Mismatch of Expectations: How Modern Learning Resources Fail Conversational Programmers

Authors

April Y. Wang, Ryan Mitts, Philip J. Guo, Parmit K. Chilana

Research Lab

Parmit Chilana

Research Themes

Software Learnability

Abstract

Conversational programmers represent a class of learners who are not required to write any code, yet try to learn programming to improve their participation in technical conversations. We carried out interviews with 23 conversation-al programmers to better understand the challenges they face in technical conversations, what resources they choose to learn programming, how they perceive the learning process, and to what extent learning programming actually helps them. Among our key findings, we found that conversational programmers often did not know where to even begin the learning process and ended up using formal and informal learning resources that focus largely on programming syntax and logic. However, since the end goal of conversational programmers was not to build artifacts, modern learning resources usually failed these learners in their pursuits of improving their technical conversations. Our findings point to design opportunities in HCI to invent learner-centered approaches that address the needs of conversational programmers and help them establish common ground in technical conversations.

Citation

April Y. Wang, Ryan Mitts, Philip J. Guo, Parmit K. Chilana. "Mismatch of expectations: How modern learning resources fail conversational programmers." In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1-13. 2018.

@inproceedings{wang2018mismatch,
  title={Mismatch of expectations: How modern learning resources fail conversational programmers},
  author={Wang, April Y and Mitts, Ryan and Guo, Philip J and Chilana, Parmit K},
  booktitle={Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems},
  pages={1--13},
  year={2018}
}