Capital Community College

Student Learning Assessment Implementation Team

REPORTING DESIGN
for data based on
ASSESSMENT OF GENERAL EDUCATION GOALS

Data Collection (Assessment Implementation Team)

Once each semester (mid-November & mid-April), the Assessment Implementation Team uses a rubric to assess samples of a cross-curricular project. In 2001-2002, the focus is on writing; in 2002-2003, the focus is on mathematical problem-solving. Each sample is scored both holistically and analytically based on a previously-established rubric.

Data Compilation (Clerical support staff)

The scoring sheets are submitted to assessment clerical support staff for compilation of results. The compilation includes

See the section below on Student Learning Assessment Data Compilation for detail.

These descriptive reports are returned to the Implementation Team for study (early December and early May). The second report of the year will be cumulative, combining both fall and spring data for a cohort total of at least 100 students.

Data Interpretation (AIT & clerical staff in support of Departments)

The descriptive reports are studied for the generation of appropriate inferential reports. For use in developing program improvements.

Archiving (AIT and clerical staff)

All scoring sheets and databases are catalogued by key number and stored in appropriate files for retrieval in future years for longitudinal studies and cohort comparisons. (June)

PROFILES (DETAIL)

Fields to be included in profiles are limited to those that are relevant to each assessment's purposes. They include primarily those areas of academic history over which the school exercises some control. They exclude ethnicity, age, gender, and other background categories that might be of interest for purposes of general research but not for purposes of designing interventions for the improvement of student learning.

Fields included:

STUDENT LEARNING ASSESSMENT
DATA COMPILATION (DETAIL)

Assessment team and clerical support will:BANNER support personnel will provide:
1. Pick the assessment samples 
 2. Profile information (by BANNER#) about each sample
3. Assign a key number to each sample 
4. Generate data through the assessment of each sample 
 :5. The averages computed for each sample
6. Connect profile information to assessment data 
7. Destroy the keys 
8. Maintain the anonymous, aggregate data 
This division of functions ensures the anonymity of participants in the assessment activity.