Kern Institute Data Science Lab

Designing surveys and research studies with sensitive measurable outcomes is vital to understand the associations between variable, make causal claims and test theories. The goal is to help in all this from aligning curriculum objectives to with outcome measures that can detect subtle differences. The data lab helps support this process and ensure important outcomes of interest are not missed or captured in a way that will bias conclusions.

Building pipelines to get data out of data capture systems and into the hands of researchers, learners and teachers is an essential goal. The data science lab works to create frictionless systems, sharing data back with all types of users through access to data for research purposes in a secure and ethical manner. In this, one of the goals of the lab is to use open-source tools and share code back to these communities to contribute and evolve the field with good data 'hygiene' practices.

The ability to link datasets can provide valuable insight that could not be achieved if the datasets are kept separate. It is not feasible to capture all of the data in one study given time, money and practical constraints. To link data, important linkage variables (often referred to as unique and/or direct identifiers) are required at the time of data collection. The Kern data science lab helps ensure the language of primary educational data collection will facilitate data linkage through 'second-order' data analyses following the most update to date legislative guidelines, security and privacy standards.

Representing data graphically aids interpretation and provides an accessible way to discover patterns by leveraging innate cognitive and perceptual strategies. One goal is to support behavioral change and life-long learning using effective, accurate and digestible visualization strategies. This requires aligning cognitive and instructional design principles with relevant and important data points.