EDUM: classroom education measurements via large-scale WiFi networks

Abstract

Behavior in classroom-based courses is hard to measure at large-scale. In this paper, we propose the EDUM (EDUcation Measurement) system to help characterize educational behavior through data collected from WLANs (WiFi networks) on campuses. EDUM characterizes students' punctuality (attendances, late arrivals, and early departures) for lectures using longitudinal WLAN data, and further characterizes the attractiveness of lectures using mobile phone's interactive states at minute-scale granularity. EDUM is easy to deploy and extensible for new types of data. We deploy EDUM at Tsinghua University where ~700 volunteer students' data are measured during a 9-week period by ~2,800 APs and two popular mobile apps. Our results show that EDUM makes it possible to obtain large-scale observations on punctuality, distraction and study performance, and quantitatively confirm or disprove numerous assumptions about educational behavior.