title: "HappyFace: Identifying and Predicting Frustrating Obstacles for Learning Programming at Scale"
authors: Ian Drosos, Philip J. Guo, Chris Parnin
venue: IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
year: 2017
links:
- <a href="happyface-paper.htm">Blog post</a>

tweet: HappyFace uses a five-level pain scale to identify causes of frustration when learning programming

abstract: >
  Unnecessary obstacles limit learning in cognitively-complex domains
  such as computer programming. With a lack of appropriate feedback
  mechanisms, novice programmers can experience frustration and
  disengage from the learning experience. In large-scale educational
  settings, the struggles of learners are often invisible to the
  learning infrastructure and learners have limited ability to seek
  help. In this paper, we perform a large-scale collection of code
  snippets from an online learn-to-code platform, Python Tutor, and
  collect a frustration rating through a light-weight learner feedback
  mechanism. We then devise a technique that can automatically identify
  sources of frustration based on participants labeling their
  frustration levels. We found 3 factors that best predicted novice
  programmers' frustration state: syntax errors, using niche language
  features, and understanding code with high complexity. Additionally,
  we found evidence that we could predict sources of frustration. Based
  on these results, we believe an embedded feedback mechanism can lead
  to future intervention systems.

bibtex: >
  @inproceedings{DrososVLHCC2017,
    author={Drosos, Ian and Guo, Philip J. and Parnin, Chris},
    title={{HappyFace}: Identifying and Predicting Frustrating Obstacles for Learning Programming at Scale},
    booktitle = {Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)},
    series = {VL/HCC '17},
    pages={171-179}, 
    doi={10.1109/VLHCC.2017.8103465}, 
    year={2017},
    month={Oct}
  }