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Using theory-informed data science methods to trace the quality of dental student reflections over time

Abstract

This study describes a theory-informed application of data science methods to analyze the quality of reflections made in a health professions education program over time. One thousand five hundred reflections written by a cohort of 369 dental students over 4 years of academic study were evaluated for an overall measure of reflection depth (No, Shallow, Deep) and the presence of six theoretically-indicated elements of reflection quality (Description, Analysis, Feeling, Perspective, Evaluation, Outcome). Machine learning models were then built to automatically detect these qualities based on linguistic features in the reflections. Results showed a dramatic increase from No to Shallow reflections from the start to end of year one (20%  →  66%), but only a limited gradual rise in Deep reflections across all four years (2%  →  26%). The presence of all six reflection elements increased over time, but inclusion of Feelings and Analysis remained relatively low even at the end of year four (found in 44% and 60% of reflections respectively). Models were able to reliably detect the presence of Description (κTEST = 0.70) and Evaluation (κTEST = 0.65) in reflections; models to detect the presence of Analysis (κTEST = 0.50), Feelings (κTEST = 0.54), and Perspectives (κTEST = 0.53) showed moderate performance; the model to detect Outcomes suffered from overfitting (κTRAIN = 0.90, κTEST = 0.53). A classifier for overall depth built on the reflection elements showed moderate performance across all time periods (κTEST > 0.60) but relied almost exclusively on the presence of Description. Implications for the conceptualization of reflection quality and providing personalized learning support to help students develop reflective skills are discussed.

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Acknowledgements

The authors would like to thank Sarita Xinying Liu for her assistance with the process of content analysis and the NYU Dental Informatics team for their help in accessing the data. This article expands on preliminary work presented at the International Conference on Learning Analytics and Knowledge in 2020.

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Correspondence to Yeonji Jung.

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Appendix

Appendix

Appendix A: model performance of the classifiers

See Tables 3, 4, 5, 6, 7 and 8.

Table 3 Model performance of the reflection elements using linguistic features
Table 4 Model performance of the depth classifier using linguistic features
Table 5 Confusion matrix of the depth classifier using linguistic features on the test data
Table 6 Model performance of the depth classifier using coded and predicted values for reflection elements as features
Table 7 Confusion matrix of the depth classifier using coded values for reflection elements on the test data
Table 8 Confusion matrix of the depth classifier using predicted values for reflection elements on the test data

Appendix B: lists of top 10 predictive features for each of the classifiers

See Tables 9, 10 and 11.

Table 9 Reflection elements classifiers: Top 10 features (with MDG mean index)
Table 10 Depth classifier using linguistic feature: Top 10 features (with MDG mean index)
Table 11 Depth classifier using coded reflection elements: Top 10 features (with MDG mean index)

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Jung, Y., Wise, A.F. & Allen, K.L. Using theory-informed data science methods to trace the quality of dental student reflections over time. Adv in Health Sci Educ 27, 23–48 (2022). https://doi.org/10.1007/s10459-021-10067-6

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  • DOI: https://doi.org/10.1007/s10459-021-10067-6

Keywords

  • Reflection
  • Health professions education
  • Educational data sciences
  • Classification