INNOVATIVE EDUCATIONAL TECHNOLOGY: SUPPORTING COGNITIVE LOAD MANAGEMENT AND SELF-REGULATED LEARNING IN ANATOMY EDUCATION

Abstract

Admin and faculty across the educational landscape are charged with understanding and deciding on how, if at all, to integrate technological advancements, such as virtual reality (VR) and artificial intelligence (AI), into their curriculum. Currently, the educational value of many of these technologies is unclear, with research studies contradicting their efficacy and value, ultimately suggesting that these tools, while innovative, must be used correctly to be effective. Therefore, educators across disciplines need evidence-based recommendations to successfully integrate the emergent, innovative technologies of the modern era and to understand how these technologies may impact student experience and learning. This thesis utilizes two complementary lenses—Cognitive Load and Self-Directed Learning—to examine technology-enhanced approaches for understanding student learning and experience within STEM courses. The following chapters recount two studies that explored how technology can be used to measure and support student learning and regulation. Chapter 1 affords a broad examination of emerging technologies within STEM education, while underscoring the value of understanding the learner’s experience using those technologies. Research design and methodology utilized in each study is briefly presented alongside the rationale for each research effort. Chapter 2 is a reproduced version of the published manuscript, Cognitive Load in Virtual Reality Anatomy Education: Comparing 2D and 3D Learning Experiences. The chapter is composed of an experiment that measured the cognitive load of students learning anatomy concepts in virtual reality. Our research team hypothesized that student cognitive load would be lower when viewing 3D content than when viewing 2D content. Quantitative data were collected via the NASA Task Load Index and the HP OMNICEPT REVERB G2 head-mounted display (HMD) for viewing virtual reality content. The HMD collects and analyzes biometric data to provide a measure for cognitive load. The NASA Task Load Index is a standardized measure for subjective reports of cognitive load experienced during an activity. Cognitive Load is defined as the total amount of mental effort required to complete a learning task. Additionally, qualitative data were collected via focus group and surveys to assess student experience, as well as the impact of various characteristics and prior experiences on cognitive load. The study highlights the value of VR technology in not only measuring student learning but helping to manage the required mental resources to complete the task. Chapter 3 is a reproduced version of the published manuscript in the Journal of Microbiology and Biology Education. The study conducted—Leveraging Generative AI to Foster Metacognition and Self-Directed Learners—utilized generative AI to foster metacognitive and self-directed learning (SDL) behaviors among graduate biomedical science students. The rapidly expanding field of AI has left many educators perplexed with respect to how, when, and why to use AI in their classroom. Our research team conducted an 8-week observational study to examine how AI could support the development of metacognitive awareness among participants, and further evaluating how the metacognitive awareness impacted the student’s readiness for SDL. Eight participants engaged with a trained AI model that facilitated reflective conversations related to the student’s learning efforts and experience in the context of their graduate neuroanatomy course. Quantitative data were collected via the Metacognitive Awareness Inventory (MAI) and the Self-Directed Learning Instrument (SDLI) to determine the impact of engaging with the model on students’ use of metacognitive practices and readiness of SDL, respectively. Qualitative data were collected via focus group interviews. The model provides a working example of how AI may be used to support the development of students’ self-regulation, rather than simply an answer generator. Chapter 4 is a broad summary of the two studies, how they are connected, and how they contribute to our understanding of emergent educational technologies. Recommendations for future research are briefly discussed alongside recommendations for immediate integration of the technologies into STEM education. The research studies described here help us understand the impact of innovation in education through technology. Both studies provide evidence that technology can support student learning by offering deeper and nuanced insights into the student experience. Virtual reality has the innate potential to reduce overall effort via the way it displays anatomical data to students. Generative AI may be utilized to support students in developing the requisite skills for success through AI-guided learning. Together, these emerging technologies provide a mechanism for innovative change within STEM education by offering educators effective tools for measuring and supporting student learning experiences beyond the typical approaches many have come to rely upon.