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Learning about learning: patterns of sharing of research knowledge among Education, Border, and Cognitive Science fields

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Abstract

This study explores the patterns of exchange of research knowledge among Education Research, Cognitive Science, and what we call “Border Fields.” We analyze a set of 32,121 articles from 177 selected journals, drawn from five sample years between 1994 and 2014. We profile the references that those articles cite, and the papers that cite them. We characterize connections among the fields in sources indexed by Web of Science (WoS) (e.g., peer-reviewed journal articles and proceedings), and connections in sources that are not (e.g., conference talks, chapters, books, and reports). We note five findings—first, over time the percentage of Education Research papers that extensively cite Cognitive Science has increased, but the reverse is not true. Second, a high percentage of Border Field papers extensively cite and are cited by the other fields. Border Field authors’ most cited papers overlap those most cited by Education Research and Cognitive Science. There are fewer commonalities between Educational research and Cognitive Science most cited papers. This is consistent with Border Fields being a bridge between fields. Third, over time the Border Fields have moved closer to Education Research than to Cognitive Science, and their publications increasingly cite, and are cited by, other Border Field publications. Fourth, Education Research is especially strongly represented in the literature published outside those WoS-indexed publications. Fifth, the rough patterns observed among these three fields when using a more restricted dataset drawn from the WoS are similar to those observed with the dataset lying outside the WoS, but Education Research shows a far heavier influence than would be indicated by looking at WoS records alone.

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Notes

  1. Just noted that Volume 117, Issue 3, of Scientometrics, contains some 44 papers, in contrast to Volume 1, Issue 1, of 40 years ago, with 7 papers.

  2. We acknowledge that this subsumes differences among ED research communities. For example, Discipline-Based Education Research (DBER) concerns with undergraduate Physics education would not necessarily be unified with, say, Medical Education Research, no less with K-12 teaching or Special Education studies.

  3. To illustrate, here are three sample cited references appearing in a WoS abstract record:

    Thus, WoS provides first author, publication year, and abbreviated source name (plus volume and first page). Note that Cited Reference content varies somewhat in format and what is included (e.g., one of these three has a DOI).

  4. These text analyses are done using VantagePoint software (www.theVantagePoint.com).

  5. I.e., from other fields, such as chemistry, sociology, or whatever—these are identified via the second stage process (next paragraph), using a thesaurus that associates sources (journals) to WCs.

  6. WoS Helpdesk notes factors considered in source assignment to WCs: journal subject matter and scope; author & editorial board affiliations; funding acknowledgements; cited and citing relationships to other journals; journal sponsor; journal’s categorizations in other bibliographic databases.

  7. Cited Reference Count per article averages 42.8 (median 39) overall. In 1994 that was 34.5 (median of 31) for 3679 articles; in 2014, it was up by almost 50% from 1994 to 50.9 (median of 46) for 10,086 articles.

    Interestingly, citation propensity differs only modestly by (Cited Reference) field:

    • 43.7 (median 40) for 20,843 ED articles

    • 46.3 (median 42) for 26,575 CogSci articles;

    • 47.7 (median 44) for 18,997 Border Field papers; and

    • 44.0 (median 40) 30,435 Other articles.

  8. In contrast, Youtie et al. (2017) used a more selective denominator of only the cited journal items appearing in the 177 journals and categorized into ED, CogSci, or Border (i.e., not drawing upon the much wider set of cited sources herein classified, and not including “Other”). We note this to explain why values differ (although our foci here are somewhat different).

  9. Somewhat surprising, we found considerable inconsistency in how cited names appeared. Given the size of the files (hundreds of thousands of authors), full cleaning was not done. We applied VantagePoint’s “person.fuz” fuzzy matching routine to consolidate name variations. We also searched for name variants for the top authors. For instance, Michael I. Posner is the most cited by the Gen0 CogSci articles; we collapsed these variants of his name to get his count: Posner, M I; Posner M. I.; Posner M.; Posner, M; Posner M., I; Posner M, I; Posner Michael I.; Posner M.I. Table 4 offers full names where available.

  10. Those authors highly cited by ED Gen0 papers include a number of organizations—in contrast to those cited by CogSci and Border Field papers. This is not a problem for our purpose of comparing top sources, but remember that these are apt to include materials generated by multiple authors (e.g., different National Academies committees). The data only denote cited first authors.

  11. We did not want to average in percentages based on 1 or 2 categorized papers—e.g., if a single citation were in, say, CogSci, to count as 100%.

  12. This set of categorized reference sources is smaller than the full set used in the prior analyses (i.e., generating Figs. 1, 2, 3). Those analyses used all reference sources categorized collectively by the three thesauri together. That allowed for a given cited source to be classified into multiple categories if, say, the 177 thesaurus located it in a Border Field whereas the Auto-categorizer also associated it with ED. Classifying WoS sources is best done based on WoS Categorization of sources (journals); so the Auto-categorizer is only applied to sources not covered by the first two thesauri. See the Supplemental Materials for details.

  13. Should others wish to pursue, we are glad to share the data with finer categorizations, as licensing permits.

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Acknowledgements

This work was supported by a grant from the US National Science Foundation, Directorate for Education and Human Resources (DRL-1348765) to Search Technology Inc. While serving at the National Science Foundation, G.S. was supported by the IR/D program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the National Science Foundation.

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Authors and Affiliations

  1. Search Technology, Inc., 6025 The Corners Pkwy., Norcross, GA, 30092, USA

    Alan L. Porter, David J. Schoeneck & Stephen F. Carley

  2. Science, Technology and Innovation Policy (STIP) Program, School of Public Policy, Georgia Tech, Atlanta, GA, 30332-0345, USA

    Alan L. Porter, Jan Youtie & Seokbeom Kwon

  3. Division of Research on Learning, Directorate for Education and Human Resources, US National Science Foundation, 2415 Eisenhower Ave., Alexandria, VA, 22314, USA

    Gregg E. A. Solomon

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  1. Alan L. Porter
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Porter, A.L., Schoeneck, D.J., Youtie, J. et al. Learning about learning: patterns of sharing of research knowledge among Education, Border, and Cognitive Science fields. Scientometrics 118, 1093–1117 (2019). https://doi.org/10.1007/s11192-019-03012-3

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