This project will employ a longitudinal and cross-sectional research design and will generate evidence that is descriptive [case study and observational] and associative/correlational [analytic essay, interpretive commentary, and causal comparative design]. Original data will be collected using observation, survey research, interviews, and teacher instructional logs.
This study will use multiple instruments to measure the status of implementation and the factors that contribute to or inhibit the implementation, spread and sustainability of Ohio’s STEM education models. The CEMSE’s fidelity of implementation (FOI) instruments including a Teacher Instructional Questionnaire, Classroom Observation Protocol, Teacher Instructional Log, and Teacher/School Leader Interview Protocols provide a foundation for measuring the status of implementation of the STEM school models and STEM instruction.
Regarding measurement of the factors that affect the implementation, spread and sustainability of STEM innovations, the FOI instruments already measure some of these factors (e.g. experiences of the user, internal social climate/culture, leadership, resources, etc.) so we will use the appropriate, already validated items in these instruments as a starting point to develop a teacher environmental questionnaire and school leader environmental questionnaire. We will also include items pertaining to the factors in the student questionnaire. Additionally, as the instrument development and adaptation process proceeds, wherever possible we will include validated items from other existing instruments. An initial review has uncovered several school climate instruments, innovation complexity measures, and student as learner questionnaires, from which items can be used to measure the constructs of interest.
Exploratory Factor Analysis will be used to validate items measuring the status of implementation and environmental factors affecting the implementation, spread and sustainability of Ohio’s STEM education models.
Correlation, path analysis and regression analysis will be used to explore how environmental factors affect the model implementation, spread and sustainability.