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BSC 1010C General Biology IAPPENDICES

A. Scientific Method and Experimental DesignB. Data Analysis and Graph PreparationC. Critically Evaluating “Scientific” Articles, Reports or Internet SitesD. Concept MappingE. Rubrics for Non-Traditional Assessments

Group Member EvaluationTips for Making a Poster

Audio visual/Poster EvaluationPresentation EvaluationReport Evaluation

Created by Mary Kay Cassani/FGCU/Ft. Myers, FL

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Appendix A

SCIENTIFIC METHOD AND EXPERIMENTAL DESIGN

HUMANS ARE INSTINCTIVELY CURIOUS

As most textbooks will tell you, the scientific method is a continuous process. It really doesn’t start with observations, but that seems like a good place to begin, because all experimentation is based on some kind of prior knowledge. Prior knowledge could be general observations made subconsciously or specific observations recorded for a purpose. At the heart of the scientific process is human curiosity about nature and a desire to figure out how things work. The scientific process builds on this instinct, and the scientific method is a way confirming that our assumptions about “how things work” are accurate.

HUMANS ARE CATEGORIZERS

Human beings are in the habit of observing and categorizing things. It helps us to remember and make generalizations about the future. It came in handy when we had to figure out which tree produces fruit at what time of year, or if the bushes were in bloom, then the grazers should be here soon to hunt. Observations and categorization create a body of information which may or may not be accurate. Things like “If we throw a virgin in the volcano, then it will stop erupting” may have worked once, and a generalization was made about it, but it probably was not reproducible.

SCIENCE RESULTS MUST BE REPRODUCIBLE

The scientific method is a way of taking the categorizations and generalizations and testing them to make sure they are right and reproducible. The method involves designing an experiment, recording the results, interpreting the results, and using the information to predict what would happen in similar circumstances. Scientific experiments come in two basic flavors: ones where we can control and manipulate all of the pieces and parts, and ones where we make observations and measurements of the natural world without manipulating any of it. Some experiments are combination of the two. All experiments should be documented in such a way that another group can do the same experiment and get the same result. If a different result happens, then the experiment is not reproducible, and the conclusions and generalizations drawn from it are not accurate.

HYPOTHESIS MUST BE TESTABLE

All experiments start with a generalization statement, called a hypothesis (an “educated guess”.) “A virgin will stop the volcano from erupting” is a hypothesis. “The deer come when the bushes are in bloom” is a hypothesis. A hypothesis must be a statement with a testable outcome. “Roses smell good” is not a testable


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hypothesis (what do you mean by “good”?). Some hypotheses are sort of testable, but can be tested only if the outcome is defined well. “Carrots make me see better” can be tested, but the term “better” needs to be defined in order to design an experiment. “Carrots improve vision from 20/40 to 20/20” is a much more testable hypothesis.

PREDICTIONS STATE THE EXPECTED OUTCOME

The next step is to form a prediction based on the hypothesis. The prediction must state the expected outcome of an experiment if the hypothesis is true. It is in the form of an if/then statement. “If a virgin will stop the volcano from erupting, then no eruptions will occur after we throw him in.” “If the deer come when the bushes are in bloom, then there will be more deer when there are flowers on the bushes.” “If carrots improve vision from 20/40 to 20/20, then eating more carrots will make my vision 20/20.” If the hypothesis is testable, and the prediction is stated in terms of the outcome, then when the experiment is finished, it will be easy to determine whether the hypothesis has been supported or disproved. Notice that a hypothesis can be disproved, but is never proven, only supported. Other conditions of an experiment may disprove the hypothesis, therefore any single experiment cannot prove a hypothesis.

CONTROL TREATMENTS PROVIDE A BASIS FOR COMPARISON

How the experiment is designed is critical to the support or disproof of a hypothesis. A poor experimental design may support a hypothesis with erroneous information. There are several critical parts to any experiment. The first of these are the control treatments, commonly abbreviated to “controls”. Controls are parts of the experiment which are used for comparisons or baseline data. If you have nothing to compare your experimental data to, how do you know it worked? For instance, if we are testing the effect of different levels of nitrogen on the growth of tomatoes, we would need some tomato plants that do not get any nitrogen so that we know how they would grow “normally”. These no-nitrogen plants are the controls, they serve as standards to compare our test plants to.

ALL CONDITIONS MUST BE THE SAME

The second critical part of any experiment are the controlled variables. Don’t let the similar terms confuse you. Controlled variables are all of the things in the experiment that you are NOT testing. In the nitrogen+tomatoes experiment, the kind and amount of soil is a controlled variable. So are the kind and amount of water, the amount of sun, the kind of tomato plant, the temperature and the humidity of the greenhouse. All of these things could affect the outcome of the experiment, so they have to be identical for all of the test plants and the control treatment plants.


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MANY INDIVIDUALS ARE NEEDED IN EACH TREATMENT LEVEL

The different levels of nitrogen are the treatment variables or treatment levels, sometimes abbreviated to “treatments”. There needs to be several individuals in each treatment level. These are replications. Replications are critical to any experiment so that if something happens to one of the individuals, then the experiment can go on. Also, mathematical statistical evaluations are performed on the replications, and can tell us just how reliable our data is. The kind of statistics to be used will determine how many individuals are needed in each treatment level. In introductory general science classes, usually three to ten replications are in each treatment level, depending on the experiment.

ONLY ONE INDEPENDENT VARIABLE, MULTIPLE DEPENDENT VARIABLES

The variable you are testing, in this case nitrogen, is called the independent variable. The measurements you make (height, weight, number of leaves, etc.) are the dependent variables. (Remember it this way: the dependent variables are dependent on the independent one. The height of the plant is dependent on the amount of nitrogen. Height is the dependent variable, nitrogen is the independent one.) There can be only one independent variable in any experiment! If there is more than one independent variable, how can you tell what caused the results you see?

“REAL WORLD” EXPERIMENTS

Observational experiments try to have all of the necessary parts, but out in the “wild” it sometimes is difficult to control all of the variables except the one you are testing. Observational experiments generally take the form of “If this hypothesis is true, then we would expect to see this happening under these natural circumstances.” An example would be: Hypothesis: “The altitude a plant grows at affects its height”. Prediction: “If altitude affects height, then the same kind of plant will be shorter when it grows higher up on a mountain.” To test this, the ecologist would find a mountain that had the plant growing on it at several elevation, and at each elevation, the soil would be the same, if possible; the rainfall would be the same, if possible; the amount of sunlight would be the same, if possible…etc. If all the variables that should be controlled were controlled as much as possible, the ecologist would measure the heights of several of the plants at each altitude to test the hypothesis. If the soil was different, however, the results of the observational experiment would not be valid, because then we couldn’t tell if it was the soil or the altitude that cause the heights to be different. The same is true for the other controlled variables. These kinds of observational experiments don’t always have clear-cut control treatments, due to the nature of the experiments. In the example, the plants grown at sea level (or the lowest altitude) could be considered a control treatment, although it is also a treatment level.


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COMPARE THE RESULTS TO THE PREDICTION

When only one condition is changed (the independent variable) and all of the other conditions remain the same (the controlled variables), then the difference in measured results (the independent variables) can be attributed to the effect of the independent variable. Only if the measured differences are significantly different from the control treatment (the baseline or zero level) can the results of the experiment be said to support or disprove the hypothesis. The results should match what the prediction says will happen in order for the results to support the hypothesis. If the results do not match the prediction, for any reason, then the hypothesis is disproved. Be careful of the use of the word “significant.” In science, this term means that certain statistical numerical tests were performed on the data. If no statistical tests were performed, do not use “significant”. In introductory general science classes statistical tests are not usually performed, and whether the results support or disprove the hypothesis is usually determined by qualitative evaluation.

MATHEMATICALLY MANIPULATING THE RESULTS

Having many replications in several treatment levels will give you a lot of results to evaluate. Be careful which data you put in what group! Averaging is the most common mathematical manipulation performed in introductory general science classes. Generally averaging the data within each treatment level is acceptable, provided it is all the same kind of measurements (don’t add stem height to leaf length, for instance.) Some experiments are set up so that an individual serves as its own baseline or control. These kinds of experiments will have a measurement taken BEFORE a treatment, and then several taken AFTER the treatment. The DIFFERENCE between the before and after measurements can be compared between individuals. The BEFORE measurements of all individuals cannot be grouped together, nor can the AFTER measurements. For example, to test the effect of a drug on hair growth, the amount of hair before treatment would be measured on a number of individuals. After treatment, the amount of hair on each individual would again be measured. Joe’s hair and Bob’s hair before treatment cannot be added together, nor can their hair measurements after treatment. The DIFFERENCE in measurement between Joe’s after and before treatment can be compared or averaged with Bob’s DIFFERENCE. The two difference numbers are comparable and therefore can be put together. The original measurements of each individual are not comparable and cannot be grouped together.

CRITICALLY EVALUATE THE PROCEDURE

When an experiment works, and supports the hypothesis, the usual thing to do is to accept the results, and design more experiments based on the results. In a well-designed experiment, the results will not support the hypothesis unless the hypothesis is correct. The opposite is not true, however. If the results do not support the hypothesis, it does not automatically follow that the hypothesis is wrong.


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Several things could be going on to make the experiment not work out the way it was expected:

Were there variables that should have been controlled that were not?Was all equipment clean and had no carry-over between treatments?Were there hidden assumptions in the hypothesis or prediction that could have affected the results?Were there electrical or other maintenance problems with the equipment that weren’t evident?

There are many similar kinds of questions to ask when an experiment produces unexpected results. All must be checked out before accepting negative results as real. A “NO” answer to a hypothesis is as valid as a “YES” answer, provided that the experiment was designed and controlled well.


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Appendix B

DATA ANALYSIS AND GRAPH PREPARATION

In this course, you will be asked to analyze and present data (usually numerical) in several different exercises. No matter what the units are that you are counting (grams, meters, species, sex), numerical data falls into two basic categories: continuous and discontinuous (sometimes called discrete.)

DATA TYPES

Continuous data spans a range of numbers, only some of which you record in your experiment. For example, if you are taking the temperature of a liquid, the range of temperatures available begin at absolute zero (-275oC) and go higher than 10,000oC. You will only be recording a few of the temperatures between 0oC (freezing point of water) and 100oC (boiling point of water.) Between each of the temperatures you record is an infinite number of subdivisions. This is what makes temperature continuous data: there are numbers between the ones that you record.

Discontinuous or discrete data is data that fall into specific categories, with nothing available in between; categories such as yes-no, pass-fail, male-female, oak-pine, etc. Your data is the number of things that fall into each category: how many trees are oak vs. how many are pine. LINE GRAPHS

To graph continuous data, use a line graph. (Fig. 1) Directions for making a line graph are in the next section. Always make a table of your data to accompany any graph. Only continuous data can be put on a line graph. The power of a line graph is the ability to use it for prediction. When you connect two points on a line graph, you are predicting what would be expected to happen in between the points, where you did not collect data. This is called interpolating between two points. You can also predict what would happen beyond the end of the data, for a short distance. This is called extrapolation. Line graphs do not always have straight lines. Some data is curved in one way or another. Interpolation and extrapolation may be more difficult for curved line graphs.

interpolate * extrapolate * *

data points

Fig. 1 A line graph example


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BAR GRAPHS

Discontinuous data is graphed on a bar graph (sometimes called a histogram or a bar chart, Fig. 2.) Data points on a bar graph cannot be connected with a line or curve, because there is no available data in between (you can’t be half-pregnant, or a half species between an oak tree and a pine tree.) Therefore, bar graphs do not have predictive value.

data points

Fig. 2 A bar graph example

PIE GRAPHS

A third kind of graph frequently used is a pie graph or pie chart. Any kind of data can be used in a pie graph, but it must be first converted to percentage or proportion. For instance, out of 300,000 residents of Lee County, only 150,000 are registered voters, and only 100,000 of them vote. This is continuous data, and must be converted to percentage before putting in a pie chart. (Fig. 3) The entire circle is 100% = 300,000 people, half of the circle is 50% = 150,000 people, one-third of the circle is 33% = 100,000 people. Discontinuous data must also be converted to percentage before using it in a pie graph. For example, The total number of trees in a plot is 1000. 800 are oak, 150 are pine, and the rest are palm. 100% = 1000 trees, 80% = 800 oak trees, 15% = pine trees, and 5% = palm trees. (Fig. 4)

VOTED PALM 5% 100,000 50 PINE 15% 33% 150 50% OAK 800REG.VOTERS 80%150,000

100% = 300,000 People 100% = 1000 Trees

Fig. 3 A pie graph example Fig. 4. A pie graph example

All graphs must be accompanied by a table containing the actual collected (raw) data. All graphs must have a figure number and a title that describes what the graph represents.


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INSTRUCTIONS FOR PREPARING A TABLE

1. Put your name, course number, section number and date at the top of the page.2. Count the number of different kinds of data to be included in the table. For

example, year, number of oak trees, number of pine trees, and number of palm trees are four different kinds of data.

3. Leaving space on each side of the paper, divide the paper in to columns, one for each kind of data. See Table 1.

4. Label each column with the kind of data in the column, and with the units for the numbers. For example, a water usage column might have the units of “millions of gallons per day” or MGD, in order to avoid having to put numbers with six, seven or eight figures in the column. If you use an abbreviation like MGD, put an asterisk after the abbreviation the first time it is used, and define the abbreviation at the bottom of the table, with an asterisk before the definition.

5. Enter your data in the rows of the table, leaving a space between the column label and the first row of data.

6. Draw a line under the column labels and units, and if desired, around the entire table.

TABLE 1: LEE COUNTY POPULATION AND WATER USE 1965-2020

YEAR POPULATION (THOUSANDS) (?

=projected)

POPULATION USING PUBLIC WATER (THOUSANDS) (* = estimated)

PUBLIC WATER SUPPLY USAGE

(MGD**)

1965 71.8 59.6 * 4.31970 105.2 87.2 * 8.31975 162.0 134.5 * 16.81977 170.0 141.1 * 19.01980 205.3 166.0 29.81985 273.7 227.2 * 31.71987 300.6 281.5 33.51989 325.4 270.1 * 40.81990 335.1 254.0 42.82000 419.6 ?2005 463.2 ?2010 506.1 ?2015 549.7 ?2020 593.8 ?

** MGD = million gallons per day


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INSTRUCTIONS FOR PREPARING A LINE GRAPH

1. Use the whole sheet of paper. Turn it sideways. Draw a vertical line (an axis) about 1” from the left side, and a horizontal line about 1" from the bottom. Where the two lines connect is your zero point. See Fig. 5.

2. Put the following LABELS on the graph:FIGURE NUMBER and TITLE – at the top of the pageYOUR NAME, COURSE NUMBER, SECTION NUMBER, DATE – in the top right hand corner.VERTICAL AXIS – the dependent variable: the set of numbers that are dependent on the other set; for example, population of a region is dependent on the year, but the year is not dependent on the population of the region. So year is the independent variable and population is the dependent variable. The year therefore would go on the horizontal axis, and population goes on the vertical axis. HORIZONTAL AXIS – the independent variable.

3. Put the units on each axis – thousands, millions, milligrams, percents, etc.4. Divide each axis into regular intervals. It is not always necessary to start at zero.

Mark intervals beyond the end of the data so you can extend the data. Label each interval.

5. Put the data on the graph, using clearly marked dots. Ask your instructor whether you should connect the dots. If there is more than one dependent variable, use different kinds of dots for each variable if both are on the same graph. An example would be heights of maple trees and heights of oak trees, dependent on amount of rainfall (independent.)

6. Your instructor may ask you to draw a straight line between the dots so that the line seems to be an “average” of the dots. This is called the “line of best fit.” (This can be done mathematically to be very precise, or by “eyeballing” the line, depending on the use of the information.)

7. Extend the line of best fit on each graph, to read projected (extrapolated) information beyond the end of the available data. This may or may not be accurate information, depending on the source of the available data and the curve of the line.

POPULATION 405 (THOUSANDS) 305 * 205 * 105 *

95 85

1970 1980 1990 2000 YEAR

Fig. 5 Population Growth in Lee County


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INSTRUCTIONS FOR PREPARING A BAR GRAPH

1. Follow steps 1-3 for a line graph.2. The data set that is continuous (eg. rainfall, altitude) goes on the vertical axis.

Divide the vertical axis into regular units. It is not always necessary to start the axis at zero. See Table 2 and Fig. 6.

3. The data set that is discontinuous goes on the horizontal axis. Mark places on the horizontal axis for each discontinuous category. For example, in a data set that contains counts of oak and pine trees related to altitude, altitude goes on the vertical axis and the number of oak trees and pine trees goes on the horizontal axis.

4. Put a dot on the graph at the appropriate height for each category. Instead of connecting the dots for the data, draw boxes from the horizontal axis up to each data point. DO NOT CONNECT THE TOPS OF THE BOXES! You can color or cross-hatch the boxes to make them distinguishable from each other, if desired.

5. You cannot interpolate or extrapolate from a bar graph.

TABLE 2. TREE SPECIES AND POPULATION

NUMBER 40 SPECIES NUMBER OF TREES 30 OAK 25 20 PINE 35 10 PALM 20 OAK PINE PALM

SPECIES

Fig. 6 Number of each species

INSTRUCTIONS FOR PREPARING A PIE GRAPH

Pie graphs require that the data be manipulated before a graph can be prepared. The data, either continuous or discontinuous, must be converted into percentage or proportional data before making the graph. The percentage has to be a useful datum or else the conversion makes no sense. For example, in a data set of heights of men named Pete, Joe, Raphael, and Sal, adding up all of their heights, then saying that Pete is 20% of the total height is mathematically correct, but is a meaningless piece of information in the real world. On the other hand, if the data set is weights of men, adding up the weights and finding out how the percentages are distributed has real world meaning if the concern is for tension on a climbing rope, an elevator, or in a helicopter.

1. Put the FIGURE NUMBER and TITLE of the graph at the top of the page. Put YOUR NAME, COURSE NUMBER, SECTION NUMBER and DATE in the top right hand corner.


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2. Determine the total of the data values, and the relative percentage or proportion of each data point. See Table 3.

3. Divide a circle into proportional parts that represent each of the data points or categories. See Fig. 7.

4. Mark each division with the category name, the percentage or proportional value, and the actual value for the category.

5. Each division can be colored or cross-hatched for clarity, if desired.

TABLE 3 TREE SPECIES AND POPULATION

SPECIES NUMBER PERCENT OAK 800 80 PINE 150 15 PALM 50 5 TOTAL 1000 100

PALM 50 PINE 1505% 15%

OAK 80080%

100% = 1000 Trees

Fig. 7. Percent of each species


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Appendix C

CRITICALLY EVALUATING “SCIENTIFIC” ARTICLES, REPORTS OR INTERNET SITES(See Appendix E for the rubric that will be used to grade your report.)

The print, visual and or internet media and frequently report items or make claims that appear on the surface to be valid and have a sound, scientific basis, but that may be frivolous, unjustified, or downright loony. How can the average person decide what to believe and what not to believe? What information is reliable and valid, and what is simply hype or self-serving? A dry, convincing written or spoken style is not enough. Claims and “facts” must have support from evidence in order to be considered reliable information.

Scientific claims, and those purportedly based on science in particular, must be able to meet the tests of science in order to be valid and believable as scientifically based. Primary scientific tests include objectivity, reproducibility, predictability and consistency (logic). Use of controlled studies (with controlled variables and control treatments) to support a claim improves the validity of the claim, as does having the claim published in a scientific journal that uses the peer-review process (such as Science or The Journal of Cell Biology). Peer-review means that other scientists with relevant expertise review the claim and evidence and approve of the work, before it is published. In addition, the claim must be logical and consistent with other known facts and processes.

To critically evaluate an article, report or Internet site, follow these steps;1. Identify the main claim of the article, report or site. This is usually located in the

title or opening paragraphs. 2. List the evidence that is used to support the claim. Determine whether each

piece of evidence is empirical, inferential, or testimonial/ propaganda. A. Empirical evidence is that which can be seen, heard, felt, tasted, smelled or

measured in some way that enhances these senses (“There were six times more cavities in the control group than in the treatment group.”) Claims based on empirical evidence are testable, reproducible, and generally reliable.

B. Inferences are interpretations (assumptions) based on empirical data (“Brand X reduces cavities compared to Brand Y”.)

C. Testimonial evidence is also known a hearsay evidence and is usually a personal experience or “gut feeling” (“I used it and I felt better.”) Testimonial evidence is not testable or reproducible.

D. Propaganda evidence includes transfer (“use Brand X and look like a movie star”); bandwagon (“Everyone else is using Brand X”); name calling (“Her teeth are yellow...she doesn’t use Brand X, she can’t be a good senator”); hasty generalization - a conclusion based on very little empirical evidence; extremism – yes/no statement with no middle option (“If you vote against a new libraby, than you don’t like to read”); circular reasoning (“Brand X works


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because it’s do good”);and emotional appeal – the choice of words appeals to emotions rather than common sense (“Brand X gives you a dazzling smile and brightens your life”).

3. Evaluate the claim against the acceptable evidence. Is there sufficient reliable evidence to support the claim? Is there a logical connection between the reliable evidence and the claim? Are the claim and the supporting evidence internally consistent? Are the claim and evidence consistent with other claims that you believe are valid?

4. Evaluate the person or group making the claim or providing the supporting evidence. Is this person or group objective? Objectivity means there is no bias in the reported evidence or claim. The person or group will not materially benefit from the claim outcome. Ask these kinds of questions:A. Have they received funding by a source that stands to gain from the

results, evidence or claim reported? B. Are the results or evidence biased or selectively reported in favor of a

funding source? C. Are they promoting themselves or their group for notoriety or to sell

something? (Being the first to report a scientific discovery conveys some notoriety in scientific circles, but is not considered a bias against objectivity unless other factors are involved.)

D. Does the person or group making the claim have a “personal mission” they are promoting?

5. Evaluate corroborating information: information that lends credence to the claim.A. Is background information presented? Such as the original source of the

information: for example, the peer-reviewed scientific journal where the experiment was published. (Eg. The Journal of the American Medical Association, or Science.) For an Internet site, are there links to other reliable sites? Are there references to the original journal where the experiment was published? (Note: Reports in peer-reviewed journals are reviewed and approved by other knowledgeable scientists before being published.)

B. How current is the information? When was the Internet site updated? What are the dates of supporting information?

C. Are there other reliable sources that agree with the claim?D. What are the credentials and/or experience of the person or group making the

claim? A physicist may be reliable for making claims in physics, but unreliable when it comes to cloning.

6. Decide whether to tentatively accept the claim, conditionally accept the claim, or reject the claim.

Tentatively accept the claim if: the evidence is consistent, logical, unbiased and the source information is available and/or the person or group providing the evidence or making the claim has the

credentials for the claim


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Accept the claim with reservations (conditionally accept) if: the evidence and claim are reasonable, consistent and logical but evidence is minimal or the source is not identified

Reject the claim if: acceptable evidence is lacking or the claim and evidence are contradictory, biased, or illogical

You may find the guides on the next page helpful in evaluating evidence and claims, and preparing critiques.

For the critique report:Summarize the article or report or site.List the claim and evidence reported in the article or site.State whether to tentatively accept, accept with reservation, or reject the

claim. Provide supporting reasons for your conclusion, using your evaluation of the evidence and other supporting information.

Attach the evaluation guide if you used it.Attach a copy of the article, or a printout of the first page of the site.

Your report will be evaluated using the rubric in Appendix E.

References: Ford, Bob, “Critically Evaluating Scientific Claims in the Popular Press”, The American Biology Teacher, 60:3, 174-180, March 1998.


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CRITICAL EVALUATION FORM NAME

ARTICLE, REPORT OR SITE NAME

SOURCE OF ARTICLE OR REPORT OR SITE URL

MAIN CLAIM

LIST OF EVIDENCE

EVIDENCE TYPE *

SOURCE OF EVIDENCE

IS SOURCE BIASED?

DATE OF EVIDENCE

*Empirical, Inference, Testimonial or Propaganda Add more rows if needed for more evidence.

Cross out (reject) all evidence that has any of these characteristics:- only testimonial- has a biased source- contradicts or fails to support the claim

Is the remaining evidence internally consistent with each other and the claim?Is the person or group making the claim objective?Corroborating information:

Are original sources cited?Are the sources reliable scientific journals?Is the information reasonable current?Are other reliable sources referenced?Does the person or group making the claim or providing evidence have the

credentials for the claim or evidence?


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Appendix D

CONCEPT MAPPING

Concept mapping, or concept diagramming, is a way of structuring knowledge that presents a visual picture of how several concepts are related to each other. For example, atoms are made of smaller particles that have different masses and charges. A concept map for this idea might look like this:

ATOMS

are made of

PARTICLES located

In orbit around the in the nucleusnucleus located NEUTRONS

ELECTRONS in the nucleus mass 1, charge 0 mass 0, charge -1

PROTONS mass 1, charge +1

Concept maps can be simple, or quite large, depending on the ideas being diagrammed.

To prepare a concept map, first list the terms or concepts to be diagrammed. Select the central or most encompassing concept(s), and place these in the center, linking them together with a line, if there is more than one. The line may or may not have arrows. Write on the line how the concepts are linked. BE SPECIFIC about how they are linked. (You may need to add to your list of terms as you work on the diagram.)

Select the next layer of concepts, or those that are most closely linked to the central one(s), and place these around the central concept. Link them to the central concept(s) with lines and specific descriptors on the lines.

Continue selecting, placing and linking terms until all of the concepts are included. When you have finished, you have a picture of the ideas, and can easily see how they relate to each other. The act of creating the diagram reinforces learning, and shows you where you may need some help. This exercise not only provides you with a good tool to study science, but can be quite useful in other disciplines as well.


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Appendix E

Rubrics for Non-traditional Assessments

Group Member EvaluationTips for Creating PostersPoster EvaluationPresentation EvaluationReport EvaluationModel Evaluation


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GROUP EVALUATION YOUR NAME_________________

PLEASE EVALUATE THE RELATIVE CONTRIBUTION OF THE MEMBERS OF YOUR GROUP TO THE COMPLETION OF THIS PROJECT.

PROJECT OR EXPERIMENT

DO NOT RATE YOURSELF

NAMES OF GROUP MEMBERS

USE 0 = NONE, POOR OR WEAK1 = SOME, A LITTLE, OK2 = A LOT, GOOD, GREAT

KNOWLEDGE OF CONTENT 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 OUTSIDE RESEARCH 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 (if applicable)

ORGANIZATION OF PROJECT OR EXPERIMENT 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2

PREPARATION OR EXECUTION OF PROJECT OR EXPERIMENT 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2

CONTRIBUTION TO PRESENTATION OF PROJECT (if applicable) 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2

CONTRIBUTION TO REPORT 0 1 2 0 1 2 0 1 2 0 1 2 0 1 2 (if applicable)

TOTAL POINTS _____ _____ _____ _____ _____


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TIPS FOR CREATING POSTERS

A poster is a static, visual presentation of information. The information is presented in an attention-getting form that is both attractive and concise. The content of the poster depends on the information to be presented. Is it an issue or debate? A concept or idea? The results of an investigation? The format of the poster will also depend on the type of information.

The following guidelines should be followed when preparing a poster:

ALL POSTERS, regardless of content;- should have a single, clear focus/ mission/ intent- text should be kept brief and concise- the title should grab attention- all authors of the poster will be identified- the course number, section, instructor and date will be identified- all references used in the preparation of the poster will be identified- texts should be in readable fonts and type sizes- the overall view should be balanced and pleasing to view from a distance- GRAPHICS (photos, drawings, graphs, diagrams, figures, tables, etc.)

- Use clean, bold lines- Use clear, simple labeling- Should be attention holding (color, size, clarity)- Should convey much of the information in the poster

In addition to the above, use the following guidelines to create specific types of posters.

CONCEPT POSTERS:- a brief abstract/statement of the concept being presented- any biological or other background information necessary to understand

the concept- how does this concept fit into the "big picture?"- other information as may be necessary

- time frame- size references- location- energy source/ utilization- heritability or other genetic information- aberrancies- history

ISSUES POSTERS:- an abstract of the issue (what is the focus of the poster? What is the issue

about?)


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- any biological or other background information necessary to understand the issue or to making an informed decision

- arguments for both sides of the issue- evaluation of the arguments- conclusions/opinions of the authors of the poster. Consider using a few

bulleted statements to reinforce your position

INVESTIGATIONS:- abstract of the investigation or experiment- materials and methods text, lists and/or photos/diagrams (be brief unless

the equipment used is original to this investigation; remember, one picture is worth 1000 words)

- results of the investigation. Graphical representation generally predominate over text in this section

- discussion/conclusions- be brief and to-the-point- consider using a few bulleted statements to summarize

The rubric to be used for evaluating the poster is on the next page.


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AUIDIO VISUAL EVALUATON YOUR NAME(S)________________

A-V TITLE

AUTHORS NAMES Rank each characteristic on a scale of 1-3, where 1 is low and 3 is (exceptional.) (0 = missing or really bad)Mark through any items which are not applicable.

THE POSTER, POWER POINT OR OVERHEADIs there a title that gets your attention? 0 1 2 3 Is the text and font large and clear? 0 1 2 3 Are the photos and diagrams labeled? 0 1 2 3Are there references for the content? 0 1 2 3Is the overall visual balanced and pleasing to look at? 0 1 2 3

SUBTOTAL :

THE CONTENTIs the information in the visual correct? 0 1 2 3Is the information in the visual complete? 0 1 2 3Is there enough detail to support the main ideas? 0 1 2 3Is the information in readable units? 0 1 2 3Is the written text easy to understand? 0 1 2 3Do the pictures and diagrams help in understanding the content?

0 1 2 3Does the content flow easily from idea to idea? 0 1 2 3If a presentation, does the poster content illustrate the presented material?

0 1 2 3SUBTOTAL :

THE AUTHORSIs the visual labeled with the authors names? 0 1 2 3If a presentation, is it "smooth"? 0 1 2 3If a presentation, do the authors understand what they are talking about?

0 1 2 3Are the authors able to answer your questions?0 1 2 3

SUBTOTAL : (Maximum is 51 points)

What questions do you have about the material in the visual and/or the presentation?

Total Points X .491 TOTAL SCORE __________ POINTS


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PRESENTATION EVALUATION

PRESENTERS NAME(S):

TITLE OF PRESENTATION:

EVALUATORS NAME(S):

Rank each characteristic on a scale of 1-3, where 1 is low (average) and 3 is high (exceptional.) (0=missing or really bad)Mark through any items which are not applicable.

The presenters:Identified the people in the group and indicated the participation

of each person 0 1 2 3Described what the presentation was about 0 1 2 3Each speaker was clear and audible 0 1 2 3

(not all presenters need to speak)SUBTOTAL :

The presentation was:Well organized – each part flowed easily to the next 0 1 2 3Well researched – the information was presented in depth 0 1 2 3Clear and complete – you had few questions about it 0 1 2 3Well presented – each presenter knew his/her part 0 1 2 3

SUBTOTAL :

Visual Aids were:Clear and visible to the audience 0 1 2 3Useful in understanding the project 0 1 2 3Pleasing and eye-catching 0 1 2 3

SUBTOTAL :

What more would you like to know about the project/presentation?

TOTAL POINTS X .835 = TOTAL SCORE ____________POINTS


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REPORT EVALUATION

Reports written for science classes involve more than just understanding the science. The format of the report may vary, depending on the kind of report, but all reports must convey the information in a clear, understandable fashion. Therefore, a science class report not only is graded on the science, but also on composition, citation and grammar.

All reports will be evaluated on the following criteria:

Science: Does the student appear to understand the scientific concepts being written about? If there is confusion, does the student acknowledge it?

Assignment: Did the report adhere to all parts of the assignment? Were parts of the assignment missing or incomplete?

Organization: is the report well-organized? Do topics and ideas flow from paragraph to paragraph? Does the opening paragraph introduce the topic and does the body of the report support the topic? Does each paragraph address only one main idea? Are opening and closing sentences supported by the body of the paragraph? Is there a closing paragraph that effectively summarizes, expresses the student’s opinion or otherwise effectively closes the report?

Grammar: Are sentences complete, not run-on, varied in structure and length, pleasant to read, and not awkward? Are words spelled correctly and is the vocabulary college-level or nearly so? Are sentences structured on a “literature” level and not a “speaking” or colloquial level?

References: Are references cited within the text for facts and opinions that are not the student’s own product? Are larger passages in quotation marks and referenced? (Rule of thumb – if you want to use more than three words in a row, put it in quotation marks.) Are the reference sources appropriate for the level of the course?

Works Cited: Both MLA and APA formats are acceptable. If you are not familiar with these formats, use the following formats for referenced (cited) sources:

BOOK:Last Name, First Name, Title of Book, City where published, Publisher, Year of publication.

ARTICLE:Last Name, First Name, “Title of Article”, Title of Magazine or Newspaper, Date of publication: pages of article.


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ENCYCLOPEDIA:Editor’s Last Name, First Name, “Title of Article”, Title of Encyclopedia, Edition number and year

JOURNAL REPORT:Last Name, First Name, “Title of Report”, Title of Journal, Volume number: Section number, pages, date of publication.

CD REFERENCE:“Title of Article”, Name of CD reference or encyclopedia, Edition number, Edition year.

INTERNET REFERENCE:Last Name, First Name, “Title of Article or Site”, Name of Web site, Website address http://, (Date).

PERSONAL COMMUNICATION:Last Name, First Name, personal communication, Date


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MODEL EVALUATION

MODEL TITLE_________________________________________________AUTHORS NAMES________________________________REVIEWERS NAMES What did your group learn in this model presentation?

Rank each characteristic on a scale of 1-3, where 1 is low (average) and 3 is exceptional.) (0 = missing or really bad)Mark through any items which are not applicable.

Did the model accurately represent the concept? 0 1 2 3 Did the model cover the entire concept? 0 1 2 3 Did the group presenting the model appear tounderstand the concept/material being presented? 0 1 2 3Was the demonstration smooth? 0 1 2 3

Total points:

Did the model work? Y___ N___Was the model active _____or passive _________

List the deficiencies/drawbacks about this model

What suggestions do you have to improve the effectiveness/accuracy of this model?

Your suggested grade for the group: A B C D F

Other comments:


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