Data Driven Knowledge Tracing to Improve Learner Outcomes
Certificate Course Description:
To see learning, we need to look at a detailed, fine-grained decomposition of knowledge to be learned (called Knowledge Components). We will look into how we can infer an individual student’s knowledge of the KCs in the cognitive model, based on performance. Then individualize the curriculum such that each student can master all KCs at their own pace.
In this course, students will learn to apply data-driven modeling to track students’ knowledge growth in advanced learning technologies and adapt the instructions based on that. They will also be able to apply different techniques to figure out the best KC model for a task via search.
You will learn to:
- Describe how cognitive modeling can help individualize learning
- Explain how to implement Bayesian Knowledge Tracing (BKT)
- Predict student performance using Bayesian Knowledge Tracing (BKT)
- Apply data-driven tuning of cognitive mastery
- Explain knowledge component modeling and ways to implement a KC model
- Perform data-driven KC model revision
- Evaluate a KC model with Additive Factors Model (AFM)
- Explain the role of cognitive task analysis in scaling instructional impact
- Discover a cognitive model via search and perform a system redesign in light of a new cognitive model
- Interpret and generalize the cognitive process
Targeted Audience: Researchers, learning engineers, data scientists, and instructional designers who want to develop adaptive and personalized educational experiences. Administrators who want to gain a deep understanding of how personalized learning is delivered. Anyone interested in edtech.
Approximately 3 weeks, 6-8 hours/week
Normally $1500. Now $750 for the course first run in September 2021 only.
Upon successful course completion, students receive a certificate of completion. Certificates do not convert into university credit.
Register and start taking the course in four steps:
1. Enter your email address
2. Watch this short video for instructions on how to register in OLI.
3. For this course, copy the course key: KEYYYY
4. Click on this link to Carnegie Mellon University’s Open Initiative to register and try out the course for 48 hours before payment is due.
5. (Optional but highly recommended) Set OLI to automatically resume from where you left off in the course.
1) Click on your name in the upper right corner to bring up your OLI profile settings.
2) Change the option on the dropdown to resume automatically.
3) Lastly, select UPDATE.
You are all set.
Dr. Ken Koedinger
is a professor of Human Computer Interaction and Psychology at Carnegie Mellon University. Dr. Koedinger has an M.S. in Computer Science, a Ph.D. in Cognitive Psychology, and experience teaching in an urban high school. His multidisciplinary background supports his research goals of understanding human learning and creating educational technologies…