Jarvie Journal - College of Dental Medicine - Columbia University

Volume 56, Spring 2013
Enhancing Diagnostic Reasoning Abilities of Pre-Doctoral Dental Students with
Introduction of Prosthodontics Education and Computer-Based Cognitive Tutor**
Francis Oh 1 , O. Roger Anderson 2 , George S. White 3 , Dae-Won Haam 4 , Candice Zemnick 3*
1 Postgraduate Prosthodontics, College of Dental Medicine, Columbia University, NY, NY;
2 Mathematics, Science, and Technology Program, Columbia University Teachers College, NY, NY;
3 Division of Prosthodontics, College of Dental Medicine, Columbia University, NY, NY;
4 Implant Center, College of Dental Medicine, Columbia University, NY, NY
*
Faculty Mentor
Introduction: Clinical diagnostic reasoning is the core competency of all healthcare professional, which involves
cognition and interaction with the environment to understand clinical situations, make diagnostic and therapeutic
decisions, and address clinical problems. In this study, we propose that diagnostic reasoning training in pre-doctoral
education can be enhanced with proper introduction of prosthodontics education and use of cognitive tutor. As
defined by ADA, the essence of prosthodontics education is the development of diagnostic reasoning ability,
commonly referred to as ‘Treatment Planning’. Cognitive tutor is a computer learning module based on cognitive
psychology theory, particularly the ACT-R (Adaptive Control of Thought-Rational) theory. In this study,
prosthodontics education and cognitive tutor technology are combined with various pedagogical theories, such as
problem-based-learning, cognitive load theory, teacher-proofing, and more, to formulate an effective learning
computer module that enhance pre-doctoral students’ diagnostic reasoning abilities.
Objective: The purpose of the study is to develop and analyze the efficacy of a cognitive tutor that enhance predoctoral
students’ diagnostic reasoning abilities, combining prosthodontics education and appropriate pedagogical
theories.
Materials & Methods: Randomized controlled mixed-method experimental study. Total N=40 (3 rd year dental
students). Control group (N=20) uses traditional textbook and national board preparation materials on paper.
Experimental group (N=20) used novel CTAT-based posthdontics education computer module (10 lessons). Both
groups were given 3 days to study with materials provided. Pre-test and post-test were given and analyzed for
quantitative analysis. 12-items qualitative questionnaire on 5-point Likert scale were given for qualitative analysis.
Results: The P-value for the control group was 7.67 x E-09, therefore, improvement was statistically significant for
the control group. The P-value for the experimental group was 4.42 x E-10, therefore, improvement was statistically
significant for the experimental group. When we compared the score increase between the control and experimental
groups, the analysis showed that a 2-tailed P-value is 0.015. It is concluded with 95% confidence interval that the
experimental group performed significantly better than the control group. Qualitative analysis results show that
students were positive toward cognitive tutor in terms of ease-of-use, contents integration, boosting confidence, and
enhancing diagnostic reasoning.
Discussion: While the results were favorable toward the initial premise of the study, there are several limitations to
the study design: 1) The cognitive tutor was authored mainly with qualitative analysis results from medical education
literatures. Since dental experts’ clinical reasoning process may differ from medical experts’, a new qualitative
coding analysis must be performed. 2) Quantitative analysis of the study only shows the efficacy of the learning
module, not the modality of reasoning process. We must conduct a qualitative coding analysis concurrently with the
experiment on same experimental group before and after an intervention to truly claim the change in cognitive
reasoning.
**This is a pilot study for a longitudinal in-depth study addressing all of the above limitations, including
cognitive coding, that is a part of Dr. Oh’s PhD dissertation thesis.
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