The project was mapped out over a comprehensive four-month timeline, utilizing the strategic framework of the IBM Activation Journey. With a strong focus on efficiency and progress tracking, this framework allowed us to effectively manage tasks and milestones throughout the project lifecycle.

This project took flight due to the rising costs tied to making course selections. For UofT students in programs other than computer science and management, a single course can top $600 for domestic students and skyrocket to ten times that amount for international students. While tools like RateMyProfessor exist to help, they all have their gaps. This gap between what students need and what's available opened up a big opportunity, promising a substantial user base eager for a more comprehensive solution.

Identifying opportunities begins with pinpointing the stage(s) in the experience journey where issues arise. Using feedback from user research, we mapped the entire journey across four phases: Research, Validate, Plan, and Commit. To ensure precise and impactful intervention, we then assessed the potential opportunity areas at each stage.

The Commit phase was promptly excluded from consideration as it marks the journey's end, rendering intervention too late. As for the Plan phase, while research results revealed certain pain points in course scheduling, the presence of established tools addressing some of these concerns limited our scope for differentiation. As such, improving the course planning experience was considered as a added value of our product, but not a primary differentiator. Our focus eventually shifted towards the Validate stages, where pain points concentrate, suggesting a significant opportunity to take advantage of.

Designing a course selection platform that facilitates easy experiential knowledge acquisition requires students to be willing and motivated to share their experiences with fellow students. To motivate students to share, I first segmented users into groups based on their sources of motivation, including those driven by altruistic tendencies and those primarily motivated by self-benefit incentives. This segmentation allowed me to design more tailored motivation mechanisms for each group later on.

In addition to low motivation, the issue of ineffective or misleading course reviews often stems from poorly formulated review prompts. Research into popular course review platforms such as UofT Hub, RMP, and Reddit reveals a lack of quality prompts for sharing experiences. As a result, the resulting course reviews tend to be biased, emotional, and lacking in specificity. This underscores the significance of well-crafted prompts in building a course selection platform that facilitates informed decision-making.

Our platform aimed to resonate with those driven by altruism, encouraging a sense of contribution and influence among users. By highlighting the lasting impact of their reviews, I prompted students to see their input as a lasting legacy that would greatly assist future peers. The goal was to tap into users' altruistic tendencies, showing them how their insights and experiences could become invaluable resources for the wider student community, motivating them to leave thorough, considerate reviews.

Additionally, I introduced a peer recognition feature allowing users to anonymously express gratitude for helpful reviews, fostering a sense of peer-to-peer appreciation and underlining the enduring significance of their contributions.

Motivating altruism-driven students to review is relatively easier due to their inherent self-motivation. To engage those not primarily motivated by altruism, several solutions were conceptualized. First, the platform incentivized reviews by unlocking more when students actively contributed, reinforcing this behaviour.

Another strategy targeted these students by framing course-related questions for easier recall and engagement. Specific, memory-anchoring questions and more extreme prompts ('favourite/least favourite assignment and why') elicited quicker, more engaging responses compared to generic queries.

Additionally, the review process was simplified for users less inclined to provide detailed answers. They could opt for a series of low cognitive load binary choice questions, providing an alternative for those finding open-ended responses too demanding.

Based on the academic program chosen by the student, the platform automatically curates and pre-prepares a tailored list of all necessary courses for the selected degree/program/major/concentration. This eliminates the need for students to manually sift through extensive program details, enabling them to focus on elective courses that align with their interests.

The platform offers a comprehensive filtering system that allows students to fine-tune their course search with high precision. Users can effortlessly filter courses based on various criteria, including program requirements, course levels, available time slots, and even professor ratings. This all-encompassing filtering capability ensures that students can easily navigate through an extensive course catalog, making the process of finding the most suitable courses more efficient.

On each course page, students can find concise descriptors indicating the nature of the course, such as "introductory," "group work," or "theoretical." These descriptors offer a quick snapshot of the course style, making it easier for students to gauge the course's essence without the need to sift through lengthy course syllabi. This feature provides students with an efficient way to align their preferences with the courses they plan to take.

The built-in timetable builder provides students with a canvas to unleash their scheduling creativity. With the ability to create and iterate multiple timetables, students can seamlessly experiment with different schedules and compare them all on a single page. This hands-on approach empowers students to craft the perfect timetable that aligns with their time commitments and personal preferences, ensuring a smooth and efficient course plan iteration process.

As students wrap up their courses, the platform prompts them to contribute by completing a quick course review. The review process engages students with a series of intuitive pairs of descriptors, each representing contradicting aspects such as "introductory vs. advanced" or "practical vs theoretical." Students are invited to choose the option that best describes the course and reflects their experience, enabling them to effortlessly share their insights without feeling overwhelmed by the added burden of extensive text-based responses. This streamlined approach encourages active participation and ensures that the course review process remains an efficient and straightforward experience for all users.

Our platform diverges from conventional course review platforms by strategically prompting specific and targeted reviews through a series of predefined questions. Unlike platforms that often yield generic answers due to a lack of specific prompts, our approach involves presenting tailored questions designed to elicit detailed insights. These questions are deliberately specific and sometimes extreme, like asking about a student's favourite or least favourite assignment within a course, followed by a 'why' to delve deeper into their reasoning. By structuring questions in this manner, we facilitate more detailed and easily recallable answers from users.