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Threshold Concepts for Modeling and Assessing Higher Education Students’ Understanding and Learning in Economics

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Assessment of Learning Outcomes in Higher Education

Part of the book series: Methodology of Educational Measurement and Assessment ((MEMA))

Abstract

In the last decade, the research carried out on threshold concepts as a content-based way to model students’ understanding and learning in several domains has increased. However, empirical evidence on this approach is still scarce. In this chapter, the authors investigate the adequacy of the threshold concepts approach in the domain of business and economics in higher education following an established differentiation between basic, discipline, and modeling thresholds. After conducting a cognitive interview study using verbal reports, a self-assessment questionnaire was used to assess the respondents’ familiarity with the content and their security to solve the tasks. Results indicate that there is a complex relation between students’ response processes, self-assessment, and test scores, which varies according to the different thresholds and that all three measures generally confirm our hypotheses yet have to be critically discussed. There are implications that test developers, test users, respondents, and other stakeholders should be aware of this complex relation; it affirms that the threshold concepts approach is at least a useful tool when conceptualizing and developing tests, which can be considered to be an addition to classic taxonomies of educational objectives.

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Notes

  1. 1.

    For a comparison between the gradual development of knowledge according to the taxonomy of Bloom et al. (1956) and the approach of threshold models, see also Davies (2012).

  2. 2.

    Due to the novelty of this approach, it might be comprehensible that only few threshold concepts have so far been identified and empirically analyzed (Davies 2012).

  3. 3.

    Modeling and Measuring Competencies in Business and Economics in Higher Education funded by the German Federal Ministry of Education and Research (BMBF), Grant No: 01PK11013.

  4. 4.

    Further indices emphasizing the concepts’ relevance and use in day-to-day life could be delivered by an analysis of the frequency with which the concepts are used in web-based search engines (e.g., Google or Yahoo). This analysis shows that 5 million mentions can be found for the aforementioned concepts at the basic concepts threshold, several hundreds of thousands for the discipline concepts, and less than 1 hundred thousand for the modeling concepts.

  5. 5.

    Familiarity with a concept correlates with the confidence in the solution of strongly concept-dependent tasks. With a correlation r = 0.73 between both characteristics, unity of the characteristics cannot necessarily be assumed. As they refer to different phases of a task solving process (familiarity refers to the perceived content and confidence to the final solution of a task), a content-related separation is necessary.

  6. 6.

    As the cell allocation falls short of the required 5% cell frequency, a Fisher-Freeman-Halton test was used as well, which also shows the insignificance of the correlation (p = 202) (Lydersen et al. 2007).

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Authors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Sebastian Brückner & Olga Zlatkin-Troitschanskaia

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  1. Sebastian Brückner
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  2. Olga Zlatkin-Troitschanskaia
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Correspondence to Sebastian Brückner .

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Editors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Olga Zlatkin-Troitschanskaia

  2. Johannes Gutenberg University, Mainz, Germany

    Miriam Toepper

  3. Humboldt-Universität zu Berlin, Berlin, Germany

    Hans Anand Pant

  4. Humboldt-Universität zu Berlin, Berlin, Germany

    Corinna Lautenbach

  5. Johannes Gutenberg University, Mainz, Germany

    Christiane Kuhn

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Brückner, S., Zlatkin-Troitschanskaia, O. (2018). Threshold Concepts for Modeling and Assessing Higher Education Students’ Understanding and Learning in Economics. In: Zlatkin-Troitschanskaia, O., Toepper, M., Pant, H., Lautenbach, C., Kuhn, C. (eds) Assessment of Learning Outcomes in Higher Education. Methodology of Educational Measurement and Assessment. Springer, Cham. https://doi.org/10.1007/978-3-319-74338-7_6

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  • DOI: https://doi.org/10.1007/978-3-319-74338-7_6

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