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A Randomized Controlled Trial on the Wild Wild West of Scientific Computing with Student Learners
被引:4
|作者:
Rafalski, Timothy
[1
]
Uesbeck, P. Merlin
[1
]
Panks-Meloney, Cristina
[1
]
Daleiden, Patrick
[1
]
Allee, William
[1
]
Mcnamara, Amelia
[2
]
Stefik, Andreas
[1
]
机构:
[1] Univ Nevada, Las Vegas, NV 89154 USA
[2] Univ St Thomas, St Paul, MN USA
来源:
ICER '19 - PROCEEDINGS OF THE 2019 ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH
|
2019年
基金:
美国国家科学基金会;
关键词:
statistics education;
programming languages;
scientific computing;
D O I:
10.1145/3291279.3339421
中图分类号:
TP301 [理论、方法];
学科分类号:
081202 ;
摘要:
Scientific computing has become an area of growing importance. Across fields such as biology, education, physics, or others, people are increasingly using scientific computing to model and understand the world around them. Despite the clear need, almost no systematic analysis has been conducted on how students in fields outside of computer science learn to program in the context of scientific computing. Given that many fields do not explicitly teach much programming to their students, they may have to learn this important skill on their own. To help, using rigorous quantitative and qualitative methods, we looked at the process 154 students followed in the context of a randomized controlled trial on alternative styles of programming that can be used in R. Our results suggest that the barriers students face in scientific computing are non-trivial and this work has two core implications: 1) students learning scientific computing on their own struggle significantly in many different ways, even if they have had prior programming training, and 2) the design of the current generation of scientific computing feels like the wild-wild west and the designs can be improved in ways we will enumerate.
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页码:239 / 247
页数:9
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