Challenge
Course options are plentiful but scattered, and people routinely spend hours comparing prices, schedules, and suitability by hand — or give up before finding a fit.
Case Study
Mobile · EdTech · UX/UI Design
COURSEMATCH
Course Discovery App
A mobile course discovery experience designed to help individuals explore, compare, and find suitable courses without having to manually navigate overwhelming amounts of information.
Project Snapshot
Solution
A guided discovery flow: lightweight onboarding captures interests, budget, and schedule, then drives a matched, filterable shortlist with cards that surface what actually influences a choice.
Responsibilities
- UX & UI lead
- Onboarding & matching flow
- Course card system
- Filtering & recommendations
Outcome
People reached a suitable shortlist about 45% faster and reported 33% higher confidence in their choice, with fewer mismatched selections.
Context
Too many options, too little guidance
Course options are plentiful but scattered, and people routinely spend hours comparing prices, schedules, and suitability by hand — then still second-guess the choice.
I led UX and UI for a discovery app that turns a sprawling set of options into a short, relevant shortlist.
Approach
Guided discovery, not endless search
A lightweight onboarding captured interests, budget, and schedule, then drove a matched set of recommendations people could refine with simple filters.
Clear course cards surfaced the details that actually influence a decision — price, timing, and fit — without burying people in fine print.
Outcome
Confident choices, less regret
People found suitable courses faster and reported higher confidence in their selections, with fewer mismatches caused by budget or schedule.
Project Context
CourseMatch is a course discovery and matching app that helps individuals find suitable courses based on their interests, budget, and schedule — instead of manually searching through and comparing endless options.
Why this project mattered
A course is an investment of time and money. Turning an overwhelming set of options into a suitable, confident choice had real stakes beyond convenience.
Who it's for
Individuals looking for courses that suit their needs and circumstances — anyone weighing options on interest, budget, and available schedule, most of them browsing on a phone.
Business goals
- Make course discovery faster and easier
- Increase confidence in course choices
- Help people find courses that actually fit
- Drive engagement through guided discovery
Market context
Course options were plentiful but scattered across providers and marketplaces, leaving people to compare prices, schedules, and suitability by hand. No tool turned personal needs into a relevant, comparable shortlist.
Problem Statement
How might we help someone move from an overwhelming set of course options to a short set of suitable ones — matched to their interests, budget, and schedule?
01
User Problems
- Too many course options to sort through
- Hard to tell whether a course actually fits
- Budget and schedule mismatches surface too late
- Comparing options is manual and tiring
02
Business Problems
- Low engagement with scattered course listings
- People abandon the search before finding a fit
- Users lack confidence in their choice
- No differentiation from a raw list of courses
03
Technical Constraints
- Integrate with varied course data sources
- Handle frequently changing availability and pricing
- Work on low-end devices
- Recommendations computed on-device where possible
04
Product Constraints
- Onboarding must stay short and low-friction
- Accessible to anyone, with no prior context
- Mobile-first for on-the-go discovery
- MVP scope focused on discovery, not enrollment or payment
Goals
01
User Goals
- Find courses that fit my needsShortlist tied to interests, budget, and schedule
- Avoid budget and schedule mismatchesPrice and timing fit shown before shortlisting
- Choose with confidenceEach card surfaces the factors that drive a decision
02
Business Goals
- Increase engagementMore people complete guided discovery
- Reduce abandoned searchesFewer drop-offs before finding a fit
03
Product Goals
- Keep onboarding shortCapture interests, budget, and schedule in a few steps
- Make recommendations legibleConsistent course card system with clear signals
Success Metrics
-45%
Time to find a course
vs. manual searching
+33%
Choice confidence
self-reported
≤ 4
Onboarding steps
to first match
-27%
Mismatched choices
vs. manual searching
Research & Discovery
We grounded the app in how people actually look for courses: conversations with course providers, sessions with people mid-search, a scan of existing discovery tools, and data on where the search broke down.
01
Stakeholder Interviews
Providers described plenty of course options but little help matching people to the right one.
- Plenty of options existed, but little guidance on fit
- Budget and schedule mismatches caused people to abandon the search
- Comparing options was left entirely to the individual
There are so many courses out there, but people still struggle to figure out which one actually fits them.
When a course doesn't match someone's budget or schedule, they usually just give up.
02
User Interviews
Ten people walked us through how they look for courses. Most relied on scattered searching and manual comparison.
I open a bunch of tabs and try to compare prices and timings myself.
By the time I find something interesting, it often doesn't fit my schedule or budget.
Fit
People lacked an easy way to judge whether a course suited them
Practicality
Budget and schedule were deciding factors, surfaced too late
Comparison
Comparing options was manual and tiring
03
Competitive Analysis
We reviewed course marketplaces and everyday ways people search for courses.
Course marketplacesLarge selectionOverwhelming, hard to compare on fit
Search enginesBroad reachNo sense of budget or schedule fit
Manual comparisonFlexible and personalSlow, tab-heavy, error-prone
Opening
Nothing turned a person's interests, budget, and schedule into a short list of suitable courses — that was the space to claim.
04
Analytics
Early research quantified how much effort course discovery took.
31%
Mismatched choices
Didn't fit needs, budget, or schedule
18%
Abandoned the search
Gave up before finding a fit
5+ hrs
Spent searching
Manually comparing options
05
User Journey Insights
The discovery journey lacked any guided path from needs to a suitable choice.
- 01Know what I wantPeople had needs but no easy way to apply them
- 02Browse coursesOverwhelmed by volume and scattered detail
- 03Compare optionsNo easy basis to judge fit, budget, or timing
- 04Choose a courseManual, uncertain, easy to second-guess
Key Research Findings
- 01People needed guidance, not more course listings
- 02Interests, budget, and schedule had to drive recommendations
- 03Surfacing practical fit early would prevent abandoned searches
- 04A short onboarding could capture enough to personalize results
Design Strategy
Research showed people needed guidance, not more listings. Strategy centered on turning interests, budget, and schedule into a short, comparable shortlist — surfacing practical fit early and keeping the path to a choice short.
01
Key Insights
- People lacked context to judge fitCapture needs up front and match courses against them
- Budget and schedule mismatches surfaced too lateShow practical fit before shortlisting, not after
- Choices ran on guesswork, not informationShow the decision factors on every course card
02
Design Principles
Guide, don't dump
There were plenty of listings; people needed direction
Surface practical fit early
Budget and schedule mismatches drove abandonment
Legible at a glance
Cards had to make fit obvious without reading everything
03
Prioritization Framework
We prioritized the guided path end-to-end before breadth, ensuring someone could go from their needs to a suitable shortlist before layering in extra filters.
NowOnboarding & matching flowThe core value: needs in, shortlist out
NextCourse cards & fit signalsMade recommendations trustworthy and legible
LaterAdvanced filters & saved shortlistsPower features once the core path worked
04
Success Criteria
- A person reaches a relevant shortlist from their needs
- Budget and schedule fit are visible before shortlisting
- Each card makes fit clear without deep reading
- Onboarding stays short enough to complete in one sitting
Design Process
A guided-discovery loop. Usability tests kept sending us back to simplify onboarding until confidence, not just speed, improved.
- Step 01
Discover
Learned how people actually look for courses, and why scattered options lead to mismatched choices.
- Interviewed people about past course choices they regretted
- Watched people search and compare options today
- Identified what really drives a confident choice
- Mapped where overload turns into poor decisions
- Step 02
Define
Reframed the problem from search to guidance — a path from needs to courses people trust.
- Defined the core job: confident, well-fit choices
- Set decision confidence as a first-class success metric
- Named the inputs that matter: interests, budget, schedule
- Scoped lightweight onboarding over a heavy filter wall
- Step 03
Ideate
Explored a guided flow that turns many options into a matched, filterable shortlist without hiding the details.
- Sketched onboarding that captures interests, budget, and schedule
- Explored matched, filterable results over raw search
- Designed cards that surface what sways a decision
- Balanced guidance with real user control
- Step 04
Prototype
Built onboarding and the shortlist at fidelity so the guided path could be walked end to end.
- Prototyped lightweight needs-based onboarding
- Built the matched, filterable results view
- Designed course cards with decision-relevant signals
- Populated with realistic course data
- Step 05
Test
Put the flow in front of people to see if it found suitable courses faster and choices they believed in.
- Ran task-based sessions finding a real course
- Timed how quickly people reached a shortlist
- Measured self-reported confidence in their choice
- Watched where the guided path lost them
- Step 06
Iterate
Simplified onboarding and clarified the cards, then re-tested until both speed and confidence rose.
- Trimmed onboarding to only the decisive inputs
- Sharpened the signals shown on each course card
- Re-tested to confirm the confidence gains held
- Shipped, then tracked faster discovery and fewer mismatches
User Flow
People open the app overwhelmed by options. The flow captures their needs up front, then loops on validation — refining matches until the recommended courses actually fit their interests, budget, and schedule.
Open App
Set Needs & Budget
Matches meet requirements?
Yes
Add to Shortlist
No
Refine Filters
Course Chosen
Key Decision Points
- 01
Capture the decisive inputs
Onboarding asks only for the interests, budget, and schedule that actually change recommendations, so people reach useful matches without a long questionnaire.
- 02
Validate against needs
Each match is checked against budget and schedule fit, so a recommendation is never just relevant — it is actually suitable.
- 03
Refine until it fits
When matches fall short, the flow loops back to adjust filters rather than dead-ending, guiding the person to a suitable choice.
Final Solution
The shipped app replaces course-search overwhelm with guided, confident choices. Three screens carry it: capturing needs, presenting matches, and confirming a course fits.
01
Guided Onboarding
Design Objective
Learn just enough about a person's interests, budget, and schedule to give genuinely useful recommendations.
Key UX Decisions
Onboarding asks only for the inputs that change results — interests, budget, availability — in short, friendly steps, and usability testing pushed it simpler until people felt confident, not just fast.
Outcome
More people completed onboarding and reached a useful shortlist, lifting overall engagement.
02
Matched Courses
Design Objective
Turn a large set of options into a short, trustworthy list a person can actually decide from.
Key UX Decisions
Course cards surface the factors that really drive a choice — fit, price, timing — and filters let people refine in place, so the list feels curated rather than searched.
Outcome
People spent less time deliberating and reported higher confidence that the courses fit.
03
Schedule Fit
Design Objective
Help a person confirm a course actually fits their available schedule before committing.
Key UX Decisions
Adding a course checks it against the person's schedule and other choices immediately, and the flow loops back to refine rather than dead-ending when something doesn't fit.
Outcome
Choosing got faster and mismatches fell as people committed to courses that actually worked for them.
Results & Impact
Replacing search overwhelm with guided, validated choices helped people find suitable courses faster, commit with more confidence, and end up with fewer mismatched selections.
Business Outcomes
User Outcomes
- Onboarding that captures only decision-changing inputs
- Matched results surfacing fit, price, and timing up front
- Budget- and schedule-aware validation on every match
- A refine-don't-dead-end loop that guides toward a suitable choice
- 01Guidance beats search when the decision, not the data, is overwhelming.
- 02Validating against real needs makes a recommendation trustworthy.
- 03Looping back to refine keeps people moving instead of stuck.
- Time to find a courseManual searching-45%
- Choice confidenceSelf-reported+33%
- Onboarding stepsLong forms≤ 4
- Mismatched choicesManual searching-27%
Reflection
Building for overwhelmed course-seekers taught me that the kindest thing an interface can do is make a decision feel smaller. The temptation was to show everything we could compute; the discipline was to show only what helped someone choose.
- Capturing just the decision-changing inputs kept onboarding short and genuinely useful.
- Validating matches against real needs made recommendations trustworthy, not just relevant.
- The refine-don't-dead-end loop kept people moving toward a suitable choice.
- I'd test the onboarding with real first-time users even earlier.
- I'd design the empty and no-match states with more care from the outset.
- I'd add a way to save and compare multiple shortlists side by side.
Challenges & Trade-offs
Personalization vs. simplicity
Better recommendations want more input; overwhelmed people want fewer questions. The trade-off was asking only what measurably changed results, accepting a slightly less tailored match for a far gentler start.
Guidance vs. autonomy
Guiding people risks feeling paternalistic to those who know what they want. We made the guided path the default but kept manual control one tap away, rather than forcing everyone through the same funnel.
- 01When the decision is the burden, guidance beats raw search and more data.
- 02Usability testing repeatedly pushed me simpler than my first instinct — and it was right.
- 03Trust comes from validation; a recommendation that doesn't fit erodes it fast.
- Longer-term learning plans that look beyond a single course.
- Peer and expert input woven into the recommendation signals.
- Integration with providers to close the loop from shortlist to enrollment.
