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Computing research in the academy: insights from theses and dissertations

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Abstract

Computational technologies have become increasingly pervasive in recent decades. Computing research in academia has commensurately seen dramatic increases, both in computing-specific research and research using computing. This article maps out the academic computing landscape by examining the connections between computing-related keywords used to describe theses and dissertations. Specifically, data from 29,435 dissertations and theses found in the ProQuest Theses and Dissertation database in the years 2009–2014 were analyzed. Results identify interdisciplinary clusters, as well as identify key differences in connections between the four computing disciplines in the database: computer science (CS), computer engineering (CE), information technology (IT), and information science (ISci). For example, authors who primarily identify with CS rarely list secondary keywords, while authors from a high variety of other disciplines list CS as a secondary keyword. CE is tightly focused and growing, but rarely seen as a relevant secondary keyword by other fields. Meanwhile, the highly interdisciplinary nature of IT and ISci is shown, along with different subgroups within those areas. While the total number of unique theses with computing-related keywords has remained steady, they have shifted dramatically between specific computing fields (e.g., away from CS toward CE and IT). Visualizations present information on the growth and change in focus of computing-related theses and dissertations, as well as networks of keywords that co-occur. We discuss potential explanations of the patterns, as well as their implications on the future of graduate work in computing disciplines.

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References

  • An, X. Y., & Wu, Q. Q. (2011). Co-word analysis of the trends in stem cells field based on subject heading weighting. Scientometrics, 88(1), 133–144. https://doi.org/10.1007/s11192-011-0374-1.

    Article  Google Scholar 

  • Andersen, J. P., & Hammarfelt, B. (2011). Price revisited: on the growth of dissertations in eight research fields. Scientometrics, 88(2), 371–383. https://doi.org/10.1007/s11192-011-0408-8.

    Article  Google Scholar 

  • Bertrand, A., & Cellier, J.-M. (1995). Psychological approach to indexing: Effects of the operator’s expertise upon indexing behaviour. Journal of Information Science, 21(6), 459–472. https://doi.org/doi:10.1177/016555159502100605.

    Article  Google Scholar 

  • Besselaar, P. V. D., & Heimeriks, G. (2006). Mapping research topics using word-reference co-occurrences: A method and an exploratory case study. Scientometrics, 68(3), 377–393. https://doi.org/10.1007/s11192-006-0118-9.

    Article  Google Scholar 

  • Bonsignore, E. M., Dunne, C., Rotman, D., Smith, M., Capone, T., Hansen, D. L., et al. (2009). First steps to Netviz Nirvana: Evaluating social network analysis with NodeXL. In 2009 International conference on computational science and engineering, 29–31 Aug. 2009 (Vol. 4, pp. 332–339). https://doi.org/10.1109/CSE.2009.120.

  • Cho, J. (2014). Intellectual structure of the institutional repository field: A co-word analysis. Journal of Information Science, 40(3), 386–397. https://doi.org/doi:10.1177/0165551514524686.

    Article  Google Scholar 

  • Connolly, R., Miller, J., Uzoka, F.-M., Lunt, B., Schroeder, M., Miller, C. S., et al. (2016). Red fish blue fish: Reexamining student understanding of the computing disciplines. In Proceedings of the 17th annual conference on information technology education, 2016 (pp. 115–120). ACM.

  • Coulter, N., Monarch, I., & Konda, S. (1998). Software engineering as seen through its research literature: A study in co-word analysis. Journal of the Association for Information Science and Technology, 49(13), 1206–1223.

    Google Scholar 

  • Courte, J., & Bishop-Clark, C. (2009). Do students differentiate between computing disciplines? ACM SIGCSE Bulletin, 41(1), 29–33.

    Article  Google Scholar 

  • Ding, Y., Chowdhury, G. G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing and Management, 37(6), 817–842.

    Article  MATH  Google Scholar 

  • Dissertation Abstracts International. (2017). http://www.proquest.com/products-services/dissertations/Dissertations-Abstract-International.html. Accessed March 1 2017.

  • Fein, L. (1959). The role of the university in computers, data processing, and related fields. Communications of the ACM, 2(9), 7–14. https://doi.org/10.1145/368424.368427.

    Article  Google Scholar 

  • Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40, 35–41.

    Article  Google Scholar 

  • Gan, C., & Wang, W. (2015). Research characteristics and status on social media in China: A bibliometric and co-word analysis. Scientometrics, 105(2), 1167–1182. https://doi.org/10.1007/s11192-015-1723-2.

    Article  Google Scholar 

  • Glass, R. L., Ramesh, V., & Vessey, I. (2004). An analysis of research in computing disciplines. Communications of the ACM, 47(6), 89–94.

    Article  Google Scholar 

  • Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. Riverside: University of California, Riverside.

    Google Scholar 

  • Hansen, D. L., Schneiderman, B., & Smith, M. A. (2011). Analyzing social media networks with NodeXL: Insights from a connected world. Burlington, MA: Morgan Kaufmann.

    Google Scholar 

  • Harel, D., & Koren, Y. (2000). A fast multi-scale method for drawing large graphs. In International symposium on graph drawing, 2000 (pp. 183–196). Springer

  • He, Q. (1999). Knowledge discovery through co-word analysis. Library Trends, 48(1), 133.

    MathSciNet  Google Scholar 

  • Hu, C.-P., Hu, J.-M., Deng, S.-L., & Liu, Y. (2013). A co-word analysis of library and information science in China. Scientometrics, 97(2), 369–382.

    Article  Google Scholar 

  • Hu, J., & Zhang, Y. (2015). Research patterns and trends of Recommendation System in China using co-word analysis. Information Processing and Management, 51(4), 329–339. https://doi.org/10.1016/j.ipm.2015.02.002.

    Article  Google Scholar 

  • Igami, M. P. Z., Bressiani, J. C., & Mugnaini, R. (2014). A new model to identify the productivity of theses in terms of articles using co-word analysis. Journal of Scientometric Research, 3, 3–14.

    Article  Google Scholar 

  • Khasseh, A. A., Soheili, F., Moghaddam, H. S., & Chelak, A. M. (2017). Intellectual structure of knowledge in iMetrics: A co-word analysis. Information Processing and Management, 53(3), 705–720. https://doi.org/10.1016/j.ipm.2017.02.001.

    Article  Google Scholar 

  • Kim, S., Hansen, D., & Helps, R. (2015) Information technology research in the academy: Insights from theses and dissertations. In Proceedings of the 4th annual ACM conference on research in information technology, 2015 (pp. 5–10). ACM

  • Kostoff, R. N. (1993). Co-word analysis. In Evaluating R&D impacts: Methods and practice (pp. 63–78). Springer.

  • Lee, L.-S., & Fang, Y.-S. (2015). Trends and issues in technology education research in Taiwan: A co-word analysis of 1994–2013 graduate theses (Online Submission).

  • Lee, B., & Jeong, Y.-I. (2008). Mapping Korea’s national R&D domain of robot technology by using the co-word analysis. Scientometrics, 77(1), 3–19.

    Article  Google Scholar 

  • Lee, P.-C., & Su, H.-N. (2010). Investigating the structure of regional innovation system research through keyword co-occurrence and social network analysis. Innovation, 12(1), 26–40.

    Article  Google Scholar 

  • Li, M., & Chu, Y. (2016) Explore the research front of a specific research theme based on a novel technique of enhanced co-word analysis. Journal of Information Science. https://doi.org/10.1177/0165551516661914.

    Google Scholar 

  • Library of Congress Collections Policy Statements (2008). https://www.loc.gov/acq/devpol/theses.pdf. Accessed March 1 2017.

  • Liu, Y., Goncalves, J., Ferreira, D., Xiao, B., Hosio, S., & Kostakos, V. (2014). CHI 1994–2013: Mapping two decades of intellectual progress through co-word analysis. In Paper presented at the proceedings of the SIGCHI conference on human factors in computing systems, Toronto, Ontario, Canada.

  • Liu, G.-Y., Hu, J.-M., & Wang, H.-L. (2012). A co-word analysis of digital library field in China. Scientometrics, 91(1), 203–217.

    Article  Google Scholar 

  • Morichika, N., & Shibayama, S. (2016). Use of dissertation data in science policy research. Scientometrics, 108(1), 221–241. https://doi.org/10.1007/s11192-016-1962-x.

    Article  Google Scholar 

  • Otte, E., & Rousseau, R. (2002). Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science, 28(6), 441–453. https://doi.org/10.1177/016555150202800601.

    Article  Google Scholar 

  • ProQuest 2014-2015 Publishing Agreement. (2014). http://media2.proquest.com/documents/umi_agreement_papersub.pdf.

  • Ravikumar, S., Agrahari, A., & Singh, S. N. (2015). Mapping the intellectual structure of scientometrics: A co-word analysis of the journal Scientometrics (2005–2010). Scientometrics, 102(1), 929–955. https://doi.org/10.1007/s11192-014-1402-8.

    Article  Google Scholar 

  • Ritzhaupt, A. D., Stewart, M., Smith, P., & Barron, A. E. (2010). An investigation of distance education in North American research literature using co-word analysis. The International Review of Research in Open and Distributed Learning, 11(1), 37–60.

    Article  Google Scholar 

  • Sedighi, M. (2016). Application of word co-occurrence analysis method in mapping of the scientific fields (Case study: the field of Informetrics). Library Review, 65(1/2), 52–64. https://doi.org/10.1108/LR-07-2015-0075.

    Article  Google Scholar 

  • Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. In Visual languages, 1996. Proceedings. IEEE symposium on, 1996 (pp. 336–343). IEEE.

  • Song, J., & He, X. (2016). Research on the master thesis of information sciences in recent five years. Technology Intelligence Engineering, 02.

  • Tsai, L. C., Hwang, S. L., & Tang, K. H. (2011). Analysis of keyword-based tagging behaviors of experts and novices. Online Information Review, 35(2), 272–290. https://doi.org/10.1108/14684521111128041.

    Article  Google Scholar 

  • Varshney, L. R. (2012). The Google effect in doctoral theses. Scientometrics, 92(3), 785–793. https://doi.org/10.1007/s11192-012-0654-4.

    Article  Google Scholar 

  • Vessey, I., Ramesh, V., & Glass, R. L. (2005). A unified classification system for research in the computing disciplines. Information and Software Technology, 47(4), 245–255.

    Article  Google Scholar 

  • Wakita, K., & Tsurumi, T. (2007). Finding community structure in mega-scale social networks. In Proceedings of the 16th international conference on World Wide Web, 2007 (pp. 1275–1276). ACM

  • Yan, B.-N., Lee, T.-S., & Lee, T.-P. (2015). Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): A co-word analysis. Scientometrics, 105(2), 1285–1300. https://doi.org/10.1007/s11192-015-1740-1.

    Article  Google Scholar 

  • Yang, Y., Wu, M., & Cui, L. (2012). Integration of three visualization methods based on co-word analysis. Scientometrics, 90(2), 659–673. https://doi.org/10.1007/s11192-011-0541-4.

    Article  Google Scholar 

  • Zong, Q.-J., Shen, H.-Z., Yuan, Q.-J., Hu, X.-W., Hou, Z.-P., & Deng, S.-G. (2013). Doctoral dissertations of Library and Information Science in China: A co-word analysis. Scientometrics, 94(2), 781–799.

    Article  Google Scholar 

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Authors and Affiliations

  1. Brigham Young University, 1031 Hunters Pointe, Bentonville, AR, 72712, USA

    Sung Kim

  2. Brigham Young University, 2571 N. 930 E., Provo, UT, 84604, USA

    Derek Hansen

  3. Brigham Young University, 1280 Cedar Ave., Provo, UT, 84604, USA

    Richard Helps

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  1. Sung Kim
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  2. Derek Hansen
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  3. Richard Helps
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Correspondence to Derek Hansen.

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Kim, S., Hansen, D. & Helps, R. Computing research in the academy: insights from theses and dissertations. Scientometrics 114, 135–158 (2018). https://doi.org/10.1007/s11192-017-2572-y

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