TeachME Professional Development

1. Students with at least a high school diploma are more likely to be employed and to have higher median weekly earnings, while individuals who drop out of high school are at risk of poorer health and a shorter lifespan in comparison to those who have graduated.

2. An early warning system identifies those who are at risk of dropping out of school by applying predictive analytics to student data to determine their risk level in relation to:

3. Early warning systems require time, money, and effort, and according to the authors, the functions of a continuous improvement process include each of the following EXCEPT:

4. As a first step in the planning process, planning teams should work together to conduct a needs assessment that identifies agency and stakeholder needs, and to identify and validate early warning indicators, data elements, and analytical models.

5. Early warning indicators measure the extent to which a student is likely to be at risk of dropping out of school, and indicator identification may occur through an analysis of preexisting internal longitudinal data, a review of external research conducted by other education agencies and organizations, or a combination of both.

6. Early warning systems commonly include indicators such as attendance, behavior, and course performance, and some early systems, particularly at the state level, include:

7. The use of a given indicator is predicated on whether the related data are valid, reliable, and:

8. Systems that assess student risk using frequently updated data may be most useful for identifying elementary school-age students that demonstrate future risk of dropping out.

9. Although some data already exist and remain relatively constant, other data are generated through one-time or recurring events and activities, and while existing data need to be mapped to or aligned with the early warning system before being imported or uploaded, new data will need to be collected as students progress through school.

10. When considering if it appropriate to use demographic data as an early warning system indicator, planning teams must determine whether there is a strong correlation between these data and on-time graduation after adjusting for student risk level based on:

11. Interpreting early warning system data with data captured in a different system, such as a student information system (SIS), can point to the underlying causes of individual student risk, help staff better understand the specific needs of individual students, and enable the development of interventions that directly address:

12. Which of the following is NOT a true statement about linking early warning systems and college readiness systems/indicators?

13. Many SEAs and LEAs have access to more sophisticated data now than in previous years, including data related to student satisfaction with school, student engagement, and social-emotional learning, and these types of data may be useful for identifying students at risk of dropping out.

14. Once early warning indicators and elements have been identified, they can be used to create detailed, documented technical specifications and system requirements, and establishing written requirements can help assess the appropriateness of different system development options.

15. The introduction of an early warning system to an agency should be supported thorough training, and the goals of such training should include helping staff understand the purpose of an early warning system, teaching staff how to collect, report, analyze, and interpret data using a system, and prompting staff to use a system and its data:

16. The need for data-informed actions and interventions should be emphasized when reporting and sharing data, and the authors recommend load balancing within a system to make it easier for users to review individual student data and provide targeted interventions.

17. Regular evaluation of an early warning system should focus on system accuracy, system performance, intervention effectiveness, and:

18. Since statewide early warning systems include data on more students than local early warning systems, they can reveal important trends that wouldn’t be visible at the LEA level.

19. The Dropout Risk Model, which was developed with the intent to identify students in need of services, uses eight early warning indicators to calculate each student’s dropout probability including each of the following EXCEPT:

20. The Montana Office of Public Instruction created a voluntary statewide early warning system, Montana EWS, to help schools lower their dropout rates and encourage data use among schools, which uses a logistic regression analytical model to produce a percentage chance of not graduating from high school on time based on a dropout probability score for each student.

21. Interpreting early warning data in combination with related datasets enriches understanding of the data and can prompt more effective interventions at the student or school level.

22. The Wisconsin Department of Public Instruction (DPI) created the Dropout Early Warning System (DEWS) to identify students in grades 6-9 who are at risk of not graduating from high school on time by addressing early interventions needed, accuracy of identification, transparency, reproducibility of predictions, and:

23. Lessons learned from SEAs and LEAs that have implemented or are in the process of implementing an early warning system include drawing on professional learning communities and the experiences of colleagues to learn about the barriers, strategies, and successes other states and districts have faced in developing, implementing, and using an early warning system.

24. Experts recommend budgeting sufficient time to develop the system’s analytical model, balancing the model’s complexity and transparency, and clearly and objectively explaining to users the capabilities, limitations, and accuracy of the analytical model and its:

25. System use recommendations include supporting each step of the early warning process by integrating early warning system use with intervention mapping, reporting, and validating.