer tutoring and the free-agency of students to determine their tutoring schedule is more impactful than it being prescribed by advisors.
This information is helpful and important; but, what if we could know more?
There is an opportunity to harness more power from tutoring data to better drive the development of curriculum, students, and faculty. Beyond measuring the effectiveness of tutoring itself, deeper layers of assessment tutoring programs are missing in their efforts. How could this be useful and serve a broader purpose?
What if tutoring assessment could provide insight into academic program needs? Learning which classes or professors prompt students to seek help and at what times in the semester could be invaluable in readying resources, normalizing help-seeking behavior for students, and improving curriculum delivery. Such information might also better shape prerequisite/corequisite course pathways. For example, understanding that students who had already taken “course x” seek tutoring less in “course y” than those who did not take “course x” could help inform course pathways in a meaningful way.
Tutoring data like this also enables administrators to evaluate where dollars should be allocated on additional support for high-demand courses. It provides an opportunity to build bridges between what an academic program aspires to achieve and what it is able to achieve by filling in gaps with better informed resources and support to create optimal learning to drive student success.
Useful tutoring data also has the potential to help administrators better understand the “consumer mindset” of students. This is especially helpful to ensure the program leans into the style and preferences of Generation Z. When and where students like to meet for tutoring services, how frequently they like to meet, and at what point in a semester they seek tutoring for certain courses (beginning of course, after first exam, before midterms/finals) is the kind of data that illuminates the behavior of students utilizing tutoring. Assessing student use of tutoring services also allows the opportunity to find out student FAQs in each course depending on what point in the semester they seek help. Understanding general themes here may prove helpful to tutoring efforts or to faculty initiatives looking to meet students where they are in their learning process in future semesters.
What would be possible if we better understood the outcomes associated with tutoring? Only data can make this possible. Looking at the hours of tutoring used by a student or the cadence of tutoring (weekly, bi-weekly, etc.) compared to a student’s performance in a course can show a level of impact. This kind of data also gives us important information related to tutors themselves, which can be an overlooked area of assessment. Examining the qualifications and experience level of a tutor compared to a student’s performance in a course may help inform future pairings. The general efficacy of tutors or examining the effectiveness of tutors who have received tutoring themselves are also areas of value to consider. Lastly, another outcome associated with tutoring worth exploring is the performance of students in their future courses in correlation with tutoring they received in their prerequisites.
With good data, there is the power to provide better academic support to students. Insights gained from further understanding can lead an institution to new levels of success. Imagine being able to predict student outcomes more accurately so interventions can take place early enough to make a difference. Rather than simply suggesting tutoring, faculty might be able to suggest a time and cadence to students of tutoring they can use as a reference point. Certainly “check out our tutoring services” is a very different statement from “80% of students in this course who seek tutoring support within the first two weeks of class go on to to perform at a B+ or higher.”
Overall, increased data collection and analysis gives us the power to shape curriculum more effectively and efficiently, resulting in a better learning experience for our students.