Graduation Date

Spring 5-4-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Programs

Medical Sciences Interdepartmental Area

First Advisor

Eleanor Rogan, Ph.D.

Second Advisor

Ethan Snow, Ph.D.

MeSH Headings

Imaging, Three Dimensional, Emotions, Technology, Augmented Reality, Cadaver, Metacognition

Abstract

A high school human anatomy course was designed to integrate three-dimensional learning modalities through the use of three-dimensional visualization technology, augmented reality, and whole-body donors (cadaveric specimens), and customized 3D digital models were created to support student learning. The course was organized so that each class meeting fell into one of six unique learning session types: lectures, augmented reality labs, gross anatomy (cadaveric) labs, content application (active learning) sessions, review sessions, and examination sessions.

An interval scale using ten emoji, each associated with a particular emotional valence (i.e., pleasantness) level, was developed and integrated into the course to track students’ emotional states before and after each learning session. Results indicated that students generally felt neutral or pleasant emotions and experienced significant valence level shifts over a variety of learning session types. Additionally, some correlations between their emotional valence levels and their exam scores were identified, and students experienced statistically significant changes in their emotions regarding learning in a cadaveric laboratory over the span of the course.

Four summative lecture and laboratory unit examinations were developed to assess the students’ abilities to remember and to understand information derived from unit learning objectives, using four unique question types. Student metacognitive shifts were analyzed by evaluating the alignment of a students’ pre-examination confidence level, measured on a four-factor scale, for a particular learning objective to a students’ post-response certainty level, measured on a four-factor scale, for a question assessing that particular learning objective. Knowledge levels were defined and could fall into one of four general categories: complete, partial, absent, or flawed, although more nuanced sub-categories were defined and analyzed. Results indicated that students performed best, were most confident, and were most certain on questions utilizing screenshots of 3D digital models, whereas students performed worst, were least confident, and were least certain on questions utilizing cadaveric specimens. Additionally, there were more complete knowledge levels and fewer absent knowledge levels acquired on the unit lecture examinations, as compared to the unit laboratory examinations.

Comments

2024 Copyright, the authors

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