What is the issue?
What grades mean.
Possible causes for the change in the grade distribution.
What should be done about grade inflation.
Grade inflation is an issue because university administrators and other individuals concerned with outcome assessment in higher education have conducted a variety of statistical analyses and identified some "alarming" trends. The average grade in some courses has gone up significantly. In fact enough courses are showing this trend that average GPA of college students in the US has gone up. Along with this trend, students have come to expect higher grades. At present, in certain courses and departments, the average student expects to get a B or A in the course and in fact most students get A's or B's in the courses. Students receiving grades of C or less feel the instructor has evaluated their performance as less than satisfactory. It is argued by individuals concerned about "Grade Inflation" that at one time, a grade of C was an average grade and grades of B and A were reserved for recognition of above average or truly exceptional performance.
The fear is that our courses have been "dumbed down" or that Universities are lowering standards and reducing requirements and that this accounts for the higher grades. The concern is that if the requirements for getting a college degree are being lowered, the meaning of a college degree may change, and a college degree may have less value. Further concern about this issue comes from the perception, (or mis-perception), on the part of some faculty that students are less well prepared or less qualified than they used to be.
This trend in grades is accompanied by a corresponding degree inflation in some disciplines. In psychology for example, the entry level degree has for many years, been the Ph.D. In recent years, however, as degree production has outstripped growth in the field, Ph.D. candidates are having more and more difficulty in obtaining positions right out of grad school. Many of the new Ph.D.'s find it necessary to take a post doc for a year. The post doc makes them more marketable and they can compete more effectively for jobs than the new Ph.D.'s . As a result, departments advertising a position frequently have applicants with a Ph.D. and at least one post doc to choose from. By default the entry level for the field has become a Ph.D. and at least one post doc.
The grade inflation issue arose from observations based on statistics. The primary concern is that the distribution of grades has changed. Technically, the concern is that the distribution is more negatively skewed than it should be, that is that there are too many A's and B's and not enough D's and F's (see diagram). The observation that the distribution has changed, however, does not tell us the cause of the change. Demonstration that more A's and B's are awarded is one thing. Figuring out why is quite another.
Part of the confusion concerning grade inflation arises from the notion that grades should be normally distributed. In point of fact, many psychological characteristics of individuals appear to be normally distributed in the population and many of the variables thought to influence psychological processes appear to follow the normal distribution. As a result psychologists frequently assume that the variables in their studies are normally distributed. This assumption then becomes the basis of the statistical treatment of the data in psychological research. When psychologists develop tests to measure the psychological characteristics of individuals in the population, they frequently base the design, validation, and scoring on similar statistical assumptions. It is natural that someone would try to apply similar logic to the development and scoring of academic achievement tests. In point of fact the function of the classroom achievement test is completely different from the function of selection and achievement tests (SAT, ACT, GRE, GMAT, etc.) designed by psychologists. In technical terms, classroom tests are "ideographic" while the selection and achievement tests designed by psychologists are "normative". The logic and statistical assumptions used in normative test design, development and validation do not apply.
In the 1950's and 1960's it was popular for university faculty to "curve" the grades in their courses. The assumption was that grades (like all the other psychological variables that were being studied by scientific psychology at the time) should be normally distributed. Early on, some faculty forced the grades into a normal distribution by applying a "true curve." Under this plan, regardless of the grades earned, the top 10% received A the next 20% were B's, the next 40% were C's, the next 20% were D's and the bottom 10% were F's, regardless of the student's actual level of performance. The popularity of curving grades was relatively short lived. Psychologists and psychometricians quickly realized that the process was based on faulty assumptions and bad logic and most had abandoned the process by the early to mid 1960's. However, one still sometimes hears students and faculty say things like, "There should be no more than 10% A's in a college course."
One of the biggest problems with curving was that under a true curve, 10% of the students had to fail regardless of how much they had learned. Even if all the students in the course had mastered the material to a satisfactory level, the lowest 10% would still be failed. As a result, curving was only used to adjust grades upward.
Another problem with curving is that it is based on the assumption that grades are a random variable. The normal distribution is the distribution of a random variable. Without getting bogged down in the technical definition of a random variable, if it isn't random it isn't a random variable. The only way that the underlying distribution for students scores on the final exam in a college course can be normally distributed is if the students are a random sample from the population. If you only administer the test to students who have enrolled in the course or students who have completed the course, the sample is clearly not random and a very different (and unknown) distribution may apply. As soon as a faculty member teaches the students something, their performance on the test is no longer random but rather a reflection of the outcome of the teaching-learning process.
One of the reasons grades cannot be normally distributed is that a variety of selective pressures operate to take less qualified students out of the pool. Grades may be close to normally distributed in a university with unrestricted admission on the pretest for the first course in which the students enroll after entering the university. If students are tested after having had 3 or 4 months of the course, their scores will be higher. After the first course or first semester, a bunch of the poorer students will fail and leave the university. As a result of these factors, scores of second semester freshmen should be higher and the grade distribution more negatively skewed. This selective process continues through graduate school and as a result, the higher the level course the greater percentage of A's and B's that can be expected.
In the hey day of statistical grade adjustment, a number of attempts were made to identify "appropriate" distributions for college courses at each level. It was hoped that by statistically adjusting the skew, the curve could be made to fit the increasingly select groups of students in higher level courses. There was no objective criterion for developing these distributions and as a result, this process met with limited success.
While the statistical assumptions underlying curving were flawed, an even greater flaw in the logic of statistical grade adjustment had been overlooked. Student's grades in a college course are a result of a number of factors including:
Changes in any of these variables can change the shape of the grade distribution in important and relevant ways. It is next to impossible to separate these factors and each factor calls for a different response. Two examples follow:
Grades reflect a students ability. The first law of psychology is that the best predictor of future performance is past performance. When a straight A student enrolls in your course, it is a real good bet that she/he will get an A in your course as well. When a struggling student enrolls in your course, it is likely she/he will struggle in your course as well. Grades are so highly correlated with intelligence that you can virtually give an IQ test to your students and assign the grades on that basis. There is clearly a problem here. While more able students can be expected to learn more, more quickly, grades should reflect more than ability.
Grades reflect a students standing in the class. This is the interpretation on which curving and statistical adjustment are based. The notion is that tests, being imperfect measures of a student's knowledge, can be used to rank students but cannot be used to accurately quantify a student's knowledge. Class standing is then taken as an indicator of the amount the student has learned and grades are based on class standing. This amounts to an admission that grades have been assigned out of ignorance. Instructors should decide what they want the students to know and what they want them to be able to do and then develop tests to determine if they have developed the required knowledge and skills. Then all students who meet the specified standards would pass, regardless of class standing.
Grades sometimes reflect a students previous knowledge or background. Students frequently come into a course with very different backgrounds. Grades may then reflect the student's background rather than what they learned in the course.
Grades should reflect a student's mastery of the material. Grades can be taken as a certification of level of mastery of the course material. Under this interpretation, the grade becomes an evaluation of competence. The student may use a variety of strategies to develop the competence. For example, she/he may attend your course, do the assignments, and take your tests to develop mastery of the material. However, if the student can pass the competence exam without regular attendance or participation in the activities, it should be irrelevant to the grading process.
The problem with this approach is that it requires the establishment of standards against which the competence of the students can be evaluated. Many college courses have no defined objectives, no measurable goals, and are not designed to enhance the students competence in any domain. These tend to be the courses that the students object so strongly to being required to take and which no one would take if they were not required.
The advantage of the mastery approach is that faculty who dedicate themselves to developing specific competence in the students can concentrate on the development of effective instructional techniques which will maximize the number of students achieving mastery. It should be noted that by default, this will result in higher grades.
Grades reflect the success or failure of the instructor. B. F. Skinner said that it is not the student who fails a class. When the student does poorly it is because the instructor failed to motivate the student, failed to captivate her/his interest, and failed to provide appropriate learning activities and instruction which would enable the student to master the content. Of course, no teacher can reach all the students in her/his classes. But it should be the objective of every faculty member to reach a higher percentage of them each semester. This necessitates getting to know the students, finding out where they are coming from, what their background is, and where they are starting from. It then necessitates designing learning activities which will lead them from where they are to mastery of the material. While it is clear that in the context of the modern higher education institution, highly individualized instruction is prohibitive, careful use of technology and experience with the needs of students over the years can enable a faculty member to develop a course which will reach a large percentage of the students. This will of course lead to higher grades. It is to be expected that faculty who have been teaching for some time will get better at it and will reach more students. Older faculty should be expected to turn out students with a higher level of mastery and hence higher grades. If a faculty member has been teaching a course for twenty-something years and the high grade on her/his midterm is still 35%, something is seriously wrong.
There are several possible reasons why the distribution of grades might have become more negatively skewed.
We should design our courses and curricula in such a way that we have specific goals and objectives for each course. We should next design our testing and grading procedures to enable us to determine if the students have mastered the knowledge and skills taught in the course. Then we should focus our attention on developing instructional technology, instructional methods, and learning experiences and activities which will captivate the students interest, motivate them, and enable them to master the material. In short, it should be our goal that every student get an A in every course.