Data sharing model for physics education research using the 70 000 response Colorado Learning Attitudes about Science Survey for Experimental Physics dataset

被引:6
作者
Aiken, John M. [1 ,2 ,3 ,4 ]
Lewandowski, H. J. [3 ,4 ,5 ]
机构
[1] Univ Oslo, Njord Ctr, N-0371 Oslo, Norway
[2] Univ Oslo, Ctr Comp Sci Educ, N-0371 Oslo, Norway
[3] Natl Inst Stand & Technol, JILA, Boulder, CO 80309 USA
[4] Univ Colorado, Boulder, CO 80309 USA
[5] Univ Colorado, Dept Phys, Boulder, CO 80309 USA
基金
美国国家科学基金会;
关键词
INTRODUCTORY PHYSICS; STUDENTS BELIEFS; IMPACT;
D O I
10.1103/PhysRevPhysEducRes.17.020144
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
We present a model for sharing quantitative data in the field of physics education research and use it to present a newly available dataset as an example. This model is in line with calls from across physics and science more generally to democratize data and results through open access. The model includes suggestions for data collection, creation of a data schema, and data sharing. It attends to the specific needs of the physics education research community, such as anonymization of human subjects data. As an example of this model, we use the Colorado Learning Attitudes about Science Survey for Experimental Physics (E-CLASS) dataset, which includes over 70 000 responses to the E-CLASS survey. These data cover 133 institutions, 599 unique courses, and 204 instructors, and was collected between 2016 and 2019. These data are made available at the time of publication and can be used freely, without the need of any institutional review board approval.
引用
收藏
页数:16
相关论文
共 70 条
[11]  
Codd E. F, 1989, Readings in Artificial Intelligence and Databases, P60
[12]  
Dataverse H., **DATA OBJECT**
[13]   Theoretical perspectives of quantitative physics education research [J].
Ding, Lin .
PHYSICAL REVIEW PHYSICS EDUCATION RESEARCH, 2019, 15 (02)
[14]  
Dounas-Frazer D., 2018, P 2018 PHYS ED RES C
[15]  
Downey A.S., 2013, SHARING CLIN RES DAT
[16]   The NOMAD laboratory: from data sharing to artificial intelligence [J].
Draxl, Claudia ;
Scheffler, Matthias .
JOURNAL OF PHYSICS-MATERIALS, 2019, 2 (03)
[17]   What Drives Academic Data Sharing? [J].
Fecher, Benedikt ;
Friesike, Sascha ;
Hebing, Marcel .
PLOS ONE, 2015, 10 (02)
[18]   Mining Big Data in Education: Affordances and Challenges [J].
Fischer, Christian ;
Pardos, Zachary A. ;
Baker, Ryan Shaun ;
Williams, Joseph Jay ;
Smyth, Padhraic ;
Yu, Renzhe ;
Slater, Stefan ;
Baker, Rachel ;
Warschauer, Mark .
EMERGENT APPROACHES FOR EDUCATION RESEARCH: WHAT COUNTS AS INNOVATIVE EDUCATIONAL KNOWLEDGE AND WHAT EDUCATION RESEARCH COUNTS?, 2020, 44 :130-160
[19]   Design, analysis, tools, and apprenticeship (DATA) Lab [J].
Funkhouser, Kelsey ;
Martinez, William M. ;
Henderson, Rachel ;
Caballero, Marcos D. .
EUROPEAN JOURNAL OF PHYSICS, 2019, 40 (06)
[20]  
Glass G. V., 1976, Educational Researcher, V5, P3, DOI [https://doi.org/10.3102/0013189X005010003, DOI 10.3102/0013189X005010003]