- Direct Support Workers
- Technology Experts
- Postsecondary Educators
- Primary Educators
- Secondary Educators
In this session we introduce and demonstrate the Classroom Teaching (CT) Scan. The CT Scan is an observational tool designed to capture teachers’ instructional practices in real time and then aide in delivery of coaching. Resulting data provides portraits of lessons including raw counts and percentages of time spent using various teaching practices, and ratios of instructional time to non-instructional time.
The CT Scan app is a cross-platform, standalone application that is straightforward to use, easily reconfigurable and customizable for a variety of scenarios. After entering details about the observation (e.g., teacher name, grade level), the user sees the main observation screen where classroom events and their occurrence times are recorded. A default list of practices is provided; users can customize these to reflect a specific scenario. Following the observation, the user answers summary questions and rates the teacher on several criteria. The app displays results in pie charts to facilitate rapid visual assessment of practices. A color-coded “timeline” is generated to show the temporal sequence and relationship of teacher and student actions, vocabulary words, visual aids, and responses.
The timeline and performance summary are available to review with instructors immediately after an observation. Raw data are stored to a file for inclusion into an offline database for a posteriori analysis. The database allows calculation of user-defined performance statistics for each instructor, including time-series charts of performance characteristics. Furthermore, advanced causal relationships between student and teacher actions can be identified, and teacher patterns can be determined based on these relationships, which allows for specific, performance-based feedback.
The aims of this session are to introduce the constructs on the CT Scan and demonstrate its operation and outputs. We will introduce preliminary reliability and validity data and discuss future research plans. We will also solicit feedback from audience members regarding the design and uses of the CT Scan as an observational tool.
- Learners will understand the development steps undertaken in developing the CT Scan and app and will hear preliminary reliability and validity data.
- Learners will understand the conceptual differences between the CT Scan and other popular observational tools (e.g., CLASS, Danielson framework).
- Learners will understand the outputs of the CT Scan app and how these outputs relate to professional development.
- Audience members will have the opportunity to interact with the CT Scan App.
- Audience members will be encouraged to ask questions through the presentation regarding tool development and design.
- Audience members will be encouraged to provide feedback on the app design and outputs to inform future iterations of the app.
Michael Kennedy is an Associate Professor of Special Education at the University of Virginia. His research focuses on providing pre- and inservice teachers with learning experiences that translate into improved implementation of evidence-based practices and outcomes for all students.