A solution for automated grading of QDA homework [HICSS 2023]

Abstract: Teaching research methods is important in any curriculum that prepares students for an academic career. While theoretical frameworks for qualitative theory building can be adequately conveyed through lecturing, the practices of qualitative data analysis (QDA) cannot. However, using experiential learning techniques for teaching QDA methods to large numbers of students presents a challenge to the instructor due to the effort required for the grading of homework. Any homework involving the coding of qualitative data will result in a myriad of different interpretations of the same data with varying quality. Grading such assignments requires significant effort. We approached this problem by using methods of inter-rater agreement and a model solution as a proxy for the quality of the submission. The automated agreement data serves as the foundation for a semi-automated grading process. Within this paper, we demonstrate that this proxy has a high correlation with the manual grading of submissions.

Reference: Kaufmann, A., Riehle, D., Krause, J. & Harutyunyan, N. (2023). A Solution for Automated Grading of QDA Homework. In Proceedings of the 56th Hawaii International Conference on System Sciences (HICSS 2023), pp 44-53.

The paper can be downloaded as a PDF file.

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