Predicting abandonment in online coding tutorials

Document Type

Conference Proceeding

Publication Date

11-9-2017

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.

Identifier

85041015390 (Scopus)

ISBN

[9781538604434]

Publication Title

Proceedings of IEEE Symposium on Visual Languages and Human Centric Computing Vl Hcc

External Full Text Location

https://doi.org/10.1109/VLHCC.2017.8103467

e-ISSN

19436106

ISSN

19436092

First Page

191

Last Page

199

Volume

2017-October

Grant

1657160

Fund Ref

National Science Foundation

This document is currently not available here.

Share

COinS