Established in 2017, the IOE Laboratory for Educational Data Science seeks to take novelty perspectives on today’s top-priority research areas in education and broader social contexts by harnessing Big Data and advanced computational tools.
The recent expansion in ICT has traversed almost every domain of human life, including academia at large and educational studies in particular. Social science data methods have been on an upswing amid their growing recognition as an important tool to unlock evidence and interpretations that can render new and deeper insights into what is fundamentally implicated in driving modern educational settings and their actors.
The Laboratory’s R&D concentrates on the following key areas:
Students’ Daily Practices and Life Contexts
The cohesive social development and sound academic progression of modern students are contingent upon a host of factors that dwell both within and outside their immediate learning perimeter. Advanced data science methods come in handy when seeking to take a deeper dig into the nature of these multiple determiners as well as in evaluating the extent to which each such factor affects various aspects of learning achievement and well-being. Employing computational frameworks to research into how students’ social environments and life patterns develop over time, including their daily schedules, academic preferences, pastimes, friendship networks, etc., helps uncover the whole range of otherwise inaccessible scholarly angles, rationales and evidence. These new research outputs push the overall frontiers of modern educational studies while enabling a more holistic understanding of what underlies students’ identity formation, academic performance and life-courses.
The advent of the internet has put virtually the entire stock of human knowledge at one’s fingertips while also empowering students to get in touch with their peers from nearly every corner of the globe. This might be suggesting that schoolers are now no longer tied to their immediate social environment and have become better placed to break out of the inequality loop. In practice, however, the opposite situation seems to have been observed where inequalities and stark division by location, status and academic achievement are still as relevant for the digital space. Our studies in this area aim to yield more compelling explanations for what triggers digital inequalities and how they evolve.
Digital Traces as Indicators of Students’ Future Socio-Educational Characteristics
As recent studies have convincingly shown, data on how one behaves in the digital realm can provide a deep well of evidence about the person’s individual traits and particular aspects of future life-course. At our Lab, we process huge arrays of various social network data to map out prospective trends in students’ socio-educational profiles, including the most likely developments in academic preference and performance, socialization habits, etc. The outputs from these studies also provide important groundwork for advancing and scaling-up our R&D agenda in Student Life Contexts and Digital Inequality.
- Gender Bias in Sharenting: Both Men and Women Mention Sons More Often Than Daughters on Social Media
- Schools are segregated by educational outcomes in the digital space
- Predicting PISA Scores from Students’ Digital Traces
- The Digital Flynn Effect: Complexity of Posts on Social Media Increases over Time
- Formation of homophily in academic performance: Students change their friends rather than performance
Lab in the Media