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Comparison of case-based and lecture-based learning in dental education using the SOLO taxonomy
Mehmet Ilgüy1, Dilhan Ilgüy2, Erdoğan Fişekçioğlu1
1Dr. Mehmet İlgüy is Associate Professor, Department of Dentomaxillofacial Radiology, Yeditepe University Faculty of Dentistry, Istanbul, Turkey; Dr. Dilhan İlgüy is Associate Professor, Department of Dentomaxillofacial Radiology, Yeditepe University Faculty of Dentistry, Istanbul, Turkey; Dr. Fişekçioğlu is Assistant Professor, Department of Dentomaxillofacial Radiology, Yeditepe University Faculty of Dentistry, Istanbul, Turkey; and Dr. Oktay is Professor, Department of Pedodontic Dentistry and Dental Public Health, Yeditepe University Faculty of Dentistry, Istanbul, Turkey.
Abstract:
The aim of this study was to compare the impact of case-based learning (CBL) and lecture-based learning (LBL) on fourth-year dental students' clinical decision making by using the Structure of Observed Learning Outcome (SOLO) taxonomy. Participants in the study were fourth-year dental students (n=55) in academic year 2012-13 taught in a large-group LBL context and fourth-year dental students (n=54) in academic year 2013-14 taught with the CBL methodology; both took place in the oral diseases course at Yeditepe University Faculty of Dentistry, Istanbul, Turkey. All eligible students participated, for a 100 percent response rate. A real case was presented to the students in both groups to assess their clinical decision making on the topic of oral diseases. Their performance was evaluated with the SOLO taxonomy. Student t-test was used for statistical evaluation, and significance was set at the p<0.05 level. A statistically significant difference was found between the mean scores of the relational and extended abstract categories of the CBL and LBL groups (p<0.05). Students who were taught with CBL had higher scores at the top two levels of the SOLO taxonomy than students taught with LBL. These findings suggest that an integrated case-based curriculum may be effective in promoting students' deep learning and it holds promise for better integration of clinical cases likely to be encountered during independent practice.
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