Introduction

Representational sampling is a sampling strategy used to select enlightening, useful, and informative student products. Although representational sampling can be more complex than random sampling, it can be a valuable approach to selecting student products for ACE assessment.

Selecting a Representational Sampling Strategy

The two central issues to consider when selecting a representational sampling strategy are the student characteristics (e.g., number of students in the course, distribution of students' class standing, etc.) and the variance of student achievement. Class size and the number of sections of a course are also important considerations.

Table 1 shows some guidance on the selection of a sampling strategy for small- to mid-sized courses. Large courses or courses with a large number of sections may also consider typical sampling in addition to maximal variation and extreme case sampling.

Table 1. Guidance on selecting a representational sampling strategy

Click on the links above to see a description and example for each of these representational sampling strategies.

Typical Sampling

Purpose: "To describe what is 'typical' to those unfamiliar with case" (Creswell, 2002).
Use when: Student achievement is fairly homogeneous or when sampling across a large number of students or sections.

I teach the capstone course for my department that enrolls about 10-15 students per year. Most students do very well in the course—the failure rate is less than 1%.
Suggested strategy: Select 3 – 5 student products (as indicated by the Certified Course Proposal) that represent the "typical" student achievement. Select no fewer than 3 products for small classes and no more than 10 from a single section of a large course.
Explanation: Students have similar demographic characteristics (all from the same major and all seniors) and similar level of achievement. Selection of a typical sample of student products will provide data on the typical student achievement of ACE outcome 10.

More than 30 sections of my course are taught every semester by full-time faculty members, part-time instructors, and graduate teaching assistants.
Suggested strategy: Select a random sample of course sections. Each selected section provides samples of student work that represent the "typical" student. These samples of student work are evaluated by a department-level committee. In addition to sharing results as part of the ACE process, results are shared with all individuals responsible for teaching a section of the course.
Enhancement: Sampling of course sections is stratified by instructor type (i.e., graduate teaching assistant, adjunct instructor, full-time faculty member).
Explanation: Selection of a typical sample of student products across the sections of the course provides data on the typical student achievement of the ACE outcome. The sharing of areas of strength and areas of weakness in student achievement of the ACE outcome with all instructors can lead to improvements in student learning.

Extreme Case Sampling

Purpose: "To describe particularly troublesome or enlightening cases" (Creswell, 2002).
Use when: Student achievement is heterogeneous or when the selection of typical cases would not accurately represent students' experiences in the course or would mask interesting connections.

I teach a course every semester that serves only freshmen. The course has about 30 students each semester. There tends to be a wide range of student achievement in the course.
Suggested strategy: Select 3 – 5 student samples that show the range of achievement levels (e.g., low, medium, high). Select no fewer than 3 products for small classes and no more than 10 from a single section of a large course. Include "troublesome or enlightening" cases and explain why these products were selected.
Enhancement: Indicate the proportion of students in each performance range.
Explanation: Selection of a range of performance levels and indication of the proportion of students achieving each performance level provides data on the range of student achievement of the ACE outcome. Inclusion of enlightening cases (e.g., a superior level of achievement by a student who recently completed a study abroad experience) may reveal directions for further exploration of achievement of the ACE outcome.

Maximal Variation Sampling

Purpose: "To develop many perspectives" (Creswell, 2002).
Use when: Student achievement of the ACE outcome is heterogeneous and characteristics of students enrolling in the course dramatically impact student achievement on the ACE outcome.

I teach a large lecture course with over 100 students. Students in the course come from many different colleges and majors and have a large variance in their level of preparation. In addition, some students in the course are freshmen and others are seniors.
Suggested strategy: Select student products based on both achievement level and student demographics. Select at least one product for each achievement / student characteristic combination (e.g., low achievement / senior, low achievement / freshman, medium achievement / senior, medium achievement / freshman, high achievement / senior, high achievement / freshman). Select no fewer than 3 products for small classes and no more than 10 from a single section of a large course.
Enhancement: Indicate the proportion of students in each performance level by class year.
Explanation: Selection of a range of performance levels by class year and indication of the proportion of students achieving each performance level provides data on the range of student achievement of the ACE outcome and the relationship to class year. Sorting results by class year may reveal directions for further exploration of achievement of the ACE outcome.

Student Achievement and Demographics Definitions

Homogeneous student achievement: Student achievement of the ACE outcome is similar across students. This situation may be most common in small or specialized courses.

Heterogeneous student achievement: Student achievement of the ACE outcome is varied across students. This situation may be most common in medium – to large-sized courses or courses that include students with a wide range of prior learning experiences.

Homogeneous student demographics: Students are similar in class year (e.g., all seniors), major, and prior learning experiences. This situation may be most common in small or specialized courses or capstone courses.

Heterogeneous student demographics: Students differ by class year, major, or prior learning experiences. This situation may be most common in medium – to large-sized courses or courses that enroll students from multiple colleges and backgrounds.