When is the right time to intervene with a student who might be struggling in their learning journey? A complex question but one we sought to answer partly with a big data project using data we had collected over many years on our students. The Project, called the Student Journey Project, was the brainchild of our COO, Associate Professor Rhys Johnson and utilized the expertise of Associate Professor Keith B. Carter and his KBC consultancy as well as our own IT, Academic and Operations talent. The home of the project and the data was the School of Diploma Studies, of which the author is Dean. This article explores the drivers, decisions, and details made on that project as well as some early results.

Firstly, we had to define what data we wanted and make it less big, or even question if it was big. You see, we’d been challenged at a recent Kaplan Industry Advisory Board Meeting, by the Chair of IT Board Mr. Shreeniwas Iyer, who described “Big Data” as a tautology. “Big is like the prefix multi in multimedia, in the 21st century it’s redundant; just as all media is now convergent and so by nature any one medium has multiplicity, so too data is of such a vast volume that the big is assumed, it’s all just data”, he asserted. He should know, Mr Iyer is the senior coder with Quantcast, a company that deals with advertising data volume in the billions every day.

We came to see what Mr Iyer meant as we completed the User Acceptance Testing on this project, which enabled us to mine 82 million data points that pertained to our students here in Singapore. Our goal was to make data-driven decisions on when to intervene with students who were at-risk of not passing or graduating.

1. The Drivers:

A. Accounting for Failure in Accounting

Accounting can be the hindrance of many a well-intentioned commerce, business or related interdisciplinary students (trust me, mine is the voice of the bookkeeping challenged). The three D’s cause much consternation: Double sided, Debits/Credits and D grades (and I’m not talking about Distinctions). At Kaplan, for non-accounting majors, first semester accounting is consistently the lowest performing subject in terms of students’ first-time pass rates.

We knew from the literature and our own experience that the challenge we had was around early intervention, which is making a meaningful connection with the student over and above that of their dedicated professor, in order to identify the barriers to their learning before it’s too late. Our courses are directed to reflect our diverse student population, ranging from part-time working adults to full-time students, so it was imperative that we needed extra real-time data to act in a timely manner. Moreover, some 13 of our courses offer accounting with different lecturers on different days at different times in different semesters, a challenge that required a technical solution to allow for the variability and scale of delivery.

B. Divergent Systems with Important Data

In order to have the right data at our disposal to arrive at the right time to intervene and rescue students, required the convergence of a myriad of systems (see Figure 1 below): The Student Management System for final performance data, The Moodle Learning Management System for students’ online participation and formative (midstream) performance data, the Digital Entry System logs for real-time attendance data and Navision Accounting Systems for payment confirmation. An aggregator lumped it all in and then Tableau allowed for dashboards.

2. Decisions to Make: Questions are the Answers

Our straightforward approach should not minimize the effort we needed to come to a conclusion. With 82 million data points available to us, our temptation was to quickly generate answers. But as we found out, formulating the right questions was crucial to the development of our program. Enter Associate Professor Keith B. Carter, National University of Singapore, School of Computing and his colleague, Ms. Ann Luo.

Associate Professor Carter came in and showed us the direction we needed to get the right answers. He started working with us in low-tech and set out to ensure we clearly defined our query to better plan out a course of action. He took the time to time to know what we wanted to know challenging our Socratic Paradox—we knew that we knew nothing.