63 questions · 27 sources · 0 syntheses
Instructional Design
The systematic design of teaching materials and methods.
Source-derived questions
Questions extracted from the papers in this topic, excluding the curated questions above.
- How can connecting instructional design patterns with software architecture patterns reduce friction between learning content and its delivery system?
- How can the CID framework explain contradictory findings in research on productive failure, where problem-solving before instruction sometimes helps and sometimes hinders learning?
- How do model-tracing cognitive tutors represent a student's problem-solving strategies and misconceptions?
- How do the computational requirements that different tasks place on neural networks parallel cognitive load in human learners?
- How does Generation Z's habituation to constant digital information flow shape the requirements for online tutoring agents?
- How does Pattern-Oriented Instructional Design (POID) model instructional design as a connection of reusable patterns?
- How does Physics-STAR use step-by-step guidance and reflective prompts instead of direct answers to support deeper learning in high school physics?
- How does educational data mining define a knowledge component, and why are KCs derived empirically from task data rather than fixed by instructional designers?
- How does elaborated feedback across verbal, graphical, and mathematical representations relate to post-test performance in high school physics?
- How does interleaving during practice affect transfer of skills to new contexts?
- How does knowledge component analysis translate into practical improvements in intelligent tutoring systems, such as personalized instruction and evidence-based selection of teaching strategies?
- How does personalized, adaptive difficulty adjustment in an LLM tutoring system align with worked-example and guidance-fading principles from cognitive load theory?
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- How does placing a deferrable task at the top of a to-do list help a procrastinator get more done?
- How does professional expertise moderate the relationship between cognitive load and performance benefits from AI assistance?
- How does the CID framework extend the KLI framework to analyze instruction that spans multiple phases, such as discovery learning followed by explicit instruction?
- How does the KLI framework resolve the apparent conflict between research favoring testing/practice versus research favoring worked examples?
- How does the KLI framework resolve the apparent conflict between testing-effect research and worked-example research in learning science?
- How does the KLI framework's causal chain from instructional methods to learning processes to knowledge types guide the selection of teaching methods for a given learning goal?
- How does the KLI framework's three coordinated taxonomies (knowledge, learning, instruction) explain why the same instructional method can help learning in one context and fail in another?
- How does this chapter distinguish between instructional intelligence and adaptivity in AI-enabled serious games?
- How should tutors respond when a low-efficacy student makes a math error, according to this research?
- In what way does CID apply the knowledge, learning, and instruction levels of analysis to transitions between phases rather than to a single instructional episode?
- In what ways can insights from deep learning models inform the design of instructional and learning technology systems?
- In what ways do intelligent tutoring systems adapt instruction to a learner's individual progress?
- In what ways might large language models, reinforcement learning, and agent-based architectures enable more integrated instructional adaptation within serious games?
- What are the four dimensions LearnLab uses to categorize knowledge components, and how do they map to different learning processes and instructional approaches?
- What are the implications of using difficulty-derived load estimates for modelling cognitive load in learning games?
- What are the three categories of learning processes KLI identifies, and how does each connect to a distinct type of knowledge component?
- What design implications does the re-encoding mechanism have for scheduling review intervals in digital learning systems?
- What do continuous physiological measures of cognitive load imply for the design of instructional materials aimed at minimizing extraneous load while fostering germane load?
- What does scaling from principles and circuit models to complex end-to-end trainable systems reveal about how the brain manages information processing demands?
- What does the finding that clustering improves understanding while removal reduces cognitive effort imply for designing human-centered explanations from symbolic AI systems?
- What does the manual-skipping tendency imply for how products should be designed?
- What does validation across 287 million learners and 22 languages suggest about the scalability of pattern-based instructional design?
- What ethical, scalability, and cognitive-adaptability gaps remain unresolved in current tutoring system research, and how might hybrid ITS-RTS solutions address them?
- What evidence links item difficulty values to existing theories of intrinsic versus extraneous cognitive load?
- What evidence shows that DRL-based metacognitive interventions in one tutoring domain (a logic tutor) transfer to prepare students for future learning in an unrelated domain (a probability tutor)?
- What experimental manipulations allow intrinsic, extraneous, and germane load to be varied independently when studying learners with low prior knowledge?
- What experimental methodology was used to measure participants' reasoning performance and extraneous cognitive load when classifying stimuli using ASP-derived explanations?
- What gains in accuracy and efficiency did students show on conceptual, computational, and informational physics questions after using Physics-STAR?
- What happened to the Kansas board's decision after the 2000 election changed its majority?
- What historical trajectory connects early computer-assisted instruction and intelligent tutoring systems to today's AI-enabled adaptive learning architectures?
- What instructional methods does the KLI framework associate with procedural/associative knowledge versus conceptual knowledge versus schema-level understanding?
- What is the Knowledge, and what does it require of London taxi drivers?
- What is the difference between the statistical model and the cognitive model in KC assessment, and how do they jointly explain student performance?
- What percentage of people were found to skip product instruction manuals entirely?
- What practical and research challenges, such as explainability, validation, and computational cost, limit the deployment of AI-enabled serious games despite their promise?
- What practical considerations should instructional designers weigh when deciding whether to implement interleaving given learners' working memory and readiness differences?
- What representation-selection strategies do students adopt when given feedback in multiple external representation formats, and how do these strategies differ by representational competence?
- What role did LearnLab's large-scale learning data play in grounding the KLI framework's claims about matching instruction to knowledge type?
- What role do GoalPattern, ProcessPattern, and ContentPattern play in systematizing the design of educational technologies?
- What role do multiple retrieval pathways created during re-encoding play in strengthening memory representations?
- What role does automated LLM-based assessment play in scaling effective tutoring practices?
- What role does explanatory (versus purely corrective) feedback play in promoting deeper cognitive processing during tutoring interactions?
- What role does intermediate knowledge generated in one instructional phase play in shaping the learning processes of a subsequent phase?
- What steps does effective learning engineering require when a real instructional context involves multiple knowledge components of different types?
- Why can tailoring an LLM's responses to individual learners be pedagogically desirable rather than a form of unfair bias?
- Why does extraneous cognitive load have a much larger negative association with task performance than intrinsic load in AI-assisted work?
- Why does guiding students to self-correct their mistakes work better than direct error correction for students with low self-efficacy?
- Why does having learners generate explanations for their reasoning improve learning outcomes, and how does this generation effect relate to germane cognitive load?
- Why does interleaved practice produce worse performance during learning but better performance on delayed tests compared to blocked practice?
- Why does using a diverse set of representations benefit students with lower representational competence more than those with higher competence?
- Why were young, technologically confident users the most likely to skip reading instructions?
Sources
- A Patterns Based Approach for Design of Educational Technologies
- AI-Enabled Serious Games: Integrating Intelligence and Adaptivity in Training Systems
- Advancing Education through Tutoring Systems: A Systematic Literature Review
- Benefits of Spaced Learning Predicted by Re-encoding Mechanisms
- Beyond Answers: Large Language Model-Powered Tutoring System in Physics Education for Deep Learning and Precise Understanding
- CID: A Framework for Cognitive Analysis of Composite Instructional Designs
- Deep Learning for Cognitive Neuroscience
- Difficulty as a Proxy for Measuring Intrinsic Cognitive Load Item
- Instruction Maps to Learning Events: Principles from the KLI Framework
- Intelligent Tutoring Systems for Generation Z's Addiction
- Interleaved Practice Benefits Implicit Sequence Learning and Transfer
- Knowledge Component Analysis in Educational Data Mining
Show 15 more sources
- Leveraging Deep Reinforcement Learning for Metacognitive Interventions across Intelligent Tutoring Systems
- Personalized Multimodal Feedback Using Multiple External Representations: Strategy Profiles and Learning in High School Physics
- Precision Proactivity: Measuring Cognitive Load in Real-World AI-Assisted Work
- Review of intelligent tutoring systems using bayesian approach
- The Dual Role of Abstracting over the Irrelevant in Symbolic Explanations: Cognitive Effort vs. Understanding
- The Knowledge-Learning-Instruction (KLI) Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning
- The Life Cycle of Large Language Models: A Review of Biases in Education
- The Role of Executive Function in Interleaved vs Blocked Learning of Science Concepts
- Use of Eye-Tracking Technology to Investigate Cognitive Load Theory
- Using Large Language Models to Assess Tutors' Performance in Reacting to Students Making Math Errors
- Using Large Language Models to Provide Explanatory Feedback to Human Tutors
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- Ig Nobel Prize in Science Education