TY - JOUR AU - Feng, Wenzheng AU - Tang, Jie AU - Liu, Tracy Xiao PY - 2019/07/17 Y2 - 2024/03/28 TI - Understanding Dropouts in MOOCs JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Special Technical Track: AI for Social Impact DO - 10.1609/aaai.v33i01.3301517 UR - https://ojs.aaai.org/index.php/AAAI/article/view/3825 SP - 517-524 AB - <p>Massive open online courses (MOOCs) have developed rapidly in recent years, and have attracted millions of online users. However, a central challenge is the extremely high dropout rate — recent reports show that the completion rate in MOOCs is below 5% (Onah, Sinclair, and Boyatt 2014; Kizilcec, Piech, and Schneider 2013; Seaton et al. 2014).</p><p>What are the major factors that cause the users to drop out?</p><p>What are the major motivations for the users to study in MOOCs? In this paper, employing a dataset from XuetangX<sup>1</sup>, one of the largest MOOCs in China, we conduct a systematical study for the dropout problem in MOOCs. We found that the users’ learning behavior can be clustered into several distinct categories. Our statistics also reveal high <em>correlation</em> between dropouts of different courses and strong <em>influence</em> between friends’ dropout behaviors. Based on the gained insights, we propose a Context-aware Feature Interaction Network (CFIN) to model and to predict users’ dropout behavior. CFIN utilizes context-smoothing technique to smooth feature values with different context, and use attention mechanism to combine user and course information into the modeling framework. Experiments on two large datasets show that the proposed method achieves better performance than several state-of-the-art methods. The proposed method model has been deployed on a real system to help improve user retention.</p> ER -