









































Emerging Perspectives 
ep.journalhosting.ucalgary.ca 
 

Corresponding author: lizmcmanus@telus.net 

 
Reducing School Dropout Rates Through Early Identification of Students at Risk 

 
Elizabeth McPherson McManus 

 
University of Calgary 

 
Students who drop out of school may face a host of poor outcomes in life. Literature 
shows that dropping out of school is evident in the middle and elementary school years 
through many indicators. Some of these warning signs including poor attendance, 
engagement in learning, and behavior have been widely studied. Statistical data-
driven systems used in some school jurisdictions show only the presence of indicators; 
they do not reveal the impact on learning, nor do they explore the reasons for the 
presence of the indicator(s). This paper is a call to action to implement identification 
of students in middle school at risk of dropping out. The use of individualized holistic 
identification approaches may more accurately identify students at risk as well as 
inform interventions.  

 
Keywords: School dropout, early identification, at risk, middle school 
 

McManus, E.M. (2020). Reducing school dropout rates through early identification of students at 
risk. Emerging Perspectives, 4(2), 67-78. 

 
 
Society views high school completion as an essential achievement in order to succeed in 

work and life in North America. Research shows that individuals who drop out of school are at 
higher risk of poor outcomes including unemployment (Bowlby, 2008), developing mental health 
disorders (Breslau, Lane, Sampson, & Kessler, 2008; Freeman & Simonsen, 2015), incarceration 
(Dynarski et al., 2008), early pregnancy, reduced marital stability (Hankivsky, 2008), reduced 
life expectancy (Rumberger, 2011) and significantly lower earning potential (Dynarski et al., 
2008; Uppal, 2017) compared to those who complete school with qualifications. Staggeringly, 
families headed by a high school dropout accumulate only one tenth of the wealth of families 
headed by a high school graduate (Lemon & Watson, 2011). The lifelong negative impact on the 
individual and their families as well as the implicit cost to society constitutes a public health 
problem (Freeman & Simonsen, 2015; Lansford, Dodge, Pettit, & Bates, 2016).  

Dropping out, defined in many Canadian studies and published statistics as failing to gain 
the high school diploma within five years of commencing high school, is a recognized problem 
in Canada. One study published a decade ago found the tangible costs of school dropout to be 
high, and estimated that the cost savings in 2008, if graduation rates were to improve by just 1%, 
to be $7.7 billion (Hankivsky, 2008). The problem is particularly concerning in Alberta, with 
69% of students reported to have graduated in 2011, compared to 85% nationally (Statistics 
Canada, 2015). The Alberta government acknowledges the significance of the problem. In 2006 
the Government of Alberta launched the Your Future Starts Here initiative which included a 
symposium to hear the voices of students. The report from this symposium demonstrated that the 
process of dropping out begins long before high school but it did not explore the possibility of 



McManus - Emerging Perspectives (2020)                68 

early identification of students at risk (Alberta Education, 2006). In 2008 a report of the Alberta 
Commission on Learning stated that improving graduation rates should be a priority and set a 
goal to achieve 90% graduation within four years of commencing high school (Gunn, Chourney 
& Poulson, 2009). Schools in Alberta had already been working on this and were using funding 
through the Alberta Initiative for School Improvement (AISI) to support projects aimed at 
improving graduation rates. Gunn, Chourney, and Poulson (2009) analyzed 18 such action 
research projects in Alberta schools identifying five common themes in promising interventions, 
however the work being done at that time was limited to high schools. The authors pointed out 
the need for this work to be extended to elementary and middle schools, however there is no 
evidence in the literature that this has been addressed. The current government initiative, the 
Alberta Education High School Completion Framework, has identified what it referred to as five 
‘STARting points’ to improve graduation rates (Alberta Education, 2016) but an emphasis on 
early identification of students at risk is missing. 

Research has demonstrated that dropping out is not sudden, but rather years of 
accumulative factors might impact a student’s decision to drop out (Statistics Canada, 2008). 
This establishes the importance of learning how to identify younger students at risk. Although 
there is empirical evidence that indicators of risk are present in younger students, there is a lack 
of research showing effective application of this knowledge in the middle school years. It is 
possible that identifying at-risk middle school students will drive the development and 
implementation of interventions for this age group aiming at reducing school dropout rates. The 
purpose of this article is to conduct a comprehensive literature review to identify indicators for 
high school dropouts among elementary and middle school aged students. Furthermore, the 
author intends to use these empirical findings to put forward recommendations for an 
individualized holistic approach for schools to employ monitoring and intervening, thus possibly 
reducing the rate of school dropout.  

Analytical Approach 

This literature review is an independent qualitative inquiry involving the interpretation of 
findings in relation to knowledge about the developmental stage of adolescents, and a synthesis 
of ideas to form recommendations. The search was carried out within the ERIC, PsychINFO and 
Educational Research Complete online databases using combinations of the following search 
terms in the title and then in the text: dropout, identification, early warning signs, intervention, 
and middle school. The initial search was limited to peer reviewed publications in English, 
published within the last 15 years. Older publications were considered in an effort to include 
Canadian data. I searched the 12 issues of the International Journal on School Disaffection.  I 
read 27 abstracts, from which 20 studies and reports were selected for a full read. Some studies 
that fit the search criteria were excluded.  

As the purpose of this article was to review literature about general school dropout rates, 
it was not applicable or feasible to examine indicators associated with specific child and youth 
populations whose unique experiences may have unique influences on their decisions to drop 
out. Therefore, I excluded articles examining only specific visible minority or marginal groups, 
or otherwise deemed too narrow to be relevant to the general population of middle school 
students in Alberta. Research that fell within my date criteria but was deemed to be outdated, for 
example on the topic of digital literacy, was excluded due to the rapidly changing nature of the 
field. I evaluated the validity of non-peer reviewed publications from the scholarly careers of 
authors, absence of conflict of interest, and the research processes described within the 



McManus - Emerging Perspectives (2020)                69 

documents themselves. The articles selected for a full read were critically analyzed and evaluated 
for relevance, credibility, and similarity according to guidelines in Hendricks (2017). To arrive at 
my findings, I evaluated the data through the lenses of applicability to the developmental stage of 
students age 10-14, and relevance in the context of urban and suburban Alberta. I analyzed the 
literature by generating a table of common themes. Through the methods summarized here, I 
synthesized new meaning that is relevant to the age group and population of interest, resulting in 
pragmatic recommendations for improvement.  

Middle School Indicators for School Drop Out 

Dropping out of school is described as a process wherein social, behavioral, demographic 
and family factors combine to negatively impact academic engagement resulting in declining 
achievement, which can ultimately lead to dropping out (Abraham, 2015). Pathways to dropping 
out are diverse and causes may be multidimensional (Orpinas, Raczynski, Wetherington Peters, 
Colman, & Bandalos, 2015) however, it is well established that early warning signs, such as 
disengagement, can indeed be evident before high school (Abraham, 2015; Janosz, Archambault, 
Morizot, & Pagani, 2008; Lee & Breen, 2007; Orpinas et al., 2015; Schoenberger, 2012; Wood et 
al., 2017). Multiple interrelated reasons for disengagement such as bullying, anxiety, and poor 
attendance create a snowball effect in the process of disengagement which can start early in the 
school career (Abraham, 2015; Schoenberger, 2012). Research, including a large scale 
longitudinal quantitative study, a qualitative study hearing the voices of older students and an 
analysis of Grade 3 engagement data, provided evidence that the identification of middle school 
age students who are at risk of dropping out is not only feasible, but recommended. Baltimore 
Education Research Consortium (2011) carried out a logistic regression analysis of longitudinal 
records of more than 7000 students in the 2000-2001 Grade 6 cohort in Baltimore providing 
strong evidence that early warning indicators in Grade 6 predict dropout in high school: 70% of 
students with an indicator did not graduate within one year of expected graduation. While 30% 
of dropouts displayed no indicator in Grade 6, the majority of these did by Grade 9.  

In another study, Abraham (2015) interviewed 20 participants from ages 18-21, attending 
a dropout recovery program in Illinois, most of whom identified the onset of disengagement to 
be in middle school. Some of these individuals wished they had received interventions early on. 
In other research, Barry and Reschley (2012) analyzed data covering a range of facets of school 
engagement in Grade 3 students, finding models indicating the risk of dropping out. While the 
psychometric measurement instruments they used are not suitable for use as teacher screening 
tools, this study nevertheless points to the presence of early warning indicators even in primary 
school.  These studies have provided strong evidence of the presence of early warning signs in 
students at risk of dropping out. 
  Early warning indicators of risk of dropping out have been widely studied and many 
authors have agreed that the key indicators are attendance, behavior, and course grades (Balfanz, 
2011; Baltimore Education Research Consortium, 2011; Barry & Reschly, 2012; Kennelly & 
Monrad, 2007; Rumberger et al., 2017; Ryan, 2011; West, 2013). In addition, some studies have 
highlighted being over-age for the grade (Baltimore Education Research Consortium, 2011; 
Kennelly & Monrad, 2007; Ryan, 2011; Wood et al., 2015) or being retained in a grade 
(Kennelly & Monrad, 2007) as critical risk factors. While low or failing grades in all core 
subjects have been found to be a significant warning sign in a number of studies, McKee and 
Calderella (2016) specifically identified scores in math to be a relevant indicator of risk. This 
finding is based on standardized testing, and, as McKee and Calderella pointed out, other studies 
have shown standardized test scores to be a weaker indication of risk of dropping out than 



McManus - Emerging Perspectives (2020)                70 

teacher-assigned grades. Furthermore, the presence of multiple indicators greatly reduces the 
chance of graduating, as shown in the study by Baltimore Education Research Consortium, 
(2011), where only 26% of students with two indicators graduated, and for students with four 
indicators this figure was a mere 8%. Social and academic risk factors are interrelated and 
accumulate to reach a critical mass at which the individual can no longer cope with being in 
school (McKee & Calderella, 2016). Several studies have focused on specific early warning 
indicators. The impact of attendance, engagement in learning, and externalizing and internalizing 
challenges are examined in the next part of this literature review.  

Attendance 

Chronic absence is considered to be one of the strongest single indicators of risk of 
dropping out (Baltimore Education Research Consortium, 2011). Although somewhat dated, a 
Canadian study in Toronto corroborated the connection between poor attendance in Grade 9 and 
increased risk of dropping out, demonstrating the relevance of this issue in Canada (Brown, 
1999). In a study in Philadelphia 78% of students who attended less than 80% in eighth grade 
later dropped out of school (Kennelly & Monrad, 2007). In another study, Wood et al. (2017) 
suggested that low academic achievement is the primary individual factor in risk of dropping out. 
Arguably, poor attendance results in missed opportunities for learning, which could then lead to 
poor academic achievement among students. Poor attendance may indicate disinterest, other 
desires or demands outside of school, lack of family support needed to attend school, decreased 
confidence, increased depression or aggression leading to avoidance (Schoenberger, 2012) or 
poor physical health, and a lack of resources or transportation to attend school. Attendance 
patterns can predict risk of dropping out (Schoenberger, 2012) thus reasons for absence should 
be thoroughly explored. 

Attendance data is appealing to use in an early warning system as it provides an objective 
measure which is collected in a consistent way over time and across schools and is easily 
accessed through online data systems (Schoenberger, 2012). Attendance data alone is of dubious 
use in identifying students at risk of dropping out, however, well-adapted students could be 
absent from school yet still be able to maintain acceptable academic achievement, and 
conversely, disengaged students with low or falling achievement may or may not have poor 
attendance. Rumberger’s (2011) description of students who are engaged in the social system of 
school but not the academic system is corroborated by a study of over 11000 students in Grades 
1-9 in Montgomery County Public Schools, which found that students who were at risk of 
dropping out did not tend to be absent or truant, rather they were present, but failing 
academically (West, 2013). Further complicating this issue, researchers do not agree on a rate of 
absenteeism that is a definitive indicator of risk of dropping out. In fact, in the Schoenberger 
(2012) study the statistical attendance threshold had to be changed from 80% to 90% to obtain 
meaningful results, illustrating this point nicely. Poor attendance is recognized as a problem in 
schools in Alberta with 2000 students per year in Alberta referred to the attendance board to 
address issues related to truancy (Alberta Education, 2014).  

In 2011-2013 the government of Alberta carried out a study into improving school 
attendance, including surveys and focus groups across 16 school authorities involving 2700 
educators, superintendents, attendance board members, 150 students with poor attendance, and 
100 parents and guardians. The result of the study was a comprehensive list of recommendations 
including proactively improving attendance through protocols to monitor attendance and act on 
poor attendance as soon as it becomes evident, creating individual support plans including 
appropriate learning pathways, and providing professional development on strategies to improve 



McManus - Emerging Perspectives (2020)                71 

attendance and engagement such as building a positive learning environment and safe and caring 
school initiatives (Alberta Education, 2014). The specific recommended supports included silent 
mentoring (a subtle approach to relationship-building and improving school engagement with an 
individual at risk), assigning specialized personnel to support indigenous families, solving 
transportation issues, and fostering goal setting, career awareness, and support for transitions 
(Alberta Education, 2014). Further recommendations included a focus on wellness by including 
social-emotional learning across all grades and by providing specific health and wellness 
information and supports to students and families (Alberta Education, 2014).  

Taken together, the review of the literature suggests that while poor attendance might be 
an important risk factor, student absence is indeed a complex factor. Specifically, studying 
individual rates of student absence alone may not provide guidance as to what interventions may 
be needed to support the student. It is important to explore the reasons for absence as well as the 
impact the absence is having on the academic progress of the individual, which points us towards 
an individualized identification system. 

Engagement in Learning 

Engagement in learning directly impacts progress in school, and poor engagement is an 
indicator of the risk of dropping out. Many theories explaining school dropout are based on the 
notion of school engagement, encompassing the idea of individual compatibility with the school 
environment (Janosz et al., 2008, Rumberger, 2011).  Janosz et al. (2008) carried out an 
accelerated longitudinal study, using multiple single cohorts starting at different ages, with 13300 
students, age 12-16 to analyze changes in engagement over several years. The authors measured 
students’ school attendance, behavioral and learning challenges, enjoyment and interest in 
schools using self-reports to track engagement. In cases where engagement in learning was either 
consistently low from the beginning of adolescence, or declined rapidly, the risk of later 
dropping out was higher. Students showing a rapid decrease in engagement and those who began 
adolescence with low engagement were at greatest risk of dropping out (Janosz et al., 2008). 
Additionally, with respect to school engagement and early drop out, Schoenberger (2012) 
predicted that children who do not feel accommodated in the environment of school due to their 
behavioral, cognitive, or emotional challenges may feel frustration or incompetence resulting in 
disengagement (Schoenberger, 2012). The complex interplay of disengagement with other 
characteristics or circumstances creates the snowball effect and thus disengagement is a 
significant risk factor. 

Overt academic or social engagement is relatively easy to observe and measure (Barry & 
Reschley, 2012; Janosz et al., 2008; Kennelly & Monrad, 2007). For example, academically 
engaged students tend to have the materials needed for class, contribute to discussions, complete 
and submit work, and help one another out. Socially engaged students are connected to others, 
contribute to the school community, and participate in formal or informal extracurricular 
activities. Cognitive engagement, such as motivation and self-determination, or affective 
engagement such as peer and teacher relationships, however, cannot be observed so readily 
(Barry & Reschley, 2012). This indicates the need for diligence in teachers to identify students 
who might be struggling to concentrate or are unhappy at school, prompting support and 
interventions, be it from the teacher, district specialists, or the school psychologist. Student 
engagement is an alterable state, however (Barry & Reschley, 2012), and interventions can be 
effective. Low or unstable engagement in school is a significant risk factor for dropping out. 



McManus - Emerging Perspectives (2020)                72 

Externalizing and Internalizing Challenges 

Externalizing and internalizing challenges contribute to school functioning of students, 
including disengagement, and thus are also risk factors for school dropout (Balfanz, 2011; 
Baltimore Education Research Consortium, 2011; Barry & Reschly, 2012; Kennelly & Monrad, 
2007; Orpinas et al., 2015; Rumberger et al., 2017; Ryan, 2011; West, 2013). Externalizing 
behavior problems include physical and emotional aggression, alcohol or drug use, participation 
in a peer subculture of excessive partying, truancy, and engagement in at-risk and impulsive 
behaviors such as having precocious sexual intercourse (Orpinas et al., 2015). Internalizing 
concerns include anxiety, depression, neuroticism, and somatization, all of which could elicit 
maladaptive behaviors in students such as social withdrawal and school avoidance (Orpinas et 
al., 2015). To enhance school’s effectiveness in identifying, monitoring, and supporting students 
with externalizing and internalizing concerns I recommend teacher training, consultation with 
psychology and mental health specialists in schools, as well as incorporation of school mental 
health strategies in classrooms. Suspension data is often used as a measure of compliance with 
school behavior expectations, however, behavior ratings by teachers were found to be more 
effective indicators of risk of dropping out than suspension data for sixth graders (Kennelly & 
Monrad, 2007) and may provide more consistent data as they nullify inconsistencies in school 
suspension policies. Students with formerly identified internalizing or externalizing challenges, 
such as anxiety disorders, should receive clinically proven specific accommodations and 
supports through an individual plan, and could show improvements in engagement in learning, 
emphasizing a need for the involvement of school based mental health staff in planning those 
supports (Green et al., 2017). Problem behavior is often symptomatic of other wellness issues 
that need to be identified and supported (Auger, 2014), and is a notable indicator of the risk of 
dropping out. If students with internalizing or externalizing behavior problems are supported 
with appropriate classroom accommodations and mental health interventions, rather than 
focusing on punitive consequences such as suspensions, then perhaps the risk of later dropping 
out would be reduced. 

Identification Systems: Data Analysis and Holistic Approaches 

Digital data analysis and individualized holistic ecological methods represent contrasting 
approaches to identification of students at risk. Data analysis systems are in place in several 
school jurisdictions in the USA to monitor and identify students at risk of dropping out (Ryan, 
2011; West, 2013). Of the five systems described by Ryan (2011) all included threshold 
measurements for attendance and achievement, three included discipline data such as 
suspensions, two included being retained in a grade or over-age for the grade, and one included 
other detail such as school mobility (moving schools in addition to the expected transitions) and 
homelessness. These jurisdictions used digital data systems to generate scores for individual 
students, thus identifying those who meet the criteria for intervention. Threshold benchmarks 
indicative of risk vary between studies (Kennelly & Monrad, 2007; Ryan, 2011) and some 
authors encourage schools to review local historical data to establish thresholds relevant to their 
community (Rumberger et al., 2017; Wood et al., 2017); a time-consuming task of dubious 
effectiveness. Factors such as attendance, behavior and course grades are interdependent in the 
way they influence engagement in learning and Janosz et al., (2008) suggested that we avoid 
limiting identification strategies to readily measurable data such as attendance. No evidence was 
found of use of such statistical data systems to identify students at risk in Canada (Alberta 
Education, 2001), which may reflect a long-held view, supported by research from the 1990s, 



McManus - Emerging Perspectives (2020)                73 

that dropping out is a complex process involving many interrelated factors. While data systems 
may be useful to identify institutional trends and highlight clusters of students at risk, these 
values do not consider ecological factors involved in the student’s life, and they are crucial for 
schools to consider planning interventions for school dropouts.  

Ecological Theory 

Proposed in the late 1970s, Bronfenbrenner’s ecological theory explains how 
environmental factors influence development, identity and life choices at the micro-, meso-, exo- 
and macro-system levels (Bronfenbrenner, 1977), which supports a holistic ecological approach 
to identification (Wood et al., 2017). More than 40 factors have been linked to risk of dropping 
out encompassing attributes of community, family, school and the individual (Dockery, 2012). 
Physical and emotional changes of adolescence coupled with new school and life demands, cause 
difficulties for some but not others, supporting a holistic wellness approach to the identification 
of students at risk of dropping out (Lemon & Watson, 2011). A few researchers have addressed 
aspects of the wellness of individuals, including physical and mental health, ethnic identity, 
acculturation and sense of belonging in relation to risk of dropping out (Lemon & Watson, 2011; 
Orpinas et al., 2015; Wood et al., 2015). Family factors such as low socio-economic status 
(McKee & Calderella, 2016; Wood et al., 2017), family turmoil, authoritarian or neglectful 
parenting (Orpinas et al., 2015), or homelessness (Ryan, 2011) contribute to the risk of dropping 
out and an individualized holistic approach seeks to identify such diverse factors. 

Classification of Groups of Students Who Drop Out 

Attempts have been made to classify students at risk of dropping out into groups to aid 
identification and streamline interventions, however two such studies discussed here also 
demonstrate the diversity of routes to dropping out, supporting an individualized approach to 
identification. Orpinas et al. (2015) carried out a study of 657 students in Grade 6 in Georgia and 
developed a classification system of social adaptive skills through a combination of teacher 
ratings and self-reporting, and the longitudinal correlation to dropping out. As anticipated the 
groups with externalizing and internalizing problems were found to have a higher risk of 
dropping out. Less expectedly, the group of students found to be average in all scales except 
social skills also had a higher dropout rate and, because of a lack of significant externalizing or 
internalizing problems, these students are more difficult to identify. They tend to have a lower 
sense of mattering (meaning a feeling of belonging and being appreciated) fewer positive peer 
connections, and are more vulnerable to bullying or exclusion (Orpinas et al., 2015). In another 
study of 1582 students in Montreal, 507 had not graduated by age 22, and were classified into 
four groups (Janosz et al., 2008). The groups at greatest risk of dropping out were the quiet 
dropouts (those who report being previously highly engaged in school) and the maladjusted 
dropouts (those with severe difficulties). Two other groups were the disengaged dropouts 
(severely unmotivated but without other significant difficulties) and lower achievers (school 
failure but without any externalizing behavior) (Janosz et al., 2008). This demonstrates that 
school dropouts follow a variety of trajectories, of differing severity, some of which may not be 
immediately obvious to school staff, indicating the need for an individualized approach to 
identification. 

Unexpected Outcomes 

Some students displaying no clear early warning signs will still drop out of school. A 
data-driven early warning system would miss these students at risk. Janosz et al., (2008) found 



McManus - Emerging Perspectives (2020)                74 

that in the normative group with stable trajectories of engagement, which had the lowest number 
of eventual dropouts, some students still dropped out, the majority of whom were girls. Factors 
contributing to unexpected dropping out can be situational factors such as pregnancy, marriage, 
entry into the workforce, or personal factors such as the onset of depression or anxiety (Jansoz et 
al., 2008) or a traumatic life event. Unexpected events, such as receiving an attractive job offer, 
could cause a student to suddenly adjust the value they place on education, influencing the 
decision to drop out (Rumberger, 2011). Pregnancy has been shown in one study to be the 
primary reason for unexpected dropout amongst girls, (Meeker, Edmonson & Fisher, 2008) 
however Rumberger, (2011) points out that the pregnancy is often not the root cause of dropping 
out, but rather the underlying factors such as poverty and poor engagement in school which 
combine to result in the pregnancy. This author proposes that an individualized, holistic approach 
provides an opportunity to identify those students whose pathway towards dropping out is more 
subtle or sudden.  

In conclusion, we need to take early warning signs of dropping out in middle school 
seriously so that we can implement effective interventions (Orpinas et al., 2015). Identification 
through an individualized, holistic approach provides a more accurate means of identification 
than a data-driven system. Furthermore, by revealing the personal strengths and challenges of the 
individual, this approach informs appropriate interventions. 

Discussion 

Students who drop out of school can be identified and supported in middle school. Poor 
attendance in this age group is of particular concern because, as well as academic interruption, 
the social disconnectedness of school absence creates an additional challenge for adolescents and 
results in a snowball effect. Early intervention is critical in the light of this snowball effect.   

As expected, the issues surrounding disengagement and risk of dropping out are complex 
and a tiered approach, such as Response to Intervention (RTI), which includes both monitoring 
and interventions at the universal, group and individual level (Brown-Chidsey & Steege, 2010) 
could be effective. The three tiers begin at the classroom level, where teachers observe and 
assess student achievement and wellbeing, and implement universal interventions. For those 
students who do not respond, schools provide second-tier interventions, typically in small 
groups. Finally, students who need yet more support receive intensive individualized 
interventions. In this way, RTI may provide a structured process to improve engagement and 
reduce the risk of dropping out of school. The key indicators of risk found in the literature, which 
form a basis for triage within the RTI model and inform intervention strategies are poor 
attendance, internalizing or externalizing behavior concerns, academic difficulties, low or falling 
academic achievement, poor engagement in learning, emotional concerns, low sense of 
mattering, poor social integration, familial factors, being overage for the grade or retained in a 
grade. 

Although statistical data-driven analysis programs might serve well to identify local or 
systemic trends, on an individual level, schools must assess the degree of risk through a more 
thorough, holistic, ecological approach in which all risk factors are examined to evaluate the 
direction and degree of impact on the individual. Problematic thresholds, for example in 
attendance rate, vary between individuals depending on achievement motivation; a long-
established theory, the origins of which are credited to John W. Atkinson (Weiner, 2010), 
explaining the differences between the effort individuals will put in to achieve their goals. 
Family factors are complex, for example, school mobility forms a risk factor for some yet a 



McManus - Emerging Perspectives (2020)                75 

protective factor for others (Kennelly & Monrad, 2007). An individualized assessment of risk, 
involving analysis of all the factors listed in the paragraph above, is therefore essential.  

While the RTI model serves to support all students, it can also be used to provide 
feedback to administrators to identify and respond to localized challenges and trends, informing 
policy changes and funding strategies. One example of a specific problem in Alberta is the low 
graduation rate of Indigenous students. The response in one large urban school board was to 
place trained learning coaches in schools across all age groups where this need was recognized, 
to mentor and support Indigenous students towards success. A similar approach could be taken if 
other clusters of students showing signs of being at high risk of dropping out are found. 

Further Research 

Noticeable gaps in the literature on early warning signs include social media, birth order, 
unexpected success and the aforementioned school mobility. Social media opens up a new realm 
of wellness-related issues for adolescents today, and up to date research is needed in this area 
(Lemon & Watson, 2011). Increased usage of cellular and digital devices may elicit risks for 
excessive internet use and school avoidance, and students with mental health concerns are at 
greater risk of such issues. Therefore, further empirical examination of these issues and its 
relationship with school dropout is warranted. Birth order is an as-yet unexplored factor in 
dropout risk (Lemon & Watson, 2011). Birth order could influence the type of family 
responsibilities students have, such as caring for younger siblings or supporting the family 
financially, which could potentially be a barrier to daily participation and attendance in school. 
Furthermore, younger children who have observed an older sibling drop out may be influenced 
to follow suit. Cultural sensitivity towards family expectations comes into play here, and further 
research is needed in this area.  Some students with serious risk factors go on to successfully 
graduate (Orpinas et al., 2015) and more research is needed to discover what distinguishes this 
group who achieve unexpected success. While we know that school mobility impacts students 
differently (Kennelly & Monrad, 2007), greater understanding of the reasons for these 
differences is needed.  

Stigma associated with school dropout is an important topic and the influence of stigma 
on the decision to drop out, as well as the impact of stigma on the individual who has dropped 
out, is missing from current research. Lee and Breen (2007) claim to have provided a “normative 
narrative” on dropping out (p. 342) by presenting 10 out of 12 cases of students who had dropped 
out of school yet found satisfying full time work. While the selection of participants through the 
author’s own social network leaves open the possibility of bias in this study, it does illustrate the 
point that early school leaving is not always a personal disaster. A small number of students 
showing no early warning signs still drop out (Janosz et al., 2008) and presumably face the 
associated stigma, as well as pressure to return to school. Could a normative narrative around 
leaving school early be helpful or would complacency become instilled among potential dropouts 
and educators? Further research into the impact of stigma surrounding leaving school early 
would be illuminating. 

Conclusion 

“When students drop out of high school, it’s a failure of the education system, plain and 
simple” (Alberta Commission on Learning, 2003, p.61). Students at risk of dropping out can be 
identified early, and middle school staff should use the findings of this research, namely an 



McManus - Emerging Perspectives (2020)                76 

individualized holistic tiered approach, to illuminate a host of complex and interrelated early 
warning signs to identify students at risk. Interventions can be applied, reducing the dropout rate 
and improving outcomes for individuals and society. Identification of students at risk has been 
well researched, with notable exceptions where more work is needed: social media use, birth 
order, unexpected success, school mobility and stigma. Nonetheless, students at risk can be 
identified in the middle school years and by providing support to effectively re-engage youth we 
can reduce the risk of later dropping out. It is imperative that we act to apply pragmatic 
monitoring in middle schools.  

In the wider society, the lack of prospects and damaging stigma of leaving school early is 
a social construct which can be changed. For those who do not successfully fit the current school 
profile we need to provide valued choices that offer genuine opportunities for a happy, healthy 
and rewarding life. This change can start in schools with the identification of those at risk of 
dropping out and by offering educational pathways on which they can thrive. If successful 
alternative educational routes become commonplace, over time such routes will become accepted 
as a part of the mainstream educational profile and stigma will dissipate.  

References 

Abraham, P. P. (2015). The child left behind: A qualitative study examining student perceptions 
regarding the dropout trajectory and best practices in dropout recovery programs. 
(Doctoral dissertation). Retrieved from ProQuest LLC. (UMI No. 3681272) 

Alberta Commission on Learning. (2003). Every child learns, every child succeeds: Report and 
recommendations. Retrieved from https://open.alberta.ca/dataset/b0ad8515-edad-419a-
968d-a30ec9975901/resource/491dd557-1f9a-4184-a3b1-
c72e543c0168/download/commissionreport.pdf 

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	Analytical Approach
	Middle School Indicators for School Drop Out
	Attendance

	Engagement in Learning
	Externalizing and Internalizing Challenges
	Identification Systems: Data Analysis and Holistic Approaches
	Ecological Theory
	Classification of Groups of Students Who Drop Out
	Unexpected Outcomes

	Discussion
	Further Research
	Conclusion
	References

