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WVU assessment resources

WVU Assessment Resources and Examples

This page collects the resources, templates, and examples developed by the Director of Assessment, the University Assessment Council, and the Teaching and Learning Commons, as well as examples of how departments have implemented those.

Assessment Plans and Reports

Blank assessment plan and report template
Detailed guidelines for using the assessment plan and report template

Examples of Data Visualization 

The goal of visualizing data graphically is to make patterns in the data stand out clearly, making  comparisons easier.  Shades of blue and gray provide a palette that’s easy on the eyes while still providing enough contrast to distinguish between the data series.  When you look at a graph, the “take-home message” (or messages) should be obvious.  Contact Robynn Shannon if you would like assistance with data visualization.

A clustered bar graph makes it possible to compare responses among questions and also to compare responses to a single question.  Horizontal bars allow plenty of space for the cluster labels.  The clusters are arranged (top to bottom) from the greatest “Strongly agree” response to the smallest.  Percentages are often preferable to actual data values because most people can more easily compare percentages in their heads (e.g., it’s easier to think about the difference between 25% and 75% than between 38 and 114).  Data labels were left off the bars because they would make the graph look cluttered.  Similarly, a stacked bar graph was not used because four sections of a single bar are harder to read than a cluster of bars.  Including the sample size helps the viewer interpret the results.

Clustered bar graph displayin resonses to an institutional evaluation of academic advising

For this graph and the next, stacked bars adding up to 100% were used because there are only two or three values for each bar, and stacked bars make for a less cluttered appearance than clustered bars.  As with clustered bars, stacked bars allow you to make two comparisons in one graph, in this case comparisons among items being scored by the rubric and scores within an item.  In this graph, the information of greatest interest was the number of “1” score in each bar, so the bars are arranged (top to bottom) from the largest value for “1” to the smallest.  That section of each bar is also the darkest.  Data labels were added mostly to make it easier to compare the size of the middle section of each bar.

Stacked bar chart showing rubric scores for GEF area 1 assessment

This graph has an additional level of complexity because an additional comparison is being made.  As in the graph above, it is possible to compare among rubric items (with abbreviated labels in this graph) and also to compare the scores (“1” vs. “2 or 3”) within an item.  Additionally, it is easy to make a comparison between course numbers (101 vs. 102) within a rubric item. Because there are only two sections to each bar, data labels were added only to the section of greatest interest. An alternative way to graph these data would be to arrange the bars in just two clusters, one for 101 and one for 102, and to have one bar for each rubric item (GS, SE, CD, and CP) within each cluster.

Stacked bar chart showing comparison between ENGL 101 and 102 in GEF area 1 assessment

This graph is an example of a “box” (or “box and whisker”) plot.  Box plots contain a lot of information about a set of numbers (values) and are therefore somewhat complicated to read (see labeled illustration).  The ends of the lines extending from the box (the “whiskers”) show the minimum and maximum values in the data set, in other words, the ends of the range of values.  The line across the inside of the box shows the median, and the “×” shows the mean; the actual value of the mean for each box is labeled. The mean and median can be quite different if the data set includes outliers.  The bottom and top lines of the box itself indicate the value for the first and third quartile, respectively (the first quartile is the number that 25% of values fall below; the third quartile is the number that 75% of values fall below). A small box with short “whiskers” indicates a small range of values in the data set.

Bow and whisker chart showing reading comprehension progression in a sequence of Spanish courses.

Generic box and whisker chart with parts labeled

CIM Course and Program Proposals

Curriculum Maps

Curriculum maps link program learning outcomes to the courses in which they are delivered, the extent to which mastery is expected in those courses, and, in some cases, the measures used to assess the program learning outcomes in those courses.

The Biochemistry curriculum map links courses in a number of different academic departments to the programs learning outcomes.

The B.S. in Math uses the curriculum map below which links the course to the program learning outcome and the extend to which the learning outcome is covered or relevant to the course.
Curriculum map showing how Math courses map to Math program learning outcomes and what level of mastery is achieved in those courses
The undergraduate Horticulture program uses a text-based approach to curriculum mapping; an example of how they do this for one of their learning outcomes is provided below.

Learning Outcome 1: Understanding/Problem Solving (Year 1 – 2018)

Demonstrate critical thinking skills and problem solving abilities in areas such as:

o    Basic business concepts

o    Integrated Pest Management (weed science, entomology, plant pathology)

o    Genetics

o    Plant physiology

o    Soil science

o    Microbiology

o    Agrochemistry

 Learning Experience:

·         Students will take courses in plant science, soil science, business, entomology, plant pathology, genetics, plant physiology. Students will be introduced to these concepts in our introductory general horticulture course (HORT 220). The concepts that are key to this learning outcome will be reinforced in two plant identification courses (HORT 260 and HORT 262) and finally the learning outcome will be enforced in our capstone course HORT 480: Case Studies in Horticulture.


·         Direct Assessment will take place by collecting the mystery hormone/plant propagation lab assignment in HORT 220, the garden plan development and renovation of a vacant area in HORT 260 and 262, respectively, and the final case study analysis assignment in HORT 480 (Case studies in Horticulture. Scoring of those assignment will be done with the Problem Solving Value Rubric 

·         Indirect Assessment will occur by a faculty advisor evaluation form, notes in degree works that will show progression in career goals and aspirations, a graduating senior survey, and polling employers of our graduates at least one year after they leave the university.

Exit (Senior), Alumni, Internship, and Employer Surveys and Rubrics

Sample undergraduate alumni survey (Women's and Gender Studies)

Sample employer survey (Horticulture)

School of Social Work's Field Assessment (administered at mid-term and final)

Student Learning Outcomes

The Faculty Senate Curriculum Committee has a guide to writing clear and measurable course and program outcomes.

The Teaching and Learning Commons at WVU has an excellent resource on writing powerful learning outcomes that work.

Program Examples
Graduate Program Examples

Course Examples


Upon successful completion of this course, students will be able to…

  • Apply techniques from feminist science studies to a scientific field (WGST 250, Women in Science).
  • Develop an export business plan (MKTG 410, Export Management).
  • Create a professional-quality poster presentation (FIS 401, Professional Forensic Communication).
  • Synthesize the skills and knowledge required in conducting performance and economic evaluation (HPML 680, Performance and Economic Evaluation for Public Health).
  • Interpret epidemiologic analyses from a range of multivariate models (including linear, logistic, Poisson, and Cox regression models) (EPID 712, Quantitative Methods in Epidemiology).


Section-level syllabi are reviewed around institutional expectations and best practices.