ADDRESSING QUANTITATIVE REASONING AND ANALYTICAL WRITING SKILLS IMPROVEMENT USING GLOBAL ADDRESSING QUANTITATIVE REASONING AND ANALYTICAL WRITING SKILLS IMPROVEMENT USING GLOBAL AND LOCAL DATA SETS IN AN INTRODUCTORY GLOBAL CLIMATE CHANGE COURSE AND LOCAL DATA SETS IN AN INTRODUCTORY GLOBAL CLIMATE CHANGE COURSE NIEMITZ, JEFFREY W., Department of Geology, Dickinson College, P.O. Box 1773, Carlisle, PA 17013, NIEMITZ, JEFFREY W., Department of Geology, Dickinson College, P.O. Box 1773, Carlisle, PA 17013, [email protected] [email protected] ABSTRACT # 63108 ABSTRACT # 63108 Many undergraduate students cannot adequately Many undergraduate students cannot adequately interpret large, complex datasets even when presented interpret large, complex datasets even when presented in graphical form. The need to improve our student’s in graphical form. The need to improve our student’s quantitative reasoning and analytical writing skills quantitative reasoning and analytical writing skills has lead to the development of a series of integrated has lead to the development of a series of integrated exercises in our introductory global climate change exercises in our introductory global climate change course. Global climate datasets are excellent course. Global climate datasets are excellent resources for helping students improve their resources for helping students improve their quantitative reasoning skills and understand of quantitative reasoning skills and understand of temporal and spatial interactive global processes. In temporal and spatial interactive global processes. In an effort to provide formative assessment for student an effort to provide formative assessment for student progress in both these critical skills, labs start progress in both these critical skills, labs start with simple data extraction from newspapers and hand with simple data extraction from newspapers and hand graphing and culminate in large and complex database graphing and culminate in large and complex database analyses using Excel with computer graphing skills and analyses using Excel with computer graphing skills and basic statistics integrated into short written basic statistics integrated into short written assignments. In advance of the first exercise, assignments. In advance of the first exercise, students gather a week’s worth of data from their students gather a week’s worth of data from their hometown newspapers. Then the students find their hometown newspapers. Then the students find their state climatologist’s website and download the same state climatologist’s website and download the same data from the year before. They graph these data for data from the year before. They graph these data for both time periods, compare them, and turn their data both time periods, compare them, and turn their data and reasoned interpretations into a two-page paper. and reasoned interpretations into a two-page paper. The following week a few students’ examples are The following week a few students’ examples are highlighted to show the range of weather and climate highlighted to show the range of weather and climate change. By analyzing student results anonymously all change. By analyzing student results anonymously all learn the kinds of misinterpretations that can result learn the kinds of misinterpretations that can result and the depth of analysis that can be done even with a and the depth of analysis that can be done even with a small dataset. Dataset size and complexity increases small dataset. Dataset size and complexity increases in subsequent labs using climate phenomena such as in subsequent labs using climate phenomena such as ENSO, monsoon intensity, and drought to explore the ENSO, monsoon intensity, and drought to explore the relationships between global climate change and local relationships between global climate change and local manifestations of those changes over time. Datasets manifestations of those changes over time. Datasets come from the websites including NCDC climate, USGS come from the websites including NCDC climate, USGS stream gauge, and LTRR tree ring records. Besides stream gauge, and LTRR tree ring records. Besides learning the basic functions of Excel, students’ data learning the basic functions of Excel, students’ data analyses include regression and basic spectral analyses include regression and basic spectral analysis. Improved quantitative and written skills do analysis. Improved quantitative and written skills do translate to other courses and, hopefully, the translate to other courses and, hopefully, the quantitative literacy all citizens need in the 21 quantitative literacy all citizens need in the 21 st st century. century. INTRODUCTION INTRODUCTION Over the last two decades the sciences at Dickinson Over the last two decades the sciences at Dickinson College have reformed their curricula from a College have reformed their curricula from a traditionally separated lecture and laboratory to an traditionally separated lecture and laboratory to an integrated active learning experience where by integrated active learning experience where by inductive reasoning students learn fundamental inductive reasoning students learn fundamental scientific principles and concepts. In Geology, we scientific principles and concepts. In Geology, we use topics of broad geologic interest (e.g., History use topics of broad geologic interest (e.g., History of Life, Plate Tectonics, Oceanography) as a context of Life, Plate Tectonics, Oceanography) as a context for giving students practice in honing basic life for giving students practice in honing basic life skills specifically writing and quantitative skills specifically writing and quantitative reasoning. Topical courses allow significant depth in reasoning. Topical courses allow significant depth in the content and thus lend themselves to using large the content and thus lend themselves to using large datasets as vehicles for teaching fundamental datasets as vehicles for teaching fundamental principles and quantitative reasoning. The following principles and quantitative reasoning. The following discussion uses as an example our Global Climate discussion uses as an example our Global Climate Change introductory course. While traditional in its Change introductory course. While traditional in its content (meteorology, climatology, paleoclimatology), content (meteorology, climatology, paleoclimatology), the difference between weather and climate, the the difference between weather and climate, the interactions between regional climate phenomena via interactions between regional climate phenomena via teleconnections, and the evidence for and teleconnections, and the evidence for and substantiation of long term climate change can be substantiation of long term climate change can be inferred using large datasets available for the most inferred using large datasets available for the most part on the World Wide Web. We have found that the part on the World Wide Web. We have found that the introduction and statistical manipulation of large introduction and statistical manipulation of large datasets needs to be progressive in nature. Starting datasets needs to be progressive in nature. Starting with simple exercises using EXCEL as a tool give the with simple exercises using EXCEL as a tool give the students confidence when more complex datasets and students confidence when more complex datasets and statistical analyses are introduced. In addition we statistical analyses are introduced. In addition we found that initially most students were unfamiliar found that initially most students were unfamiliar with the basic functions of EXCEL. As time goes on we with the basic functions of EXCEL. As time goes on we see less and less need for remedial spreadsheet see less and less need for remedial spreadsheet instruction and can “raise the bar” with regard to the instruction and can “raise the bar” with regard to the complexity of the datasets and exercise objectives. complexity of the datasets and exercise objectives. Moreover, we are finding that the students are readily Moreover, we are finding that the students are readily translating the EXCEL and data analysis skills to translating the EXCEL and data analysis skills to other classes as we track those who take introductory other classes as we track those who take introductory classes and continue on to other electives or courses classes and continue on to other electives or courses in the major. Here we present three exercises which in the major. Here we present three exercises which require the acquisition and analysis of different require the acquisition and analysis of different EXERCISE I: UNDERSTANDING WEATHER AND CLIMATE EXERCISE I: UNDERSTANDING WEATHER AND CLIMATE ASSIGNMENT: ASSIGNMENT: Collect one week of local weather data (predicted and actual temperature and precipitation) from your hometown Collect one week of local weather data (predicted and actual temperature and precipitation) from your hometown newspaper. newspaper. The data collected is typical for most newspapers i.e. max. , min., and average temp for the day, the normal max., min. and The data collected is typical for most newspapers i.e. max. , min., and average temp for the day, the normal max., min. and average temps for that day, the extreme temps for the day, and the precipitation for the data and record average temps for that day, the extreme temps for the day, and the precipitation for the data and record rainfall for the day. They are informed they will need to do this for the first class of the semester. rainfall for the day. They are informed they will need to do this for the first class of the semester. During the first class we talk about the difference between weather and climate. They are then asked to find the climate During the first class we talk about the difference between weather and climate. They are then asked to find the climate data for the same week of days from any other year in the climate record for their hometown or nearby city. This requires data for the same week of days from any other year in the climate record for their hometown or nearby city. This requires them to search the Web for historical climate data for their town. them to search the Web for historical climate data for their town. OBJECTIVES: OBJECTIVES: 1) To start collecting and analyzing weather data; 2) to begin searching the Web for the required climate 1) To start collecting and analyzing weather data; 2) to begin searching the Web for the required climate data; 3) to learn to graphically present all data subsets using EXCEL; 4) to begin to understand basic statistics such data; 3) to learn to graphically present all data subsets using EXCEL; 4) to begin to understand basic statistics such as maximum, minimum and averages, and the concept of standard deviation; 5) the difficulty of predicting weather even 24 as maximum, minimum and averages, and the concept of standard deviation; 5) the difficulty of predicting weather even 24 hours in advance; and 6) the difference between weather and climate in terms of time and meteorological variability. hours in advance; and 6) the difference between weather and climate in terms of time and meteorological variability. EXAMPLE: EXAMPLE: W EATHER DATA FO R HARRISBURG ,P A (JA N U A R Y 15-22,2000) Day M ax Tem p M in Tem p M ean N orm X Lastyr hi Lastyr lo R ecord hiR ecord lo pred hi pred lo PPT M DTppt N orm ppt 15-Jan 33 18 26 28 25 13 67 -3 0 0.97 1.26 16-Jan 54 27 41 28 32 17 62 -4 42 24 0 0.97 1.35 17-Jan 24 12 18 28 40 15 65 -6 26 13 0 0.97 1.44 18-Jan 19 7 13 28 52 25 66 -6 25 18 0 0.97 1.53 19-Jan 39 17 28 28 44 32 66 14 32 22 0 0.97 1.62 20-Jan 31 26 29 28 47 31 68 -16 32 16 0.01 0.98 1.71 21-Jan 19 12 16 28 42 29 64 -22 25 0 0.18 1.16 1.8 22-Jan 23 7 15 28 39 27 64 -9 26 14 0 1.16 1.89 M eans-Extrem e Tem perature -30 -20 -10 0 10 20 30 40 50 60 70 80 15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan T (oF) Daily Norm R ecord hi R ecord lo 1937 1990 1990 1990 1951 1951 1959 1967 1964 1893 1982 1994 1994 1994 1994 1936 M ax-M in-M ean Tem peratures 0 10 20 30 40 50 60 15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan T(oF) M ax Tem p M in Tem p Daily Norm M ax-M in-Predicted Tem peratures 0 10 20 30 40 50 60 15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan T (oF) M ax Tem p M in Tem p pred hi pred lo M ean Tem peratures 10 15 20 25 30 35 40 45 15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan T (oF) Daily Norm Lastyr Precipitation 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan Inches 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Inches P ptto date N orm ppt PPT LeftScale R ightScale H arrisburg M in T -Dec.1999,2000 0 10 20 30 40 50 1 8 15 22 29 Datesin Decem ber Temp(F) m in 2000 m in 1999 m in norm H arrisburg M ax T -Dec.1999,2000 20 30 40 50 60 70 80 1 8 15 22 29 Datesin Decem ber Temp(F) m ax 2000 m ax 1999 m ax norm Harrisburg Avg.T -Dec.1999,2000 10 20 30 40 50 60 1 8 15 22 29 Datesin Decem ber Temp(F) avg 1999 avg 2000 avg norm Plot A shows a typical data set for Plot A shows a typical data set for one week in January. Students would one week in January. Students would recognize that the max., min., average, recognize that the max., min., average, and range of temperatures varies even and range of temperatures varies even over a short time period over a short time period Plot B shows the standard deviation Plot B shows the standard deviation of temperatures for one week of temperatures for one week Including the extreme range. Note Including the extreme range. Note that several days of extreme lows that several days of extreme lows and highs were set in one year and highs were set in one year Plot C shows the differences between Plot C shows the differences between predicted and actual high and low predicted and actual high and low temperatures. Students note the temperatures. Students note the difficulty in even short-term forecasts difficulty in even short-term forecasts Plot D (precipitation) shows that in Plot D (precipitation) shows that in the long-term record it has rained the long-term record it has rained every day and in the year-to-date every day and in the year-to-date record Harrisburg was behind and in record Harrisburg was behind and in fact in a prolonged drought fact in a prolonged drought Plot E shows the students the variability Plot E shows the students the variability of daily mean temperatures from one of daily mean temperatures from one year to the next compared to the long- year to the next compared to the long- term mean term mean When comparing the months of December for 1999 and 2000, the maximum (F), minimum (G), and average (H) temperatures for 1999 are significantly warm When comparing the months of December for 1999 and 2000, the maximum (F), minimum (G), and average (H) temperatures for 1999 are significantly warm 2000. December 1999 was one of the warmest on record for Harrisburg while December 2000 was one of the coldest. Students note that having a very 2000. December 1999 was one of the warmest on record for Harrisburg while December 2000 was one of the coldest. Students note that having a very year just one year apart suggests no particular trend in the context of global warming warnings. year just one year apart suggests no particular trend in the context of global warming warnings.