1. Taxonomy of Research on At-Risk Students
John M. Charles
Abstract
Two of the major challenges in the United States higher educational foundations today are:
making the path to graduation easy to navigate and refining the quality schooling for at-risk
students. The “at-risk” status refers to higher education students who are in danger of attrition
due to academic or pedagogical as well as non-scholastic risk factors. In this paper, we take a
scrupulous look, from several angles, at what, in terms of research and findings, has already been
done on the subject of at-risk students by conducting an extensive review of the body of
literature on at-risk students in the higher education environment in the United States and around
the globe. The paper attempts to describe and explain the notion of at-risk student, as well as
examine the many risk factor variables associated with the “at-risk” status. At-risk student is an
important topic for HE institutions because dropouts can be very costly and detrimental to these
institutions. In order to improve student retention and graduation rates at American colleges and
universities, the most critical factors that put student at risk to drop-out must be identified and
scrutinized in the most meticulous fashion. We hope this paper will lead us toward a taxonomy
of at-risk student.
Introduction
While many of the world’s wealthiest countries such as Australia, Belgium, Canada, Denmark,
Japan, South Korea, Luxembourg, New Zealand, Norway, Sweden, etc. have managed to
impressively increase their university retention and graduation rates in the past few decades,
relatively high dropout rates in higher education (HE) continue to be one of the major and most
critical threats faced by colleges and universities across the United States of America; a country
which at one point was one of the world’s preeminent super powers when it comes to education
in general, but also when it comes to retaining and graduating students after four years of HE.
Dropout rates can be controlled and reduced if effective policies and programs are put in place to
identify, monitor and support at-risk students. The at-risk student status refers to a variety of
college and university students, especially those who have, officially or unofficially, reached a
specific threshold that put them, compared to other students, at a critically higher risk of attrition
due to academic or pedagogical (low grades, inferior secondary education, bad study habits, bad
test-taking strategies, lack of engagement, etc.) and/or non-academic (social, financial, familial,
emotional, and behavioral instabilities, language barriers, ect.) risk factors. Two of the continual
2. challenges in the United States educational foundations today are: making the path to graduation
easy to navigate and refining the quality schooling for at-risk students. At-risk student is an
important topic for HE institutions because dropouts can be very costly and detrimental to these
institutions, which may see their brand, prestige, revenues, funding and some of their programs
negatively impacted by high dropout rates.
American HE institutions have been under more and more scrutiny, in recent years, due
to their inefficiency when it comes to identify at-risk students, to accommodate them, and
provide sufficient and adequate support to those students so that they can achieve their socio-
intellectual goals and graduate within four years of HE. In the past few decades, however, higher
education institutions have used a wide variety of admission tools and techniques to reduce
dropouts and concomitantly improve retention and graduation rates. They have created and
implemented student success centers which mission is to provide students with the necessary
resources and advisements to stay the course during their higher education journey all the way
through graduation and beyond. Nonetheless, retention and graduation efforts have not yet been
proven to be significantly productive and consistent. According to the National Center for
Education Statistics (NCES), the percentage of full-time students at four-year institutions who
fail to complete their undergraduate degree within four years is well-above 60%, while the
completion rate after six years is only 59%. Dropping out of school has a serious socio-economic
impact on families, communities and the country as a whole. When myriads of HE students fail
academically and fail to graduate (which certainly implies that they hypothetically lack the skill
set required to perform necessary job functions, such as basic math, increased reading and
communication ability, technology skills, lifelong learning and computer abilities) this create
skills shortages and skills gap in the job market meaning that multitudes of jobs go unfilled,
which, in turn, can badly hurt the economy and compromise competiveness against other
preeminent industrialized nations such as Germany, China, Japan, etc.. Furthermore, students
who dropout from higher education constitute a major burden for their community and for
society as a whole given the fact that they earn, in general, far less income than students with
degrees, which put them in a position where they often have to rely on the taxpayers’ dollars,
entitlement programs, government welfare and other benefits to survive their daily living. The
number of tax dollars spent on welfare and other benefits and entitlement programs increases
each year. In 2014, it was estimated that about two-thirds of all federal spending went to these
types of programs and benefits. For all these reasons, it is critical for colleges and universities
and the responsible authorities to put in place the policies and mechanism needed to effectively
and efficiently improve the quality of schooling of at-risk students and make the path to
graduation easy to navigate for these students.
In this paper we take a meticulous look, from several angles, at what, in terms of research
and findings, has already been done on the subject of at-risk students by conducting an extensive
review of the body of literature on at-risk students in the higher education environment in the
3. United States and around the globe and on university dropout. The paper attempts to describe
and explain the notion of at-risk student, as well as examine the many risk factor variables
associated with the “at-risk” status. In other words, the paper is a theoretical synthesis of recent
research on the subject matter of at-risk student; our overall goal, here, is to pore over and
analyze the work that has already been done by scholars throughout the world on the notion of
at-risk students in HE as well as analyze the risk factors that are often associated with students
with the at-risk status. In order to help at-risk students reach their full potential and
concomitantly improve student retention and graduation rates at colleges and universities, the
most critical factors that put student at greater risk to drop-out must be identified and scrutinized
in the most careful fashion. We hope this paper will lead us toward a taxonomy of “at-risk”
higher education student.
IDENTIFYING AT-RISK STUDENTS: FACTORS THAT INFLUENCE STUDENT DROPOUT
The subject matter of this taxonomic research paper is “at-risk” student. Definitions of key
concepts such as “at-risk” are various and often problematic when it comes to attach such label
to specific individuals. The notion of “at-risk” started to take root in the American pedagogical
literature in April 1983 after it was used in a soi-disant report to the Nation and the Secretary of
Education titled "A Nation at Risk: The Imperative for Educational Reform" and published by
the National Commission on Excellence in Education. The National Commission on Excellence
in Education was created in 1981 in order to inspect the quality of education in the United States
and to make a report to the Nation and to Terrel Howard Bell, the Secretary of Education under
President Ronald Reagan. By and large, the primary purpose of that report was to identify and
describe the problems afflicting American education and to provide effective and efficient
remedies to these problems. The report highlighted that the level of mediocrity in the United
States educational system was flagrant and that American educational institutions appear to have
even forgotten the basic goals of schooling, and of the hard work and relentless determination
needed to reach those goals.
As we mentioned earlier, for the context of this paper, at-risk students are those students
who have been labeled as being in danger of attrition. In the United States, different scholars,
educationalists and different states define "at-risk" differently, so it is quite problematic to liken
state policies on the subject matter. Nonetheless, if one fact is certain, it remains that students
who are labeled as "at-risk" face a number of needs, circumstances and challenges that other
students do not. From intellectual deficiencies, to financial hardships, debilitating social
conditions and emotional instability, the challenges faced by at-risk students are wide-ranging
and multifaceted. To be able to provide any form of solutions that could more or less improve the
circumstances of those students, HE institutions must first be able to identify them before the
problem becomes far too complex to solve. We drew heavily on the literature to examine the
traditional risk factors that are often used by schools and educators to identify and classify
4. students as at-risk. Several risk factor variables are recognized by a wide range of scholars to be
worth taking a look at when attempting to identify higher education at-risk students. These
factors can be organized in three (3) major categories:
1. Academic Risk Factors
2. Socio-Economic Risk Factors
3. Emotional/Psychological Risk Factors
In the past decade, numerous studies have been conducted on university dropout with the
aim of identifying and assessing the different risk factors that generally cause HE students to fail
academically and subsequently relinquish their degrees before completion. While academic
performance is one of the most visible and measurable factors that influence dropout behaviors,
it is clear that the characteristics of at-risk students go far beyond scholastic and pedagogical
abilities. The literature shows that in many circumstances even students with above average
academic performance can be in danger of attrition while there are many students with
substandard academic performance who often find ways to stay the course in college until they
graduate. In their research, Belloc, Maruotti and Petrella (2010) showed that superior secondary
academic achievements did not predict lower probability of university dropout. Still, while
student dropout may be attributed to a large variety of factors other than academic savoir faire
and aptitude, it is widely accepted by scholars throughout the globe that higher education
students who tend to be at higher risk of giving up their studies are often those who performed
poorly in high school as well as those who perform poorly in college (Wu, et al., 2007).
A meticulous analysis of the recent literature shows that the characteristics of students
who are at higher risk of dropping out from higher education and the determinants that influence
that decision are quite diverse and complex in nature and often vary by individuals, by
environments, and mainly by majors (Araque, et al., 2009). For instance, Meyer and Marx
(2014) mentioned that lack of integration, lack of motivation and program toughness were
frequently cited as a driving factor of dropouts among at-risk engineering students. Dobele, et al.
(2013) found that the statistical association between study load and at-risk students was highly
significant and Gury (2010) suggested that students who are not admitted in their preferred major
as well as students with deficient living conditions were more at-risk of relinquishing their
education. There are, however, some common variables (such as scholastic skills and ability,
student involvement, demographics, etc.) that are more often than not considered by scholars as
effective identifiers of at-risk students and productive predictors of student retention (Campbell
& Mislevy, 2013).
Non-academic & Personal
5. Table 1: Review of the research literature on factors
influencing university dropout among at-risk students.
No. VARIABLES
(TRADITIONAL FACTORS)
REFERENCES OVERALL
SIGNIFICANCE
1 Academic Ability
(Study Skills)
Campbell & Mislevy, 2013;
Heublein, 2014;
Hoffman & Lowitzki, 2005;
Highly Significant
2 Age Araque et al., 2009;
Delen, 2012;
Di Pietro, 2004;
Dobele, et al., 2012;
Dobele, et al., 2013;*
Grebennikov & Skaines, 2008;
Singell & Waddell, 2010;
Highly Significant
3 Attendance Status
(Part-time vs. Full-Time)
Dobele, et al., 2012;
Grebennikov & Skaines, 2008;
Slightly Significant
4 Citizenship Belloc, et al., 2010;
Dobele, et al., 2013;
Significant
5 College GPA Araque et al., 2009;
Gifford, et al., 2006;
Grebennikov & Skaines, 2008;
Hoffman & Lowitzki, 2005;
Reason, 2009;
Wu, et al., 2007;
Highly Significant
6 Employment Commitments
(Work-related Risk Factors)
Araque et al., 2009;
Campbell & Mislevy, 2013;
Grebennikov & Shah,2012;
Hoffman & Lowitzki, 2005;
Meyer & Marx, 2014;
Weissman, 2010;
Significant
7 Gender Araque et al., 2009;
Belloc, et al., 2010;
Campbell & Mislevy, 2013;
Delen, 2012;
Di Pietro, 2004;
Dobele, et al., 2013;*
Gifford, et al., 2006;
Grebennikov & Skaines, 2008;
Gury, 2011;
Hoffman & Lowitzki, 2005;
Mattson, 2007;
Reason, 2009;
Singell & Waddell, 2010;
Significant
7. Pedagogical Risk Factors
From a purely pedagogical perspective, Gury (2011) indicated that pre-college factors
like high school GPA and strong college readiness (or strong high school preparation) were, in
general, very significant and consistent in predicting high student retention rates. In addition,
Belloc, Maruotti and Petrella (2010) observed that when students perform well in courses
associated with their specific major, they are less likely to dropout regardless of their
performance in other courses. This finding suggests that higher education institutions should
consecrate a substantial amount of resources in implementing tools, policies, as well as strategies
that would boost students’ performance and allow them excel in courses related to their
respective major.
The subject of test scores is quite a divisive issue when it comes to identifying at-risk
students and predicting retention and dropout. While some authors question the overall
usefulness of test scores in retention research and strongly argue the legitimacy of frequently
using such scores especially SAT scores as predictors of success (Hoffman & Lowitzki, 2005;
Mattson, 2007; Reason, 2009), others believed that test scores can be used successfully, using
statistical modeling, to identify at-risk students even before they arrive on campus (Gifford, et
al., 2006; Singell & Waddell, 2010).
Non-Academic and Personal Risk Factors
The recent research literature agrees that traditional variables like gender, race, ethnicity
and other demographic factors are often relatively significant in identifying at-risk students and
predicting retention and dropout. These variables, nonetheless, can be affected by personal
experiences and other confounders which, in turn, lead to interaction effects that create the need
for complicated statistical models to perform any reliable analysis (Reason, 2009). Non-
academic and personal factors such as age (Araque, et al., 2009; Dobele, et al., 2012; Dobele, et
al., 2013), gender (Belloc, et al, 2010; Gury, 2011), race and religion (Hoffman & Lowitzki,
2005), socioeconomic and familial backgrounds (Gury, 2011), employment-related issues
(Meyer & Marx, 2014; Weissman, 2010), etc. have all been proven to play highly productive
roles in identifying at-risk students and predicting university dropout. When it comes to age,
Araque, et al. (2009) observed that start age played a non-negligible role in university dropout as
students who started working on their degrees at over 20 years old were at higher risk of attrition,
while most of those who remained in school started at either 18 or 19 years old. On a closely
related note, Grebennikov and Skaines (2008) also found that older students were at higher risk
of dropping out from higher education. Surprisingly however, Dobele, et al. (2013) found that
the statistical association between age and at-risk students was not significant.
Dobele, et al. (2013) also suggested that gender is not significantly associated with at-risk
status among higher education students. Nevertheless, a large body of literature treated gender as
8. critical factor when it comes to at-risk students and higher education dropouts considering that
males and females often display divergent dropout behaviors. Gury (2010) indicated that females
were more likely to attrite during the first year, while males were more likely to drop-out in the
subsequent years. However, contrarily to what is often found in the literature, Belloc, Maruotti
and Petrella (2010) found that male students were, in general, less likely to attrite than females.
Research by Heublein (2014) showed that motivational issues, false expectations, and
financial issues are often at the forefront of the at-risk students and dropout impediment. In
related research, Doolen and Long (2007) focused on measuring how university students (mainly
engineering freshman) regard the traditional factors that are often proved to be correlated to
retention. By and large, their survey found that difficulty of classes, excessive workload, cost of
their education were the most frightening concerns for these students when it comes to retention.
Furthermore, Dobele, et al. (2012) found that attendance status (part-time vs. full-time) was only
significant for certain disciplines while citizenship was highly significant considering that
domestic students were proven to be more likely to be at-risk than international students, a
finding that was also backed by Belloc, Maruotti and Petrella (2010), Grebennikov and Skaines
(2008) and Dobele, et al. (2013). Language background was found to be statistically significant
in identifying at risk cohorts by both Grebennikov and Skaines (2008) and Dobele, et al. (2013).
Dobele, et al. (2012) and Grebennikov and Shah (2012) found that employment
commitments, financial difficulties, personal educational issues (such as poor exam preparation
and study skills, poor time-management or organizational skills, exam stress/nerves, general
difficulty understanding the material, and poor writing or language comprehension skills) were
some of the most common reasons used by at-risk students to spell out their substandard
academic performance or their decision to drop-out.
RESPONDING TO THE NEEDS OF AT-RISK STUDENTS
As we have already mentioned, from intellectual deficiencies, to financial hardships, debilitating
social conditions and emotional instability, the challenges faced by at-risk students are wide-
ranging and multifaceted. To help these students cope with these issues, schools need to be
proactive and relentless in their approach. To support at-risk students with purely academic
challenges, Choi et al. (2013) suggested a wide range of clear and specific steps that higher
education institutions should take starting with offering personalized tutoring services with
respect to clearly recognized needs, to using Rich Site Summary (RSS) feeds to communicate
frequently with at-risk students and keep them up-to-date with class assignments, reducing the
number of course that at-risk students must take each semester which will reduce their scholastic
load, improving student evaluation systems, and making enrollment systems more flexible so
that students can choose their own learning pace. Laskey & Hetzel (2011) also suggested that
tutoring services can have a significant impact on students struggling academically. They found
9. that when students meet with a tutor at least once a week, this often leads to higher grades,
higher GPA, better achievement in classes and retention. Furthermore, they suggested that higher
education institutions should create tutoring centers as well as introducing developmental
courses so that at-risk students can received the pedagogical help needed to build their academic
(reading, writing, math, communication, etc.) skills and improve their critical thinking.
Many scholars believe that talking and engaging with at-risk students on a regular basis is
the key to at-risk student success. Early intervention and outreach with vulnerable students can
be considerably beneficial when it comes to control dropout behaviors. It is a fact that whenever
a student that is facing a multitude of challenges – whether personal or academic – is left alone,
the chances of academic recovery for that student are slim to none. Heisserer & Parette (2002)
suggested that intrusive advising is a vital factor in preventing at-risk students’ dropout. Meaning
that that the more present and active advisors and faculties are in at-risk students’ life, the more
support they are likely to obtain, which can considerably reduce their likelihood of relinquishing
their studies and even change their status from “at-risk” to “not at-risk”. It is clear that intrusive
advising can have a real and significant impact on at-risk students. As we already mentioned,
higher education institutions have used a wide variety of admission tools and techniques to
reduce dropout rates by creating student success centers which are primarily designed to provide
students with the resources and advisements necessary to succeed; however, in most cases
advisors have only been working to provide academic and enrolment information to students
without really focusing on the true challenges and needs at-risk individuals.
A lot of work has been done to improve the quality of schooling for at-risk students.
However, the statistics clear show that much more needs to be done if we want to reduce
considerably dropout rates in higher education. From this perspective, Essa & Ayad (2012)
proposed a Student Success System that uses analytics to identify and design effective, efficient
and personalized intervention strategies for at-risk students. By and large, this system (S3)
applies statistics, data visualizations, and predictive modeling using a strategy of decomposition
in order to detect academically at-risk students and provide a case-based approach for managing
interventions. In a world where technology is now at the forefront of any form of advanced
development, it is critical for schools to start relying on these types of technologies when it
comes to combating university dropout. Such technologies, nonetheless, should be built using a
well-designed and effective conceptual framework that would ensure that their predictions are
accurate and their proposed intervention strategies are fair, feasible, efficient and beneficial for
both the school and the student identified as at-risk.
10. Figure 1. A Conceptual Framework for Identifying and Supporting At-Risk Students
Conclusions
In this paper, we have outlined what has already been done to identify at-risk students in the
higher education environment and provide them the support needed to succeed academically.
First, drew heavily in the literature to identify the traditional and most common risk factors that
are often used by scholars to identify at-risk students pre-emptively. Secondly, we explored some
proposed intervention tools and strategies that we deemed worth mentioning due to their proven
efficacy. It is clear that although higher education institutions have put a lot of effort and energy
in fighting dropout, they have shown a flagrant lack of effectiveness considering the current
dropout rates. We believe that those institutions should rely more on new technology and
software to solve the dropout problem. It is clear that the characteristics of students who are at
higher risk of dropping out from higher education and the determinants that influence that
decision are quite diverse and complex in nature and often vary by individuals, by environments,
and mainly by majors. Therefore, it is important that research on the at-risk student’s topic
continues and expands to analyze how the pedagogical techniques used by professors in their
classroom are correlated with the at-risk status.
11. References
Araque, F., Roldan, C., & Salguero, A. (2009). Factors Influencing University Drop Out Rates.
Computers & Education, 53(3), 563-574.
Bai, H., & Pan, W. (2010). A Multilevel Approach to Assessing the Interaction Effects on
College Student Retention. Journal Of College Student Retention: Research, Theory &
Practice, 11(2), 287-301.
Belloc, F., Maruotti, A., & Petrella, L. (2010). University Drop-Out: An Italian Experience.
Higher Education: The International Journal Of Higher Education And Educational
Planning, 60(2), 127-138.
Berkovitz, R. A., & O'Quin, K. (2007). Predictors of Graduation of Readmitted "At Risk"
College Students. Journal Of College Student Retention: Research, Theory & Practice,
8(2), 199-214.
Brown, J. L. (2012). Developing a Freshman Orientation Survey to Improve Student Retention
within a College. College Student Journal, 46(4), 834-851.
Campbell, C. M., & Mislevy, J. L. (2013). Student Perceptions Matter: Early Signs of
Undergraduate Student Retention/attrition. Journal Of College Student Retention:
Research, Theory & Practice, 14(4), 467-493.
Choi, H., Lee, Y., Jung, I., & Latchem, C. (2013). The Extent of and Reasons for Non Re-
Enrollment: A Case of Korea National Open University. International Review Of
Research In Open And Distance Learning, 14(4), 19-36.
Dekker, G. W., Pechenizkiy, M., Vleeshouwers, J. M., & International Working Group on
Educational Data, M. (2009). Predicting Students Drop Out: A Case Study. International
Working Group On Educational Data Mining.
Delen, D. (2012). Predicting Student Attrition with Data Mining Methods. Journal Of College
Student Retention: Research, Theory & Practice, 13(1), 17-35.
Di Pietro, G. (2004). The determinants of university dropout in Italy: a bivariate probability
model with sample selection. Applied Economics Letters, 11(3), 187-191.
Dobele, A. R., Kopanidis, F., Gangemi, M., Thomas, S., Janssen, R., & Blasche, R. E. (2012).
Towards a Typology of At-Risk Students: A Case Study in Singapore. Journal Of Higher
Education Policy And Management, 34(1), 3-13.
Dobele, A. R., Gangemi, M., Kopanidis, F., & Thomas, S. (2013). At Risk Policy and Early
Intervention Programmes for Underperforming Students: Ensuring Success?. Education
& Training, 55(1), 69-82.
12. Doolen, T. L., & Long, M. (2007). Identification of retention levers using a survey of
engineering freshman attitudes at Oregon State University. European Journal Of
Engineering Education, 32(6), 721-734.
Duarte, R., Ramos-Pires, A., & Gonçalves, H. (2014). Identifying at-risk students in higher
education. Total Quality Management & Business Excellence, 25(7/8), 944-952.
Essa, A., & Ayad, H. (2012). Improving Student Success Using Predictive Models and Data
Visualisations. Research In Learning Technology, 2058-70.
Gifford, D. D., Briceno-Perriott, J., & Mianzo, F. (2006). Locus of Control: Academic
Achievement and Retention in a Sample of University First-Year Students. Journal Of
College Admission, 191,18-25.
Grebennikov, L., & Shah, M. (2012). Investigating Attrition Trends in Order to Improve Student
Retention. Quality Assurance In Education: An International Perspective, 20(3), 223-
236.
Grebennikov, L., & Skaines, I. (2008). University of Western Sydney students at risk: profile and
opportunities for change. Journal of Institutional Research, 14(10), 58-70.
Gury, N. (2011). Dropping out of higher education in France: a micro‐economic approach using
survival analysis. Education Economics, 19(1), 51-64.
Heisserer, D. L., & Parette, P. (2002). ADVISING AT-RISK STUDENTS IN COLLEGE AND
UNIVERSITY SETTINGS. College Student Journal, 36(1), 69.
Heublein, U. (2014). Student Drop-out from German Higher Education Institutions. European
Journal Of Education, 49(4), 497-513.
Hoffman, J. L., & Lowitzki, K. E. (2005). Predicting College Success with High School Grades
and Test Scores: Limitations for Minority Students. Review Of Higher Education, 28(4),
455-474.
Lance R. Angell (2009). Construct Validity of the Community College Survey of Student
Engagement (CCSSE), Community College Journal of Research and Practice, 33:7, 564-
570.
Laskey, M. L., & Hetzel, C. J. (2011). Investigating Factors Related to Retention of At-Risk
College Students. Learning Assistance Review, 16(1), 31-43.
Lewis, S.E., & Lewis, J.E. (2007). Predicting at-risk students in general chemistry: Comparing
formal thought to a general achievement measure. Chemistry Education Research and
Practice, 8, 32–51.
13. Lizzio, A., & Wilson, K. (2013). Early intervention to support the academic recovery of first-
year students at risk of non-continuation. Innovations In Education & Teaching
International, 50(2), 109-120.
Lowis, M., & Castley, A. (2008). Factors Affecting Student Progression and Achievement:
Prediction and Intervention. A Two-Year Study. Innovations In Education And Teaching
International, 45(4), 333-343.
Marcia A. Roman, Rosemarye T. Taylor & Debbie Hahs-Vaughn (2010). The Retention Index of
the Community College Survey of Student Engagement (CCSSE): How Meaningful Is
It?, Community College Journal of Research and Practice, 34:5, 386-401.
Mattson, C. E. (2007). Beyond Admission: Understanding Pre-College Variables and the
Success of At-Risk Students. Journal of College Admission, 196, 8-13.
McCluckie, B. (2014). Identifying students ‘at risk’ of withdrawal using ROC analysis of
attendance data. Journal Of Further & Higher Education, 38(4), 523-535.
McClenney, K., Marti, C. N., Adkins, C., & Community College Survey of Student Engagement
(2012). Student Engagement and Student Outcomes: Key Findings from "CCSSE"
Validation Research. Community College Survey Of Student Engagement.
Meyer, M., & Marx, S. (2014). Engineering Dropouts: A Qualitative Examination of Why
Undergraduates Leave Engineering. Journal of Engineering Education, 103(4), 525-548.
Nelson, K. J., Duncan, M. E., & Clarke, J. A. (2009). Student success: The identification and
support of first year university students at risk of attrition. Studies in Learning,
Evaluation, Innovation and Development, 6(1), 1-15.
Niemi, D., Gitin, E., & International Association for Development of the Information Society, (.
(2012). Using Big Data to Predict Student Dropouts: Technology Affordances for
Research. International Association For Development Of The Information Society.
O'KEEFFE, P. (2013). A SENSE OF BELONGING: IMPROVING STUDENT RETENTION.
College Student Journal, 47(4), 605-613.
Reason, R. D. (2009). Student variables that predict retention: Recent research and new
developments. Naspa Journal, 46(3).
Rogulkin, D. (2011). Predicting 6-Year Graduation and High-Achieving and At-Risk Students.
Online Submission.
Schroeder, C. C. (2013). Reframing Retention Strategy: A Focus on Process. New Directions For
Higher Education, (161), 39-47.
14. Singell, L. D., & Waddell, G. R. (2010). Modeling retention at a large public university: can at-
risk students be identified early enough to treat?. Research in Higher Education, 51(6),
546-572.
Tamin, S. K. (2013). Relevance of mental health issues in university student dropouts.
Occupational Medicine, 63(6), 410-414.
Weissman, E. J. (2010). A Study of the Academic and Life Factors that Contribute to Attrition of
Male Adult At-Risk Students Attending For-Profit Degree-Granting Institutions.
ProQuest LLC.
Whannell, R. (2013). Predictors of Attrition and Achievement in a Tertiary Bridging Program.
Australian Journal Of Adult Learning, 53(2), 280-301.
Wu, D., Fletcher, K., & Olson, L. (2007). A Study of College Student Attrition Via Probabilistic
Approach. Journal Of Mathematical Sociology, 31(1), 89-95.
Yasmin, D. (2013). Application of the Classification Tree Model in Predicting Learner Dropout
Behaviour in Open and Distance Learning. Distance Education, 34(2), 218-231.
