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Comparative Study
, 79 (6), 697-704

Utilizing Self-Assessment Software to Evaluate Student Wax-Ups in Dental Morphology

Comparative Study

Utilizing Self-Assessment Software to Evaluate Student Wax-Ups in Dental Morphology

Karen R McPherson et al. J Dent Educ.


Traditionally, evaluating student work in preclinical courses has relied on the judgment of experienced clinicians utilizing visual inspection. However, research has shown significant disagreement between different evaluators (interrater reliability) and between results from the same evaluator at different times (intrarater reliability). This study evaluated a new experimental software (E4D Compare) to compare 66 student-produced tooth wax-ups at one U.S. dental school to an ideal standard after both had been digitally scanned. Using 3D surface-mapping technology, a numerical evaluation was generated by calculating the surface area of the student's work that was within a set range of the ideal. The aims of the study were to compare the reliability of faculty and software grades and to determine the ideal tolerance value for the software. The investigators hypothesized that the software would provide more consistent feedback than visual grading and that a tolerance value could be determined that closely correlated with the faculty grade. The results showed that a tolerance level of 450 μm provided 96% agreement of grades compared with only 53% agreement for faculty. The results suggest that this software could be used by faculty members as a mechanism to evaluate student work and for students to use as a self-assessment tool.

Keywords: E4D Compare; assessment; dental education; grading; preclinical education.


Figure 1
Figure 1. Process by which E4D Technologies software calculates feedback on student wax-up
Note: The software scans the ideal and student models and uses millions of data points from the adjacent teeth and typodont base to combine and overlay the two. The differences are measured, and both numerical and visual feedback are instantly provided.
Figure 2
Figure 2. Color-coded map of differences showing where student has made errors
Note: The software codes green as acceptable, red to yellow as overreduced or undercontoured, and blue as underreduced or over-contoured. Numerical values of each color are presented as surface area. For this study, the percentage that was green was used as the grade. The instructor has control over the strictness of the tolerance value.
Figure 3
Figure 3. Differences in software tolerance values that result in varying amount of acceptable color (green)
Note: Alterations in the strictness or tolerance value generate different results. With a smaller number such as 0.2 mm entered as the acceptable tolerance, little green is displayed, and more errors are highlighted. If a higher number such as 0.5 mm is used, almost the entire tooth is green indicating the student was within 0.5 mm of the faculty wax-up.
Figure 4
Figure 4. Mean difference between faculty and machine grades for each tolerance value under consideration
Note: Error bars represent the 95% confidence interval around the mean; 450 tolerance most closely correlates with faculty scores.

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