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Reflections on study abroad: a computational linguistics approach

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Abstract

Study abroad and the associated sociocultural experience has been a subject of substantial interest to social science scholars and university administrators. Shedding novel light on the phenomenon, we draw on a corpus of student-authored reflective essays and apply machine learning methods for analysis of text-as-data to examine the features and the determinants of salient themes emphasized by students in their study abroad reflections. Our analysis identifies 18 different topics spanning the domains of distinctly cultural cognition, interaction with people, physical environment, and personal change. Specifics of the experience such as duration and location, timing of reflections, and observable student characteristics including gender, major, academic performance, extracurricular involvement, and socioeconomic status are all important determinants of student’s reflections. Different factors, however, matter differently with respect to students’ emphases on particular topics, a finding indicative of the complex nature of the study abroad experience.

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Notes

  1. A Scopus search on 'study abroad' appearing in the title, abstract, or keywords identifies more than 2000 published contributions, with the vast majority of publications dated after year 2008. An analogous search using Google Scholar reveals many more works. For a necessarily limited set of sample contributions and further references, see, e.g., Carlson and Widaman [6], Ryan and Twibell [33], Dwyer [9], Rundstrom Williams [32], Hadis [15], Anderson et al. [2], Paige et al. [26], Collentine [7], Norris and Gillespie [25], Basow and Gaugler [3], and Terzuolo [37].

  2. For example, the words 'dog' and 'bark' will appear more often in a topic about dogs, 'cat' and 'purr' in a topic about cats, while 'pet' and 'vet' may appear roughly equally in both. Documents feature multiple topics in different proportions. A document that is 20% about cats and 80% about dogs will tend to feature four times as many dog words as cat words.

  3. See https://www.structuraltopicmodel.com for an updated list of published applications of STM.

  4. For an exposition of the formal statistical structure of the STM and computational aspects of estimation, see Roberts et al. [28].

  5. This figure does not include shorter spells abroad as part of regular coursework offered by resident faculty.

  6. We dropped four essays of students who by the time of completion of our data collection had not yet turned in both the early and the ex-post reflection essay in the required format.

  7. The STM also allows for the possibility to model topical content as a function of metadata (see, e.g., Roberts et al. [27, 28]). We do not utilize this feature of STM.

  8. In studying the words lists, it is important to keep in mind that STM-based estimates of topics are driven by correlations across documents in the occurrence of words. Thus, estimated word lists will also contain words that are on their own not particularly informative about the core ideas underlying a topic. (For example, 'can' and 'will' are among highest probability words for several topics.) Indeed, this is an aspect of STM that human readers cannot easily match. An author's use of a topic might rely on specific combinations of words in patterns that a human reader might find hard to discern.

  9. Roberts et al. [30: 12] note that to implement the regressions, "…the topic model should contain at least all the covariates contained in the estimateEffect regression". Accordingly, the set of metadata variables that we utilize to estimate the effect of specific metadata covariates on topical prevalence conceptually coincides with the set of metadata covariates that we utilize to estimate the topics. Practically, to estimate the effects associated with categorical and numeric variables that take on multiple values (such as, e.g., Region and GPA; see Table 2), we define and utilize in the analysis corresponding binary variables that highlight the effects of interest (e.g., Africa vs. other regions; above median GPA vs. below median GPA).

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Acknowledgements

We are grateful to Mark Rush and Marc Conner for making this project possible. Griffin Noe provided excellent research assistance. An anonymous reviewer offered valuable comments and suggestions on an earlier draft of the manuscript.

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Authors and Affiliations

  1. Department of Economics, The Williams School of Commerce, Economics, and Politics, Washington and Lee University, Lexington, VA, 24450, USA

    Peter Grajzl

  2. CESifo, Munich, Germany

    Peter Grajzl

  3. Center for International Education, Washington and Lee University, Lexington, VA, 24450, USA

    Cindy Irby

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Correspondence to Peter Grajzl.

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Grajzl, P., Irby, C. Reflections on study abroad: a computational linguistics approach. J Comput Soc Sc 2, 151–181 (2019). https://doi.org/10.1007/s42001-019-00038-8

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