Blog Post #1 – Motivation Autopsy

At the end of last year, I began the Machine Learning Specialization course from Coursera, with a high level of motivation and confidence. At the time, I was already studying machine learning theory at UVic, so I assumed that this course would be a relatively smooth transition into practical application. My expectation was that it would help me strengthen my hands-on skills while reinforcing concepts I was already familiar with.

However, as I progressed through the course, my motivation gradually began to shift. Around one-third of the way in, I started to feel increasingly disengaged. Although I understood the theoretical foundations of machine learning, the practical component particularly the heavy use of the Python library scikit-learn was far more challenging than I expected. My prior Python experience was mostly focused on data cleaning and numerical methods, the logic and workflow of scikit-learn, however, felt fundamentally different. The course seemed to assume that learners were already comfortable with these tools, leaving little space to bridge that gap.

Looking back, this disengagement was not simply due to the inherent difficulty of machine learning, but rather to aspects of the learning design. The pacing was fast, explanations were often high-level, and there was limited scaffolding to help learners transition from theory to practice. While I had anticipated that theory and practice would closely align, the connection between the two was weaker than I had expected, which made it harder to stay motivated.

From a learner needs perspective, this experience did not sufficiently support my sense of relatedness and competence. The course did not strongly connect new practical skills to learners’ existing knowledge, nor did it provide enough opportunities to build confidence through gradual progression. Without these supports, it became increasingly difficult to feel capable of continuing, even though I remained interested in the subject itself.

If this learning experience were designed differently, I believe it could better support motivation by incorporating more low-risk, step-by-step practice activities early on. Clearer explanations of tool-specific thinking, along with opportunities for early success, might help learners develop confidence and maintain engagement. This reflection has helped me see how important learning design is—not just in presenting content, but in supporting learners as they navigate the challenging process of changing and extending their understanding.