addressing quantitative reasoning and analytical writing skills improvement using global and local...
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ADDRESSING QUANTITATIVE REASONING AND ANALYTICAL WRITING SKILLS IMPROVEMENT USING ADDRESSING QUANTITATIVE REASONING AND ANALYTICAL WRITING SKILLS IMPROVEMENT USING GLOBAL AND LOCAL DATA SETS IN AN INTRODUCTORY GLOBAL CLIMATE CHANGE COURSEGLOBAL 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, [email protected], JEFFREY W., Department of Geology, Dickinson College, P.O. Box 1773, Carlisle, PA 17013, [email protected]
ABSTRACT # 63108ABSTRACT # 63108 Many undergraduate students cannot adequately interpret large, complex Many undergraduate students cannot adequately interpret large, complex datasets even when presented in graphical form. The need to improve our datasets even when presented in graphical form. The need to improve our student’s quantitative reasoning and analytical writing skills has lead to the student’s quantitative reasoning and analytical writing skills has lead to the development of a series of integrated exercises in our introductory global development of a series of integrated exercises in our introductory global climate change course. Global climate datasets are excellent resources for climate change course. Global climate datasets are excellent resources for helping students improve their quantitative reasoning skills and understand helping students improve their quantitative reasoning skills and understand of temporal and spatial interactive global processes. In an effort to provide of temporal and spatial interactive global processes. In an effort to provide formative assessment for student progress in both these critical skills, labs formative assessment for student progress in both these critical skills, labs start with simple data extraction from newspapers and hand graphing and start with simple data extraction from newspapers and hand graphing and culminate in large and complex database analyses using Excel with culminate in large and complex database analyses using Excel with computer graphing skills and basic statistics integrated into short written computer graphing skills and basic statistics integrated into short written assignments. In advance of the first exercise, students gather a week’s assignments. In advance of the first exercise, students gather a week’s worth of data from their hometown newspapers. Then the students find worth of data from their hometown newspapers. Then the students find their state climatologist’s website and download the same data from the their state climatologist’s website and download the same data from the year before. They graph these data for both time periods, compare them, year before. They graph these data for both time periods, compare them, and turn their data and reasoned interpretations into a two-page paper. and turn their data and reasoned interpretations into a two-page paper. The following week a few students’ examples are highlighted to show the The following week a few students’ examples are highlighted to show the range of weather and climate change. By analyzing student results range of weather and climate change. By analyzing student results anonymously all learn the kinds of misinterpretations that can result and anonymously all learn the kinds of misinterpretations that can result and the depth of analysis that can be done even with a small dataset. Dataset the depth of analysis that can be done even with a small dataset. Dataset size and complexity increases in subsequent labs using climate phenomena size and complexity increases in subsequent labs using climate phenomena such as ENSO, monsoon intensity, and drought to explore the relationships such as ENSO, monsoon intensity, and drought to explore the relationships between global climate change and local manifestations of those changes between global climate change and local manifestations of those changes over time. Datasets come from the websites including NCDC climate, over time. Datasets come from the websites including NCDC climate, USGS stream gauge, and LTRR tree ring records. Besides learning the USGS stream gauge, and LTRR tree ring records. Besides learning the basic functions of Excel, students’ data analyses include regression and basic functions of Excel, students’ data analyses include regression and basic spectral analysis. Improved quantitative and written skills do basic spectral analysis. Improved quantitative and written skills do translate to other courses and, hopefully, the quantitative literacy all translate to other courses and, hopefully, the quantitative literacy all citizens need in the 21citizens need in the 21stst century. century.
INTRODUCTIONINTRODUCTION
Over the last two decades the sciences at Dickinson College have reformed Over the last two decades the sciences at Dickinson College have reformed their curricula from a traditionally separated lecture and laboratory to an their curricula from a traditionally separated lecture and laboratory to an integrated active learning experience where by inductive reasoning students integrated active learning experience where by inductive reasoning students learn fundamental scientific principles and concepts. In Geology, we use learn fundamental scientific principles and concepts. In Geology, we use topics of broad geologic interest (e.g., History of Life, Plate Tectonics, topics of broad geologic interest (e.g., History of Life, Plate Tectonics, Oceanography) as a context for giving students practice in honing basic life Oceanography) as a context for giving students practice in honing basic life skills specifically writing and quantitative reasoning. Topical courses allow skills specifically writing and quantitative reasoning. Topical courses allow significant depth in the content and thus lend themselves to using large significant depth in the content and thus lend themselves to using large datasets as vehicles for teaching fundamental principles and quantitative datasets as vehicles for teaching fundamental principles and quantitative reasoning. The following discussion uses as an example our Global reasoning. The following discussion uses as an example our Global Climate Change introductory course. While traditional in its content Climate Change introductory course. While traditional in its content (meteorology, climatology, paleoclimatology), the difference between (meteorology, climatology, paleoclimatology), the difference between weather and climate, the interactions between regional climate phenomena weather and climate, the interactions between regional climate phenomena via teleconnections, and the evidence for and substantiation of long term via teleconnections, and the evidence for and substantiation of long term climate change can be inferred using large datasets available for the most climate change can be inferred using large datasets available for the most part on the World Wide Web. We have found that the introduction and part on the World Wide Web. We have found that the introduction and statistical manipulation of large datasets needs to be progressive in nature. statistical manipulation of large datasets needs to be progressive in nature. Starting with simple exercises using EXCEL as a tool give the students Starting with simple exercises using EXCEL as a tool give the students confidence when more complex datasets and statistical analyses are confidence when more complex datasets and statistical analyses are introduced. In addition we found that initially most students were introduced. In addition we found that initially most students were unfamiliar with the basic functions of EXCEL. As time goes on we see less unfamiliar with the basic functions of EXCEL. As time goes on we see less and less need for remedial spreadsheet instruction and can “raise the bar” and less need for remedial spreadsheet instruction and can “raise the bar” with regard to the complexity of the datasets and exercise objectives. with regard to the complexity of the datasets and exercise objectives. Moreover, we are finding that the students are readily translating the Moreover, we are finding that the students are readily translating the EXCEL and data analysis skills to other classes as we track those who take EXCEL and data analysis skills to other classes as we track those who take introductory classes and continue on to other electives or courses in the introductory classes and continue on to other electives or courses in the major. Here we present three exercises which require the acquisition and major. Here we present three exercises which require the acquisition and analysis of different datasets and increase in complexity over time. Each analysis of different datasets and increase in complexity over time. Each exercise requires a certain amount of dataset extraction, manipulation, and exercise requires a certain amount of dataset extraction, manipulation, and analysis as substantiation of inferences made in a 2-3 page fully formatted analysis as substantiation of inferences made in a 2-3 page fully formatted analytical paper.analytical paper.
EXERCISE I: UNDERSTANDING WEATHER AND CLIMATEEXERCISE I: UNDERSTANDING WEATHER AND CLIMATE
ASSIGNMENT:ASSIGNMENT: Collect one week of local weather data (predicted and actual temperature and precipitation) from your hometown newspaper. Collect one week of local weather data (predicted and actual temperature and precipitation) from your hometown newspaper. 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 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 rainfall for the day. They are informed they will need to do this for the first class of the semester. 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.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 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 them to search the Web for historical climate data for their town. the climate record for their hometown or nearby city. This requires 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 data; 3) to learn to graphically present all data 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 as maximum, minimum and averages, and the concept of standard deviation; 5) the difficulty of 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 hours in advance; and 6) the difference between weather and climate in terms of time and meteorological variability.predicting weather even 24 hours in advance; and 6) the difference between weather and climate in terms of time and meteorological variability.
EXAMPLE:EXAMPLE:
WEATHER DATA FOR HARRISBURG, PA (JANUARY 15-22, 2000)Day Max Temp Min Temp Mean Norm X Last yr hi Last yr lo Record hi Record lo pred hi pred lo PPT MDTppt Norm ppt
15-Jan 33 18 26 28 25 13 67 -3 0 0.97 1.2616-Jan 54 27 41 28 32 17 62 -4 42 24 0 0.97 1.3517-Jan 24 12 18 28 40 15 65 -6 26 13 0 0.97 1.4418-Jan 19 7 13 28 52 25 66 -6 25 18 0 0.97 1.5319-Jan 39 17 28 28 44 32 66 14 32 22 0 0.97 1.6220-Jan 31 26 29 28 47 31 68 -16 32 16 0.01 0.98 1.7121-Jan 19 12 16 28 42 29 64 -22 25 0 0.18 1.16 1.822-Jan 23 7 15 28 39 27 64 -9 26 14 0 1.16 1.89
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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 evenand range of temperatures varies evenover 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 betweenPlot C shows the differences betweenpredicted and actual high and lowpredicted and actual high and lowtemperatures. Students note the temperatures. Students note the difficulty in even short-term forecastsdifficulty in even short-term forecasts
Plot D (precipitation) shows that in Plot D (precipitation) shows that in the long-term record it has rainedthe long-term record it has rainedevery day and in the year-to-dateevery day and in the year-to-daterecord Harrisburg was behind and inrecord Harrisburg was behind and infact in a prolonged drought fact in a prolonged drought
Plot E shows the students the variabilityPlot E shows the students the variabilityof daily mean temperatures from oneof daily mean temperatures from oneyear to the next compared to the long-year to the next compared to the long-term meanterm mean
When comparing the months of December for 1999 and 2000, the maximum (F), minimum (G), and average (H) temperatures for 1999 are significantly warmer than for December 2000. December 1999 was one of the warmest on record When comparing the months of December for 1999 and 2000, the maximum (F), minimum (G), and average (H) temperatures for 1999 are significantly warmer than for December 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 cold and very warm year just one year apart suggests no particular trend in the context of global warming warnings.for Harrisburg while December 2000 was one of the coldest. Students note that having a very cold and very warm year just one year apart suggests no particular trend in the context of global warming warnings.
EXERCISE II: ANALYSIS OF EFFECTS OF LATITUDE, ATTITUDE, AND CONTINENTALITY USING HISTORICAL TEMPERATURE AND PRECIPITATION RECORDSEXERCISE II: ANALYSIS OF EFFECTS OF LATITUDE, ATTITUDE, AND CONTINENTALITY USING HISTORICAL TEMPERATURE AND PRECIPITATION RECORDSASSIGNMENT: ASSIGNMENT: Using the National Climate Data Center’s Global Historical Climatology Network find historical temperature and precipitation data for two cities, one in North America and one on any continent but North America.Using the National Climate Data Center’s Global Historical Climatology Network find historical temperature and precipitation data for two cities, one in North America and one on any continent but North America.Your two cities should be at least 40Your two cities should be at least 40o o latitude apart. One city should be on a coastline, the other at least 500 km from the coast.latitude apart. One city should be on a coastline, the other at least 500 km from the coast.
OBJECTIVES: OBJECTIVES: 1) A geography lesson in finding appropriate cities; 2) Finding good historical records in a dataset with 7000+ stations with records back to 1800; 3) manipulating, graphing, and analyzing a large dataset in EXCEL; 4) 1) A geography lesson in finding appropriate cities; 2) Finding good historical records in a dataset with 7000+ stations with records back to 1800; 3) manipulating, graphing, and analyzing a large dataset in EXCEL; 4) synthesizing the data to determine the cause(s) of climate differences between the two cities and writing a short paper about the synthesis.synthesizing the data to determine the cause(s) of climate differences between the two cities and writing a short paper about the synthesis.
EXAMPLE: EXAMPLE:
Plots above come from the CLIMVIS website. Students would work in EXCEL to average the max and minPlots above come from the CLIMVIS website. Students would work in EXCEL to average the max and mintemperatures and with precipitation graph the monthly values for the period of record. This would be temperatures and with precipitation graph the monthly values for the period of record. This would be discussed in the paper in terms of relative temperature range and amount of precipitation at each site. Extremediscussed in the paper in terms of relative temperature range and amount of precipitation at each site. Extremeand average years for Temp and Ppt for each city would also be compared (see plots A and B below) graphically. and average years for Temp and Ppt for each city would also be compared (see plots A and B below) graphically.
DATA SHEETDATA SHEET
City #1 PORT TOWNSEND, WASHINGTON, USACity #1 PORT TOWNSEND, WASHINGTON, USALongitude:122.75Longitude:122.75oo W WLatitude: 48.1Latitude: 48.1oo N NElevation (meters): 0 m Elevation (meters): 0 m Distance from coast (km): 0 km Distance from coast (km): 0 km # of years of record: Temp: 86 PPT: 86# of years of record: Temp: 86 PPT: 86Other geographically interesting facts:Other geographically interesting facts: In rain shadow of Olympic Mts.In rain shadow of Olympic Mts. On the Straits of Juan de FucaOn the Straits of Juan de Fuca
City #2 BOULIA, AUSTRALIA City #2 BOULIA, AUSTRALIA Longitude: 139.9Longitude: 139.9oo E ELatitude: 22.9Latitude: 22.9oo S S Elevation (meters): 146 mElevation (meters): 146 mDistance from coast (km): 780 KMDistance from coast (km): 780 KM
# of years of record: Temp: 86 PPT: 86# of years of record: Temp: 86 PPT: 86 Other geographically interesting facts: Other geographically interesting facts:
Middle of Great Australian DesertMiddle of Great Australian Desert
Version 1 Precipitation Data:Rainfall Data Through1990
Version 2 Temperature Data:Regularly UpdatedMax/Min Temperature Data*
http://www.ncdc.noaa.gov/oa/climate/ghcn/ghcn.SELECT.htmlhttp://www.ncdc.noaa.gov/oa/climate/ghcn/ghcn.SELECT.html
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EXERCISE III: ENSO, CLIMATE CHANGE, AND STREAM FLOW IN THE WESTERN U.S.EXERCISE III: ENSO, CLIMATE CHANGE, AND STREAM FLOW IN THE WESTERN U.S.ASSIGNMENT:ASSIGNMENT: Determine whether the Southern Oscillation in the southwest Pacific has an effect on temperature, precipitation and stream flow volume in the western states of the United States Determine whether the Southern Oscillation in the southwest Pacific has an effect on temperature, precipitation and stream flow volume in the western states of the United States
OBJECTIVES: OBJECTIVES: 1) to determine if regional climate phenomena have global control of weather historically 2) to use EXCEL to do regression and time series analyses; 3) to derive comparable from multiple databases1) to determine if regional climate phenomena have global control of weather historically 2) to use EXCEL to do regression and time series analyses; 3) to derive comparable from multiple databases
EXAMPLES: SNOHOMISH, WA and TOMBSTONE, AZEXAMPLES: SNOHOMISH, WA and TOMBSTONE, AZ
Students are given a list of weather stations for which there are associated USGS gauging station in the states of Arizona, California, Oregon, and Washington. Road maps of these states help the students to find the locations of the Students are given a list of weather stations for which there are associated USGS gauging station in the states of Arizona, California, Oregon, and Washington. Road maps of these states help the students to find the locations of the Locale they choose. The students already know how to get to the NCDC site, now they must find a site with good ENSO data and explore the USGS Water Resources site to ascertain historical stream flow data. Frequently the range of Locale they choose. The students already know how to get to the NCDC site, now they must find a site with good ENSO data and explore the USGS Water Resources site to ascertain historical stream flow data. Frequently the range of dates for each station varies so it is reasonable to choose a time span which will provide enough El Nino and La Nina events to show a trend against the other variables.dates for each station varies so it is reasonable to choose a time span which will provide enough El Nino and La Nina events to show a trend against the other variables.
Southern Oscillation Index (SOI)http://www.cgd.ucar.edu/cas/catalog/climind/soi.html http://water.usgs.gov./
Water Resources of the United States
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Two examples of association of SOI with Two examples of association of SOI with Precipitation, temperature, and stream discharge Precipitation, temperature, and stream discharge show relatively weak correlation For Tombstone, show relatively weak correlation For Tombstone, AZ and a relatively strong correlation for AZ and a relatively strong correlation for Snohomish, WA. Plot A shows good correlation of Snohomish, WA. Plot A shows good correlation of rainfall with discharge because rains tend to be rainfall with discharge because rains tend to be monsoonal with significant runoff potential. monsoonal with significant runoff potential. However, in Plot B discharge and SOI do not show However, in Plot B discharge and SOI do not show consistent correlation. Monsoonal rains are consistent correlation. Monsoonal rains are correlated to El Nino and La Nina events. Plot C correlated to El Nino and La Nina events. Plot C shows small temperature range variations. shows small temperature range variations. Abnormally low high and low temps for a year Abnormally low high and low temps for a year show inconsistent correlation with La Nina events show inconsistent correlation with La Nina events (note circled years). Rain-Discharge relations in (note circled years). Rain-Discharge relations in Snohomish (plot D) is attributable to runoff and Snohomish (plot D) is attributable to runoff and snowmelt depending on time of year. Both snowmelt depending on time of year. Both discharge and rainfall versus SOI show strong discharge and rainfall versus SOI show strong correlations with abnormally high rain and correlations with abnormally high rain and discharge being correlated with La Nina events discharge being correlated with La Nina events and low rain and discharge correlated with El and low rain and discharge correlated with El Nino events. 12-point moving averages help in Nino events. 12-point moving averages help in delineating trends.delineating trends.
SNOHOMISH
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DISCUSSION AND CONCLUSIONS: DISCUSSION AND CONCLUSIONS:
At Dickinson College we have used large datasets obtained from the World At Dickinson College we have used large datasets obtained from the World Wide Web for a variety of disciplines including climate change, environmental Wide Web for a variety of disciplines including climate change, environmental geology, oceanography, geochemistry, geomorphology, and others. Large data sets geology, oceanography, geochemistry, geomorphology, and others. Large data sets can be generated from long-term studies of various geologic processes for example, can be generated from long-term studies of various geologic processes for example, stream chemistry and changes in meanders of small streams. I have also used tree stream chemistry and changes in meanders of small streams. I have also used tree ring and ice core databases to do quantitative reasoning in my climate course. ring and ice core databases to do quantitative reasoning in my climate course.
With each analysis a paper is required which asks the students to formulate With each analysis a paper is required which asks the students to formulate the problem, the methodologies used to obtain and synthesize the data, and a the problem, the methodologies used to obtain and synthesize the data, and a discussion of the data analysis. In this way the students receive practice in discussion of the data analysis. In this way the students receive practice in analytical writing. By the third or fourth exercise the students have become quite analytical writing. By the third or fourth exercise the students have become quite good at manipulating data in EXCEL and better at synthesizing data. They learn good at manipulating data in EXCEL and better at synthesizing data. They learn as we see in the SOI vs. climate of the Western US exercise that positive results will as we see in the SOI vs. climate of the Western US exercise that positive results will not always be achieved and frequently the data are not easy to analyze. However, not always be achieved and frequently the data are not easy to analyze. However, there is always an explanation for the data which may require a change in ones there is always an explanation for the data which may require a change in ones hypothesis. hypothesis.
One other benefit we have found is that the statistical analysis skills have One other benefit we have found is that the statistical analysis skills have translated to other courses minimizing the time spent reteaching basic EXCEL translated to other courses minimizing the time spent reteaching basic EXCEL techniques.techniques.
There are some pitfalls to these kinds of exercises:There are some pitfalls to these kinds of exercises:
•In the teaching of EXCEL for use as a statistical analysis tool, instructions must be In the teaching of EXCEL for use as a statistical analysis tool, instructions must be very clear and detailed otherwise the students will be lost before encountering the very clear and detailed otherwise the students will be lost before encountering the data from which we desire them to learn about process. A primer in EXCEL will data from which we desire them to learn about process. A primer in EXCEL will help those who are totally unfamiliar with EXCEL and be a good refresher for help those who are totally unfamiliar with EXCEL and be a good refresher for those who are familiar with EXCEL. those who are familiar with EXCEL. •Add new statistical techniques gradually and re-use the familiar techniques. Some Add new statistical techniques gradually and re-use the familiar techniques. Some redundancy is good.redundancy is good.
•In crease the sophistication of the databases from week to week to challenge In crease the sophistication of the databases from week to week to challenge students.students.
•Make sure databases used do not contain complexities for searching which would Make sure databases used do not contain complexities for searching which would confuse introductory students. If they do and are still useful, the instructor may confuse introductory students. If they do and are still useful, the instructor may need to tailor the database searching as was done in Exercise III. This requires a need to tailor the database searching as was done in Exercise III. This requires a lot of pre-lab trial and error experimentation on the part of the instructor.lot of pre-lab trial and error experimentation on the part of the instructor. •The expected outcomes of exercises can be obtained more rapidly if part of the The expected outcomes of exercises can be obtained more rapidly if part of the database search is done for the students ahead of time. This is particularly useful if database search is done for the students ahead of time. This is particularly useful if you are using data from a dataset searched in previous labs and re-searching would you are using data from a dataset searched in previous labs and re-searching would be unnecessarily time consuming.be unnecessarily time consuming.