Investigating Course-Level Analytics in Online Classes

Concurrent Session 3

Brief Abstract

This presentation focuses on our work understanding course-level analytic data both during a particular semester and across course offerings.

Archivist Notes

Extended Abstract

Everyone is talking about analytics as "big data" but is ignoring the fact that teachers are surrounded by data every day and have no idea how to make use of it. Some of the research we are doing shows that it's actually helpful for teachers to access the data at their fingertips and use it to guide their teaching, pivot when needed mid-semester, and to inform pedagogical changes. Understanding how to use data at the course level helps instructors to be prepared to work with larger data sets emerging institutionally in many places, as well as to help guide curricular discussions about how analytics apply across course, programmatic, school/college and institutional levels.

We plan to use a course we have developed and collected data from as an example to kick off the discussion, but then will use the following questions to guide the conversation and engage participants.

1. How is your institution looking at learning analytics? What support is there for more focused, course-based work?
2. Since participants at the conference will be coming from a variety of institution types and LMS types, some basics of a) Where are the data in your system? b) What kinds of data are available to you?
3. Is there a component of every day teaching which you would want to get data about? What questions do you have about your teaching? Where could you find this? What would it look like?
4. Are there data points in your LMS or elsewhere which can deliver information that would inform teaching, but which were not necessarily intended to provide that information?
5. How do you navigate issues of validity, statistical power, etc. when working with course level data? How can you make decisions with relative certainty, even if they are not widely generalizable.
6. What data and analysis tools do you need and how might you use them yourself or with faculty?