Overview of the CICSL Assessment System

The Continuous Improvement of Candidate and Student Learning (CICSL) is the assessment system of the Professional Education Unit.

Main Page Content

CICSL Assessment System

The preparation of the education professional candidate to be a competent and effective education professional is central to the system and the Professional Education Unit's (PEU) Conceptual Framework provides the foundation for the CICSL system. The Conceptual Framework for the PEU is guided by and grounded in social constructivism.

Key assessments for both Candidate Performance and Unit Operations enables the PEU to collect and analyze data to evaluate and improve the performance of candidates, the unit, and its programs.

Candidate Performance

Five candidate performance matrices outline key candidate performance assessments by certification preparation program at four CICSL Data Checkpoints in each candidate's program. The matrices can be found in our assessment handbook.

Assessment Handbook

CICSL Data Checkpoints

  1. Program Admission/First Semester
  2. Prior to Culminating Experience
  3. Program Completion
  4. Post-Graduation

Key Performance Areas

  1. Content Knowledge
  2. Pedagogical Knowledge
  3. Professional Knowledge and Skills
  4. Reflective Skills
  5. Professional Dispositions
  6. Impact on K-12 Learning

Unit Operations

Assessment of unit operations and programs is done through data collection and review in four main areas using a broad array of data and information.

  • Governance and Administration
  • Resources
  • Program Delivery
  • Candidate Satisfaction & Support

Data-Driven Decision Making and Tracking

Assessment data are analyzed during three assessment days held each year. The attendees make recommendations to improve our programs and candidate performance based on data. Those recommendations are brought to the appropriate stakeholders to be implemented.

The assessment committee completes data-driven decision making forms that document the data, the interpretation of the data, changes implemented based on the data, and a closing the loop section that

Last Updated 2/19/21