@inproceedings{be749fce8ea74287bf60613315f33035,
title = "Predicting abandonment in online coding tutorials",
abstract = "Learners regularly abandon online coding tutorials when they get bored or frustrated, but there are few techniques for anticipating this abandonment to intervene. In this paper, we examine the feasibility of predicting abandonment with machine-learned classifiers. Using interaction logs from an online programming game, we extracted a collection of features that are potentially related to learner abandonment and engagement, then developed classifiers for each level. Across the first five levels of the game, our classifiers successfully predicted 61% to 76% of learners who did not complete the next level, achieving an average AUC of 0.68. In these classifiers, features negatively associated with abandonment included account activation and help-seeking behaviors, whereas features positively associated with abandonment included features indicating difficulty and dis-engagement. These findings highlight the feasibility of providing timely intervention to learners likely to quit.",
author = "An Yan and Lee, {Michael J.} and Ko, {Andrew J.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2017 ; Conference date: 11-10-2017 Through 14-10-2017",
year = "2017",
month = nov,
day = "9",
doi = "10.1109/VLHCC.2017.8103467",
language = "English (US)",
series = "Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC",
publisher = "IEEE Computer Society",
pages = "191--199",
editor = "Peter Rodgers and Henley, {Austin Z.} and Anita Sarma",
booktitle = "Proceedings - 2017 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2017",
address = "United States",
}