There have been numerous efforts documenting the effects of open science in existing papers; however, these efforts typically only consider the author's analyses and supplemental materials from the papers. While understanding the current rate of open science adoption is important, it is also vital that we explore the factors that may encourage such adoption. One such factor may be publishing organizations setting open science requirements for submitted articles: encouraging researchers to adopt more rigorous reporting and research practices. For example, within the education technology discipline, theACM Conference on Learning @ Scale (L@S) has been promoting open science practices since 2018 through a Call For Papers statement. The purpose of this study was to replicate previous papers within the proceedings of L@S and compare the degree of open science adoption and robust reproducibility practices to other conferences in education technology without a statement on open science. Specifically, we examined 93 papers and documented the open science practices used. We then attempted to reproduce the results with invitation from authors to bolster the chance of success. Finally, we compared the overall adoption rates to those from other conferences in education technology. Although the overall responses to the survey were low, our cursory review suggests that researchers at L@S might be more familiar with open science practices compared to the researchers who published in the International Conference on Artificial Intelligence in Education (AIED) and the International Conference on Educational Data Mining (EDM): 13 of 28 AIED and EDM responses were unfamiliar with preregistrations and 7 unfamiliar with preprints, while only 2 of 7 L@S responses were unfamiliar with preregistrations and 0 with preprints. The overall adoption of open science practices at L@S was much lower with only 1% of papers providing open data, 5% providing open materials, and no papers had a preregistration.
All openly accessible work can be found in an Open Science Framework project.
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This content will become publicly available on July 1, 2024
How to Open Science: Analyzing the Open Science Statement Compliance of the Learning@Scale Conference.
There have been numerous efforts documenting the effects of open
science in existing papers; however, these efforts typically only
consider the author’s analyses and supplemental materials from
the papers. While understanding the current rate of open science
adoption is important, it is also vital that we explore the factors that
may encourage such adoption. One such factor may be publishing
organizations setting open science requirements of submitted arti-
cles: encouraging researchers to adopt more rigorous reporting and
research practices. For example, within the education technology
discipline, the ACM Conference on Learning @ Scale (L@S) has been
promoting open science practices since 2018 through a Call For Pa-
pers statement. The purpose of this study was to replicate previous
papers within the proceedings of L@S and compare the degree of
open science adoption and robust reproducibility practices to other
conferences in education technology without a statement on open
science. Specifically, we examined 93 papers and documented the
open science practices used. We then attempted to reproduce the
results with intervention from authors to bolster the chance of suc-
cess. Finally, we compared the overall adoption rates to those from
other conferences in education technology. Our cursory review sug-
gests that researchers at L@S were more knowledgeable in open
science practices, such as preregistration or preprints, compared
to the researchers who published in International Conference on
Artificial Intelligence in Education and the International Conference
on Educational Data Mining as they were less likely to say they
were unfamiliar with the practices. However, the overall adoption
of open science practices was significantly lower with only 1%
of papers providing open data, 5% providing open materials, and
no papers with a preregistration. Based on speculation, the low
adoption rates maybe due to 20% of the papers not using a dataset,
at-scale datasets and materials that were unable to be released to
avoid security issues or sensitive data leaks, or that data were being
used in ongoing research and are not considered complete enough
for release by the authors.
All openly accessible work can be found in an Open Science
Framework project
more »
« less
- Award ID(s):
- 1931523
- NSF-PAR ID:
- 10443568
- Date Published:
- Journal Name:
- Proceedings of the Tenth ACM Conference on Learning@Scale (L@S '23), July 20-22, 2023, Copenhagen, Denmark
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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