Program Goals/Target Population
Check & Connect is a school-based, structured mentoring program. The goals of the program are to reduce the number of days that students miss school and increase their engagement with academic activities when they are in school. The program primarily targets students in grades K–12 who are at risk of disengagement or dropping out.
Program participants are identified using alterable indicators of student engagement risk that are most relevant to the specific school or school district of implementation such as absences, tardiness, behavioral referrals, out-of-school suspensions, and course failings.
Eligible students are assigned a mentor, who is typically an in-school staff member. Mentors receive training and a program manual to guide their interactions with students. Mentors are expected to develop nurturing and supportive relationships with students and support student engagement through two primary channels: the Check component and the Connect component. During the Check component, mentors use school data to monitor student attendance (e.g., absences, tardiness, skips), social–behavioral performance (e.g., referrals, suspensions, detentions), and academic performance (e.g., grades, credits earned). During the Connect component, mentors share checked data with students, reinforce the salience of education, and facilitate personalized interventions to boost engagement, including activities to foster participation in school activities, enhance academic and social competencies, practice problem-solving strategies to overcome barriers to school attendance and engagement, and connect students to social service and school-based resources. Mentors are encouraged to use information about the student’s school engagement level and family circumstances and available school and community resources to tailor the ways they intervene with students. Mentors are also encouraged to connect with families of students, through home visits or over the phone, to serve as liaisons between home and school, and to partner with parents to increase student engagement.
Mentors meet with students individually, both formally (scheduled) and informally (unscheduled), for a minimum of 2 years, often following students who switch schools within a district. Each mentor carries a caseload of several students (for example, in one version of the program, mentors had up to 48 students). Mentors routinely collect and report implementation data using a standard monitoring form, including 1) check data on student grades, tardiness, suspension, and whether the student was considered high risk based of check data and mentor’s judgment; 2) length of interaction with students (in minutes) and mode of communication (in person, via phone, text, email); and 3) list of specific interventions provided to student.
Mentors are paid program staff with a variety of educational and employment experiences. Essential qualifications include familiarity with school and community resources, a willingness to persist, a belief that all students have abilities, willingness to work closely with families and school staff, and advocacy and communication skills, including the ability to negotiate, compromise, and confront conflict constructively (Sinclair et al. 1998).
Check & Connect is centered on student engagement, which refers to a student’s level of engagement and active participation in school and school-related activities (Finlay 2006; Fredricks et al. 2004). School engagement is viewed as a multidimensional construct involving behavioral (e.g., attendance, participation in classroom discussion), affective (sense of belonging, connectedness to school), and cognitive (perceived relevance of schoolwork, self-regulation toward goals) engagement (Heppen et al. 2017). More active participation in classroom learning activities among children has been associated positively with academic achievement and negatively with skipping school and dropping out (Anderson et al. 2004; Finlay 2006; Fredricks et al. 2004). The program’s goal of improving engagement outcomes for youth by using a nurturing and supportive relationship with an adult is consistent with Rhodes and colleagues’ model of youth mentoring, which theorizes that support and role modeling provided within mentoring relationships can strengthen youth academic and behavioral outcomes (Rhodes et al. 2006).
The use of mentors to advocate for and connect youths to services and resources based on their unique needs and circumstances and to help them develop skills to overcome obstacles to school success is consistent with empowerment theory (Zimmerman 2000). Such support also may broaden the youth’s network of social ties in ways that lead to new connections and opportunities, thus increasing social capital (Heaney and Israel 2002). Finally, the emphasis on collaborative relationships among mentors, school personnel, and families in Check & Connect is consistent with Bronfenbrenner’s (1979) ecological model of human development and Stokol’s (1996) social–ecological theory, both of which emphasize the interplay of personal attributes, social contexts, and environmental conditions in influencing individual behavior.
The findings from the study by Guryan and colleagues (2017) showed that, at the end of the 2-year intervention period, students who participated in the Check & Connect program had fewer days absent and a larger percentage of days present in school, compared with control group students. This difference was statistically significant. However, students in the Check & Connect program also had lower standardized MAP math scores, compared with control group students, which was also a statistically significant difference between the groups. There were no statistically significant between-group differences on outcomes related to GPA, course failures, and standardized MAP reading scores.
There was a statistically significant difference between students in the Check & Connect intervention group, compared with those in the control group in days absent, at the end of the 2-year study period. Students in the intervention group had 0.709 fewer days absent.
There was a statistically significant difference between students in the intervention group, compared with those in the control group in percentage of days present, at the end of the 2-year study period. Students in the intervention group had 0.455 percent more days present.
There were no statistically significant differences between students in the intervention and control groups in their GPA over the 2-year study period.
There were no statistically significant differences between students in the intervention and control groups in course failures over the 2-year study period.
Standardized MAP Math Score
There was a statistically significant difference in MAP math scores between students in the intervention group, compared with those in the control group, at the end of the 2-year study period. Students in the intervention group had scores that were 0.083 lower than the control group scores.
Standardized MAP Read
There were no statistically significant differences between students in the intervention and control groups in standardized MAP reading scores over the 2-year study period.
Overall, the findings from the study by Heppen and colleagues (2017) showed that at 3 years post-baseline, there were no statistically significant differences between youths in the Check & Connect program, compared with the control group, on any of the outcomes assessed.
There were no statistically significant differences between students in the intervention and control groups on attendance (whether they had attended school for at least 90 percent of the days they were enrolled during the year), at 3 years post-baseline.
There were no statistically significant differences between the intervention and control groups on citizenship grades (receipt of “Unsatisfactory” grade in two or more courses), at 3 years post-baseline.
There were no statistically significant differences between the intervention and control groups on dropout (having dropped out of high school within 5 years of grade 9), at 3 years post-baseline.
There were no statistically significant differences between the intervention and control groups on scores for the teacher-student relationship subscale of the Student Engagement Instrument (SEI), at 3 years post-baseline.
There were no statistically significant differences between the intervention and control groups on student engagement scores, at 3 years post-baseline.
There were no statistically significant differences between the intervention and control groups on on-time graduation (graduation within 4 years if entering Grade 9), at 3 years post-baseline.
Control and Relevance of Schoolwork
There were no statistically significant differences between the intervention and control groups on scores for the control and relevance of schoolwork subscale of the SEI, at 3 years post-baseline.
Future Aspirations and Goals
There were no statistically significant differences between the intervention and control groups on scores for the future aspirations and goals subscale of the SEI, at 3 years post-baseline.
There were no statistically significant differences between the intervention and control groups on the number of credits earned at the beginning of the 4th year of high school, at 3 years post-baseline.
Fewer than Two Course Failures
There were no statistically significant differences between students in the intervention and control groups on whether they failed two or more classes during the school year, at 3 years post-baseline.
Guryan and colleagues (2017) used a randomized controlled study to evaluate the Check & Connect program among two cohorts of Chicago Public Schools (CPS) students; each cohort was followed for 2 school years. Sixty-nine schools serving students in grades K–8 were selected for participation, based on their representativeness of the district in terms of student demographic and socioeconomic characteristics, and on the availability of large enough groups of students in each grade with 10 to 35 absences in the previous year. These schools were placed in three groups based on geographic location, student race and ethnic demographics, and school-level absence rates. One school from within each group of three was randomly selected to be part of the first study cohort. Within these 23 schools, five grades out of seven (grades 1 to 7) were randomly selected to be in the intervention; the remaining two grades formed the control group. Students within the five grades selected for participation were then placed in groups of three, were matched based on baseline absences, and one of the three students in each group was randomly selected to be in the intervention group. Eligible students within each school and grade were then sorted in descending order by baseline absences and were offered the chance to participate in the intervention. In cohort 2, nine schools with nine returning mentors were selected, and students within each school were placed in one of the following five randomization blocks: 1) students who were assigned to the intervention group in cohort 1 (those selected from this group would receive the intervention for 4 years); 2) students who were assigned to the control group in cohort 1; 3) students who were in grades 1 and 2 in the first year of cohort 2 (since no students from cohort 1 were in these grades at start of cohort 2); 4) students who were new to the school since the randomization for cohort 1; and 5) students who were nominated for the study by their principals. Students in the control group were 1) those from a school randomly selected to be a control group, in a grade randomly selected to be a control grade within an intervention group; or 2) randomly selected to be a control within an intervention grade in an intervention school.
In cohort 1, 933 students were assigned to the intervention group, and 2,958 were assigned to the control group. At baseline, the mean ages of cohort 1 students in the intervention and control groups were 9.0 and 8.7 years, respectively. Fifty-three percent of students in both the intervention and control groups were male. Additionally, 59 percent of students in the intervention group and 57 percent of students in the control group were black, followed by Hispanic (38 percent and 40 percent, respectively). During the year prior to randomization, students in the intervention and control groups attended 151.0 and 150.3 days out of a possible 170 days of school; this difference was statistically significant. At baseline, cohort 1 students in the intervention and control groups did not differ significantly on a number of demographic and outcome measures, including age, gender, race/ethnicity, learning disability, course failures, GPA, or days absent. In cohort 2, 1,038 students were assigned to the intervention group, and 1,111 were assigned to the control group. At baseline, the mean ages of students in the intervention and control groups were 8.4 and 8.5 years, respectively., In terms of race/ethnicity, 47 percent of intervention group and 46 percent of the control group were black, followed by Hispanic (47 percent and 49 percent, respectively). During the year prior to randomization, students in the intervention and control groups attended 159.9 and 159.2 days out of a possible 170 days of school. Cohort 2 students in the intervention and control groups did not differ significantly on any of the baseline demographic and outcome measures assessed. Fifty-two percent of students assigned to the intervention group in cohort 1 and 33 percent of students assigned to the intervention group in cohort 2 participated in the program.
Student demographic, attendance, and achievement data was collected from CPS administrative records. Demographic data included birth date, race/ethnicity, eligibility for free and reduced-price lunch, and presence of an Individualized Education Plan (as an indicator for having a learning disability). Measures of attendance and absences included data on days absent (the percentage of enrolled days a student was absent over the school year) and days present (the percentage of enrolled days a student was present over the school year). Measures of achievement were annual GPA and math and reading test scores. Since CPS administered multiple tests over the study period, the study used scores from the Illinois Standards Achievement Test (ISAT), which was administered among students in the third through eighth grades annually, and the Measure of Academic Progress (MAP), which was administered to students in third through eighth grades and to students in second grade in the last 2 study years.
Regression analyses were used to evaluate the effects of the intervention within an intent-to-treat framework, adjusting for baseline demographic and outcome measures, including gender, race/ethnicity, age, presence of a learning disability, days present, days absent, GPA, and course failures. Randomization block fixed effects analysis was used for cohort 2 data to adjust for the unequal probability of selection into the intervention group across the five blocks of students. Estimated effects of the program on outcomes were pooled across both program years and the two cohorts of students.
Heppen and colleagues (2017) used a 3-year randomized controlled study to evaluate the Check & Connect program in a sample of grade 9 students at risk of not graduating on time. The study was conducted in 10 comprehensive high schools within a large urban district. Students in the study schools were 48 percent Hispanic, 21 percent white, 15 percent Asian, and 14 percent African American. Of the total sample, 21 percent were English language learners, and 67 percent were eligible for free or reduced-price lunches. Potential participants were identified based on their probability of on-time graduation as determined by the following risk factors: 1) absence for 10 percent or more of enrolled days in grade 9; 2) failing grade in at least one course in grade 9; 3) failing grade in Algebra I in grades 8 or 9; and 4) at least one “Needs Improvement” or “Unsatisfactory” citizenship grade (indicating general behavior and participation in class) in grade 9 courses. One hundred to 150 of the students who entered grade 9 in 2010–2011 and had the lowest predicted probabilities of on-time graduation were identified in each of the participating schools for participation in the study.
Five-hundred and fifty-three eligible students agreed to participate in the study and were randomly assigned to the intervention (n=276) and control (n=277) groups. More than 30 percent of students in the sample were current English language learners, about 34 percent were previous English language learners (met state criteria to be reclassified as fluent English proficient prior to grade 9), and 72 percent were Hispanic. About 23 percent of students were absent 10 percent or more of enrolled days in grade 9, about 98 percent had a failing grade in at least one course in grade 9, about 80 percent had a failing grade in Algebra I, and about 90 percent had at least one “Needs Improvement” or “Unsatisfactory” citizenship grade in grade 9. The average predicted probability of on-time graduation was 0.55. There were no statistically significant differences between students in the intervention and control groups on any of these risk factors.
Outcome measures were sourced from district administrative data and student surveys in the spring of 2012, 2013, and 2014. District administrative data provided information on student demographic characteristics, academic backgrounds (e.g., prior attendance, behavior and course performance grades, achievement test scores), enrollment in each academic year, attendance, course-level transcript data, exit exam status (i.e., pass/fail), graduation date, and diploma type. Dropout, on-time graduation, and 5-year graduation information was also collected from the state data system to capture students who had left the district (but remained in a public high school within the state) prior to the end of the study. Student surveys measured student engagement in social and academic activities. The Student Engagement Instrument (SEI), a 33-item scale, was used to measure affective engagement (teacher–student relationships, peer support for learning, and family support for learning) and cognitive engagement (control and relevance of school work and future aspirations and goals). The 12-item Student Engagement Questionnaire (SEQ-C) was also used to measure student engagement in the school, reflecting perceived relevance of schoolwork, teacher–student relationships, teacher expectations, and satisfaction with school. In addition, students were asked to report on their involvement in targeted academic supports, including tutoring, online credit recovery programs, and college preparatory programs, as a measure of the extent to which students in the control group accessed interventions similar to those provided to students in the intervention group.
Effects of the program on outcomes were evaluated using regression analyses with an intent-to-treat (ITT) framework. Analyses controlled for baseline student demographic and academic background characteristics. Analyses also controlled for attrition and nonresponse by applying inverse probability weights, which are based on the probabilities of having missing data.
Cost information is available on the program Web site: http://checkandconnect.umn.edu/.
Information on how to implement the Check & Connect (C&C) program is available in a manual that can be purchased at the program website (http://checkandconnect.umn.edu/
). Contents of the manual include how to determine indicators of student disengagement; identify students at risk of disengagement or dropout; select or hire mentors; organize existing resources for intervention; get to know students, teachers, and parents; use “check” procedures and the monitoring form; implement “connect” interventions; strengthen the family-school relationship; monitor the person-environment fit; provide mentor support and supervision; and evaluate program implementation.
Mentors, once selected or hired, receive a program manual and attend training sessions that cover a variety of programmatic areas, including the context of the dropout problem, the role of the mentor, the criteria mentors should use to classify students as needing basic or intensive intervention based on check data, and types of interventions that mentors can use.
In the study by Gurvan and colleagues (2017), students in the intervention were assigned a mentor. Full-time mentors (n
=15) were hired by the implementing agency (SGA Youth and Family Services) and were supervised by a full-time project manager. In addition to overseeing the work of the mentors, the project manager organized and led weekly mentor meetings and provided guidance and feedback to mentors about how to work most effectively with the students. A project manager within CPS oversaw the implementation of the C&C program, oversaw the SGA project manager, and helped to collect data on participation and implementation. Mentors received training and guidance on how to implement the intervention with fidelity by members of the research team (at the University of Minnesota) once or twice a year. Mentors met with students, one-on-one or in small groups, an average of five times a month and connected with parents or guardians twice a month. Mentors completed biweekly reporting forms documenting the number of interactions they had with students and their families, indicating whether these interactions were in person or over the phone. The program manager maintained records on the students who were approached to participate and those who did participate in the program.
In the study by Heppen and colleagues (2017), five mentors were hired, and each was assigned to a caseload of 50 or 60 students. Mentors received the program manual and attended a 2-day training session provided by program developers. Ongoing professional development was provided each year of the study. Mentors also had weekly in-person meetings with the program coordinator, who had biweekly phone meetings with program developers. Mentors were expected to check student progress weekly using monitoring forms and to meet with each student on their caseload at least twice per month. The intervention began during the 2011–2012 school year, corresponding to students’ second year of high school, and ended in the spring of 2013–2014, corresponding to students’ fourth year of high school.
Guryan and colleagues (2017) also assessed the effects of the intervention on days present (the percentage of enrolled days a student was present over the school year) and membership days (the total number of days the student was officially enrolled in a CPS school). They found that students in the intervention group had more days present, compared with those in the control group at the end of the 2-year study period. However, there was no statistically significant difference on membership days between the groups. Guryan and colleagues (2017) also assessed the effects of the program on all outcomes for intervention group students who participated in the intervention using a treatment-on-the-treated (TOT) framework. For these analyses, students who were assigned to the intervention group, approached to participate, and chose to participate were considered as having received the intervention. Results were similar to those for ITT analyses for all outcomes assessed.
Heppen and colleagues (2017) also found a statistically significant difference between intervention and control groups on successful completion of at least one summer course, but not on participation in at least one extracurricular activity, peer support for learning, family support for learning, ever passing a high school exit exam, and 5-year graduation. Additionally, Heppen and colleagues (2017) assessed the effects of the program on all outcomes using a TOT framework. For these analyses, students considered as having received the intervention were those who met with their mentor at least 20 times in a given year and for whom check data was recorded on all monitoring forms. Results indicated a statistically significant difference between intervention and control groups on successful completion of at least one summer course, but not on any of the other outcomes assessed.
Evidence-Base (Studies Reviewed)
These sources were used in the development of the program profile:Study 1
Guryan, Jonathan, Sandy Christenson, Amy Claessen, Mimi Engel, Ijun Lai, Jens Ludwig, Ashley C. Turner, and Mary C. Turner. 2017. The Effect of Mentoring on School Attendance and Academic Outcomes: A Randomized Evaluation of the Check & Connect Program
. Institute for Policy Research, Working Paper Series. Evanston, Ill.: Northwestern University.Study 2
Heppen, Jessica B., Kristina Zeiser, Deborah J. Holtzman, Mindee O'Cummings, Sandra Christenson, and Angie Pohl. 2017. “Efficacy of the Check & Connect Mentoring Program for At-Risk General Education High School Students.” Journal of Research on Educational Effectiveness
. Advanced online publication.https://dx.doi.org/10.1080/19345747.2017.1318990
These sources were used in the development of the program profile:
Abrams, Ryan D. 2015. Using the Check and Connect Program to Decrease the Absent Rate of High School Students
. Dissertation. Chicago, IL: Concordia University.
Anderson, Amy R., Sandra L. Christenson, Mary F. Sinclair, and Camilla A. Lehr. 2004. “Check & Connect: The Importance of Relationships for Promoting Engagement with School.” Journal of School Psychology
Bronfenbrenner, Urie. 1979. The Ecology of Human Development
. Cambridge, Mass.: Harvard University Press.
Fredricks, Jennifer. A., Phyllis C. Blumenfeld, and Alison H. Paris. 2004. “School Engagement: Potential of The Concept, State of The Evidence.” Review of Educational Research
Heaney, Catherine A., and Barbara A. Israel. 2008. “Social Networks and Social Support.” In K. Glanz, B. K. Rimer, and K. Viswanath (eds.). Health Behavior and Health Education: Theory, Research, and Practice, Fourth Edition. San Francisco, Calif.: Jossey-Bass, p. 189–210.
Lehr, Camilla A., Mary F. Sinclair, and Sandra L. Christensen. 2004. “Addressing Student Engagement and Truancy Prevention During the Elementary School Years: A Replication Study of the Check & Connect Model.” Journal of Education for Students Placed at Risk 9(3):279-301.
Maynard, Brandy R., Elizabeth K. Kjellstrand, and Aaron M. Thompson. 2014. “Effects of Check and Connect on Attendance, Behavior, and Academics: A Randomized Effectiveness Trial.” Research on Social Work Practice 24(3):296-309.
Rhodes, Jean E., Renée Spencer, Thomas E. Keller, Belle Liang, and Gil Noam. 2006. “A Model for the Influence of Mentoring Relationships on Youth Development.” Journal of Community Psychology 34(6): 691–707.
Sinclair, Mary F., Sandra L. Christenson, David L. Evelo, and Christine M. Hurley. 1998. “Dropout Prevention for Youth with Disabilities: Efficacy of a Sustained School Engagement Procedure.” Exceptional Children 65(1):7-21.
Sinclair, Mary F., Sandra L. Christenson, and Martha L. Thurlow. 2005. “Promoting School Completion of Urban Secondary Youth with Emotional or Behavioral Disabilities.” Exceptional Children 71(4):465-482.
Stokols, Daniel. 1996. “Translating Social Ecological Theory into Guidelines for Community Health Promotion.” American Journal of Health Promotion 10(4):282-98.
Strand, Paul S., and Nicholas P. Lovrich. 2014. “Graduation Outcomes for Truant Students: An Evaluation of a School-Based, Court-Engaged Community Truancy Board with Case Management.” Children & Youth Services Review 43:138-144.
Zimmerman, Marc A. 2000. “Empowerment Theory: Psychological, Organizational and Community Levels of Analysis.” In J. Rappaport and E. Seidman (eds.). Handbook of Community Psychology. New York, NY: Kluwer Academic/Plenum Publishers, p. 43–63.