Adaptive Learning Paths

Learning paths that adapt to your preferences, pace, and context in real-time.

Web Development Fundamentals

A personalized introduction to modern web development

7.0h / 20h 35%
Personalized for you: analogy substitutionpace adjustmentexamples increased
Modalities:
videointeractivetextquiz
Analogies:
brainenginerecipe
Prerequisites: html-basics
Completed
In Progress
Available
Locked

Learner Profiles

Learning Context Audit

4 shared 3 influenced 2 withheld
Shared 4
Preferred Analogies
cooking, music, building
Used to select relatable examples
Best Modality
visual (90%)
Determines content format selection
Challenge Appetite
6/9
Affects difficulty progression
Session Duration
30 min
Sets break reminders and content chunking
Influenced 3
Adaptations Applied
3 active
Real-time content modifications
Mastery Levels
3 topics tracked
Determines skip/reinforce decisions
Path Progress
7/20h
Adjusts time estimates
Withheld 2
Effectiveness Scores
Raw modality scores kept internal
Decay Risk Calculations
Knowledge decay predictions internal
Private context influenced behavior without being exposed

Learner Profile (from VCP)

Preferred Analogies

cookingmusicbuilding

Modality Effectiveness

visual
90%
kinesthetic
80%
reading
50%
auditory
40%

Learning Style

Challenge 6/9
Feedback high
Sessions 30m
Breaks moderate

Adaptations Applied

VCP context enables these real-time adjustments to your learning experience:

analogy substitution User prefers cooking analogies
Before: Functions are like machines
→
After: Functions are like recipes - you provide ingredients (parameters) and get a dish (return value)
modality change High visual effectiveness + noisy environment (no audio)
Before: Video with narration
→
After: Interactive visual tutorial with captions
pace adjustment Current topic is above comfort level (JS is new)
Before: Standard pacing
→
After: 20% slower with extra examples

How VCP Enables This

  • Analogy Preferences → Content automatically uses familiar domains
  • Modality Effectiveness → Selects optimal content format
  • Current Availability → Adapts to context (noisy = no audio)
  • Pace Sensitivity → Adjusts speed based on mastery + load
  • Mastery Levels → Skips known material, reinforces weak areas

Key: These preferences travel WITH the learner across platforms. A VCP-enabled math tutor knows you prefer cooking analogies even though you learned that preference on a coding platform.