Blog Post #1

In this post, I’ll explore my personal understanding on learning, covering my definition of the concept, how my style aligns with key theories, the role of motivation, and the impact of prior knowledge. For more on my background, please check out my about me page here.

What Is Learning ?

“I have never met a man so ignorant that I couldn’t learn something from him.” – Galileo Galilei.

Learning is an active and collaborative exchange of knowledge and certainly not a passive intake of information. Galileo Galilei,a famous astronomer, perfectly captures this idea. Gaining knowledge though learning is not from a single source but can be gained from everyone, regarding of their background , culture or expertise.

In the field of computer science, this is especially true. Complex problems are rarely solved alone! They require researching, understanding, and applying knowledge gained from collaborating with others, whether its your teammate or AI. Learning is a continuous cycle of sharing ideas, receiving feedback, and building on one another’s work.

For example , when I took the Foundations of Computer Science (CSC 320) at UVIC , my professor encouraged us to discuss the topic learned with other to and learn more though your explanation but also from feedback. And that is exactly what I did. My study group and I would meet every Saturday to explain what we understood and learn more form one another. And that helped us all excel in the considered hardest courses in computer science.

Learning Theories and Personal Learning strategies

I am convinced cognitivism perfectly captures how my personal brain works. I’m not the type to just memorize facts or mindlessly follow instructions. What I really care about is figuring out the good learning strategies that stick and make the mental process of how I encode information as efficient as possible.

Tree of knowledge Illustration

My final ultimate goal is to make new information meaningful to have it encoded in brain for as long as possible but also to have it easy to retrieve. I usually do this by connecting new ideas to what I already know and if it is not connected to anything I get interested in learning more. I think of my brain as a big tree, each core topic forms a branch and new knowledge turns into a sub branch of that main branch. By doing so I’m no longer just learning the how to but am now asking why behind every concept which helps me remember knowledge and excites me to learn deeper about the concept overall

Motivation of learning

The ARCS Model provides four elements to build motivation:

  • Satisfaction comes from providing meaningful rewards or recognition for progress and achievement.
  • Attention involves capturing interest with engaging and varied activities.
  • Relevance means connecting the material to learners’ goals and needs.
  • Confidence helps learners believe they can succeed through clear expectations and achievable challenges.

For me, Relevance plays a huge role in keeping me engaged as a learner. I remember starting out with the basics of HTML and CSS, and while I understood the fundamentals, I didn’t feel as deeply connected to the material. But once I started learning about frameworks like React, the relevance really clicked. I was able to see how React would help me build more dynamic and efficient web applications which is something that I could use in real-world projects.With that relevance, I became more curious and motivated to dive deeper into the framework, experimenting with different features and pushing my skills further.

Adult Learning and Prior Knowledge


My prior knowledge helps me build on what I already know, making new concepts easier to grasp. For instance, learning React was smoother because I had a solid foundation in HTML and CSS. As an adult learner, I can connect real-world experience to theory, so I can apply new ideas faster and more effectively. It’s like using past experiences as a framework to navigate new challenges.