TY - GEN
T1 - Towards a Model of API Learning
AU - Kelleher, Caitlin
AU - Ichinco, Michelle
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - In today's world, learning new APIs (Application Programming Interfaces) is fundamental to being a programmer. Prior research suggests that programmers learn on-the-fly while they work on other project-related tasks. Yet, this process is often inefficient. This inefficiency has inspired research seeking to understand and improve API learnability. While the existing research has provided insight into API learning, we still have a fractured understanding of the process of learning a new API. In this paper, we take the first steps towards developing a theoretical model of API learning by combining predictions from Information Foraging Theory (IFT) to describe information search behavior, Cognitive Load Theory (CLT) to describe learning, and External Memory (EM) to describe how API learners augment their short term memories. Our proposed model is consistent with existing research on barriers to learning APIs and helps to provide explanations for these barriers as well as suggest new research directions.
AB - In today's world, learning new APIs (Application Programming Interfaces) is fundamental to being a programmer. Prior research suggests that programmers learn on-the-fly while they work on other project-related tasks. Yet, this process is often inefficient. This inefficiency has inspired research seeking to understand and improve API learnability. While the existing research has provided insight into API learning, we still have a fractured understanding of the process of learning a new API. In this paper, we take the first steps towards developing a theoretical model of API learning by combining predictions from Information Foraging Theory (IFT) to describe information search behavior, Cognitive Load Theory (CLT) to describe learning, and External Memory (EM) to describe how API learners augment their short term memories. Our proposed model is consistent with existing research on barriers to learning APIs and helps to provide explanations for these barriers as well as suggest new research directions.
KW - API Learning
KW - Cognitive Load Theory
KW - External Memory
KW - Information Foraging
UR - https://www.scopus.com/pages/publications/85078873101
U2 - 10.1109/VLHCC.2019.8818850
DO - 10.1109/VLHCC.2019.8818850
M3 - Conference contribution
AN - SCOPUS:85078873101
T3 - Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC
SP - 163
EP - 168
BT - Proceedings - 2019 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2019
A2 - Smith, Justin
A2 - Bogart, Christopher A.
A2 - Good, Judith
A2 - Fleming, Scott D.
PB - IEEE Computer Society
T2 - 2019 IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC 2019
Y2 - 14 October 2019 through 18 October 2019
ER -