The Continuous Improvement of Candidate and Student Learning (CICSL) is the assessment system of the Professional Education Unit (PEU) depicted in this link
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CICSL Diagram. 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-Conceptual Framework (PEU-CF) provides the foundation for the CICSL system. The Conceptual Framework for the unit 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.
Five Candidate Performance matrices outline key candidate performance assessments by certification preparation program at four CICSL Data Checkpoints in each candidate's program.
Candidate Performance Matrices
Matrix 1: Initial Teacher Education Preparation leading to NYS Initial Teacher Certification
Matrix 2: Second Initial Teacher Education Preparation leading to a second NYS Initial Teacher Certification
Matrix 3: Advanced Teacher Education Preparation leading to NYS Professional Teacher Certification
Matrix 4: School Counselor Education Preparation leading to NYS School Counselor Certification
Matrix 5: School Leadership Education Preparation leading to NYS Education Administration Certification
CICSL Data Checkpoints
- Program Admission/First Semester
- Prior to Culminating Experience
- Program Completion
Key Performance Areas
- Content Knowledge
- Pedagogical Knowledge
- Professional Knowledge and Skills
- Reflective Skills
- Professional Dispositions
- Impact on K-12 Learning
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
- Program Delivery
- Candidate Satisfaction & Support
Data-Driven Decision Making and Tracking
Assessment data are analyzed during three PEU 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 (DDDs) that document the data, the interpretation of the data, changes implemented based on the data, and a closing the loop section that measures the impact of changes made.
Data review templates
- ORID Focused Conversation Data Analysis Guide and Comment Sheet (.pdf) (.docx)
- PEU Data Review Form (.pdf) (.docx)
- PEU Data-Driven Decision Making Flowchart
Annual Data Review