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Assessing Student Work
This page contains
- Guidelines for establishing a fair grading method
- Know the University or College grading policy
- Instructor role in grading
- Grading systems
- Examples of Scaled Grade Systems
- Selecting a method
- Determining Grades
Assess student work
A. Guidelines for establishing a fair grading method
- Guiding light: "be objective and fair in your evaluation"
- Take the time to think, plan and prepare before you begin grading
- Show no reluctance to re-evaluate part way through the grading task
- Written comments should be clear and objective (avoid personal judgments or comments).
- Be prepared to explain and justify your grading methods and style.
- Grading is not a popularity contest – have a strong ego!
- Teaching evaluations should not drive grading assessment
B. Know the University or College Grading Policy
Whether you have been at a college or university for a month or for years, it is imperative that you have an understanding of the school grading policies. Often these are a synthesis of the Office of Academic Affairs and the Academic Senate. Students expect that instructors will develop a grading method that is in compliance with the institutional standards on grading. It makes no sense to be in contradiction to these standards or guidelines.
| SFSU Academic Senate policy: SFSU Bulletin Definitions of Grades:
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C. Instructor Role in Grading
There is a wide variation in instructor roles in grading. For example, an instructor may be the sole person in charge of a class, one of many instructors in a multi-section course (most likely a lab course), or member of a team that is presenting a course. Each situation requires a little different effort for assigning grades. In the first situation, the instructor exercises her/his judgment alone. In the second situation, agreements regarding grading are usually established at the beginning of the semester by a coordinator. An example of this type of situation is providing a lab coordinator with the points accumulated by each student in your class, and the lab coordinator then assigns the grade. A variation of this is that using agreed upon guidelines, each section instructor assigns a grade. In the third situation, the members of the team agree to grading policies at the beginning of the semester. Each situation requires a different level of effort for assigning grades.
D. Grading Systems
Shown below are two types of grading systems frequently used in science. The types are self-explanatory. It is imperative that students know the type you are using so that there will not be any misunderstandings at the end of the course.
- "no scaling" – A’s >89, B’s 80-89, C’s 70-79, etc
- "scaling" – the traditional grade assignment as shown in "no scaling" is not followed, but a system based upon student performance in the given class is used. This method factors in such variables as challenge level of paper topics, quizzes and exams, stockroom prep., instrument response, grading style of instructor, and similar factors. In general, students seem to respond most positively to this grading method. It is most helpful and calming to students if you can post for their perusal performance of other classes from previous semesters. This information allows them to compare their performance to others and determine a sense of where they stand in the class regarding their grade.
E. Examples of Scaled Grade Systems
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| Excel Spreadsheet 1 View Spreadsheet (pop-up) Download Grading Spreadsheet |
1. Qualitative Scaling Method
As you read a paper, lab report or the like, you decide on A’s, B’s, C’s, etc.; a re-reading may be necessary as may be an adjustment of your evaluation. You are in essence scaling grades relative to the class performance.
For help in organization and as an aid to grading, one may use a grid or rubric to try to ensure grading consistency. One such grid is shown below for grading a lab report in the form of a scientific journal article.2. Quantitative Scaling Method 1
Use the highest grade (e.g., 94) or "clump" as an "A" grade; divide this by 2 (e.g.: 94/2 = 47), and apply the following method:
- failing grades are awarded if they fall below this figure (<47)
- divide the range, 47 - 94, into 4 equal parts (47/4 = ~12)
- the following grade distribution applies:
47-58, "D"; 59-70, "C"; 71-82, "B"; 83-94, "A".
Plus/minus distinctions are easily worked out within the
grade range using approximately equal ranges.3. Quantitative Scaling Method 2
Compute average "u" and standard deviation "s" (avoid outliers) Example: u = 56; s = 11. Using these numbers, you can calculate the C grade range. Once the C grade range has been calculated, some simple arithmetic will yield the other ranges.
- F grade: anything less than the lower limit of the range for D grades.
- D grade: subtract s from the lower limit of C range. Numbers between this value and the lower limit of the C range are D grades.
- C grade: the values for C grade range are computed by applying the formula C = u ± s/2. In this case the two values would be 56 + 5.5 and 56 - 5.5. Rounding 5.5 up to 6 our two values would be 50 and 62. The C grade range is therefore 50 - 62.
- B grade: add s to the upper limit of the C range. Numbers between this value and the upper limit of the C range are the B grades.
- A grade: anything above the upper limit of the range for B grades.
Selecting a Method
A legitimate question to ask at this point is which method do I select for use or is there a rationale for selecting one method over the other. As you will see in the LEARNING ACTIVITY below, the methods provide different scaling ranges, and thus different grade distributions. Method 1 responds more to student performance in a particular exercise or semester, while Method 2 is more objective as it is based upon statistics. As a guide, I would suggest that if your student sample is sufficiently large, i.e., > 20, then Method 2 should be invoked. If the student sample is < 20, then Gaussian statistics may not apply, and Method I would be more appropriate.
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LEARNING ACTIVITY In this exercise, we will use a spreadsheet of class grades, and will calculate the final grades for this class using quantitative scaling methods 1 and 2. The grades for this group of students on their hour and final exams are shown in Spreadsheet 2. |
 Excel Spreadsheet 2 View Spreadsheet (pop-up) Download Grading Spreadsheet |
 Excel Spreadsheet 3 View Spreadsheet (pop-up) Download Grading Spreadsheet |
First we will calculate the sum total of grades. The hour exam sum is based upon the two highest hour exams. The lowest hour exam or a missed exam is dropped. The sum of the 2 highest hour exams and final exam points is shown (Exam Point Sum).
| Bin | Frequency | |
|---|---|---|
| 0 |
0 |
0 |
| 9 |
9 |
0 |
| 19 |
19 |
1 |
| 29 |
29 |
4 |
| 39 |
39 |
4 |
| 49 |
49 |
9 |
| 59 |
59 |
6 |
| 69 |
69 |
7 |
| 79 |
79 |
3 |
| 89 |
89 |
0 |
| 99 |
99 |
0 |
| 100 |
100 |
0 |
| More | 0 |
In the Exam Point Sum column, “0” is awarded to students who withdrew from the course. For ease of determining grades, let’s reduce the exam sum points to the base 100. One does this simply by dividing the exam point sum by the total possible points, 400, and then multiplying by 100.
Using the spreadsheet statistical functions, the average and standard deviation are computed to be: u = avg. = 48.3 std. dev = s = 15.8. It is important when computing these values to eliminate the students who did not complete the course, i.e., those who did not sit for the final exam to avoid biasing these calculations. It is helpful at this time to plot a frequency distribution using the spreadsheet, which will lead to a histogram. In Excel, this is created by selecting Tools > Data Analysis > Histogram. The distribution in Table 1 shows you what you will obtain.
A bar graph is a useful visual representation of this distribution. Using the Chart Wizard in Excel, choose Clustered Column (top left) to obtain the graph shown below in Figure 1.
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| Figure 1 |
This frequency distribution will be helpful in drawing the divisions between grades. Such a distribution can be completed for each hour exam and provides suggestive performance information to both students and the instructor. This is shown below in Figure 2 for the hour exams in this class.
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| Figure 2 |
Addition of a second set of data to a graph or histogramIt is a simple process to add a second set of data which is called a new series to an existing plot or histogram. It does not matter that the second series has fewer data points than the first series. From the frequency table for a second or third set of data, highlight the calculated frequency . With the mouse pointer still in the highlighted data area, depress the right mouse button. From the dialog box that appears select Copy. Now select the chart to which you want to add the additional data set at the bottom of the Excel window. From the main menu under Edit, choose Paste Special. A version of the Paste Special dialog box comes into view. Under options, choose either “add cells as New Series or New Points. For this application, choose the former. For “Values (Y) in “, select Columns, since the data is in columns. At the bottom, make certain that “Series Names in First Row” is unchecked (unless you have included it in highlighting), and that “Categories (X values) in First Column” is also unchecked. Click on “OK” and a second histogram should appear on the chart. |
Determining grades
Now we will determine grade assignments using the two quantitative scaling methods described previously and the data shown previously.
Quantitative Scaling Method 1
The highest course average is 74 with several grades at 73, 71, and 68.
Divide the highest course average by 2: 74÷2 = 37; a failing grade
is awarded to scores below 37. Therefore, the range of passing grades is
37 to 74. Dividing into four equal parts of
9.2 each, we obtain
- F range: below 37
- D range: 37 to 46
- C range: 47 to 56
- B range: 57 to 63
- A range: above 64
In effect there are 9 points for each major division, and thus +/- distinctions are easily defined (e.g., “D-“, 37-39; “D”, 40-43; “D+”, 44-46). One can do this division for each grade range.
Quantitative Scaling Method 2
The average and standard deviation for this sample data are: u
= 48.3 s = 15.8. Using these values we obtain:
- F range: below 25
- D range: 25 to 39
- C range: 40 to 56
- B range: 57 to 72
- A range: above 72
One expects differences between the two quantitative scaling methods, since they are based upon different hypotheses and calculations. Since doing both computations is not difficult, it is revealing to do both and compare the results. In this example, Method 1 seems more liberal in grade assignments than Method 2.
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To demonstrate competence in assigning grades for a class, determine the grade assignments for the following spreadsheet of grades for a class that includes laboratory grades using both Quantitative Scaling Methods. The total point values for hour exams, the final exam, and the lab component of the course are shown. The lab and lecture contribute equally to the grade. Do not drop any grades. Sheet 1 of the downloadable Excel file is blank. Sheet two contains a completed version of the activity so that you may check your results.
View the spreadsheet (pop-up) |
It is expected that you will compute a total point score for each student to the base 100, and will create a frequency distribution and histogram chart to help in determining the grade distribution by the two scaling methods discussed. The results for this exercise may be obtained from CET.