Learning Analytics: Metacognition, Motivation, and Discourse
Start Any Time
Work on your pace and you will have instructors available to help you answer any questions.
Duration
Approximately 4 weeks, 3-4 hours/week
Fee
$1000 Professional Rate
$400 Full-time Student Rate
*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
Certificate Course Description:
This course delves into the intricate aspects of learning analytics with a focus on metacognition and motivation, providing a deep understanding of how learners think and what drives them. You will learn to apply four advanced learning analytics techniques aimed at researching these areas. The course also covers the pivotal role of feature engineering in understanding and enhancing learner metacognition and motivation. In addition, you will gain practical skills in discourse analysis, learning how to interpret and analyze communication within educational settings. The course culminates in a project or final exam option, allowing you to apply the concepts learned to real-world data or demonstrate your knowledge through a comprehensive set of questions.
Module 1: Metacognition and Motivation
- Apply the four learning analytics techniques for researching learner metacognition and motivation
- Explain how feature engineering can be used to aid in metacognition and motivation research
Module 2: Discourse Analysis
- Explain the function of discourse analysis
- Apply tools and methods of discourse analysis
Module 3: Course Project
At the end of the course, you’ll have an opportunity to do a little project where you will have choice to analyze discourse data. 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 self-graded and you will receive a sample solution.
No prerequisites but experience with a programming language (e.g Python) will be helpful.
Researchers, educational data scientists, learning analysts, instructional designers, and students who want to learn about various techniques and considerations for handling educational datasets. Anyone interested in the science behind learning processes and outcomes.
What you'll learn
This course will help you:
- Apply the four learning analytics techniques for researching learner metacognition and motivation
- Explain how feature engineering can be used to aid in metacognition and motivation research
- Explain the function of discourse analysis and apply tools and methods pertinent to it
Course Instructors
Dr. John Stamper
is an Associate Professor of Human-Computer Interaction at Carnegie Mellon University. Dr. Stamper has a PhD in Computer Science from the University of North Carolina at Charlotte. His main area of research is focused on using “Big Data” from educational systems to improve learning. He is also the lead researcher behind DataShop, which is the largest open repository of log data from learning systems….
Certificate
Upon successful completion of the program, participants will receive a verified digital certificate of completion from Carnegie Mellon University’s Open Learning Initiative.
In addition to the knowledge and immediately applicable frameworks you will gain by attending your selected courses, you will benefit from:
- A digital, verified version of your Executive Certificate (Smart Certificate) you can add to your resume and LinkedIn
- Networking with a global group of your peers and instructors for advancing your career
Register Now
Register and start taking the course in four steps:
1. Enter your email address
2. Click on this link to Carnegie Mellon University’s Open Initiative https://proton.oli.cmu.edu/sections/join/h7ltz to register and try out the course for 48 hours before payment is due.