By Ha Diep, Lead Math Specialist

In the LEC, I help MCNY students solve complex equations in addition to overcoming their fears about all things related to math. There is really no limit to my quantitative-based work. Anyone who has passed through the LEC knows that we are devoted to supporting our students’ every academic need.

My colleagues and I do a lot of other things in support of our tutoring efforts. We talk with professors, promote our services, and develop learning materials for students. One aspect of the LEC that I, personally, have always worked with is data.

Capturing important information

LEC data is mostly in the form of “scheduled actions.” Every time a student schedules an LEC tutoring session, we consider that a scheduled action. Since 2006, when the LEC was founded, we have recorded over 26,000 scheduled actions.

We keep our scheduled actions in an Excel file, which allows us to sort them by campus (Bronx or Manhattan), skill (math or writing), specialist, program, and many other categories. It also allows us to analyze our data.

Analyzing data is about helping students

Our data analysis always starts with questions in plain English. For example, at the end of every semester, we ask ourselves, in terms of writing tutoring, which curriculum we saw the most students for. We find the answer in our data and compare it with other data points.

The heart of analysis is always the question: “Why?” As we try to understand why—and how—we saw the most students for a certain curriculum, we also try to come up with strategic next steps, like coming up with innovative ways to promote the LEC to students and developing stronger relationships with professors.

Ultimately, everything in the LEC comes back to the matter of best serving our students. Although we rely on data, as life-long educators and proponents of self-directed learning, we know that data only show us the information that has been collected. It is up to us to study it and apply it in a meaningful way that will benefit our students.

Getting geeky with data

In the book The Undoing Project, the author Michael Lewis writes how Daryl Morey, the general manager of the Houston Rockets of the National Basketball Association, used sophisticated data analysis to predict which players in the draft would become the best professionals.

The models were often successful, but sometimes they failed. To improve, Morey revisited them, incorporating new data points he had previously overlooked and subjecting them to new analysis (in this case, players’ precise physical traits).

“[He] needed,” Lewis writes, “to be even more geeky.”

In the LEC, I know that we will be getting geekier with our data for as long as it helps us best support our students.