Elivate: A Real-Time Assistant for Students and Lecturers as Part of an Online CS Education Platform

Abstract

We present Elice, an online CS (computer science) education platform, and Elivate, a system for (i) taking student learning data from Elice, (ii) inferring their progress through an educational taxonomy tailored for programming education, and (iii) generating the real-time assistance for students and lecturers. Online courses suffer from high average attrition rates, and early prediction can enable early personalized feedback to motivate and assist students who may be having difficulties. Elice captures detailed student learning activities including intermediate revisions of code as students make progress toward completing their programming exercises and timestamps of student logins and submissions. Elivate then takes those data to analyze each student's progress and estimate the time to completion. In doing so, Elivate uses a learning taxonomy and automatic clustering of source code revisions. Using more than 240,000 code revisions generated by 1,000 students, we demonstrate how Elivate processes large-scale student data and generates appropriate real-time feedback for students.