2014/09/20 by Tanmay Sinha, Nan Li, Sinha, Tanmay +5
Computer Science · Medicine · #Advanced Graph Neural Networks #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Mosquito-borne diseases and control #Online Learning and Analytics #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1409.5887
openalex publication_date 2014/09/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This work is an attempt to discover hidden structural configurations in\nlearning activity sequences of students in Massive Open Online Courses (MOOCs).\nLeveraging combined representations of video clickstream interactions and forum\nactivities, we seek to fundamentally understand traits that are predictive of\ndecreasing engagement over time. Grounded in the interdisciplinary field of\nnetwork science, we follow a graph based approach to successfully extract\nindicators of active and passive MOOC participation that reflect persistence\nand regularity in the overall interaction footprint. Using these rich\neducational semantics, we focus on the problem of predicting student attrition,\none of the major highlights of MOOC literature in the recent years. Our results\nindicate an improvement over a baseline ngram based approach in capturing\n"attrition intensifying" features from the learning activities that MOOC\nlearners engage in. Implications for some compelling future research are\ndiscussed.\n