Using Synthetic Data Generators to Promote Open Science in Higher Education Learning Analytics

Data sharing is a common contribution to open science. The creation of open datasets can speed up research advancements by allowing researchers to focus efforts on developing and validating analytical techniques, rather than on obtaining data. Open datasets also allow researchers to benchmark new analytical approaches against a known standard, and increase the reproducibility of research. The field of higher education learning analytics could benefit from the creation of open, shared datasets on higher education students as these data do not currently exist in open and accessible formats. Here, we propose the use of synthetic data generators to create open access versions of student data. Synthetic datasets have an advantage over real data, as private student data is protected by federal laws. We compare the characteristics of the synthetic data to the original data and illustrate a model for how the synthetic data can be leveraged for developing and optimizing a common learning analytics algorithm.