Author: zackd

For our Learning Design Blueprint, my part is about the legal and ethical aspects of Patient Data Privacy and Security. One of my planned learning activities requires learners to analyze a clinical case study to distinguish between public FOIPPA and private PIPEDA privacy statutes (Office of the Privacy Commissioner of Canada, 2019). The activity also asks learners to identify active compliance hazards and define the legal boundaries of a patient’s circle of care. Cognitive overload and limited accessibility pose substantial barriers to student achievement in this activity. Our target audience consists of neurodivergent students, people who speak languages other than English, and people with diverse educational backgrounds. Presenting complex legal frameworks through standard text-based case studies might easily irritate and divert these students. When legal terminology dominates the conversation, the actual application of patient data protection is overlooked.

To overcome these obstacles, I want to modify this exercise by converting the static text into an interactive branching scenario using H5P. Reflecting on my experience creating immersive clinical training environments using Unreal Engine 5 and the Meta Quest Pro, I’ve had an idea of how spatial and visual simulations boost understanding. While we cannot create a complete virtual reality simulation for this web-based lesson, we may replicate the immersive learning method. Instead of reading a long narrative about a data sharing compliance issue, learners will navigate a visual storyboard. They will step into the shoes of an allied health professional and make compliance choices in real time. For example, if a learner chooses to share health data outside the established circle of care, the scenario will immediately pause to provide plain language, constructive feedback. This change immediately supports our objective of offering numerous learning formats that use visual aspects rather than depending simply on written information. By changing from a reading comprehension job to an interactive decision-making process, we assist students in navigating the gray regions of digital health data without becoming overwhelmed.

Guidance Documents – Office of the Information and Privacy Commissioner for BC. (2024). Oipc.bc.ca. https://www.oipc.bc.ca/resources/guidance-documents/

Office of the Privacy Commissioner of Canada. (2024, May 1). PIPEDA in brief. Priv.gc.ca; Office of the Privacy Commissioner of Canada. https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/pipeda_brief/

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Hi there! I’m a fourth year combined in Combined Major in Computer Science and Health Information Science at Uvic. I enjoy doing creative works and going out with my friends.

My most valuable educational experience came when I utilized Unreal Engine 5 and Meta Quest Pro to construct a medical training simulation with the purpose of supporting clinical workers in creating procedural muscle memory using virtual reality technology. This project began as a part of my health information class, which sparked my interest. My goal was to address practical clinical training limits by bridging the gap between healthcare informatics theory and software engineering. This was completely consistent with our constructivist learning principles because instead of just remembering material from a textbook, I was actively developing a rich, dynamic mental model of spatial computing and medical user experience through practical testing.

But at the technical optimization stage, my motivation started to change. The instructional materials and engine documentation I had to rely on were organized in a very strict, behaviorist way, despite the assignment’s need for originality because the program behaves in a very specific way. My desire for independence was a little shaken by this enormous conflict. Every time I try something new that I thought would work, such as by experimenting with new UI layouts or special physics-based hand interactions, the strict underlying technological limits provided little margin for error and continuously broke the build.

In addition, because there were only two of us in the small group, my competence was pushed to its utmost. The learning process seems particularly difficult owing to the significant cost of troubleshooting sophisticated bugs with such a small team. Especially with the 3D modelling process as that took up the most time. Simply put, we lacked both the ability and manpower required to fix these sophisticated system issues and refine our features.

The course approach might have featured a collaborative, problem-solving tech sandbox for the entire class, rather than forcing little, isolated couples to battle prescriptive paperwork alone. To improve relatedness and strengthen our collective expertise and the experience overall, we may have partnered in larger peer development cohorts to share the workloads and strategies better.

For blog post 2, I will be focusing on Cooperative Learning Environment and the topic my group has decided to talk about is Patient Data Privacy and Security in the Digital Age. First, I’ll explain briefly what cooperative learning is.

Cooperative learning is fundamentally about students working together in small groups to maximize their own and their peers’ learning. Instead of just putting people together and hoping for the best, effective cooperative learning relies on a few key components. First, there is positive interdependence, which implies that students recognize that they must cooperate as a team to succeed or fail. Second, there is personal accountability, so no one can just get away with it. Furthermore, it involves direct promotional participation, which teaches essential interpersonal skills and allows the group to assess how well they collaborate (Yang, 2023). The major goal is to promote active and social knowledge building.

So, in terms of how this learning environment approach connects to our topic of patient data security and privacy, in many ways, it is an ideal fit. In the real world, data protection is rarely a one-time task. It requires a culture of collective accountability, and everyone should know and be aware about it. Cooperative learning allows us to spread this shared awareness. In a learning environment, students can work together to identify hazards or debate ethical norms. Furthermore, they can also learn about how healthcare teams must collaborate to ensure system security. However, cooperative learning may only be appropriate to a certain degree of contexts in our topic. Individual compliance and memorization are critical in several aspects of data security, such as creating strong passwords and understanding severe regulatory criteria. Although peers can test one another, implementing specific security measures is ultimately a personal duty.

For technology-mediated environments, if we want cooperative learning to occur digitally, we can’t just open a forum and expect cooperation to happen on its own. The interaction is determined by the platform we use. For example, adopting a shared digital workplace for sorting requires collaboration and compromise. Given all of this, cooperative learning might be a key component of our final interactive learning design. We plan to create a basic cooperative exercise in which small groups must examine a hypothetical privacy scenario and determine the best course of action as a group. To ensure that everyone learns the ideas on their own, we will create a balance between cooperative learning and individual evaluations. It all comes down to finding the right balance so that our students realize that protecting patient data necessitates collaboration.

I also found this really cool short video that explain the cooperative learning concept well.

Reference:

Yang, X. (2023). A historical review of collaborative learning and cooperative learning. TechTrends, 67, 718–728. https://doi.org/10.1007/s11528-022-00823-9