Blog inspired by #SAChat tweet: “FT: I wish I had more connected data across areas to trace student progression & impact of respective services. (pt 1)#sachat #saassess”
Universities collect SO much data!
Even before students step foot on campus for their first classes, institutions already have demographic information, cognitive factors, and involvement information on incoming students. Once classes start, institutions have grades, academic usage behaviors, and – lest we forget – surveys, surveys, surveys! Metrics, measures, and statistics per student exponentially increase with every course and campus activity.
All this data can prove pretty impactful. Institutions are looking at cognitive and non-cognitive factors to create prediction models generating early alerts for student failure or withdrawal. Systems can use student interests to predict appropriate campus involvement or student leadership opportunities to enhance student experience. Pairing service usage with survey feedback can prompt faculty or staff intervention to follow up on a poor student experience or provide additional support.
Data carries power when it is used. Data power is strengthened when paired with other data. Unfortunately, data typically remains separated and, thus, unrealized in their potential. While this can occur for a variety of reasons, here are three common reasons:
It’s not easy. It can be difficult to quantifiably combine the data. For example, GPA values do not automatically crosswalk or aggregate with survey responses.
Data isn’t interpreted. Faculty and staff collect data and may share reports but do not reflect on what results mean. They do not take time to translate data into information in order to enable action.
Silos are alive and well. Even when they are taking the time to execute on the previous two points, keeping to themselves prevents the involvement of stakeholders who could provide complementary data or take additional action.
Here are a few steps to prevent or work to counteract these effects:
Always check for existing data. Before you or others collect new data, check to see what you have available – and not just in your department. Ask around; you’ll be surprised what is already available!
Chart the student lifecycle. Outline the path a student takes as they progress through the university. Identify what helps or hurts them and the university interventions (passive or active) along the way. List possible data points for each and consider this data in relation to assessment, retention, and persistence efforts.
Use data to tell a story. Look to use individual and combined data to describe different types of students or predictive consequences of students under certain circumstances. With data from everything a student does or thinks, there are a large number of stories to be told!
Question existing gaps. Thinking about those student stories, be critical of where there are plot holes. Think about why the holes or gaps exist and brainstorm data that might connect one dot to the next.
Set aside time for analysis and reporting. You shouldn’t collect data if you don’t have time to report your findings. Reporting means more than aggregating numbers; explanations and interpretations in a narrative add meaning to your findings.
Share often. Don’t let your data hide on a shelf; share it, use it, and allow it fuel action. Every assessment effort has multiple stakeholders – challenge yourself to share effectively to each of them.
Several institutions are doing pretty remarkable things connecting data sets, expanding the conversation to include big data efforts. If that seems intimidating, know there are simple combinations of complementary data sets institutions can leverage to better understand and intervene to improve the student experience. With data on so many student behaviors and outcomes, the first step is digging into that information to better understand it. Challenge your peers to take the time to interpret the data and share it, so as to not deprive the rest of the institution from that data. Benign information may gain significant importance or use when paired with complementary data.
Allow me to summarize with a variation of math’s Transitive Property of Equality (if a = b, and b = c, then a = c): I believe data is equivalent to power, and power comes with responsibility, so data comes with responsibility. As such, please collect with a conscience and act with accountability. Do this for you, your colleagues, and the students you serve.
> BONUS <
Podcast With Kedrick Nicholas on Assessment of Student Programming