A Bibliographic Analysis of Digital Learning Platforms as Research Infrastructure
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| Title: | A Bibliographic Analysis of Digital Learning Platforms as Research Infrastructure |
|---|---|
| Language: | English |
| Authors: | Zak Risha, Jeremy Roschelle, Digital Promise, Empirical Education Inc., Institute of Education Sciences (ED) |
| Source: | Digital Promise. 2026. |
| Availability: | Digital Promise. 1001 Connecticut Avenue NW Suite 935, Washington DC 20036. Tel: 202-450-3675; e-mail: contact@digitalpromise.org; Web site: https://digitalpromise.org/ |
| Peer Reviewed: | N |
| Page Count: | 16 |
| Publication Date: | 2026 |
| Document Type: | Information Analyses |
| Descriptors: | Electronic Learning, Educational Research, Educational Technology, Technology Uses in Education |
| Abstract: | Modern learning at scale relies on data at scale. Methods for using learning platform data have been evolving, particularly to allow comparative or experimental research within existing digital learning platforms (DLPs). By providing researchers with large-scale data from active learners, DLPs can reduce costs to collect data, pilot new interventions, and run A/B comparisons, enabling Learning Engineering to yield improvements that benefit student learning. This potential has given rise to an emerging DLPs-as-research-infrastructure subfield, with support from multiple funders. Progress depends on community-building and collaboration, as strong relationships are necessary to develop sound methods and approaches as well as to utilize the available data well. This subfield does not yet have a dedicated journal, conference, or scholarly society that serves a broader community. And yet the DLP-as-research-infrastructure subfield is aware of broader connections and precedents for its work. To understand these connections and precedents, a bibliometric analysis was conducted. Reporting on the bibliometric analysis yields two contributions: first, the immediate findings can advance the work of those who attend Learning @ Scale; second, the combined bibliometric approach could help other emerging subfields to better locate their more expansive community and related literature. Studying such newer subfields is difficult because clear communities around a single journal or conference have not yet formed, so we must rely on other sources and methods. Our analyses use the proceedings of the Annual Workshop on A/B Testing and Platform-Enabled Learning Research (which occurs at the ACM Learning @ Scale Conference each year) as a seed because submissions are open (whereas the set of funded projects are more restrictive). By analyzing a combination of social ties (co-authorship), shared intellectual foundations (bibliographic coupling), and semantic similarities (embeddings) analyses, we map the people, publications, and ideas that help shape this emerging area. In doing so, we demonstrate how bibliometric techniques can be used in service of solidifying and growing emerging subfields of research. |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2026 |
| Accession Number: | ED682371 |
| Database: | ERIC |
| Abstract: | Modern learning at scale relies on data at scale. Methods for using learning platform data have been evolving, particularly to allow comparative or experimental research within existing digital learning platforms (DLPs). By providing researchers with large-scale data from active learners, DLPs can reduce costs to collect data, pilot new interventions, and run A/B comparisons, enabling Learning Engineering to yield improvements that benefit student learning. This potential has given rise to an emerging DLPs-as-research-infrastructure subfield, with support from multiple funders. Progress depends on community-building and collaboration, as strong relationships are necessary to develop sound methods and approaches as well as to utilize the available data well. This subfield does not yet have a dedicated journal, conference, or scholarly society that serves a broader community. And yet the DLP-as-research-infrastructure subfield is aware of broader connections and precedents for its work. To understand these connections and precedents, a bibliometric analysis was conducted. Reporting on the bibliometric analysis yields two contributions: first, the immediate findings can advance the work of those who attend Learning @ Scale; second, the combined bibliometric approach could help other emerging subfields to better locate their more expansive community and related literature. Studying such newer subfields is difficult because clear communities around a single journal or conference have not yet formed, so we must rely on other sources and methods. Our analyses use the proceedings of the Annual Workshop on A/B Testing and Platform-Enabled Learning Research (which occurs at the ACM Learning @ Scale Conference each year) as a seed because submissions are open (whereas the set of funded projects are more restrictive). By analyzing a combination of social ties (co-authorship), shared intellectual foundations (bibliographic coupling), and semantic similarities (embeddings) analyses, we map the people, publications, and ideas that help shape this emerging area. In doing so, we demonstrate how bibliometric techniques can be used in service of solidifying and growing emerging subfields of research. |
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