Digital Projection of the Learning Self.
by Daniele Di Mitri
1. Multimodal Data Collection
1.1. Longitudinal Input space
1.1.1. Data sources
1.1.1.1. Biosensors
1.1.1.2. Activity Trackers
1.1.1.3. Contextual sensors
1.1.2. Event types
1.1.2.1. Random Events
1.1.2.2. Fixed Events
1.2. Outcome space
1.2.1. Indicator
1.2.1.1. Mastery skills
1.2.1.2. Overall success
1.2.1.3. Competencies
1.2.1.4. Mistakes
1.2.2. Sampling
1.2.2.1. Random sampled
1.2.2.2. Self-report
2. Learning Pulse
2.1. Challenges
2.1.1. Real-Time processing issues
2.1.2. Poor explanatory power of varaibles
2.1.3. No "Ground Truth"
3. Feedback Strategy
3.1. Time
3.1.1. Next states
3.1.2. Current states
3.2. Media
3.2.1. Dashboard
3.2.2. Feedback Cubes
3.2.3. Augmented Reality
3.2.4. Virtual Reality
4. Virtual/Digital
5. Infrastructure
5.1. Real-Time
6. WEKIT WP3
6.1. Sensor Fusion
6.2. Mastery Skills
6.3. Real-Time system
6.4. Object recognition
6.5. Checklists
7. Machine Learning
7.1. Modelling
7.1.1. State-Action
7.1.2. Causal inference
7.1.3. Sequence Mining
7.1.4. Probabilistic modelling