From Early Warning to Professional Development ...kc-communications.com/wp-content/uploads/2015/09/... · From Early Warning to Professional Development: Streamlining the Process

From Early Warning to Professional Development:

Streamlining the Process and Expanding the Scope of

Dropout Prevention

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EXECUTIVE SUMMARYWhen a student abandons school without graduating, school and district leaders face two major questions: “How could we have foreseen this?” and “How could we have prevented it from happening?”

Research indicates that these questions should be asked of district administrators and principals in the earlier stages of a student’s educational path, not just at the secondary level. The Center for Social Organization of Schools at Johns Hopkins University calls dropping out “the culmination of a gradual process of disengagement from school (Curran Neild, 2010).” This “process of disengagement” can take years, even beginning in early childhood.

Improving dropout prevention efforts means resolving a student’s disengagement process as early as possible. Educators need to identify and monitor the academic measures and social factors that signal that a student is in trouble and take appropriate action.

improves on what’s gone before.

BACKGROUND: THE SCOPE AND TOLL OF DROPPING OUT

Educators and the general public have reason to celebrate when it comes to dropout prevention. In 2014, Education Secretary Arne Duncan announced that the latest National Center for Education

disheartening disparities;; certain districts still do not graduate even 60 percent of their student

America’s public high schools without graduating currently stands at more than 3 million (Statistic Brain, 2014). Despite the improving graduation rates, we are faced with over 3 million young people who failed to

a toll on the school systems being abandoned, the individual making the decision to leave school, and the community as a whole.

Despite the improving graduation rates, we are faced with over 3 million young people who failed to receive the support they needed.


deal with the loss of average daily attendance (ADA) or average daily membership (ADM) funding whenever a student leaves before graduation. Since the

funds (National Education Association, 2014), the annual number of dropouts previously cited means that school systems across the United States are losing a staggering amount of money (up to $16,962,000,000).*

amount per student at $7,021. The Arizona Department of Education cited the number of dropouts for that school

having severely hampered their ability to make money. Individuals without a high school diploma or its

jobs (Statistic Brain, 2014). In 2013, 11 percent of workers over 25 years of age who did not have a high school diploma or its equivalent were unemployed. Those who were earning wages received a median paycheck of $472 a week. In comparison, only 7.5 percent of high school graduates in the same age range were unemployed in 2013 and their median earnings were $651 per week (Bureau of Labor Statistics, 2014).

facing dropouts. According to research, this group is much more likely to engage in criminal behavior, require social services, and be in poor health (Bridgeland, DiIulio, Jr. & Morison, 2006). Those factors, in turn, mean that dropouts levy a very high cost, not only on their own quality of life but also on their local communities and society in general.

* Approximated number based on average ADA funding amount of $11,308 and half of a school year since most dropouts

do so during their senior year, (Van der Ark, 2013). Each state differs in how they fund based upon ADA or ADM.

** Approximated number based on 2012-13 ADA funds for Arizona reported by the NEA and half of a school year.

Individuals without a high school diploma or its equivalent have extremely limited job prospects since they are ineligible for 90 percent of U.S. jobs.


FORESEEING THE PROBABILITY OF A STUDENT’S DROPPING OUT

student data collected by various administrative software applications to identify factors that make one student

during the workshops revealed a set of identifying

where there is little home support to keep the student coming to school.

support at home to turn this trend around.

particularly if scores are reported throughout the year.

the district, and living with caregivers rather than parents.

Course: High Standards and High Graduation Rates, a joint project by Achieve, Inc. and Jobs for the

the following student data for tracking (Jerald, 2006):


The majority of the 12 indicators cited above match the identifying factors from the Darby workshops.

related issues, advocated tracking the following factors in order to calculate middle and high school students’ potential risk for eventually dropping out:

courses in any subject for high school students (Bowles Therriault, et al., 2013)

DROPOUT PREVENTION SYSTEMS: WHAT HAS BEEN DONE AND WHAT IS NEEDED

Darby workshops discussed how to reduce and even eliminate the likelihood that any particular student would drop out of school. The consensus was that schools and districts need support in the following:

for dropping out of school 2. Monitoring the progress of those students as they go through activities, events, interventions, and other efforts designed to prevent their dropping out.

3. Assessing the success of the activities, events, interventions and other efforts for other students with similar characteristics in order to determine best practices.

evaluation data to more quickly and effectively discover opportunities for professional development that boost dropout prevention.

The good news for administrators is that a school’s student records hold all the data required to give

the risk factors cited in the previous section. In fact, many districts and teachers already measure some combination of the indicators. The drawback is that educators often manually perform those measurements on paper or on spreadsheets, an ineffective use of their time and an added burden to

The good news for administrators is that a school’s student records hold all the data required to give educators insight into which students are exhibiting the risk factors.


A few systems (both technological and otherwise) have been developed, and a representative sampling has been included below:

The On-Track Indicator for the Chicago Public Schools District

high school freshmen (ninth graders who were not designated as ninth graders in the previous school year). This statistical indicator of student progress uses only two indicators: the number of credits

social studies (Allensworth & Easton, 2005).

The Value-Added Research Center

(VARC) Tool for Milwaukee Public Schools

who are likely to struggle in high school and eventually drop out, but it uses a combination of academic, behavioral and demographic information to do so. Educators use that data to assign interventions designed

The Early Warning Intervention

and Monitoring System (EWIMS)

calculate indicators of risk. Created by the National High School Center for districts nationwide,

monitors student progress with the interventions.

The Dropout Early Warning System (DEWS) from the state of Wisconsin

At the other end of the technological spectrum is DEWS, an online tool available through Wisconsin’s secure WISEdash data portal for districts statewide. DEWS uses demographic and student outcome variables reported at the state level and a predictive statistical model to calculate students’ risk of dropping out or graduating late in grades seven through nine.

Weaknesses in Early-Warning SystemsWhile each of the tools cited above represents a step in the right direction, they all have weaknesses that need to be addressed in order to provide the comprehensive support educators need:

Customization of Weighted Factors

Institutional culture, practices and resources differ from district to district and taking these differences into account enables educators to more effectively identify those students at highest risk for dropping out of school. However, only the EWIMS allows for true customization. When the system is designed

Institutional culture, practices and resources differ from district to district and taking these differences into account enables educators to more effectively identify those students at highest risk for dropping out of school.


is not an issue. But when districts do not have the resources to design or tailor a system, the lack of

The DEWS does make a concession toward customization by encouraging the state’s districts

to consider the case of each student as well as other data available in WISEdash,” (Wisconsin

also increase the possibility of untrained or distracted users overlooking, improperly integrating or misinterpreting data.

Half of the systems in the representative sampling do not record what activities, interventions, events, etc. are assigned to address dropout risk factors and whether or not they work. While VARC’s data is used to assign

Using separate systems to assign and track remedial activities divides the function and potentially compromises users’ ability to effectively connect which particular activities or events have the best

Identifying Opportunities for Professional Development

Of the systems mentioned, only the EWIMS has a process that supports professional development. Since educators’ actions are the essential component in turning around a student’s prospects for dropping out, not tying teacher practices to the risk factors is a missed opportunity. Knowing what works under what circumstances and which educators are or are not obtaining positive results from interventions, activities, events, etc. is critical to creating a repository of proven solutions as well as providing professional support to staffers where needed.

Anywhere, Anytime Ability to Determine Positive Effects of Remediation Early On

Only the EWIMS tracks remedial activities and events but the EWIMS process includes a number of

imported or entered manually into the system by trained staff, so the timeline for analysis depends on the schedules of those staffers. Second, the EWIMS process relies on a team approach to monitoring

school and (iii) after the end of each grading period. Consequently, the speed with which a particular remedial action event is assessed depends on whether that team meets more frequently than the recommended minimum.


Addressing At-Risk Factors as Early as Elementary School

of high school” (Allensworth, Easton, 2005). Additionally, VARC states that “substantial research indicates that

late in many cases (VARC, 2013).”

However, none of the systems mentioned above permit

indicators for students in middle school (VARC, 2013). The National High School Center’s Early Warning System already does so.

Unfortunately, starting at the middle grades is not likely to be enough to support all students. The National

“behavioral problems that develop into school adjustment

(Smink & Reimer, 2005).”

Technological tool

Customization

Elementary students

Supports professional

development opportunities

for activities/interventions

Anywhere, anytime ability

to determine effect of

intervention early

DEWS

Y

N

N

N

N

N

EWIMS

Y

Y

Y

N

Y

N

On-Track Indicator

N

N

N

N

N

N

VARC

Y

N

N

N

N

N

The dropout problem is not one that can be addressed exclusively at the middle or high school levels;; by then it is too late for some students.


THE MINDSHINE SOLUTION: STUDENT RISK ASSESSMENT SYSTEM

Technologies has developed a Student Risk Assessment System. This system uses a statistical validity and probability model to predict the probability that a student will not complete high school, and it tracks the effectiveness of any countermeasures the school or district employs.

The Student Risk Assessment System obtains the necessary academic and demographic data from a district’s student information system (SIS). Once the district acquires the MindShine system, an

the SIS at scheduled times. This means that, unlike the EWIMS, the integration of district data with the Student Risk Assessment System is seamless, and the results are readily available throughout the school year. It also means that the information follows students from school to school within the district, providing educators with an uninterrupted view of each child’s challenges, any measures already taken to address those challenges, and the student’s subsequent progress. It also enables districts to track successful efforts so they can identify and share best practices.

the Darby workshops, plus any local attributes that the district considers relevant, to evaluate the

that allows educators to determine the likelihood a

of dropping out due to attendance, reading, and home environment risk factors.

The Risk score is created by the Student Risk Assessment System’s cross referencing students with similar learning patterns and demographic attributes who dropped out

automatically rebuild its statistical model as more longitudinal and demographic data become available on student cohorts. This means that while the Risk score is already highly predictive of each student’s drop out risk, as more districts participate in this program, the score’s predictive accuracy will increase, reaching 100 percent accuracy with a sample of 1 million students. To protect student privacy, the system will include safeguards that ensure that actual student data is

system’s predictions.

The Student Risk Assessment System uses a statistical validity and probability model to predict the probability that a student will not complete high school, and it tracks the effectiveness of any countermeasures the school or district employs.


with Multiple Risk Factors

Unlike other systems currently available, the Student

do not have to wait until students’ risk factors become

time, personnel and funds to resolve the issue than if the factors had been addressed in a timelier manner.

Component #2: Selection of Relevant Risk Factors

established view or notion of what risk factors should be included, this new system allows district leaders to select,

users can opt for the system’s prescribed set of risk factors. Whatever they choose, the Student Risk Assessment System can easily tag and aggregate the data for each student within the district.

Component #3: Set/Customized Weighting

In order to measure the impact of each risk factor, users must assign them a “weight” via the Student

weighting embedded within the tool, or weight the factors in the way they consider relevant based

informed us that if a student is living in a homeless environment, the weight given to that single factor should be high enough to identify that student as being likely to drop out of school and therefore designate him or her as a candidate for action.

Component #4: RFI Acceptable/Action - Established Ranges

Scores below that level indicate that only regular monitoring is needed to ensure that a particular

Component #5: Implementing and Tracking Prescribed Activities, Interventions, Events, Etc.

students with a similar set of circumstances. The Student Risk Assessment System will allow district personnel to track the success of the action assigned, whether it involves behavioral plans, academic interventions, counseling sessions, peer project work, providing breakfast or anything else that the school/district believes will make a difference for the student.

The Student Risk Assessment System

of risk factors at any age, even pre-school.


breakfasts so that particular students can concentrate in class, is important to building a database

requires attention, educators will immediately know the best solution to implement.

and views of how interventions should work or what materials should be used, discovering best

efforts that have worked for that district, but also staff members that may perform the prescribed

staff development since the more successful staffers would be able to train or collaborate with other school and district staff.

after a few weeks of an academic intervention should show an improvement. If it does not, some scrutiny may be needed to determine if the low reading scores have another cause.

CALL TO PARTICIPATE IN STUDENT RISK ASSESSMENT SYSTEM PILOT

MindShine recently began piloting the Student Risk Assessment System with two Mississippi districts, and the company is looking for additional districts interested in trying out the system’s functions and capabilities at no charge. The only prerequisite for this pilot phase is that participating

In addition to streamlining a school’s dropout prevention process, the system can provide support for other initiatives such as Race To The Top as educators strive to accomplish the following:

principals about how they can improve instruction;;

where they are needed most;; and

participate in the Student Risk Assessment System pilot.


ABOUT MINDSHINE

computational mathematics company. Through consulting services as well as custom algorithm and

world problems for some of the world’s largest companies for more than a decade.

REFERENCES

Allensworth, E.M., & Easton, J.Q. (2005) The on-track indicator as a predictor of high school graduation.

Arizona Department of Education. (n.d.). Dropout rate study report.

High school

early warning intervention monitoring system implementation guide: For use with the national high

school center’s early warning system high school tool. http://betterhighschools.org/documents/EWSHSImplementationguide.pdf

Middle

grades early warning intervention monitoring system implementation guide: For use with the

national high school center’s early warning system middle grades tool. http://www.betterhighschools.org/documents/EWSMGImplementationguide.pdf

Bridgeland, J.M., DiIulio, J.J., Jr., & Morison, K.B. (2006). The silent epidemic: Perspectives of high school dropouts: A report by Civic Enterprises in association with Peter D. Hart Research

Associates for the Bill & Melinda Gates Foundation.

Bureau of Labor Statistics. (2014). Earnings and unemployment rates by educational attainment. http://www.bls.gov/emp/ep_chart_001.htm

Curran Neild, R. (2010). Using early warning indicators to identify students at highest risk of dropping out

University.

Jerald, C.D. (2006) Identifying potential dropouts: Key lessons for building an early warning data system.

National Education Association. (2014). Rankings and estimates: Rankings of the states 2013 and estimates of school statistics 2014.


Fifteen Effective Strategies for Improving Student

Attendance and Truancy Prevention https://www.dpi.state.nd.us/title1/progress/present/15ways.pdf

Statistic Brain. (2014). High school dropout statistics.

Van der Ark, T. (2013). “How can we prevent high school seniors from dropping out?” Vander Ark on Innovation blog. http://blogs.edweek.org/edweek/on_innovation/2013/10/how_can_we_prevent_high_school_seniors_from_dropping_out.html

warning system (DEWS) dashboards. http://wise.dpi.wi.gov/wise_dashdews


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From Early Warning to Professional Development ...kc-communications.com/wp-content/uploads/2015/09/... · From Early Warning to Professional Development: Streamlining the Process