Original discovery in the learning process: Is it worth the effort?

Les Hasbargen, , SUNY Oneonta

I teach geosciences at a 4-year public liberal arts college. Courses that I teach are woven into Geology, Earth Science, and Environmental Sustainability programs on campus. Geology and Earth Science programs are housed in my department (Earth & Atmospheric Sciences), while Environmental Sustainability is maintained by the Geography Department. The learning outcomes for these programs differ somewhat (indeed, they continue to evolve), but a common thread centers on fostering critical thinking and the development of analytical skills. Because I work from a geoscience framework, I wed the thinking and analytical skills with real world data exploration and analysis. When I went through college, most data was static, restricted to a printed page, and often the analysis I was asked to perform required hand calculations and drawing on printed maps and sketching on graph paper. Times have changed. Data is becoming increasing accessible via online data servers. Tools for manipulating digitized data are proliferating. From my perspective it is a rich time in education, and the easy data access opens up the possibility of original discovery by students during the learning process. Enticed by the possibility that discovery in the learning process could motivate student participation, I have started to teach extensively with real world data. I am now wrestling with how to get students excited about these resources as they are confronted with the very real frustrations of mastering spreadsheets.

My approach varies between courses. For 100 level courses for non-science majors, I provide extra credit opportunities for students to access online data about climate, rivers, and groundwater from the National Climate Data Center and USGS, CUAHSI, and others. The projects involve data discovery, data filtering, charting, statistical summaries, cross correlations, and inferences based on observations. For upper division geoscience major courses, I make these types of projects mandatory. I often frame the exploration with a guiding question, such as "does a stream at a single location always exhibit the same rate of baseflow decline?" or "how do wells and streams respond to precipitation events?" or "how has climate changed in the US, locally and/or regionally?". Part of the motivation is to elucidate systematic interactions between surface and subsurface hydrology.

My hope is that students:
1. build procedural skills in working with data. And there are a lot of these procedures! The skills range from moving data from an online server into a spreadsheet, to filtering data and summarizing by some attribute of the data (averaging by month, year, etc), to writing equations in cells, to charting and curve fitting;
2. gain insight into how Earth works based on their own interactions with the data; and
3. can apply these generic procedures to other data sets, and thus demonstrate that their newly won skills are transferable.

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