Continue the eduKateSG Learning Node Series through the reading routes below. Return to the Learning Node reading index or the Diagnostics & Recovery Hub for the wider map.
Cognitive diagnosis and learner profiles
- How Attribute Hierarchies in Cognitive Diagnosis Work | Model Prerequisites Without Turning a Curriculum Into a Cage
- How Higher-Order Cognitive Diagnosis Works | Connect Fine-Grained Skills to a Broader Proficiency Without Collapsing the Profile
- How Longitudinal Cognitive Diagnosis Works | Track Skill Mastery Over Time Without Counting Noise as Learning
- How Cognitive-Diagnostic Adaptive Testing Works | Choose the Next Question to Reduce Uncertainty About the Skill Profile
- How Polytomous Cognitive Diagnosis Works | Model No, Basic and Advanced Mastery Without Flattening Skill Levels Into a Binary Switch
- How Partial-Mastery Cognitive Diagnosis Works | Measure Degrees of Skill Without Pretending Every Attribute Is a Switch
- How Response-Time Cognitive Diagnosis Works | Use Accuracy and Time Together Without Calling Fast Mastery
- How Missing-Response Cognitive Diagnosis Works | Keep Skipped and Unadministered Items From Becoming Wrong Answers
- How Cognitive Diagnostic Model Selection Works | Choose the Skill-Combination Rule Before the Rule Chooses the Diagnosis
- How Cognitive Diagnostic Model Fit Works | Check Whether the Skill Model Can Explain the Response Patterns It Claims to Diagnose
- How Multiple-Strategy Cognitive Diagnosis Works | Let More Than One Valid Solution Route Count as Evidence
- How MAP and EAP Cognitive Diagnosis Work | Choose Between Whole-Profile and Attribute-Wise Classification Without Hiding the Trade-Off
- How Cognitive-Diagnostic Test Assembly Works | Build a Test That Can Distinguish the Skill Profiles You Intend to Report
- How Nonparametric Cognitive Diagnosis Works | Classify Skill Profiles Without Forcing One Item-Response Formula
- How Local Item Dependence Works | Detect When Questions Share More Than the Construct the Model Explains
- How Latent Class Analysis Works | Find Hidden Response Patterns Without Treating Statistical Classes as Natural Types
Adaptive tutoring and teaching decisions
- How Model Tracing Works | Compare Each Learner Step With a Cognitive Model Without Forcing One Solution Path
- How Constraint-Based Tutoring Works | Diagnose Violated Principles Without Enumerating Every Correct Solution
- How Bayesian Networks in Assessment Work | Combine Prerequisites, Evidence and Uncertainty Into a Coherent Learner Model
- How POMDP Teaching Works | Choose the Next Teaching Move When the Learner State Is Hidden
- How Contextual Bandit Tutoring Works | Personalise the Next Hint Without Pretending One Action Is Best for Every Learner
- How Off-Policy Evaluation in Education Works | Test a New Teaching Policy With Old Logged Decisions Without Inventing Missing Outcomes
- How Student Simulators Work | Test Adaptive Tutors Without Mistaking Synthetic Learners for Real Students
- How Safe Exploration in Adaptive Learning Works | Improve a Teaching Policy Without Turning Learners Into Unbounded Experiments