Intermediate level
No prior experience required
Uncovering Implicit Knowledge with Cognitive Task Analysis
Flexible schedule
3 weeks, 6 to 8 hours per week
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*Proof of full-time student enrollment required. Acceptable forms of ID include a letter from your university’s registrar office or an unofficial transcript. Email your documents to learnlab-help@lists.andrew.cmu.edu.
What you will learn
- Explain why implicit expert knowledge creates problems for learning design and assessment.
- Use structured interviews, contextual inquiry, and think-aloud methods to surface expert thinking.
- Analyze expert performance to identify critical decisions, subskills, and common learner difficulties.
- Translate CTA findings into stronger instructional supports, tasks, and assessment choices.
Course description
Many learning experiences fall short because experts skip over the hidden steps, judgments, and cues that novices need in order to succeed. Cognitive task analysis helps learning engineers uncover that implicit knowledge so instruction, practice, and assessment can be built around what experts actually do.
In this course, you will learn practical cognitive task analysis methods such as structured interviews, contextual inquiry, think-aloud protocols, and difficulty factors assessment. You will use these methods to surface expert thinking, translate it into teachable components, and make better decisions about course design, supports, and assessments.
Syllabus
Module 1: Introduction to Cognitive Task Analysis & Contextual Inquiry
- Explain backward design and the interrelationships between goals, assessment, and instruction.
- Explain why knowledge goals must be inferred from models of data.
- Explain why “if-then” production rules are a good way to express the output (model & insights) of a Cognitive Task Analysis.
- Explain and exemplify how Cognitive Task Analysis can be used to identify learning goals.
- Recall the four Contextual Inquiry Principles principles and their definitions.
Module 2: Empirical CTA - Structured Interviews
- Compare and contrast Clark's 5 steps of CTA via structured interviews with the principles of Contextual Inquiry.
- Explain the importance of focus setting in Contextual Inquiry and preparation in Clark's structured interview approach to CTA.
- Compare and contrast the interview process of CTA via structured interviews with Contextual Inquiry.
- Use structured interviews (Cognitive Task Analysis) and contextual inquiry to identify course goals.
Module 3: Think Aloud & Theoretical CTA
- Distinguish between four kinds of CTA (theoretical vs. empirical, descriptive vs. prescriptive).
- Perform the steps of doing a think aloud study.
- Model think aloud results using subgoals and if-then production rules.
- Use model to design and improve instruction.
- Apply theoretical CTA to a task domain.
- Compare and contrast think aloud and theoretical CTA with other CTA methods.
Module 4: Quantitative Cognitive Task Analysis - Difficulty Factors Assessment
- Given a task, identify a difficulty factor in it and design a matched task without that difficulty.
- Create assessment forms for Difficulty Factors Assessment (DFA) and analyze errors.
- Model Difficulty Factors Assessment (DFA) results and use to design instruction.
- Explain how the Difficulty Factors Assessment (DFA) steps are related to the Instructional Design Big Picture.
- Compare and contrast Difficulty Factors with prior CTA techniques.
Module 5: Course Project or Final Exam
At the end of the course, you'll have an opportunity to do a little project where you will conduct a Cognitive Task Analysis to improve elementary math learning. You will also get a chance to analyze and update two existing CTA models related to personal finance. Finally, you will redesign existing instruction and assessment based on CTA implications given to you. That will provide you with a nice experience to apply the fundamentals you will learn in the modules to a larger, more authentic, context. It will be graded by the instructor and you will receive personalized feedback along with a sample solution.
You will also have the option to take a final exam with 20 questions. The exam can be taken multiple times, and each attempt draws new questions randomly from a pool of questions.
You may also complete both the course project and the final exam. The higher of the two scores will count toward the certificate.
Meet the instructor
Dr. Ken Koedinger
Ken Koedinger is the Hillman University Professor of Computer Science at Carnegie Mellon University, with appointments in Human-Computer Interaction and Psychology. He holds an M.S. in Computer Science and a Ph.D. in Cognitive Psychology and has experience teaching in an urban high school. He has developed data-sharing and analytics infrastructures that support innovations in learning, including DataShop and LearnSphere, and has used them to improve learning as illustrated in his hundreds of publications. He directs LearnLab and co-founded Carnegie Learning in 1998, the first AI in Education company to bring intelligent tutoring technology into widespread use in schools. His PLUS project provides hybrid human-AI tutoring to middle school math students in schools around the country. He is a fellow of the Cognitive Science Society, the Association for Psychological Science, and the Association for Computing Machinery.
Where to go next
Evidence-Based Backward Design for Online Learning
Design online learning experiences by aligning goals, assessments, and instruction with evidence-based methods and iterative improvement.
Active Learning: Practice and Feedback
Create evidence-based active learning activities by choosing the right types of practice and feedback for different learning goals.
Writing Better Questions for Online Learning
Create stronger questions for online learning by selecting the right question types and aligning them with clear learning goals.