Using Learning Analytics in SoTL

In the SoTL blog series thus far, we’ve been exploring what constitutes SoTL and sources of previously collected data. In this blog, I’d like to discuss another source of previously collected data for SoTL researchers: learning analytics. Broadly defined, learning analytics is the “collection and analysis of data generated during the learning process in order to improve the quality of both learning and teaching” (Society for Learning Analytics Research – SOLAR). Learning analytics data is useful for SoTL work, because this type of data is often already collected for you in various databases across campus.

Projects using learning analytics may include a variety of data to expand and enrich your SoTL study—from external factors that may influence student learning in your course to indicators of long-term impacts of your interventions. This data tends to focus on student records data such as student demographic characteristics (e.g., ethnicity, gender, year in school), student preparation (e.g., transfer credits, prerequisites), student performance (e.g., GPA, grades in follow-on courses), and student choice (e.g., programs of study, co-curricular involvement).

This data is often collected by multiple offices across campuses such as the registrar, student affairs, residential life, student success offices, institutional research, or other offices related to the question being explored. At IUB, the Bloomington Assessment and Research office (BAR), the Center for Learning Analytics and Student Success (CLASS), and the Center for Innovative Teaching and Learning (CITL) can offer various types of support for your SoTL project using learning analytic data. Staff in these areas can help you identify the types of information that might be relevant to your study, as well as help you locate the data you need and the tools to analyze it.

If you are interested in using learning analytics in your next SoTL project, consider participating in the Learning Analytic Fellows program. Proposals (due December 22, 2017) should focus on explorations in courses taught by you or someone else in your department. Collaborative proposals are encouraged, especially if they address questions of interdisciplinary programs.