Global Certificate in Data-Driven Instruction Models

• Data-Driven Instruction Fundamentals: Understanding the basics of data-driven instruction, its importance, and how to use data to inform instructional decisions.
• Data Collection Techniques: Exploring various methods for collecting data, including formative and summative assessments, and learning to choose the most appropriate techniques for different situations.
• Data Analysis Strategies: Discovering various data analysis methods and tools, and learning to interpret and draw meaningful conclusions from data.
• Actionable Data-Driven Insights: Turning data into actionable insights that can be used to improve student learning outcomes and inform instructional strategies.
• Data-Driven Instructional Models: Examining various instructional models that are driven by data, including differentiated instruction and response to intervention.
• Assessment and Evaluation: Understanding the role of assessment and evaluation in data-driven instruction, including how to use assessment data to inform instruction and evaluate student progress.
• Data Security and Ethics: Learning about best practices for ensuring data security and privacy, as well as ethical considerations when using data to inform instruction.
• Collaborative Data Use: Exploring the benefits of collaborative data use and learning how to work effectively with colleagues to analyze and use data.
• Continuous Improvement: Understanding the importance of continuous improvement in data-driven instruction and learning how to use data to inform ongoing instructional improvement.

Note: The above list of units focuses on the primary keyword "data-driven instruction" and includes secondary keywords such as "data collection techniques," "data analysis strategies," "data-driven instructional models," and "continuous improvement."