A colleague of mine attended a day long symposium related to student performance at their institution. Data analytics groups from various student assistance programs presented the results of multiple-year studies illustrating how different student subgroups performed in core and major classes. The presenters showed informative plots, tables of summarized data, and indicated the students most at risk of poor performance (low grades, failing classes, or not graduating). The analytics groups’ ability to identify the at risk students is impressive. Unfortunately my colleague left the symposium dejected. While the analysts adeptly indicated the at risk students they did not provided methods of improving the possibility for student success other than “the instructors and professors need to do more.” The suggestion of “doing more” is not a solution. What the analytics groups did not put themselves in the position of the instructor and develop potential ways to better engage the students; the problem was identified (why some students do poorly) but a collection of viable solutions was not provided.
Initially I shared by colleague’s frustration with the symposium. But slowly my frustration has shifted to the group leaders and symposium organizers. Each group’s analysis was spot-on. The indicated cohorts were struggling and the indicated reasons were plausible. Unfortunately suggestions were missing on how to improve students’ learning, comprehension, and retention by reallocate resources or develop new ways to engage the students. The analysts presenting their findings at the symposium were likely not provided with the ability -- or the requirement -- to develop solutions beyond those they provided or did not fully understand their audience (instructors and professors) and the constraints placed on them due to their teaching, administrative, and research obligations.
Data science -- and to some extent data analytics groups -- need to remember the goal of their projects is to provide insight to the questions posed along with probable and reliable solutions. Remember, analysis of the data is integral to solving a problem but the solution is paramount.