Technology
A design thinking exploration of the online graduate education experience: Empathy and beyond
Bayer, Ilana (1,2); Elma, Asiana (3); Grierson, Lawrence (4,5)
Introduction:
A design thinking approach is being used to explore user experience and to inform areas for improvement and enhancement in the online Health Sciences Education Graduate Program at McMaster University. This human-centered design process engages end-users (i.e., student, faculty, or staff) at the centre of design decisions. The core stages of this approach describe processes that empathize, define, ideate, prototype, and test.
Methods:
Data gathered from a one-course pilot study, student focus groups, and a facilitated program retreat was used to create empathic concept maps of user experience. The themes arising from these maps were then used to collate additional data to provide deeper insight into the needs, emotions, challenges, and opportunities experienced by users of this online graduate program. Specifically, the data was used to develop questions for semi-structured interviews with representatives from different end-user groups within the program (e.g., part-time course-based student, full-time thesis student, course coordinators and administrative staff). Following completion of the empathize stage, the next steps will be to identify the meaningful opportunities for enhancement, and to prototype and test innovative solutions.
Results:
The collected data was used to create empathy maps to represent the experiences of the end-users in the program that illustrated examples of what the end-user did, said, thought and felt as it related to their experience in the online graduate program. The themes arising from these maps included connections, communication, support, and workload management.
Conclusion:
The use of design thinking as a process to guide quality improvement and program enhancement in an educational context will be discussed. Results from the empathize phase of the design thinking approach and next steps will be shared. We will discuss user engagement strategies and team collaboration, strategies for data collection, lessons learned, and how the process may apply to participants’ work.
Utilizing Eye Tracking Technology to Understand How Novices Learn Neuroanatomy
Nguyen, Angela; Cheung, Beata; Saini, Jessica; Leclair, Rebecca; Lyons, Jim; Heisz, Jennifer; Wainman, Bruce; Brewer-Deluce, Danielle
Introduction:
Neurophobia is the fear experienced by students when learning and applying neuroscience. Although the reason remains unclear, neurophobia presents a major barrier to student success in neuroanatomy. A preliminary study suggests that individuals with low working memory capacity (WMC) benefit from using high-contrast images when learning neuroanatomy. The reason for the experienced improvement, however, is unknown. This study employs eye-tracking technology to better understand how individuals of varying WMC study neuroanatomical images. We aim to assess differences in gaze patterns between students of high and low WMC when viewing high and low contrast brain slices to understand the potential impact of staining on performance.
Methods:
Undergraduate students with no prior anatomical education (n=125) were recruited. During an eye-tracking session using the SR Research Eyelink II system, participants were given 5 minutes to study 12 neuroanatomical structures on digital images of 4 brain slices (either coronal or transverse slices in high or low contrast) and were then tested on their ability to identify the learned structures on similar low-contrast images. This procedure was repeated so that all participants were exposed to a set of coronal/transverse and high/low contrast images in a randomized fashion. Participants’ eye tracking data and test accuracy were recorded throughout the protocol. After testing, participants completed the Automated Operation Span Task (OSPAN) to quantify WMC.
Results:
During testing, no statistical significance was found between the amount of time spent focusing on the correct structure (IA Dwell %) based upon the slice orientation (F(1, 241) = 0.999, p = 0.318), contrast (F(1, 241 = 0.074, p = 0.786), WMC (F(1, 241) = 0.201, p = 0.654), or their interactions (F(1, 241) = 0.053, p = 0.819).There was also no significant correlation between % Accuracy, IA Dwell %, and OSPAN scores.
Discussion/Conclusion:
These results demonstrate that WMC does not significantly influence participants’ accuracy or the time spent looking at areas of interest. Critically, this offers quantitative insight into how neuroanatomical information is viewed and learned by novices. Future work will address other eye tracking metrics to better establish the impact of staining on neuroanatomical learning which will inform future teaching practices in neuroanatomy.