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In this introductory course, you will develop a solid understanding of fundamental learning analytics theories and processes, and explore different types of educational data. You will gain experience working with educational data sets and the R programming language, and hear from a diverse set of voices in the field. Finally, you will also consider ethics and privacy issues, as well explore how to work as part of a team in a domain that is becoming increasingly cross-disciplinary.
By grasping these fundamental areas, you will have a better understanding of the field of learning analytics and be able to apply skills to any occupation that utilizes educational data.
What you'll learn
- The field of learning analytics and explore how data and information are used
- Common learning analytics methods and approaches, such as data wrangling and cleaning, structure discovery, and basic prediction modeling
- How to conduct basic data wrangling and analyses
- Ethics and privacy considerations
- Working in a collaborative, cross-disciplinary setting
- Common toolsets used in the UTArlingtonX Learning Analytics courses (R and GitHub)
Week 1: What are learning analytics?
Week 2: What types of data will you work with?
Week 3: What types of things will you do?
Week 4: How do you work as part of a team or within an organization?