Utilizing Data to Improve Student Knowledge and Outcomes

CEU Course Description

Part 1: Personalized learning aims to tailor instruction to the needs, talents, and skills of each individual learner, and rapid advances in technology platforms and digital content over the last decade have enabled more widespread use of personalized learning.  The purpose of this continuing education course, developed using information from the National Forum on Education Statistics, U.S. Department of Education, is to provide an overview of personalized learning and describe best practices to collect data for personalized learning, to use those data to meet personalized learning goals, and to support the relationships, resources, and systems needed for effective use of data in personalized learning.  In addition, data considerations needed by different locations depending on their approach to personalized learning are presented as well as questions such as which data elements are appropriate at the district or state levels.

Part 2: In recognition of the critical need to support educational attainment and graduation, early warning systems have been developed to strategically use data to identify students in need of additional support and interventions that will help improve their success. The purpose of this continuing education course, developed using information from the National Forum on Education Statistics, U.S. Department of Education, is to help state and local education agencies plan, develop, implement, and use early warning systems within their agencies. The course provides a description of early warning systems and illustrates the ways in which these systems can help education agencies act on early warning data to improve student outcomes. In addition, the essential role of timely and actionable quality data in early warning systems is presented as well as case studies from state and local education agencies that have implemented or are in the process of implementing an early warning system.

CEU Course Objectives

Part 1:

1. Define personalized learning, identify its key components, and discuss effective use of technology in this type of learning.
2. Describe the categories and considerations involved in personalized learning data and differentiate between structured and unstructured data.
3. Outline strategies to create a strategic plan to support personalized learning and to modify infrastructure and learning environments when implementing the plan.
4. Illustrate ways to sustain personalized learning, to ensure the privacy of data, and to develop and maintain support structures.
5. Highlight case studies from states and districts that have implemented or are transitioning to personalized learning and evaluate how they are managing and using personalized learning data.
6. Review the challenges of personalized learning and consider continuing questions that need to be addressed.

Part 2:

1. Define early warning systems, review their use in education agencies, and explain how to adopt an effective early warning system planning process.
2. Illustrate the ways in which these systems can help education agencies act on early warning data to improve student outcomes.
3. Review the role of indicators and quality data in early warning systems and summarize techniques for analyzing early warning data.
4. Analyze trends and innovations in early warning systems, indicators, data, and analytical models.
5. Identify strategies and best practices for education agencies that are considering implementing, or have already implemented, an early warning system.
6. Highlight case studies from state and local education agencies that have implemented or are in the process of implementing an early warning system.