How to Open Science: A Principle and Reproducibility Review of the Learning Analytics and Knowledge Conference
How to Open Science: A Principle and Reproducibility Review of the Learning Analytics and Knowledge Conference
复制标题
如何开放科学:学习分析和知识会议的原理和可重复性回顾
DOI:
10.1145/3576050.3576071
复制
发表时间:
2023
期刊:
影响因子:
--
通讯作者:
Heffernan, Neil
中科院分区:
文献类型:
--
作者:
Haim, Aaron;Shaw, Stacy;Heffernan, Neil
Within the field of education technology, learning analytics has increased in popularity over the past decade. Researchers conduct experiments and develop software, building on each other’s work to create more intricate systems. In parallel, open science — which describes a set of practices to make research more open, transparent, and reproducible — has exploded in recent years, resulting in more open data, code, and materials for researchers to use. However, without prior knowledge of open science, many researchers do not make their datasets, code, and materials openly available, and those that are available are often difficult, if not impossible, to reproduce. The purpose of the current study was to take a close look at our field by examining previous papers within the proceedings of the International Conference on Learning Analytics and Knowledge, and document the rate of open science adoption (e.g., preregistration, open data), as well as how well available data and code could be reproduced. Specifically, we examined 133 research papers, allowing ourselves 15 minutes for each paper to identify open science practices and attempt to reproduce the results according to their provided specifications. Our results showed that less than half of the research adopted standard open science principles, with approximately 5% fully meeting some of the defined principles. Further, we were unable to reproduce any of the papers successfully in the given time period. We conclude by providing recommendations on how to improve the reproducibility of our research as a field moving forward.All openly accessible work can be found in an Open Science Foundation project1.
登录
查看更多内容
DOI:
--
发表时间:
--
期刊:
影响因子:
--
作者:
通讯作者:
--
影响因子:
4.3
作者:
Johndan Johnson
通讯作者:
Johndan Johnson
DOI:
10.1007/978-3-030-01768-2_3
发表时间:
2018
期刊:
LAK23: 13th International Learning Analytics and Knowledge Conference
影响因子:
--
作者:
Leo Lahti
通讯作者:
Leo Lahti
影响因子:
5.4
作者:
Miguel Ángel Conde González;F. García;M. Rodríguez;M. A. Forment;María José Casany Guerrero;Jordi Piguillem
通讯作者:
Jordi Piguillem
影响因子:
3.3
作者:
Arnoud Engelfriet
通讯作者:
Arnoud Engelfriet