DOING MORE WITH LESS: USING ONLINE DATA SETS FOR INQUIRY-BASED LAB INSTRUCTION

Clovis Community College (CCC) is a new and fast-growing 2YC in Central California, accredited in 2015. Its geology program serves an average of 250 students per year in a growing number of lecture/lab courses (7, 12, and 15 respectively in the last 3 years). However, the 2019-2020 school year was the first in which there was a full-time geology faculty with official responsibility for program development; in the four years before that, geology courses were taught exclusively by a team of up to six adjuncts. With little formal coordination or budget, no lab coordinator, limited access to tools or materials other than a rock collection, and official discouragement for field trips, many introductory geology lab exercises were not feasible. The geology teaching team therefore needed to develop exercises that made good use of lab time by exploiting its opportunities for peer teaching and high student-teacher contact and avoiding “paper labs”, but without relying on many physical resources. Additionally, from March 2020 onward, all classes have had to be fully online.

We have responded to these constraints by designing data-driven, inquiry-based lab activities that use the abundance of data and data visualization tools provided online by various scientific organizations (e.g. USGS Quake Map, Exoplanet Orbits Database, Paleobiology Database) and published data sets. Such exercises give students experience in manipulating and interpreting real data, including hypothesis generation, dealing with investigation constraints (e.g. outliers, detection limits, taphonomic biases), and other scientific practices. This approach also suits online learning because it allows asynchronous access to all lab materials at no additional cost. Students new to college-level science need intensive scaffolding to have an authentic science inquiry experience since they may have difficulty with basic scientific practices such as understanding what the data represents, reading graphs, manipulating numbers, and estimating regression lines. However, this is a strength of this approach, because implementing data-analysis exercises as labs rather than homework encourages instructors to devote time to developing those skills. In this presentation, we will share several activities and discuss scaffolding methods we have found useful for instruction in science practices.