Solving Challenges of Integrating Large Datasets into Community College Asynchronous Online Science Classes by Using a Scaffolding-Learning Cycle Approach to Teaching and Learning

Community College classes are comprised of an extremely diverse group of students ranging from high school dual-enrolled or recent graduates to non-traditional students, all with varying backgrounds and skill sets in math, science and English. Students in introductory science classes can sometimes enter college under-prepared, have math anxiety, lack confidence in scientific questioning, data analysis, and the ability to synthesize or apply concepts. Even for online students, using data analysis tools or having to download software required to view data can hinder success. Large authentic datasets can be unfamiliar, messy with high variability or missing data, have lots of numbers, complex graphs or images. Students often find these data intimidating, may fail to see patterns in the data or they may misinterpret data to match their personal or prior knowledge. Students interacting with large datasets in online, asynchronous classes can often feel lost without the in-person instructor guiding them through the data step-by-step. By using a scaffolding-learning cycle approach to teaching and learning in online classes with large datasets from research organizations such as Ocean Observatories Initiative (OOI) and International Ocean Discovery Program (IODP), instructors can incrementally introduce data with a variety of short activities such as virtual gallery walks, best-fit lines for complex graphs, basic graphing questions, or discussions focused on simple observations of images prior to engaging in more complex data interpretation, comparisons or application. These activities can improve data literacy, critical thinking skills, and increase student confidence to engage with large datasets while helping them build better concept connections.