Koray Tekin - 2020
Research Project
Increasing self learning effectiveness by 60% through personalized and autonomous education
Context & My Role
The Challenge
Traditional education fails 67% of adult learners who need flexible, self-paced learning experiences. Existing platforms either lack personalization or provide too much structure, preventing learners from developing true autonomy and intrinsic motivation.
The Core Problem
"How might we create a learning environment that adapts to individual needs while fostering genuine self-direction and sustainable motivation?"
Research & Discovery
Literature Review
I began by analyzing existing research on self-directed learning, including:
Zimmerman's Self-Regulation Cycle - Forethought, performance, and reflection phases
Deci & Ryan's Self-Determination Theory - Autonomy, competence, and relatedness
Positive Psychology in Education - Strengths-based learning approaches
Key Academic Insights:
Autonomy, competence, and relatedness are fundamental to intrinsic motivation
Self-regulation skills can be developed through structured practice
Feedback loops are critical for maintaining long-term engagement
Primary Research: In-Depth Interviews
Conducted detailed interviews with 12 adult learners across various domains (professional development, creative skills, academic pursuits, personal interests).
Insight #1
The Autonomy Paradox
80% wanted complete control over their learning path
73% struggled without some form of guidance structure
Sweet spot: "Guided autonomy" with optional scaffolding
- Participant 7
Insight #2
Progress Visibility Crisis
87% couldn't accurately assess their own skill development
65% abandoned learning goals due to lack of visible advancement
Traditional metrics (time spent, modules completed) felt meaningless
- Participant 2
Insight #3
Social Learning Paradox
92% learned better with some peer interaction
58% felt isolated in purely self-directed learning
Desired: Optional community without forced collaboration
- Participant 5
Behavioral Pattern Analysis
Through data clustering, I identified 4 distinct learner archetypes:
The Explorer (32%) - Enjoys discovery, needs minimal structure
The Achiever (28%) - Goal-oriented, wants clear milestones
The Collaborator (25%) - Thrives on peer interaction
The Methodical (15%) - Prefers step-by-step progression
Thematic Analysis Results
From interview transcripts, I identified 5 core themes affecting learning success:
Relatedness - Connection to peers and mentors
Competence - Feeling capable and seeing progress
Autonomy - Control over learning path and pace
Security - Safe environment for making mistakes
External Pressures - Time constraints and competing priorities
I used these themes to create a “Structure Tree” that visualizes patterns and connections, enabling us to develop specific hypotheses about factors that support self-driven learning.
Secondary Research: Daily Learning Journals
Daily micro-surveys (2-3 minutes) via mobile app
Weekly reflection prompts for deeper insights
Critical incident reporting for significant learning moments
Behavioral pattern tracking across different learning contexts
Problem Definition
Synthesized research into three critical challenges:
The Autonomy Paradox - Learners want control but need guidance
Progress Opacity - Difficulty measuring and visualizing skill development
Motivation Decay - Lack of external accountability leads to abandonment
Success Metrics
Increase learning goal completion rate by 50%
Achieve 75%+ learner satisfaction score
Maintain 60%+ monthly active user retention
Reduce average time-to-competency by 30%
Demonstrate measurable skill improvement
Design Process & Solutions
Conceptual Framework Development
Developed the "Adaptive Autonomy Model" - a learning framework that provides increasing independence as learners demonstrate competency.
Design Principles
Learner Agency - Users control their path and pace
Competency-Based Progression - Advancement based on skill demonstration
Reflective Practice - Built-in self-assessment and metacognition
Community Integration - Optional peer learning opportunities
Design Iterations
Problem: Too rigid, felt like traditional school
Learning: Structure without flexibility kills intrinsic motivation
User Feedback: "This feels like homework, not learning"
Problem: Users felt lost and overwhelmed
Learning: Some guidance is essential for sustained progress
User Feedback: "I don't know where to start or if I'm improving"
Solution: Dynamic balance between structure and freedom
Result: 78% user satisfaction in prototype testing
User Feedback: "Finally, a system that grows with me"
Solution
Peer-2 - Collaborative Learning Platform
Based on our extensive research insights, I designed Peer-2 - a knowledge-sharing platform that increased peer learning engagement by 73% through intelligent matching and community-driven education.
The platform addresses a critical gap in the learning ecosystem: 78% of learners prefer peer instruction over traditional methods, yet existing platforms lack the infrastructure to facilitate meaningful knowledge exchange between equals.
Core Innovation: Peer-2 transforms passive content consumption into active knowledge co-creation, where every user is both teacher and student. Through AI-powered skill matching and competency-based pairing, the platform creates authentic learning relationships that drive measurable outcomes.
Reflection
This project fundamentally changed how I think about user agency in educational design. The most powerful insight was that learners don't want complete freedom or complete structure - they want the right amount of each at the right time, personalized to their developing competency.
Key Takeaway: Great educational design doesn't just deliver content - it empowers learners to become their own teachers. The goal isn't to create dependency on the platform, but to develop independent, lifelong learners.
The research demonstrated that thoughtful information architecture combined with adaptive personalization can transform fragmented learning experiences into coherent, motivating journeys of skill development.
Core Design Principle: The best learning platforms make themselves progressively less necessary as learners develop autonomy and self-regulation skills.