Learn · Designing AI-driven Personalised Learning Environments
Lecture 56 of 80Week 9
Lecture 56 : Realtime Feedback From Students
Prof. Ramkumar Rajendran · IIT Bombay
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- Designing AI-driven Personalised Learning Environments, IIT Bombay (at NPTEL)
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- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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All the lectures
- Lecture 1 : What is Learning?
- Lecture 2 : Issues in Learning and PAL
- Lecture 3 : Self-Reguated Learning
- Lecture 4 : History of PAL Environments
- Lecture 5 : The Architecture of PAL
- Lecture 6 : UI and Domain Module
- Lecture 7 : Learner and Pedagogy Module
- Lecture 8 : What is Learning Analytics?
- Lecture 9 : Course Structure
- Lecture 10 : Introduction to Domain Model in PAL
- Lecture 11 : Rule Based Model
- Lecture 12 : Rule Based Model - Fraction Addition
- Lecture 13 : Limitations of RBM
- Lecture 14 : Constraint Based Model
- Lecture 15 : Application of CBM
- Lecture 16 : RBM and CBM
- Lecture 17 : Podcast for week 2
- Lecture 18 : Introduction to Learner Model
- Lecture 19 : Importance of Learner Model in PAL
- Lecture 20 : Overlay Model
- Lecture 21 : Learner Modeling using IRT
- Lecture 22 : Limitations of IRT as Learner Model
- Lecture 23 : Demo of IRT
- Lecture 24 : Podcast for Week 3
- Lecture 25 : Introduction to bayesian Network
- Lecture 26 : Dynamic Bayesian Network
- Lecture 27 : Conditional Probability Table
- Lecture 28 : Bayesian Knowledge Tracing
- Lecture 29 : Limitations of BKT as Learner Model
- Lecture 30 : Demo of Dyanamic Bayesian Network
- Lecture 31 : Podcast for Week 4
- Lecture 32 : Introduction to the Pedagogy Module
- Lecture 33 : Content Based and Collaborative Filtering
- Lecture 34 : Open Learner Models
- Lecture 35 : Standard Pedagogy Rules - 1
- Lecture 36 : Standard Pedagogy Rules - 2
- Lecture 37 : Pedagogy Rules from Literature
- Lecture 38 : Podcast for Week 5
- Lecture 39 : Cognitive Tutor: MathTutor
- Lecture 40 : Existing ITS: MetaTutor
- Lecture 41 : Existing ITS: MetaGuru
- Lecture 42 : Existing ITS: Betty's Brain
- Lecture 43 : Tools to develop PAL
- Lecture 44 : Demo of GIFT Tutor
- Lecture 45 : E-Learning Authoring Platforms and the SCORM Standard
- Lecture 46 : Demo of Articulate 360
- Lecture 47 : Building PAL using Gen AI
- Lecture 48 : PAL Using Multimodal Data
- Lecture 49 : Affective Computing
- Lecture 50 : Affectaware Tutor
- Lecture 51 : GBM Based PAL
- Lecture 52 : PAL using Eye Gaze Data
- Lecture 53 : Podcast for Week 8
- Lecture 54 : PAL in other learning environments
- Lecture 55 : PAL in Classroom Environment
- Lecture 56 : Realtime Feedback From Students
- Lecture 57 : PAL in Virtual Reality Learning Environments
- Lecture 58 : Data logging in VRLE
- Lecture 59 : Personalized VRLE
- Lecture 60 : Podcast for Week 9
- Lecture 61 : The GenAI Revolution in PAL Architecture
- Lecture 62 : Automated Assessment Generation
- Lecture 63 : Capturing the Handwritten Learner Input
- Lecture 64 : Data Privacy and Ethics
- Lecture 65 : Podcast for Week 10
- Lecture 66 : High-Level System Architecture with LLMs
- Lecture 67 : Developing Domain Model with LLMs
- Lecture 68 : Pedagogy Model with LLM
- Lecture 69 : Building Learner Models Using LLMs
- Lecture 70 : System Demo
- Lecture 71 : Experimental Designs for Intelligent Tutoring Systems
- Lecture 72 : Quantifying Impact
- Lecture 73 : Demo of ITS using Gen AI
- Lecture 74 : Summary of Course Contents
- Lecture 75 : Modelling the Domain
- Lecture 76 : Learner Modeling
- Lecture 77 : Pedagogy Module Revisited
- Lecture 78 : Authoring Standards
- Lecture 79 : Multimodal Data and VRLE
- Lecture 80 : Recent Trends in PAL