How data sets are captured in learning experiences
What basic procedures to use to manipulate these data sets
The use of statistical models to predict student behavior
The deployment of personalized support actions for the students
This course will benefit educational designers, learning technology managers, and academics that are interested in how to use data to guide the design and improvements of a learning experience.
Technology has the ability to collect a large amount of data about how people participate in a learning experience. How can this data be used to increase our understanding of how learning occurs? How can data be translated into actionable knowledge? How can data help improve the overall quality of a learning experience? These are the questions that are explored during the activities in the course. You will need basic knowledge about data manipulation and statistical analysis, and you will learn how to use them to translate data into actionable knowledge to apply in a learning experience.
Week 1: Computer Logs
Exploration of the type of computer logs that are produced, how the logs can be processed, and the type of information available.
Week 2: From logs to indicators
Basic procedures to transform log files into meaningful indicators that are connected with the learning environment. Explore how these transformations need to be driven by the structure of the learning design.
Week 3: Combining data sources and deploying student support actions
Apply data management techniques to create data structures by mixing multiple data sets. Translate the information and knowledge derived from the data set into student support actions.
We highly recommend that you take the previous course in this series before beginning this course:
Feature Engineering for Improving Learning Environments
This course is intended for those who have a bachelor’s degree and are interested in developing learning and data science skills for employment in education, corporate, nonprofit, and military sectors. Experience with programming and statistics will be beneficial to participants.