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Early Warning Systems: Moving From Reaction to Prevention



Early learning begets later learning, and success begets later success. The later in life we try to attempt to repair early deficits, the costlier remediation becomes…
—James Heckman (2000)

The Elementary and Secondary Education Act (ESEA, 2001) charges us with graduating all students from high school with the essential knowledge and skills necessary for success in postsecondary education and/or high-skill, high-wage employment. Unfortunately, the reality of high school graduation rates across our nation falls significantly short of this charge. The impact of nongraduates on our nation’s economy is extraordinary, both in terms of lost income, taxes, and productivity and in relation to increased involvement in the social service and criminal justice systems (Bridgeland, Dilulio, & Burke Morison, 2006).

High schools have long been targeted as the source of low graduation rates, despite the reality that high schools inherit the strengths, weaknesses, and skill gaps acquired by students while in elementary and middle schools. Dropping out of school is a gradual process that typically begins years before a student even enters high school (Bridgeland et al., 2006). Thus, combating our nation’s nongraduation and dropout rates will require a whole-system response. All educators will need to begin to extend their vision beyond their own grade level or course and to understand the impact of current school experiences on the likelihood that their students will eventually successfully complete high school. Vertical articulation and programming between elementary, middle, and high school levels is critical to improving student graduation outcomes, as keeping all students on track for graduation will require a collective effort and an aligned system of student supports from pre-kindergarten through 12th grade.

“The conventional wisdom that dropping out is a highly idiosyncratic process driven by entirely personal factors is not true for most students who leave school. Most dropouts follow identifiable pathways through the education pipeline” (Jerald, 2006, p. 3). Further, the pathways students take leading to school dropout or delayed graduation can be identified as early as 1st grade for some students and 6th grade for the majority of students, allowing for earlier and more effective intervention (Hammond, Linton, Smink, & Drew, 2007). For instance, 40% of the nongraduates in Philadelphia schools could be predicted utilizing four 6th grade risk factors—attending school less than 80% of time, poor behavior/conduct grade, failing math, and failing English (Balfanz & Herzog, 2005). By 9th grade, 85% of eventual dropouts can be accurately identified using readily available student data such as absenteeism, course failures, credits earned, and grade point average (GPA; Allensworth, 2005).

Developing and employing an early warning system (EWS) that identifies at-risk students through the analysis of readily available and highly predictive student academic and engagement data (e.g., absenteeism, course failure, GPA, credits, discipline) is critical to secondary school Response to Intervention (RtI) implementation efforts. Utilizing data systematically to identify at-risk students as early as possible will allow for the application of more effective prevention and early intervention services. A thorough analysis of risk indicator patterns and associated relevant information will help districts better understand the root causes of student disengagement and academic failure (i.e., problem analysis). Armed with this information, districts and schools will have a greater likelihood of implementing effective prevention and intervention services and maximizing student graduation rates. Reviewing the EWS data over time assists schools in determining the effectiveness of intervention programming overall, for groups of students, and for individual students.

EWSs at Work

Recognizing the benefit of intervening with students at the first sign of disengagement or academic failure, several Florida districts are actively involved in the development and early implementation stages of EWSs tailored to their local data sources and management systems as a cornerstone of their RtI implementation efforts. Although most districts are only in the initial stages of implementation, the results of utilizing EWS data for problem solving within an RtI framework are more than promising. 

One School’s Story

One of the best examples of the power of a well designed and implemented EWS is found in Pasco County, Florida’s, Ridgewood High School, a school previously designated as one of the state’s lowest performing schools. Ridgewood High School first conducted a systematic review of its EWS data at the end of the first semester of the 2009–2010 school year. The data review revealed a shocking reality: 36% of 9th graders were already off track for graduation. Worse yet, the longer students spent at the school, the more off track for graduation they became, with roughly half of the 11th- and 12th-grade cohorts off track for graduation. 

Although the results were sobering, the school’s new administrative team wasted no time responding to the immediate needs of the senior class. The school developed new credit recovery labs and staffed the labs with a certified teacher who was known for connecting with students and serving as a student mentor. All seniors who were off track due to insufficient credits (i.e., 212 students) were enrolled in the credit recovery program during the school day and monitored closely by the credit recovery teacher and the school leadership team. During spring semester, 96 semester credits were recovered and 98% of the previously off-track seniors graduated on time.

Despite the effectiveness of the credit recovery program, the school’s leadership team expressed a belief that the program was simply an “intensive care Band-Aid” that did not address the real reasons students got off track in the first place. To make real, sustainable gains, the school would have to engage in problem analysis to learn more about what contributed to the students getting off track and provide prevention and early intervention services for students before they became disengaged or failed academically. To this end, the school leadership team held focus groups with teachers, parents, and off-track students. The focus groups revealed a need for a comprehensive high school transition program that included frequent advisement, mentoring, and progress monitoring as well as increased academic support for specific courses. To address these concerns, the school implemented a 9th grade academy that included a daily advisement period, goal setting and frequent progress monitoring, and adult and peer mentoring programs. Additionally, the school adopted and implemented robust credit and course recovery options for all students and a school-wide positive behavior support program to teach students the readiness skills (e.g., organization, study skills, goal setting, progress monitoring) necessary for academic and social success. 

The positive impact of the school’s efforts is evident in the dramatic reduction in the number of students off track for graduation. In 1 year of implementation, the number of 9th graders off track for graduation was reduced from 36% to 28%, and approximately 75% of the previous year’s off-track 9th graders returned to on-track status for graduation. Student trend data now reflect that students become more engaged with the school over the course of their high school years. By the end of the fall semester of 2010–2011 school year, 84% of the senior class was on track for graduation, allowing for more resources to be redirected to support at-risk 9th graders. Amazingly, the school’s graduation rates rose from 74% to 88% in only 3 years, and 80% of the student population received no office discipline referrals during the school year, a reduction of more than 50% from previous graduating classes. All in all, students are more engaged in school and are achieving at higher levels.

The school’s leadership team monitors the student response to the prevention and intervention services by reviewing attendance and course failure data at regular intervals (i.e., mid-quarter and end-of-quarter) using progress report data.. The school also continues to use data to investigate the presence of additional barriers, which need to be addressed within their multi-tier student support system. Vertical articulation with the school’s feeder middle schools has increased the commitment to EWS and development of proactive practices to provide at-risk students with a greater chance to successfully complete high school. 

Strengthening EWSs by Incorporating Student Engagement Indicators

Although successful school completion is dependent on more than just academic performance and an absence of inappropriate behavior (Dynarski & Gleason, 2002; McPartland, 1994), most EWSs incorporate only academic (e.g., course failures, credits earned, and GPA) and behavioral risk indicators (e.g., attendance, discipline referrals). The inclusion of cognitive (e.g., perceptions of instructional irrelevancy, personal incompetence, and lack of control over educational trajectory) and psychological (e.g., few or problematic relationships with adults and peers at school) disengagement indicators is less common. However, collecting data related to social and psychological engagement along with academic and behavioral indicators will help schools to more thoroughly analyze identified student outcome issues (e.g., graduation rates, course failures, etc.). 

Disengagement from school is a gradual process that includes impaired or reduced participation in learning and school activities, reduced perceptions of belonging at school, and decreased academic outcomes (Finn, 1989). Identifying students at the first sign of disengagement significantly improves the likelihood of re-engagement and successful school completion (Anderson, Christenson, & Lehr, 2004). The inclusion of these indicators will allow districts and schools to identify and serve students earlier and assist educators in shifting from focusing on the prevention of negative outcomes (e.g., dropout) to promoting student competence and support (Jimerson, Reschly, & Hess, 2008).

Psychological Engagement

Social and intrapersonal aspects of schooling are among the most cited reasons for high school dropout (Bridgeland et al., 2006; Reschly & Christenson, 2006). Students’ perception of support at school, affiliation with school, and sense of belonging at school are significantly related to whether students remain in school (Reschly & Christenson, 2006). Providing supports for students to develop and maintain positive and productive relationships with adults and peers at school is a worthwhile endeavor. In fact, early intervention in this area is associated with reduced grade retention, misbehavior, violence, sexual activity, and teen pregnancy (Christenson et al., 2008). To provide timely intervention for socially disengaged students, schools must engage in a systematic identification process. The following identification strategies are offered:

  • List all students’ names and grade levels and have adults in school initial next to students with whom they have a personal relationship.
  • Students with no initials by their names may be socially disengaged.
  • Utilize a survey to identify students who are bullied, alienated by peers, or who simply perceive that they have difficulty connecting with peers.
  • Employ a systematic student-nomination process for school personnel to indicate the students whom they have concerns about regarding few or problematic peer and/or adult connections.
  • Determine which students are not actively engaged in extracurricular activities through the review of club and sport rosters and attendance logs.

Cognitive Engagement

Students’ cognitive engagement in school, including their sense of competence and control, their perception of the relevance of school and instruction to their future goals, and their use of learning and problem-solving strategies to achieve their goals, is significantly related to academic achievement, school attendance, and high school graduation (Christenson et al., 2008). For instance, in a study of high school dropouts, 45% of the high school dropouts reported feeling ill-prepared for high school and 29% expressed doubt that they could have met high school graduation requirements even if they had put in the necessary effort, demonstrating that students felt they were not competent enough and/or not in control of their school success (Bridgeland et al., 2006). Eighty-one percent of the high school dropouts reported that schools needed to do a better job of providing the real-world, experiential learning opportunities students need to make the connection between school and successful employment (Bridgeland et al., 2006). 

Addressing students’ cognitive engagement through intervention has been shown to increase student engagement, and persistence with academic tasks and students’ perceptions of instructional relevancy, individual competence, and personal control over their educational experience and success is not simple. Educators cannot simply review data routinely collected in schools to identify psychologically disengaged students but must depend on self-report data from students themselves regarding these variables. Employing a student survey to assess students’ sense of control, relevance of schoolwork, and future aspirations and goals can help schools identify students who perceive little or no control over their educational experiences and success and/or lack future education/career goals or fail to see the connection between their future goals and school. 

One School’s Story

The importance of systematically and comprehensively collecting, analyzing, and incorporating student engagement data into a school’s EWS became quite evident to the leadership team at one high school targeted for intervention through Florida’s state accountability system. After reviewing EWS data including student proficiency levels in math and reading, absenteeism, and office discipline referrals, the schools’ leadership team determined that not only were far too many students below grade level in reading and mathematics (as indicated by the state’s summative assessment), but also that many were behaviorally disengaged (i.e., missed 20 or more school days in a year and/or accrued six or more office discipline referrals or two or more suspensions) and an even greater percentage were off track for graduation due to failed courses, GPAs less than 2.0, and insufficient credits. The members of the leadership team collectively expressed an urgency to provide intervention supports for students in order to improve school engagement, course taking success, and student proficiency levels.

Once current levels were understood by all team members and goals were set, the team moved on to analyzing the problem. The team discussed their perceptions of why the problem (i.e., difference between goal level and current level) existed. Although many hypotheses were offered, the team quickly focused on two main hypotheses. First and foremost, the team generally believed that parents from the community did not value education and consequently did not provide adequate support for their children to be successful at school. A second but related hypothesis emerged—the leadership team hypothesized that because many of the parents in the community were unemployed and did not value education, students consequently did not hold much hope for their futures, did not value their education, and did not plan to pursue education after high school. When pressed to explain the multitude of students who came out of the same community but were successful at school, the leadership team hypothesized that these students were successful because the school was the students’ family, providing these successful students with a sense of community that was lacking in their homes.

The leadership team, after some discussion, agreed to collect information to confirm the validity of their hypotheses. Utilizing the Student Engagement Instrument (Appleton, Christenson, Kim, & Reschly, 2006), the school gathered student perception data. The use of the Student Engagement Instrument allowed the school leadership team to collect information regarding students’ perceptions of teacher–student relationships, peer support for learning, family support for learning, control and relevance of school work, future aspirations and goals, and extrinsic motivation. 

When the survey results were reviewed, the school’s leadership team was shocked by the results. The students overwhelming reported that they had aspirations and goals for the future, that education was important to those goals, and that they had plans to pursue additional education following high school. In fact, nearly 100% of the students agreed or strongly agreed with the following items: My education will create many future opportunities for me, Going to school after high school is important, I plan to continue my education following high school, School is important for achieving my future goals, and I am hopeful about my future. Similarly, most students perceived that their families felt education was important and were supportive of their educational goals. For instance, 94% of students agreed or strongly agreed with the statement, My family/guardian(s) are there for me when I need them, while 91% of students agreed or strongly agreed with the statement, My family/guardian(s) want me to keep trying when things are tough at school. The information provided by the students did not validate the leadership team’s hypotheses regarding the source of student underachievement.

Further review of the survey results also failed to validate the leadership team’s hypotheses related to why some students were successful despite the perceived lack of parental support and involvement. In fact, the survey results indicated significant issues in the areas of teacher–student relationships and peer support for learning. For instance, a significant percentage of students disagreed or strongly disagreed with the following items: My teachers are there for me when I need them; Adults at my school listen to the students; The school rules are fair; Most teachers at my school are interested in me as a person, not just as a student; Overall adults at my school treat students fairly; and I feel safe at school. Similarly, a significant proportion of students indicated peer relationship issues by disagreeing or strongly disagreeing with these items: Other students here care about me, Students at my school are there for me when I need them, and Students here respect what I have to say.

The student survey information provided by student focus groups guided the leadership team toward the identification of relationship issues as a primary source of student underachievement. Triangulating the information allowed the leadership team to move forward with the action planning process with confidence. Instead of outreach to community religious leaders and career planning courses for all students, the school leadership team planned the implementation of relationship-building activities within the students’ home room period, a more active student advisement/mentoring program, a peer mediation program, and more frequent and varied modalities for gathering student input and feedback. 

Without spending time up front to explore all student engagement areas and gather data to validate hypotheses, the school’s leadership team would have misdirected valuable resources and most likely would not have achieved the same student outcome increases. 

Collecting and analyzing the most relevant and predictive data in order to identify student outcome problems, understand the root causes of student outcome problems, and select, implement, and evaluate the impact of intervention supports on improving student outcomes (e.g., graduation rates) is critical. As such, developing and utilizing an EWS within a larger RtI framework is essential to districts’ and schools’ efforts to prevent academic skill deficits and disengagement from occurring in the first place and to more effectively respond to these issues when they occur. 

References


ACT. (2008). The forgotten middle: Ensuring that all students are on target for college and career readiness before high school. Iowa City, IA: Author.

Allensworth, E. (2005). Graduation and dropout trends in Chicago: A look at cohorts of students from 1991 to 2004. Chicago, IL: Consortium on Chicago School Research.

Anderson, A. R., Christenson, S. L., & Lehr, C. A. (2004). Promoting student engagement to enhance school completion: Information strategies for educators. In A. Canter, L. Paige, M. Roth, I. Romero, & S. Carroll (Eds.), Helping children at home and at school II. Bethesda, MD: National Association of School Psychologists.

Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring cognitive and psychological engagement: Validation of the student engagement instrument. Journal of School Psychology, 44, 427–445.

Balfanz, R., & Herzog, L. (2005, March). Keeping middle grades students on track to graduation: Initial analysis and implications. Presentation given at the second Regional Middle Grades Symposium, Philadelphia, PA.

Bridgeland, J., Dilulio, J., & Burke Morrison, K. (2006). The silent epidemic: Perspectives of high school dropouts. Washington, DC: Civic Enterprises.

Christenson, S. L., Reschly, A. L., Appleton, J. J., Berman-Young, S., Spanjers, D. M., & Varro, P. (2008). Best practices in fostering student engagement. In A. Thomas & J. Grimes (Eds), Best practices in school psychology (5th ed., pp. 1099–1121). Bethesda, MD: National Association of School Psychologists.

Dynarski, M., & Gleason, P. (2002). How can we help? What we have learned from recent federal dropout prevention evaluations. Journal of Education for Students Placed at Risk, 7, 43–69.

Finn, J. D. (1989). Withdrawing from school. Review of Educational Research, 59, 117–142.

Florida Department of Education. (2008). Education information and accountability brief. Tallahassee, FL: Florida Department of Education.

Hammond, C., Linton, D., Smink, J., & Drew, S. (2007). Dropout risk factors and exemplary programs. Clemson, SC: National Dropout Prevention Center, Communities in Schools, Inc.

Jerald, C. (2006). Identifying potential dropouts: Key lessons for building an early warning data system. New York, NY: Achieve & Jobs for the Future.

Jimerson, S., Reschly, A. L., & Hess, R. (2008). Best practices in increasing the likelihood of school completion. In A. Thomas & J. Grimes (Eds), Best practices in school psychology (5th ed., pp. 1085–1097). Bethesda, MD: National Association of School Psychologists.

McPartland, J. M. (1994). Dropout prevention in theory and practice. In R. J. Rossi (Ed.), Schools and students at risk: Context and framework for positive change (pp. 255–276). New York, NY: Teachers College.

Reschly, A., & Christenson, S. L. (2006). Prediction of dropout among students with mild disabilities: A case for the inclusion of student engagement variables. Remedial and Special Education, 27, 276–292.

U.S. Department of Education. 2001. Elementary and Secondary Education Act. www.nochildleftbehind.gov.


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