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On My Honor: A Quasi-Experimental Analysis of Honors Students’ Perceptions of Workload and Cognitive Challenge

Author

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  • K. C. Culver

    (The University of Alabama)

  • Nathaniel Bray

    (The University of Alabama)

  • John Braxton

    (Vanderbilt University)

Abstract

The assumption that honors programs are more academically challenging is rarely interrogated. Using multi-institutional, longitudinal quantitative data from a larger study, we use quasi-experimental methods to examine students’ experiences of course rigor, including workload and cognitive challenge, for honors participants compared to non-participants. Honors students perceive greater workload but not cognitive challenge in their first year, especially in terms of the amount of reading and writing they are asked to do. In their fourth year, honors participants experience less cognitive challenge than non-participants. Results of subgroup analyses suggest that these differences are likely driven by students who participate in centralized honors programs rather than departmental honors as well as those attending more selective institutions, with implications for honors program instructors and administrators.

Suggested Citation

  • K. C. Culver & Nathaniel Bray & John Braxton, 2024. "On My Honor: A Quasi-Experimental Analysis of Honors Students’ Perceptions of Workload and Cognitive Challenge," Research in Higher Education, Springer;Association for Institutional Research, vol. 65(4), pages 679-704, June.
  • Handle: RePEc:spr:reihed:v:65:y:2024:i:4:d:10.1007_s11162-024-09788-5
    DOI: 10.1007/s11162-024-09788-5
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    References listed on IDEAS

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    1. Sophia Rabe‐Hesketh & Anders Skrondal, 2006. "Multilevel modelling of complex survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 805-827, October.
    2. K. C. Culver & John M. Braxton & Ernest T. Pascarella, 2021. "What We Talk about When We Talk about Rigor: Examining Conceptions of Academic Rigor," The Journal of Higher Education, Taylor & Francis Journals, vol. 92(7), pages 1140-1163, November.
    3. Arpino, Bruno & Mealli, Fabrizia, 2011. "The specification of the propensity score in multilevel observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1770-1780, April.
    4. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    5. Glynn, Adam N. & Quinn, Kevin M., 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator," Political Analysis, Cambridge University Press, vol. 18(1), pages 36-56, January.
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