Grade Distribution and Its Impact on CIS Faculty Evaluations David McDonald, Ph.D. Roy D. Johnson, Ph.D. Georgia State University Computer Information Systems Dept. Atlanta, GA. (

Introduction Over half of Harvard undergraduates receive “A”s 91% graduate with honors Compared with Yale (51%) and Princeton (44%), one might conclude grade inflation exists at one of the nation’s top institutions (Healy 2001)

Review of the Literature Most research to date is broad in scope done by the educational discipline Ample evidence that grade inflation exists in higher education (Nagle 1998; Carey 1993; Young 1993; Crumbley & Fliedner 1995) Students also manipulate faculty behaviors with performance reviews (Stone 1995; Damron 1996) Student evaluations change focus from a consumerist approach by measuring and improving a course to a mercantilist approach of faculty “pleasing the customer.” (Renner 1981; Goldman 1993; Bonetti 1994)

Problem Sparsity of longitudinal studies on the effects of grade inflation within the IS discipline An “A” should indicate superior performance among a peer-group – not the general population of students in the U.S. Faculty face increasing pressures to give higher grades Many faculty equate a better grade with better student evaluations Parents want perceived “value” for their investment College administrators use GPA and student evaluations as a measure of quality from many departments

Research Questions Has grade inflation occurred within the degree programs of a CIS department in a large, southeastern university over the past ten years? What is the impact on the expected grades students receives on their performance evaluation of faculty?

Hypotheses H1: There has been a steady increase of higher grades given to students in the CIS Department by full-time faculty over the past decade H1a: The percentage of “A”s given has steadily increased H1b: The percentage of “B”s given has steadily increased H1c: The percentage of “C”s given has steadily decreased H1d: The percentage of “D”s given has steadily decreased H1e: The percentage of “F”s given has steadily decreased

Hypotheses (cont’d) H2: Students will give higher evaluations to full-time faculty if they expect a high grade. H2a: Students expecting an “A” will give faculty higher evaluations H2b: Students expecting an “B” will give faculty higher evaluations H2c: Students expecting an “C” will give faculty lower evaluations H2d: Students expecting an “D” will give faculty lower evaluations H2e: Students expecting an “F” will give faculty lower evaluations

Research Design Ten year period utilized (Fall 1992 to Fall of 2002) Only permanent, faculty-of-rank used Criteria-based testing methodology courses were eliminated from the dataset Percentage of grades within each course was used rather than raw numbers Usable data consisted of 36,147 grades assigned by faculty-of-rank in 1,382 courses ( original data set included 58,315 grades assigned in 1931 courses) Student Evaluation of Instructor Performance (SEIP) standardized form used to measure faculty performance

Results – Grade Inflation Significant increase in the percentage of “A”s, while a concurrent decrease in “C”s and “F”s (p-values ≤.000) When the analysis is broken down to undergraduate (20,708 grades in 780 courses) vs. graduate 15,295 grades in 593 courses), the results indicate the problem arises primarily with the undergraduate program However, the percentage of “C”s given to graduate students has decreased over the decade Support for hypotheses H1a, H1c, and H1e

Results – The effect of the students’ expected grades on their evaluation of faculty Giving “A”s did not significantly increase overall positive evaluations of faculty Giving “A”s to undergraduates marginally effected undergraduate evaluations of faculty (p-value ≤.05) Giving “C”s to undergraduate student significantly effected faculty evaluations at p-value ≤.000 Giving “D”s to undergraduates significantly effected faculty evaluations at p-value ≤.01 Fairly good support for hypotheses H2c and H2d

Limitations Use of secondary data limits the strength of the relationships found; i.e., no data on possible mediating variables or covariates E.g., graduate students are older, more mature with better work experience Variation in testing and grading philosophies of faculty; e.g., some faculty may use the same exams even though the quality of the CIS major has increased over the decade Similarly, the exam quality and validity differ from faculty member to faculty member Graduate students are placed on academic probation if they earn a “C” and therefore, faculty may be reticent to give lower grades to graduates. Difficult to show significance when “A”s and “B”s are the primary grades distributed. Generalizability of results may not hold The students’ expected grades were used as the independent variable for H2. The actual grade for each student and his or her evaluation would add more credence to the results. However, since the SEIPs were administered anonymously, this was not possible It is not known whether SEIP evaluations had an impact on how faculty graded future courses (further study needed)

Conclusions Undergraduates experience the greatest levels of grade inflation In part, grade inflation exists because of pressures on faculty to achieve high teaching evaluations A de-coupling of the existing student evaluation methodology is needed There is little support from college and university administrators to change the status quo Faculty committees need to place a higher priority to adequate teaching evaluation of faculty with college and university administration Undergraduates expecting a “C” will negatively evaluate faculty’s performance Possibly because faculty treat undergraduate students differently than their graduate counterparts and are willing to give “C”s Less maturity in age and job experience with undergraduate students may result in blaming the faculty member rather than accepting responsibility Faculty should provide students with a more realistic expectation and explanation of their grading system

Future Research Other relationships in the existing dataset E.g. “Well-prepared?” “Explains Clearly?” “Knows if the class is understanding him/her or not?” “Motivates me to do my best work” “Considers the course worthwhile” Popularity of the class (using overflow requests)

Results of Data Analysis

TABLES Table 1. Grade Distribution: Graduate and Undergraduate Programs ( ) Dependent Variable Standardized Beta Coefficient Adjusted R 2 FSignificanc e Percent “A”s * Percent “B”s Percent “C”s * Percent “D”s Percent “F”s * * indicates p-value <.001 Table 2. Grade Distribution - Undergraduate Program ( ) Dependent Variable Standardized Beta Coefficient Adjusted R 2 FSignificance Percent “A”s * Percent “B”s Percent “C”s * Percent “D”s Percent “F”s * * indicates p-value <.001 Table 3. Grade Distribution: Graduate Program ( ) Dependent Variable Standardized Beta Coefficient Adjusted R 2 FSignificance Percent “A”s Percent “B”s Percent “C”s ** Percent “D”s Percent “F”s ** indicates p-value <.05

Table 4. The Effect of the Expected Grade Students Would Receive on Their Perception of Faculty Effectiveness: Graduate and Undergraduate Programs ( ) Independent Variable Standardized Beta Coefficient Adjusted R 2 FSignificance Number of “A”s ** Number of “B”s Number of “C”s * Number of “D”s ** Number of “F”s ** * indicates p-value <.001 ** indicates p-value <.05 Table 5. The Effect of the Expected Grade Students Would Receive on Their Perception of Faculty Effectiveness: Undergraduate Programs ( ) Independent Variable Standardized Beta Coefficient Adjusted R 2 FSignificance Number of “A”s Number of “B”s Number of “C”s * Number of “D”s ** Number of “F”s * indicates p-value <.001 ** indicates p-value <.05 Table 6. The Effect of the Expected Grade Students Would Receive on Their Perception of Faculty Effectiveness: Graduate Programs ( ) Independent Variable Standardized Beta Coefficient Adjusted R 2 FSignificance Number of “A”s Number of “B”s Number of “C”s Number of “D”s Number of “F”s

Student Evaluation of Instructor Performance (SEIP) form