English

Capturing "attrition intensifying" structural traits from didactic interaction sequences of MOOC learners

Computers and Society 2014-09-23 v1 Machine Learning Social and Information Networks

Abstract

This work is an attempt to discover hidden structural configurations in learning activity sequences of students in Massive Open Online Courses (MOOCs). Leveraging combined representations of video clickstream interactions and forum activities, we seek to fundamentally understand traits that are predictive of decreasing engagement over time. Grounded in the interdisciplinary field of network science, we follow a graph based approach to successfully extract indicators of active and passive MOOC participation that reflect persistence and regularity in the overall interaction footprint. Using these rich educational semantics, we focus on the problem of predicting student attrition, one of the major highlights of MOOC literature in the recent years. Our results indicate an improvement over a baseline ngram based approach in capturing "attrition intensifying" features from the learning activities that MOOC learners engage in. Implications for some compelling future research are discussed.

Keywords

Cite

@article{arxiv.1409.5887,
  title  = {Capturing "attrition intensifying" structural traits from didactic interaction sequences of MOOC learners},
  author = {Tanmay Sinha and Nan Li and Patrick Jermann and Pierre Dillenbourg},
  journal= {arXiv preprint arXiv:1409.5887},
  year   = {2014}
}

Comments

"Shared Task" submission for EMNLP 2014 Workshop on Modeling Large Scale Social Interaction in Massively Open Online Courses

R2 v1 2026-06-22T06:01:31.934Z