دورية أكاديمية
Using theory-informed data science methods to trace the quality of dental student reflections over time.
| العنوان: | Using theory-informed data science methods to trace the quality of dental student reflections over time. |
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| المؤلفون: | Jung Y; Learning Analytics Research Network (NYU-LEARN), New York University, 370 Jay Street, 5th Floor, Brooklyn, NY, 11201, USA. yeonji.jung@nyu.edu., Wise AF; Learning Analytics Research Network (NYU-LEARN), New York University, 370 Jay Street, 5th Floor, Brooklyn, NY, 11201, USA., Allen KL; Department of Cariology and Comprehensive Care, College of Dentistry, New York University, 137 E. 25th Street, 6th Floor, New York, NY, 10010, USA. |
| المصدر: | Advances in health sciences education : theory and practice [Adv Health Sci Educ Theory Pract] 2022 Mar; Vol. 27 (1), pp. 23-48. Date of Electronic Publication: 2021 Sep 02. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Springer Netherlands Country of Publication: Netherlands NLM ID: 9612021 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-1677 (Electronic) Linking ISSN: 13824996 NLM ISO Abbreviation: Adv Health Sci Educ Theory Pract Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: <2009- > : [Dordrecht] : Springer Netherlands Original Publication: Dordrecht ; Boston : Kluwer Academic Publishers, c1996- |
| مواضيع طبية MeSH: | Data Science* , Students, Dental*, Humans ; Writing |
| مستخلص: | 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 (κ (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.) |
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| فهرسة مساهمة: | Keywords: Classification; Educational data sciences; Health professions education; Reflection |
| تواريخ الأحداث: | Date Created: 20210903 Date Completed: 20220419 Latest Revision: 20220419 |
| رمز التحديث: | 20231215 |
| DOI: | 10.1007/s10459-021-10067-6 |
| PMID: | 34476651 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1573-1677 |
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| DOI: | 10.1007/s10459-021-10067-6 |