Designing Learning with Purpose: My Reflection on Module 2

williamli

Introduction

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Module 2 introduced several frameworks for thinking about learning design, including Backward Design (UbD), Design Thinking, Learning Outcomes and Taxonomies, Better Learning Design, and Inquiry and Project-Based Learning. These ideas are not only theoretical but also deeply relevant to my own experiences as a computer science student. In this reflection, I will connect each concept with examples from my learning journey and highlight how these frameworks shape the way I study, solve problems, and design projects.

Backward Design

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The principle of Backward Design, developed by Wiggins and McTighe (2005), emphasizes starting with the desired learning outcomes and then planning assessments and activities to reach those goals.This approach resonates with me because, in past programming assignments, I tended to start coding right away and figure things out as I went. But that approach often left me with messy code and unclear goals. UbD made me realize that starting with the end goals—what I want the final product or learner outcome to look like—actually makes the whole process smoother. For example, when I planned an algorithm assignment with the end goal of “clean, efficient code that solves the problem in O(n log n),” I was able to structure my steps much better and avoid wasting time on unnecessary detours. Research also emphasizes that backward design prevents the “coverage trap,” where too much emphasis is placed on content rather than meaningful outcomes (Wiggins & McTighe, 2005). That experience taught me the value of clarifying outcomes early, which I can also apply to learning and teaching.

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Design Thinking

The idea of Design Thinking also stood out to me. I liked how it emphasizes empathy and iteration. In computer science, empathy might sound unrelated, but it matters. Last summer, I helped a classmate who struggled with recursion. Instead of just showing him the code, I tried to put myself in his shoes—what exactly was confusing him? By prototyping small examples step by step, we managed to work through the problem together. That moment showed me how empathy and iteration can make learning more meaningful, not just efficient. Studies have shown that embedding design thinking in education can improve creativity, problem-solving, and student engagement (Alvarado et al., 2025; Prayogi et al., 2023). This connects directly to my experience of breaking down problems iteratively and approaching them from the learner’s perspective.

Learning Outcomes and Taxonomies

When it comes to Learning Outcomes and Bloom’s vs. SOLO Taxonomies, I personally find Bloom’s taxonomy more intuitive because it breaks learning into stages like remembering, understanding, applying, and creating (Anderson & Krathwohl, 2001). To help readers visualize this framework, please take a look at the video above. When it comes to understanding learning outcomes, Bloom’s taxonomy provides a hierarchical structure that ranges from lower levels, such as remember and understand, to higher levels like analyze, evaluate, and create. For me, the difference between surface and deep learning became clear in my algorithms course. Memorizing formulas was “surface,” but applying them to design a solution for a real-world graph problem was “deep.” Bloom’s verbs helped me recognize when I was just memorizing versus when I was truly applying knowledge. Scholars also argue that taxonomies help teachers (and students) clarify expectations and support constructive alignment with assessments (Biggs & Tang, 2011). For me, this distinction has made my own learning goals clearer and encouraged me to push toward higher-order tasks.

AspectBloom’s TaxonomySOLO Taxonomy
FocusCognitive processes (remember → create)Depth of understanding (surface → deep)
StructureHierarchical, with six stagesProgressive levels of complexity
Use in teachingHelps design learning outcomes with clear verbsHelps assess how well knowledge is integrated

Better Learning Design

Better Learning Design also connects strongly to my own experience. In high school, I often engaged in surface learning, cramming for exams just to get through them. At UVic, I’ve experienced deeper learning when projects required me to build something functional and practical, like coding an algorithm visualizer. That’s when design and alignment between tasks and outcomes really mattered—because the assignments pushed me to move beyond memorization into problem-solving. This reflects Biggs and Tang’s (2011) highlighting of this principle repeatedly in their work. To support a deeper understanding, the video vividly illustrates what alignment means, why it is important, and how it can be implemented in course design. concept of constructive alignment, where learning activities and assessments must directly support the intended learning outcomes. Without this alignment, tasks risk being disconnected or superficial. In my case, I could clearly see the difference: projects that aligned with deeper goals transformed my approach, while misaligned tasks felt like busywork.

Inquiry and Project-Based Learning

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Finally, I see Inquiry and Project-Based Learning as closely tied to my computer science studies. Coding projects are essentially open-ended problems: there’s no single “right” way, but multiple possible approaches. This freedom is exciting but also challenging, because it forces me to ask questions, explore alternatives, and take ownership of my learning process. For example, when I worked on a project that visualized sorting algorithms, I had to research visualization libraries on my own. It was frustrating at times, but ultimately it gave me a stronger sense of independence and confidence. Research shows that project-based learning not only increases motivation but also helps students develop transferable problem-solving skills (Thomas, 2000; Sun et al., 2023). I can see how this connects to professional life, where projects rarely come with step-by-step instructions.

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Conclusion

In short, Module 2 helped me realize that effective learning design is about more than just “getting the task done.” It’s about planning with purpose, using empathy in problem-solving, and aligning tasks with meaningful outcomes. These ideas don’t just apply to education—they connect directly to how I study, collaborate, and approach challenges in computer science.

References

  • Alvarado, L. F., et al. (2025). Design thinking as an active teaching methodology in education. Frontiers in Education. https://doi.org/10.3389/feduc.2025.1462938
  • Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.
  • Biggs, J., & Tang, C. (2011). Teaching for quality learning at university (4th ed.). Open University Press.
  • Prayogi, S., Ardi, R. F. P., El Yazidi, R., Tseng, K.-C., & Mustofa, H. A. (2023). The analysis of students’ design thinking in inquiry-based learning in routine university science courses. International Journal of Essential Competencies in Education, 2(1), 1–14. https://doi.org/10.1016/ijece.2023.01.001
  • Sun, Y., Wang, C., & Hu, S. (2023). An overview of the literature on design thinking in education and educational research. Journal of Education and Educational Research. https://drpress.org/ojs/index.php/jeer/article/view/7745
  • Thomas, J. W. (2000). A review of research on project-based learning. Autodesk Foundation.
  • Wiggins, G. P., & McTighe, J. (2005). Understanding by design (2nd ed.). Pearson.