Returning to High School Online: A Phenomenological Study Exploring the Student Experience of Internet-Based Learning Self-Efficacy and Persistence
Dissertation Manuscript Submitted to Northcentral University Graduate Faculty of the School of Education
in Partial Fulfillment of the Requirements for the Degree of
DOCTOR OF PHILOSOPHY
by
SUZANNE DARROW-MAGRAS
Prescott Valley, Arizona October 2015
Returning to High School Online: A Phenomenological Study Exploring the Student Experience oflnternet-Based Learning Self-Efficacy and Persistence
By
Suzanne Darrow-Magras
Approved by:
LJ,du.)~ 11/2/15
Chair: Leah Wickersham-Fish, Ph.D. Date
Certified by:
~~~
uW1s-Dean of School: Dr. Rebecca Wardlow, Ed.D. Date
iii Abstract
Online high schools provide alternatives for non-graduates opting to return to school, yet student attrition from these programs is a known problem. Internet-Based Learning Self-Efficacy (IBLSE) is a construct used to indicate student self-belief in the ability to succeed in an online course or online learning activity. IBLSE influences student persistence in online courses, yet non-graduates often have low self-efficacy due to previous negative school experiences. Despite a lack of research on student experiences within online high schools, investment in these programs continues. This research gap presents a problem, as educational stakeholders are unable to leverage data to inform programming decisions and reduce student attrition. The purpose of this qualitative phenomenological study was to explore the lived student experiences of IBLSE and persistence in an online high school, in an effort to provide stakeholders with this necessary data. Self-efficacy theory served as the study’s framework and as a lens to evaluate findings. Purposive sampling identified five individuals who completed at least three courses at Career Online High School or who graduated within the past year. Phenomenological techniques of epoché, reduction, and imaginative variation helped identify the shared essences of the phenomenon under review. A coding process allowed for detailed data analysis and the identification and interpretation of common themes. Eleven major composite themes were identified from the interview data: perseverance and resilience, diploma required for future goals/understand the importance of education on success, high level of IBLSE, self-regulated learner, sense of responsibility to others, support, self-advocacy, belief in a higher power, and the following participant-identified self-efficacy sources: performance accomplishment, verbal persuasion and vicarious
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experience. Findings demonstrated alignment with the hypothesized sources of self-efficacy and to concepts of self-regulated learning, expectancy, and adult learning theories. Findings also aligned with research on IBLSE and the achievement of student goals, course performance, course satisfaction and persistence in the online learning environment. Study findings aligned with research on online high school best practices as well as research on common student challenges in online learning environments. Results contributed to understanding the factors that promote student IBLSE and persistence in online high schools. This study assists educational stakeholders in understanding and supporting student IBLSE and persistence in online high school environments.
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Acknowledgements
I first learned about the “Law of Attraction” through the book The Secret by Rhonda Byrne and further explored the influence of thought on manifestation through the work of inspirational teachers like Esther and Jerry Hicks, Michael Losier, Mike Dooley, Darryl Anka and Matt Khan. Internalizing the concept that I attract whatever I think about, good or bad, significantly changed how I approached life decisions and interacted with others.
The topic of virtual high schools has long been of interest to me. I live in the U.S. Virgin Islands where only 60% of adults have high school diplomas. I have also taught in a St. Thomas public high school and I know there is a need for school choice,
especially for students that need flexible schedules and for those that struggle in
traditional school environments. I wanted to study the use of virtual high schools by non-graduates, in the hopes that this modality could provide a viable option for local
residents. In particular, I wanted to understand the perspective of successful online students and use this information to inspire, motivate and support others to graduate.
As I explored theoretical frameworks, I came across the work of Dr. Albert Bandura and self-efficacy theory. Dr. Bandura demonstrated that an individual’s ability to achieve a goal depends on whether or not the individual believes he or she can achieve the goal. With this information in mind, I was able to align my personal beliefs with validated research and demonstrate to my family and friends that I wasn’t so crazy after all. I sent an email to Dr. Bandura, thanking him for his work, and told him about my research proposal. Dr. Bandura is 89 years old and professor emeritus at Stanford University. While I did not expect a reply, I was thrilled to receive his one-sentence response, “May the efficacy force be with you.” I often reflected on Dr. Bandura’s wish for me, as the dissertation journey required immense dedication, focus, perseverance and most of all, super Jedi-like self-efficacy powers. I’ve found that a self-efficacy mindset makes all the difference.
I would like to thank the Career Online High School staff for sharing their data and students with me. Thank you Dr. Howard Liebman, Wendy Kauffman, Teresa Salafrio and Dr. John Padgett for trusting and believing in me. I know that you will use the study findings to continue your uplifting work. I am also humbled and sincerely
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thankful to the students interviewed for this study. Your personal stories of triumph over adversity were inspirational and speaking with you was heart-opening.
I would also like to thank the experts that helped to review my study proposal and interview protocol and whose work is used extensively in this study: Dr. Dale Schunk, Dr. Ellen Usher, and Dr. Chin-Chung Tsai. Thank you to my Dissertation Chairs, Dr. Gregory Hickman and Dr. Leah Wickersham-Fish, and to my committee members Dr. Eugene J. Polles and Dr. Scott Burrus, for your feedback and support.
I sincerely appreciate the support I received from my employer and colleagues at International Capital & Management Company and the Cancer Treatment Centers of America (CTCA). The CTCA approach to cancer care has long incorporated the mind-body connection of fostering hope and healing through self-efficacy.
I also received immeasurable support from family and friends who never stopped believing that I could earn my PhD. Thank you to my amazing husband Dana Magras for your kindness, patience and love over the past six years of this dissertation journey. To the entire Magras family circle, thank you for always loving and supporting me.
I am blessed to share this accomplishment with my mother, MaryAnn Darrow, my father, Michael Darrow and my stepmother Irene Darrow. Thank you for always giving me your unwavering support. To my sisters, Christine Darrow, Lynn Chylinski,
Maribeth Darrow and extended family members, thank you for your love and encouragement.
To my daughters, Jade Sunshine Barber and Alice Moon Barber, I dedicate this dissertation to you. You are my life, my heart and my love. Always believe in your dreams and follow your passion. I love you.
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Table of Contents
Chapter 1: Introduction ... 1
Background ... 3
Statement of the Problem ... 5
Purpose of the Study ... 5
Theoretical Framework ... 8
Research Questions ... 14
Nature of the Study ... 14
Significance of the Study ... 17
Definition of Key Terms ... 19
Summary ... 25
Chapter 2: Literature Review ... 27
Documentation ... 28
The Problem of High School Dropout ... 29
Returning to High School ... 36
Online High Schools for Reentry Purposes ... 41
Human Development, Learning and Self-Efficacy ... 47
Student Motivation and Internet-Based Learning Self Efficacy ... 57
Self-Efficacy, Expectancy, and Self-Regulation in Online Learning Environments .. 60
Summary ... 65
Chapter 3: Research Method ... 66
Research Methods and Design(s)... 70
Population ... 74
Sample... 77
Materials/Instruments ... 80
Data Collection, Processing, and Analysis ... 81
Assumptions ... 87 Limitations ... 89 Delimitations ... 90 Ethical Assurances ... 91 Summary ... 95 Chapter 4: Findings ... 97 Results ... 97 Evaluation of findings ... 166 Summary ... 182
Chapter 5: Implications, Recommendations, and Conclusions ... 186
Implications... 189
Recommendations ... 196
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References ... 213
Appendixes ... 223
Appendix A: Permission to Conduct Study and Publish Program Names ... 223
Appendix B: Participant Recruitment Email/Script ... 224
Appendix C: Participant Introductory Email ... 225
Appendix D: Participant Informed Consent Form ... 226
Appendix E: Interview Scheduling Email ... 228
Appendix F: Interview Guide ... 229
Appendix G: Follow-Up Interview Email and Guide ... 231
Appendix H: Member Check Email, Transcript ... 232
Appendix I: Member Check Email, Individual Textural Description ... 233
ix List of Tables
Table 1. Participant Demographic Information ... 100 Table 2. Composite Textural Themes and Definitions ... 142
x List of Figures
Figure 1. Moustakas’ (1994) Modified van Kaam Method of Data Analysis. ... 85 Figure 2. Composite Themes by Participant ... 143
Chapter 1: Introduction
This phenomenological study explored the lived student experiences of Internet-Based Learning Self-Efficacy (IBLSE) and persistence within an online high school. IBLSE is a construct used to indicate student self-belief in the ability to succeed in an online course or online learning activity (Tsai, Chuang, Liang, & Tsai, 2011). Study participants were former high school non-graduates who opted to return to school online.
This study used the term ‘non-graduate’ to indicate individuals who have formally withdrawn from traditional high school. Prior phenomenological research with this population revealed that individuals prefer to be called non-graduates as the term
‘dropout’ has negative connotations that signify quitting and those that drop out of school are often perceived as failures (Hynes, 2014). Use of this terminology allowed for
avoidance of judgment of a marginalized population. This phenomenological study aimed to highlight the voices of non-graduates who returned to high school online. These students were the experts on online high school persistence and their stories deserved to be heard and honored (Hynes, 2014).
According to data from the U.S. Census Bureau (2012), approximately 3.1 million students formally withdraw from school each year. A highly skilled, educated population is fundamental to the economic strength of the United States, yet this is threatened as individuals that leave school have lower median incomes, higher rates of unemployment, greater incarceration rates, and poorer health in comparison to their graduate counterparts (Balfanz, Bridgeland, Moore, & Fox, 2013; Chapman, Laird, & KewalRamani, 2010; Hynes, 2014; Miller, McCardle, & Hernandez, 2010; Wilkins 2011). Adverse effects of high school dropout on communities include lower tax contributions, greater reliance on
federal programs such as welfare and Medicaid, and higher rates of crime (Amos, 2008; Aud, Wilkinson-Flicker, Kristapovich, Rathbun, Wang, & Zhang, 2013; Balfanz et al., 2013; Chapman et al., 2010; Wilkins 2011). High school dropout remains a serious national concern despite improvements in the dropout rate and significant investment in student dropout prevention and recovery (Amos, 2008; Balfanz et al., 2013; Chapman et al., 2010; Wilkins, 2011).
There is some balance to this negative trend as the majority of non-graduates eventually obtain completion certificates (Aud, Wilkinson-Flicker, Kristapovich, Rathbun, Wang, & Zhang, 2013). According to the National Center for Education Statistics approximately 88% of adults ages 25 to 29 had high school completion certificates in 2012. Non-graduates can return to school through in-person adult
education programs or through online high school programs (Wilkins, 2011). They can also opt to take a high school equivalency exam, such as the General Educational Development (GED) test (Wilkins, 2011).
Student attrition from online high schools is a known problem, with dropout rates in excess of 60% (Barbour & Reeves, 2009; Roblyer, 2006). There is a lack of high-quality research on the effectiveness of online learning at the high school level, yet this lack of data has not hindered program expansion efforts (Hawkins, Graham,
Sudweeks & Barbour, 2012; Molnar, Rice, Huerta, Shafer, Barbour, Miron, & Horvitz, 2014). As investment and enrollment in online high school programs grows, educational stakeholders have an increased urgency to understand student attrition from these
Support of student self-efficacy in online high school programs can mitigate the problem of student attrition from these environments, as high student self-efficacy levels correlate with educational program persistence (Artino & Stephens, 2009; Caprara, Fida, Vecchione, Del Bove, Vecchio, Barbaranelli, & Bandura, 2008; Tsai et al., 2011). The exploration of the lived experiences of students who are successful in online high schools can fill literature gaps on student self-efficacy and persistence in this environment. Educational stakeholders can use study data to inform programming decisions and reduce student attrition.
This chapter includes background information on the utilization of online high schools by former non-graduates and student self-efficacy in these environments. The study’s problem and purpose statement are included within this chapter, followed by information on self-efficacy theory, which served as the study’s theoretical framework, and related information on IBLSE. This chapter also includes research questions, the nature and significance of the study, definitions of key terms and concludes with a summary.
Background
Online high schools have existed since the mid-1990’s (Oliver, Osborne, Patel, & Kleiman, 2009). Initial online programs catered to accelerated students by providing access to advanced coursework and specialized, remote instructors (Oliver et al., 2009). Early online high school efforts also targeted rural students, the underserved and special needs populations (Roblyer & Davis, 2008). Critics of online high schools identified concerns about teacher and course quality, program funding, school certification, and social and moral aspects of learning online (Roblyer, 2006).
Online education utilization in the United States, across all academic levels, continues to grow exponentially (Queen & Lewis, 2011). In the 2003–2004 school year, U.S. public school districts reported that 0.3 million students were enrolled in K-12 online programs and by the 2008-2009 school year this number increased to over 1 million students (Queen & Lewis, 2011). Today’s online high schools provide students with a range of individualized opportunities including college-level courses for advanced students and credit-recovery opportunities for students at-risk of failure or dropout (Barbour & Reeves, 2009; Cavanaugh, Repetto, & Wayer, 2013; Oliver et al., 2009; Roblyer & Davis, 2008).
For students that have formally withdrawn from traditional school, there are online high schools that cater specifically to non-graduate populations (Wilkins, 2011). Online learning components such as student-paced programming, flexible schedules, and individualized curricula appeal to these individuals (Cavanaugh et al., 2013; Collins & Halverson, 2010). However, students with previous negative school experiences often struggle in online high school programs and fear of failure can cause students to drop out once again (Cavanaugh et al., 2013; Collins & Halverson, 2010; Hammond, Linton, Smink, & Drew, 2007).
Student attrition from online high schools is a known problem, with dropout rates in excess of 60% (Barbour & Reeves, 2009; Roblyer, 2006). High attrition rates may indicate student dissatisfaction with online learning and this can affect student success (Roblyer, 2006). Students may leave online high school for several reasons including feeling that the school does not meet their needs or that family or employment obligations interfere with coursework (Cavanaugh et al., 2013; Oliver et al., 2009; Wilkins, 2011).
Some students may struggle with independent work and feel isolated in the online environment (Wilkins, 2011).
Support of student self-efficacy in online high school programs can mitigate the problem of student attrition from these environments as high student self-efficacy levels correlate with educational program persistence (Artino & Stephens, 2009; Caprara et al., 2008; Tsai et al., 2011). Self-efficacy theory comes from Albert Bandura’s (1977a) seminal work on social cognitive theory and indicates a person’s belief in his or her competence to perform tasks and attain goals. Internet-Based Learning Self-Efficacy (IBLSE) is a construct used to indicate student self-belief in the ability to succeed in an online course or online learning activity (Tsai et al., 2011).
Self-efficacy can influence the choices students make, the amount of effort exhibited on tasks, and the level of anxiety or comfort experienced when approaching tasks (Artino & Stephens, 2009; Caprara et al., 2008; Kim & Frick, 2011; Oliver et al., 2009; Petty & Loboda, 2011; Usher & Pajares, 2008). Practitioner support of student self-efficacy discourages student procrastination in the online environment and improves student use of learning strategies (Artino & Stephens, 2009). Students with
self-professed high self-efficacy levels may be at an advantage, as self-efficacy over-estimation can result in higher student motivation and achievement and in improved student self-confidence in comparison to those with a more realistic sense of their capabilities (Gonida & Leondari, 2011; Schunk & Meece, 2006).
Statement of the Problem
Over the past decade, student enrollment in online high schools has increased exponentially (Queen & Lewis, 2011), yet student dropout from these programs remains
a known problem with dropout rates in excess of 60% (Barbour & Reeves, 2009;
Roblyer, 2006). Research on the use of online high school programs by non-graduates is limited as university-level students are typically the subject of online learning studies and research is primarily quantitative (Tsai et al., 2011). Despite stakeholder investment into online high schools and increased student enrollment there is little high-quality research on learning effectiveness in this environment (Barbour & Reeves, 2009; Hawkins et al., 2012; Molnar et al., 2014; Queen & Lewis, 2011; Roblyer & Davis, 2008). There is an increased urgency to understand and reduce student attrition from these environments (Barbour & Reeves, 2009; Roblyer & Davis, 2008). Researchers also do not fully understand how high school students acquire the necessary skills, attitudes, and habits to learn online (Barbour & Reeves, 2009; Molnar et al., 2014). Although high student self-efficacy levels are indicative of persistence in learning environments (Artino & Stephens, 2009; Caprara et al., 2008), non-graduates can have low self-efficacy due to previous negative school experiences and fear of failure can cause students to leave school once again (Cavanaugh et al., 2013; Collins & Halverson, 2010; Hammond et al., 2007; Schunk & Mullen, 2012). The aforementioned research gaps and concerns present a problem as educational stakeholders have a lack of information on student experiences of IBLSE and persistence in online high school environments (Barbour & Reeves, 2009; Miller et al., 2010; Molnar et al., 2014; Roblyer, 2006). As a result, stakeholders are unable to leverage research-based data to reduce student attrition and inform
programming decisions (Roblyer & Davis, 2008; Tsai et al., 2011; Usher & Pajares, 2008).
Purpose of the Study
The purpose of this qualitative phenomenological study was to examine the lived student experiences of IBLSE and persistence in an online high school in an effort to provide educational stakeholders with data to inform programming decisions and reduce student attrition. Former non-graduates who have experienced success within online high schools are the experts on online high school persistence and their stories deserve to be heard (Hynes, 2014). The themes that emerged from the study interview data provide necessary information for the development of strategies to improve student persistence and enhance student academic performance in online learning environments (Hawkins et al., 2012; Molnar et al., 2014). Research on how students approach difficult academic experiences and generate positive outcomes is necessary and beneficial (Hynes, 2014; Usher & Pajares, 2008).
The setting for the study was Career Online High School (COHS), which is within the world’s first accredited online, private school district, Smart Horizons Career Online Education (“About Us,” 2015). The district is located in Pensacola, Florida (“About Us,” 2015). COHS program managers assisted with participant recruitment and participants included a purposive sample of five students that completed at least three COHS courses or graduated within the past year. There are no rules regarding sample size in qualitative studies and research often included 5 to 25 participants (Mason, 2010).
The researcher conducted semi-structured, telephone-based interviews, which were approximately one hour in length. Data was analyzed with a seven-step
phenomenological process designed by the seminal work of van Kaam (1966) and modified by Moustakas (1994). This process included: listing and preliminary grouping
of relevant expressions, reduction and elimination of expressions, clustering of expressions to establish core themes, theme validation, development of individual textural and structural descriptions, and development of textural-structural meanings of the experience (Moustakas, 1994). A coding process allowed for detailed data analysis and the identification and interpretation of common themes (Moustakas, 1994). A composite description of the phenomenon synthesized all data as a whole (Moustakas, 1994).
Theoretical Framework
Self-efficacy theory, Bandura’s (1977a) seminal work, served as the study’s framework and lens to evaluate findings on student experiences of IBLSE and persistence in an online high school. Self-efficacy theory was appropriate for this study as theory application can indicate foundational information about an individual’s personal belief system and what the individual perceives himself or herself capable of accomplishing in an online learning environment. Study findings extended the literature on IBLSE as it pertained to the experiences of former non-graduates in online high schools. Educational stakeholders can use this data to inform programming decisions and reduce student attrition from online high school programs.
In his seminal work, Bandura (1977b) developed social learning theory to explain human learning through social interaction and observation of others. Social learning theory consists of three central concepts that influence human behavior: (a) cognitive or personal factors, such as knowledge, expectations and attitudes; (b) environmental factors, such as social norms, and one’s ability to influence one’s environment; and (c) behavioral factors, such as skill and practice (Bandura, 1977b). Initially, researchers
based learning theory on behavioral tenants and Bandura’s inclusion of cognitive processes indicated a fundamental change in how researchers interpreted the learning process (Schunk, 2008). With this transformation, researchers viewed learners as active seekers and processors of knowledge and not as passive receivers of information or blank slates (Schunk, 2008).
Social cognitive theory emerged from social learning theory to encompass the effects an individual’s cognitive processes have on all types of human behaviors, including learning behaviors (Bandura, 1986). Social cognitive theory includes the following constructs: reciprocal determinism, self-regulation and self-efficacy (Bandura, 1986). Reciprocal determinism is the interrelationship between an individual’s learned behavior and the environment (Bandura, 1986). Self-regulation is a self-influence process consisting of self-monitoring one’s behavior to include: (a) causes and effects; (b) self-judgment of one’s behavior to include the influence of personal morals, standards and circumstances; and (c) self-reaction to one’s behavior, to include self rewards and self-punishments (Bandura, 1986). Self-efficacy indicates a person’s belief in his or her competence to perform tasks and attain goals (Bandura, 1977a).
There is a long history of philosophical and psychological interest in beliefs about personal control and the term ‘self-efficacy’ is a modern development (Maddux, 2012). Researchers used self-efficacy beliefs as predictors to determine whether an individual will be motivated to engage in a particular goal and to what degree an individual will persevere and achieve a goal (Bandura, 1977a). There is no all-purpose self-efficacy assessment tool as self-efficacy is relative to particular situations or domains of function (Bandura, 1997, 2006).
Self-efficacy is not be confused with intention (what one says one will do), although research showed that intentions can be influenced by self-efficacy beliefs (Maddux, 2012). Self-efficacy is also not be confused with self-esteem (one’s perception of one’s competence and self-worth), yet self-esteem can influence student academic achievement (Maddux, 2012; Zuffiano, Alessandri, Gerbino, Kanacri, Di Giunta, Milioni, & Caprara, 2013). Self-efficacy is not defined as a personality trait, but as belief in one’s ability to attain desired goals within specific domains and circumstances (Maddux, 2012).
Individuals have differentiated self-efficacy beliefs as they relate to specific functional domains (Bandura, 2006). As such, an individual’s expectations about performing a specific task is relative to a particular situation (Bandura, 2006). High self-efficacy in one domain does not automatically correlate with high self-self-efficacy in another (Bandura, 2006). For example, an individual with a high sense of academic self-efficacy may feel confident in achieving academic goals, but may have a low sense of physical activity self-efficacy and may shy away from physical activity-related goals (Bandura, 2006).
The higher an individual’s self-efficacy in a particular domain, the more likely he or she will achieve a goal and vice versa; individuals with low self-efficacy in a domain are likely to struggle to attain goals in that area (Bandura, 1977a). For individuals lacking requisite knowledge and skills, no amount of self-efficacy will produce competent performance (Schunk & Meece, 2006). Perceived self-efficacy beliefs can fluctuate due to changes in environmental conditions or personal conditions such as an individual’s motivation level or state of mind (Schunk & Meece, 2006).
information sources: (a) performance accomplishment, which refers to an individual’s personal accomplishment of a task or goal; (b) vicarious experience, which refers to personal witness of task attainment by others; (c) verbal persuasion, which refers to attempts by others to influence an individual’s behavior through suggestion; and (d) physiological and emotional states, which refers to the physiological arousal an individual exhibits in consideration of a particular task or goal (Bandura, 1977a).
Performance accomplishment has the greatest effect on self-efficacy as repeated successes or failures can raise or lower personal expectations (Bandura, 1977a; Usher & Pajares, 2008). When students complete academic tasks, they interpret and evaluate the results and develop personal competence judgments according to their interpretations (Usher & Pajares, 2008). The experience of personal mastery in a domain has enduring effects on individual self-efficacy (Usher & Pajares, 2008).
Verbal encouragement from trusted parents, teachers and peers can boost student confidence on academic tasks (Usher & Pajares, 2008). A student’s strong emotional reaction to school-related tasks can provide clues to the expected success or failure and high anxiety can undermine student self-efficacy (Usher & Pajares, 2008). Increasing a students’ physical and emotional well-being and reducing stress and negative emotional states strengthens self-efficacy (Usher & Pajares). Self-efficacy beliefs are known to have a biological impact and can influence the release of catecholamines which are neurotransmitters related to management of stress and perceived threats (Bandura, 1997; Maddux, 2012). Self-efficacy beliefs play a role in the release of endorphins which can impact sensations of pain and euphoria (Bandura, 1997; Maddux, 2012).
achievement, to improve student performance, and facilitate student goal attainment (Artino & Stephens, 2009; Caprara et al., 2008; Tsai et al., 2011; Usher & Pajares, 2008). Researchers also used self-efficacy theory to indicate optimism levels in students, to identify and mitigate student anxiety, and facilitate academic help-seeking behaviors (Usher & Pajares, 2008). Research findings demonstrated that student self-efficacy had implications on achievement, motivation and self-regulation in online learning
environments (Artino & Stephens, 2009; Kim & Frick, 2011; Tsai et al., 2011). IBLSE, which indicates a student’s confidence and self-belief in the ability to succeed in an online course or online learning activity (Tsai et al., 2011), has been used to predict student motivation to enroll, exert effort, and complete an online course, and to predict student satisfaction with learning online (Kim & Frick, 2011; Oliver et al., 2009; Petty & Loboda, 2011).
Locus of control is the degree to which individuals perceive that outcomes result from their own behaviors (internal control) or from forces external to themselves
(external control) (Lefcourt, 2014). Individual attribution of control can influence behaviors, attitudes, and outcomes experienced as well as self-efficacy beliefs (Lefcourt, 2014). Those with an internal control may believe they are responsible for their success while those with an external control may believe that external forces, like luck, impact outcomes (Lefcourt, 2014).
Researchers found that students with self-professed high self-efficacy levels may be at an advantage as self-efficacy over-estimation can result in higher student
motivation, achievement and student self-confidence, in comparison to students with a realistic sense of their capabilities (Bandura, 1997; Gonida & Leondari, 2011; Schunk &
Meece, 2006). There is some controversy on this topic as researchers found that among academically at-risk high school students, student over-estimation of academic self-efficacy resulted in poor social skills, behavior problems, and lower academic competence (Gonida & Leondari, 2011). The challenge for educators is to enhance student self-efficacy, while ensuring students have the requisite skills needed for success (Schunk & Meece, 2006).
Other conflicting self-efficacy research indicated that individuals with high levels of self-efficacy may overestimate abilities to attain goals and may experience failure, while those with low self-efficacy levels can feel motivated to achieve goals to prove they can accomplish challenging tasks (Settlage, Southerland, Smith, & Ceglie, 2009). Additionally, an individual who is highly skilled, yet has low self-efficacy, can be limited in what he or she can achieve (Schunk & Meece, 2006; Usher & Pajares, 2008).
Some researchers claimed that Bandura’s utilization of self-efficacy to predict performance is limited as self-efficacy may only indicate an individual’s momentary perception of capability, whereas self-concept or self-esteem, (one’s perception of one’s competence and self-worth), may provide a more valid prediction of performance (Maddux, 2012). Additional known sources of self-efficacy not explored by Bandura include the role of optimism, the use of motivational self-talk, and the use of imagery (Usher & Pajares, 2008). There is a need to understand how student self-efficacy is fostered and sustained in the online learning environment and to understand how existing theories can be adapted for this modality (Bekele, 2010; Hartnett, St. George, & Dron, 2011; Tsai et al., 2011).
Research Questions
Phenomenological research questions are interpretive and generate data that reveals lived experiences and shared essences of a phenomenon (Moustakas, 1994). For this study, the phenomenon under investigation was the student experience of IBLSE and persistence in an online high school. The study problem, a lack of information on student experiences of this phenomenon and the resulting inability for educational stakeholders to leverage this data to reduce student attrition was addressed through Research Question 1. Research Question 2 addressed the study’s purpose, to understand how educational stakeholders can leverage student experiences of IBLSE and persistence in online high school environments and use this information to inform programming decisions.
Q1. What are the student experiences of IBLSE and persistence within an online high school?
Q2. How can educational stakeholders leverage student experiences of IBLSE and persistence to reduce student attrition and inform online high school programming decisions?
Nature of the Study
This qualitative phenomenological study described the student experiences of IBLSE and persistence within an online high school. A qualitative approach was appropriate as this method allowed for the exploration of a social issue from the participants’ point of view and indicated the meaning attributed to this experience (Marshall & Rossman, 2011). A qualitative design aligned with theoretical contribution as this approach generated information on student self-efficacy in an online learning environment.
A phenomenological approach was optimal for the study as researchers utilized this method to understand the essence of shared experiences and to convey fundamental meanings (Moustakas, 1994). Phenomenological methods allow researchers to adopt a holistic, inductive, non-judgmental approach to the exploration of how others perceive events (Moustakas, 1994). This approach was appropriate based on study goals as it allowed the researcher to understand the phenomenon from the participant’s point of view and revealed data on how the phenomenon interacted with other factors in a participant’s life (Moustakas, 1994). Phenomenology provided a deliberate, sensitive approach to research that was appropriate given the study population. A
phenomenological approach allowed for the voices of marginalized groups, like non-graduates, to be heard (Hynes, 2014; Moustakas, 1994).
In their critical review of the literature on self-efficacy in schools, Usher and Pajares (2008) cited several studies that utilized a qualitative phenomenological approach, including an interview protocol, to assess student self-efficacy. Other researchers were successful in using a phenomenological approach to capture the lived experiences of former non-graduates engaged in high school/adult education programs (Hynes, 2014). There is a need to hear the voices of students and incorporate their ideas into the development of high school reentry strategies (Hynes, 2014).
The researcher considered other methodologies for the study but they were not appropriate based on study goals. Quantitative methodologies use objective data collection and analysis methods, which did not align with the study’s framework or the goal to understand the phenomenon from the participant’s point of view (Maxwell, 2013). Quantitative data does not indicate the rich, detailed descriptions that make a study
unique (Moustakas, 1994).
The researcher explored other research designs, but they were not appropriate for the study. A case study would allow for in-depth investigation of students and programs through observation, interview and document review, but this method would not allow for exploration of lived experiences (Yin, 2014). A grounded theory approach involves theory development and this method would not allow for the application of a self-efficacy framework (Maxwell, 2013). Narrative inquiry was a less desirable approach as it
involved studying individuals through the stories they tell about their lives and would not target the experience of student IBLSE and persistence in an online high school
(Maxwell, 2013).
Ethnography is similar to phenomenology and was under consideration as the research design. Ethnography involves holistic descriptions and interpretations of cultural behaviors and cultural groups over a prolonged period, yet a phenomenological study was preferred as it focused on understanding meaning through the lens of the participant (Maxwell, 2013). The study had ethnographic elements due to the nature of the research, but the study’s focus was the unique lived experience of the phenomenon and its relation to theory, making a phenomenological approach the preferred design route.
The setting for the study was Career Online High School (COHS), which is within the world’s first accredited online, private school district, Smart Horizons Career Online Education and is located in Pensacola, Florida (“About Us,” 2015). Study participants included a purposive sample of five students that completed at least three COHS courses or graduated within the past year. There are no rules regarding sample
size in qualitative studies and research often included 5 to 25 participants (Mason, 2010). The researcher examined participants via a semi-structured telephone interview.
Bandura’s (2006) ‘Guide for Constructing Self-Efficacy Scales’ aided interview question development, along with sample qualitative self-efficacy interview questions found within Usher and Pajares (2008) and sample phenomenological interview questions found within Moustakas (1994).
Interviews were transcribed verbatim and phenomenological analysis of interview data followed a modified van Kaam methodology to generate common themes
(Moustakas, 1994). As part of the data analysis process, the researcher identified student efficacy expressions and related them to Bandura’s (1977a) theorized sources of self-efficacy. Computer-Assisted Qualitative Data Analysis Software (CAQDAS) managed study data.
The data collection plan was appropriate and aligned with the purpose of the study, to examine the lived student experiences of IBLSE and persistence in an online high school in an effort to provide educational stakeholders with data to inform programming decisions and reduce student attrition. Data collection from multiple participants generated this needed empirical information and analysis processes indicated how participant experiences related to the theoretical framework. Findings provided insight into the shared characteristics of former non-graduate students in online high school environments.
Significance of the Study
The problem of high school dropout, at the rate of approximately 3.1 million students per year, is a serious national concern (Amos, 2008; Balfanz et al., 2013;
Chapman et al., 2010; Wilkins, 2011). A highly skilled, educated population is fundamental to the economic strength of the United States, yet this is threatened as individuals that leave school can expect to have lower median incomes, higher rates of unemployment, greater incarceration rates, and poorer health in comparison to their graduate counterparts (Balfanz et al., 2013; Chapman et al., 2010; Hynes, 2014; Wilkins 2011). Adverse effects of high school dropout on communities include lower tax
contributions, a greater reliance on federal programs such as welfare and Medicaid, and higher rates of crime (Amos, 2008; Aud et al., 2013; Balfanz et al., 2013; Chapman et al., 2010; Wilkins 2011). Research that addresses the problem of high school dropout and fosters development of dropout recovery and reentry strategies is deemed worthwhile (Hynes, 2014; Wilkins, 2011).
Although online high schools provide viable alternatives for non-graduates, student attrition from these programs remains a known problem with dropout rates in excess of 60% (Barbour & Reeves, 2009; Roblyer, 2006). As investment and enrollment in online education programs grows, there is an increased urgency to understand and reduce student attrition in these environments (Barbour & Reeves, 2009). High levels of student self-efficacy correlate with student persistence in learning environments (Artino & Stephens, 2009; Caprara et al., 2008; Tsai et al., 2011), and practitioner support of
IBLSE in online high schools may mitigate the problem of student attrition. Researchers do not fully understand how high school students acquire the
necessary skills, attitudes, and habits to learn online (Barbour & Reeves, 2009; Molnar et al., 2014). Although the use of online schools by non-graduates is commonplace, there is little research in this topic area (Roblyer & Davis, 2008). During the literature review, no
qualitative studies on former non-graduate experiences of self-efficacy in online high schools were identified. The lack of research on student experiences of IBLSE in online high schools results in a problem as educational stakeholders are unable to leverage critical data to reduce student attrition and inform programming decisions (Roblyer & Davis, 2008; Tsai et al., 2011; Usher & Pajares, 2008). This study generated this necessary data. As a result, more students may receive the support they need to earn completion degrees, improve earning power, and obtain an overall higher quality of life.
Phenomenological research is an important methodology as it allows the voices of marginalized groups, like non-graduates, to be heard (Hynes, 2014; Moustakas, 1994). There is a need to incorporate student ideas into the development of high school reentry strategies (Hynes, 2014). This phenomenological study provided a venue for students to express themselves and share their experiences of IBLSE and persistence. Study findings extended the literature on student IBLSE and persistence as it pertained to the
experiences of former non-graduates in an online high school environment. Definition of Key Terms
Academic Self-Efficacy (ASE). The construct of ASE indicates a learner’s perception of academic learning; this term is interchangeable with that of student self-efficacy (Tsai et al., 2011).
Andragogy. Andragogy is a Greek term, which means ‘man-leading,’ and can be contrasted with the Greek term, pedagogy, which means ‘child leading’; within
education, andragogy is the science of helping adults learn (Knowles, 1980).
Averaged Freshman Graduation Rate. The averaged freshman graduation rate is the proportion of public high school freshmen who graduate with a regular diploma
four years after starting ninth grade; this rate is an estimate of on-time graduation from high school (Chapman et al., 2010).
Computer Self-Efficacy (CSE). CSE is a construct indicating an individual’s perceived confidence in utilizing computer technologies in a variety of capacities and situations (Tsai et al., 2011).
Concrete Operational Stage. In constructivist learning theory, the concrete operational stage occurs during the elementary school years as children apply cognitive operations to problems that involve concrete objects (Meece & Daniels, 2008).
Constructivist Learning Theory. Developed by Swiss psychologist Jean Piaget (1896-1980), constructivist learning theory indicated that individuals create their
understanding of the world they live in through interactions with their environment and with others (Meece & Daniels, 2008).
Credit Recovery. Credit recovery is an educational program feature where student retake failed coursework for high school credit or obtain high school credit through activities such as mastery testing, community service, or work/life experience (Wilkins, 2011).
Disconnected Youth. Disconnected youth is a term that describes young high school non-graduates that are neither working or in school (Bloom, Thompson, & Ivry, 2010).
Dropout Recovery. Dropout recovery involves activities conducted by school districts or community organizations to identify and re-enroll non-graduates back into traditional school (Wilkins, 2011).
school to obtain a high school credential; within the context of the study, this term is unrelated to the criminal justice system and the reintegration of former offenders into mainstream society (Wilkins, 2011).
Educational Stakeholder. The term educational stakeholder refers to any
person, group or organization that has an interest or concern in education (Saxena, 2014). Epoché. Epoché is a Greek term meaning ‘suspension of judgment’; in
phenomenological research, epoché, or ‘bracketing’, refers to researchers refraining from judgment and setting aside bias (Moustakas, 1994).
Event Dropout Rate. The event dropout rate is the percentage of students who dropped out of high school between the beginning of one school year and the next, without earning a high school credential (Chapman et al., 2010).
Expectancy. In Vroom’s (1964) expectancy theory, expectancy refers to an individual’s perception that greater efforts will yield greater results; in regards to workplace learning, expectancy refers to an individual’s perception that training participation will lead to the acquisition of knowledge, skills or abilities (Mathieu, Tannenbaum & Salas, 1992)
Expectancy Theory. Developed by business school professor Victor Vroom, expectancy theory indicated that individuals are motivated to behave in certain ways based on perceived results of behavior, with correlations between perceived desirability of an outcome and an individual’s motivation level (Vroom, 1964).
Formal Operations Stage. In constructivist learning theory, the formal operations stage occurs in early adolescence and continues into adulthood and is demonstrated by an individual’s ability to solve complex problems, infer possibilities,
hypothesize, and think ahead (Meece & Daniels, 2008).
Four-Year Adjusted Cohort Graduation Rate. The four-year adjusted cohort graduation rate is the common calculation used by all U.S. state governors for tracking high school dropout; to calculate the rate, divide the annual number of on-time graduates by the number of first-time ninth graders, four years earlier (Balfanz et al., 2013).
High School Credential. A high school credential refers to any type of high school completion degree, including a General Educational Development (GED) test credential and demonstrates that a student has met all state requirements for high school graduation (Chapman et al., 2010).
Instrumentality. In Vroom’s (1964) expectancy theory, instrumentality refers to an individual’s perception that greater efforts will result in expected outcomes; in regards to workplace learning, instrumentality refers to an individual’s perception that
knowledge, skills, or abilities gained from training will lead to specific outcomes, such as respect from peers, pay increases, and improved job performance (Mathieu et al., 1992)
Internet-Based Learning (IBL). The construct of IBL refers to learning that occurs within a general online learning environment (Tsai et al., 2011).
Internet-Based Learning Self-Efficacy (IBLSE). The construct of IBLSE indicates student self-belief in the ability to succeed in an online course or an online learning activity (Tsai et al., 2011).
Internet Self-Efficacy (ISE). The construct of ISE indicates an individual’s confidence in general skills and knowledge in using the Internet (Tsai et al., 2011).
Non-graduate. The term non-graduate refers to an individual who has officially withdrawn, or ‘dropped out’ of high school (Hynes, 2014).
Performance Accomplishment. In self-efficacy theory, performance accomplishment refers to an individual’s personal accomplishment of a task or goal (Bandura, 1977a).
Physiological and Emotional States. In self-efficacy theory, physiological and emotional states refers to the physiological and emotional arousal an individual exhibits in consideration of a particular task or goal (Bandura, 1977a).
Preoperational Stage. In constructivist learning theory, the preoperational stage occurs during the preschool years and includes the development of symbolic schemes, meaning children are able to represent objects and events with symbols such as language, mental images, and gestures (Meece & Daniels, 2008).
Reciprocal Determinism. In social cognitive theory, reciprocal determinism is the interrelationship between an individual’s behavior and the environment (Bandura, 1986).
Reentry Programs. Reentry programs are high school credential programs for non-graduates; this term is not to be confused with reentry as it relates to the correctional system (Wilkins, 2011).
Schemes. In constructivist learning theory, schemes are patterns of thoughts or actions that children use to interact with the environment (Meece & Daniels, 2008).
Self-Efficacy. Self-efficacy refers to the theoretical construct that an individual’s ability to achieve a goal depends on whether or not the individual believes he or she can achieve the goal (Bandura, 1977a).
Self-Regulated Learning (SRL). SRL refers to the metacognitive, self-directed practices and beliefs students use to obtain academic skills and self-monitor learning
effectiveness (Schunk, 2008; Zimmerman, 1986).
Sensorimotor Stage. In constructivist learning theory, the sensorimotor stage occurs in infancy and includes simple and action-oriented schemes such as reaching for, grasping, and pulling objects, goal-directed behavior, and object permanence (Meece & Daniels, 2008).
Social Cognitive Theory. Developed by American psychologist Albert Bandura, social cognitive theory emerged from social learning theory to encompass the effects an individual’s cognitive processes have on all types of human behaviors, including learning behaviors (Bandura, 1986).
Social Learning Theory. Developed by American psychologist Albert Bandura, social learning theory indicated that humans learn from one another through observation, imitation and modeling (Bandura, 1977b).
Status Completion Rate. The status completion rate is the percentage of individuals in a particular age range who are not in high school and have earned a high school credential (Chapman et al., 2010).
Status Dropout Rate. The status dropout rate is the percentage of individuals in a particular age range who are not in high school and have not earned a high school credential (Chapman et al., 2010).
Student Self-efficacy. Student self-efficacy refers to a student’s personal belief in his or her ability to complete an academic task and this term is interchangeable with that of academic self-efficacy; students demonstrate this construct by setting specific and proximal goals, self-evaluating, self-motivating, and self-regulating learning behaviors (Zimmerman, 2008).
Urban Youth. The term urban youth refers to young people that live in metropolitan areas and whose families are recent immigrants or who are financially impoverished (Schunk & Mullen, 2012).
Valence. In Vroom’s (1964) expectancy theory, valence refers to an individual’s perception of the importance of an expected outcome; in regards to workplace learning, valence refers to the personal importance of training outcomes to the individual (Mathieu et al., 1992)
Verbal Persuasion. In self-efficacy theory, verbal persuasion refers to attempts by others to influence an individual’s behavior through suggestion (Bandura, 1977a).
Vicarious Experience. In self-efficacy theory, vicarious experience refers to personal witness of task attainment by others (Bandura, 1977a).
Summary
This chapter included background information on online high schools, the utilization of these programs by former non-graduates and the impact of student self-efficacy in learning environments. The study’s problem and purpose statement were included in this chapter, followed by information on self-efficacy theory and related information on IBLSE. This chapter included the study’s research questions, information on the nature and significance of the study and definitions of key terms.
The utilization of a phenomenological research design indicated alignment with the study problem, purpose and research questions. Rich, detailed descriptions, which reflect the lived experiences of individuals, are possible with qualitative
phenomenological methodology and can indicate shared patterns, themes, and essences of experiences (Moustakas, 1994). Bandura’s (1986) self-efficacy theory provided a robust
study framework, which aligned with the nature of the study. Study data indicated shared essences of the phenomenon under review and educational stakeholders can use this information to reduce student attrition and inform programming decisions. Research that supports non-graduate attainment of high school completion degrees and fosters
development of dropout recovery and reentry strategies is considered worthwhile (Hynes, 2014; Wilkins, 2011).
Chapter 2: Literature Review
Although there is a plethora of research on the topics of high school dropout and dropout prevention, there is little research on the use of online high schools to mitigate the problem of high school dropout (Wilkins, 2011). While online high schools have been in existence for decades, researchers still do not fully understand how students learn within these environments and research on the non-graduate experience of these
programs is even more limited (Barbour & Reeves, 2009; Cavanaugh et al., 2013; Hynes, 2014; Molnar et al., 2014; Usher & Pajares, 2008).
Self-efficacy research validity improves when it is domain-specific as individual self-efficacy beliefs are relative to particular environments and tasks (Bandura, 1997, 2006). Although there is research on self-efficacy within academic domains, there is a lack of information on Internet-Based Learning Self-Efficacy (IBLSE) which indicates a student’s confidence and self-belief in the ability to succeed in an online course or in an online learning activity (Tsai et al., 2011). Additionally, research on the topic of online learning environments and student self-efficacy is primarily quantitative and focused on the experiences of university-level students (Tsai et al., 2011).
This chapter familiarizes readers with current literature on topics relevant to the study and demonstrates the need for research on the experiences of former non-graduates enrolled in online high schools. The review provides foundational information in support of the study purpose: to examine the phenomenon of student experiences of IBLSE and online high school persistence in an effort to provide educational stakeholders with information to reduce student attrition. This review incorporates various seminal, current and peer-reviewed resources to provide the appropriate perspective.
A review of the phenomenon of high school dropout indicates the gravity of this national problem. Information on high school reentry provides an overview of student pathways back to high school. A review of the use of online high schools for reentry purposes indicates the utilization of this modality with non-graduate populations. Information on self-efficacy, development and learning provides theoretical context on the human learning experience. A discussion of student motivation and IBLSE provides insight into the online student experience. Additional information on expectancy theory and Self-Regulated Learning (SRL) theory, as they related to self-efficacy theory,
provides perspective on theory development, theory overlaps, and practical applications. Documentation
A search of Northcentral University databases utilizing the Roadrunner Search Discovery Service facilitated in the location of scholarly, peer-reviewed articles from the following databases: (a) Ebrary, (b) EBSCOhost Education Research Complete, (c) EdIT Digital Library, (d) ERIC Education Research, (e) ProQuest Educational Journals, (f) Sage Journals Online, (g) Science Direct, (h) SpringerLink, (i) Taylor and Francis Online, and (j) Wiley Online Library. Additional Northcentral University resources included the Northcentral Dissertation Database and ProQuest Dissertation and Theses. Professional books served as resource and seminal material and the Google Scholar search engine helped locate full text, peer reviewed, and seminal resources.
Searches of the following government and professional websites and education clearinghouses assisted in identifying education statistics and trends: (a) U.S. Department of Education and the National Center for Education Statistics; (b) U.S. Census Bureau; (c) America’s Promise Alliance, Civic Enterprises and Everyone Graduates Center; (d)
Manpower Demonstration Research Corporation; (e) National Dropout Prevention Center; (f) National Education Policy Center; and (g) Alliance for Excellent Education. Key search terminology included various combinations of the following terms: high school dropout, online high school, virtual high school, online learning, Internet-based learning, online education, online education for high school dropouts, student attrition and persistence from online programs, social cognitive theory, self-efficacy theory, expectancy theory, regulated learning theory, adult learning theory, online self-efficacy, and academic motivation.
The Problem of High School Dropout
School reform efforts have led to significant improvements in the high school dropout rate, yet each year more than one million public high school students fail to graduate on time with their original freshman class; this equates to one-fifth of the United States freshman student body (Balfanz et al., 2013). The statistics for urban youth and minority groups such as African Americans, Native Americans and Hispanics are even more distressing as only 50% of the members of this population graduate on time (Balfanz et al., 2013; Schunk & Mullen, 2012). In 2008, the national status dropout rate was 8%, indicating that approximately 3.0 million 16 to 24-year-olds were either not enrolled in high school or had not earned a high school credential (Chapman et al., 2010). High school dropout is a crisis in the United States as more than 3.1 million students leave school each year (Aud et al., 2013).
Historically, national dropout rates have been difficult to gauge as state education agencies use varied calculations to report dropout data (Balfanz et al., 2013). Due to these inconsistencies, researchers from the National Center for Education Statistics
analyzed four specific rates in a compendium report which included graduation and dropout data from 1972–2008 (Chapman et al., 2010). Rates studied included: (a) the event dropout rate (percentage of students who dropped out of high school between the beginning of one school year and the next without earning a high school credential); (b) the status dropout rate (percentage of individuals in a particular age range who are not in high school and have not earned a high school credential); (c) the status completion rate (percentage of individuals in a particular age range who are not in high school and have earned a high school credential); and (d) the averaged freshman graduation rate
(proportion of public high school freshmen who graduate with a regular diploma four years after starting ninth grade and is an estimate of on-time graduation from high school) (Chapman et al., 2010).
In 2010, all state governors agreed to utilize a single calculation, the four-year adjusted cohort graduation rate, to track dropout and ensure for standardized
accountability (Balfanz et al., 2013). To calculate this rate the annual number of on-time graduates is divided by the number of first-time ninth graders, four years earlier (Balfanz et al., 2013). Improvements have occurred across all rates over time, yet findings showed that females and White and Asian/Pacific Islander students were more likely to graduate on time and experience fewer instances of dropout in comparison to males and to African American, American Indian/Alaska Native and Hispanic students (Chapman et al., 2010).
Student dropout can have serious impacts on individuals, communities, and economies (Amos, 2008). Non-graduates can expect to have lower median incomes, higher rates of unemployment, greater incarceration rates, and poorer health in
2011). Non-graduates earn on average, $10,000 a year less than those with a high school diploma and over the course of a lifetime a college graduate will earn approximately $1 million dollars more than a high school dropout (Amos, 2008). Thus, researchers called dropping out of high school “a million dollar mistake” (Amos, 2008, p. 8). Negative effects of high school dropout on communities included economic hardship due to lower tax contributions, a greater reliance on federal programs such as welfare and Medicaid, and higher rates of crime (Amos, 2008; Chapman et al., 2010; Wilkins 2011).
Individuals that leave school are likely to be navigating toxic environments in their homes, schools and neighborhoods that are not typical of the wider U.S. population (Hynes, 2014). Many are exposed to violent behaviors, affected by negative family health issues, and subjected to school environments and policies that are dangerous, unsupportive or disrespectful (Hynes, 2014). Findings from the National Dropout Prevention Center showed student dropout correlated with 25 risk factors in four major areas: individual, family, community and school (Hammond et al., 2007).
Examples of individual risk factors included having special needs such as learning disabilities, early adult responsibilities, poor school attendance, and misbehavior in school (Hammond et al., 2007). Family risk factors included low socioeconomic status, low education level of parents, needing to care for a family member and high family mobility (Hammond et al., 2007). Community risk factors include urban and geographic location, impoverishment, a high minority population, and high levels of violence and drug-related crime (Hammond et al., 2007).
School related dropout risk factors are numerous and included school size, high student-teacher ratio, student body characteristics (such as percentage of minority and
low-income students), student body academic performance and problems with
attendance, violence, and school safety (Hammond et al., 2007). Other school-related risk factors included school policies and structures that can impede student promotion such as high-stakes testing and standards-based reforms (Hammond et al., 2007). Lack of relevant curriculum, uninteresting coursework, and harmful school discipline practices can also cause students to leave school (Hammond et al., 2007). The aforementioned risk factors can contribute to the student experience of educational trauma, which is the unintentional ill-treatment or discrimination of students by an educational system (Gray, 2015). This type of trauma can impact individuals, families and communities and can lead to feelings of helplessness and disempowerment (Gray, 2015).
The problem of high school dropout often reflects a disconnect with adults in the academic community (Cavanaugh et al., 2013; Wilkins, 2011). When interviewed, non-graduates cited compounding reasons for leaving school including parenthood, the need to obtain employment, poor grades, lack of connection to, or dislike of school, unfair school discipline practices, uninteresting classes, unsupportive teachers, trouble getting along with teachers, and court involvement (Bloom et al., 2010; Burzichelli, Mackey, & Bausmith, 2011; Hammond et al., 2007; Wilkins, 2011). Other factors include
homelessness, incarcerated parents and being in foster care, yet these types of risk factors are often out of the control of young people who feel they must leave school in order to meet basic needs (Hynes, 2014).
Non-graduates often remain in environments that are unsupportive of academic and vocational achievement (Hynes, 2014). Young people value their connections with others and the value placed on relationships can influence whether or not an individual
stays in school or drops out (Hynes, 2014). Researchers advised practitioners to provide additional support to students experiencing risk factors and build upon student strengths to encourage persistence and/or reentry (Hynes, 2014).
Researchers advised educational stakeholders to use caution when labeling a student as ‘at-risk’ (Hickman & Wright, 2011). In a study exploring at-risk students involved in a mentoring program, it was revealed that the earlier a student enrolled in the program, the less likely he or she would be to complete the program and graduate high school (Hickman & Wright, 2011). Findings indicated that the younger a student was labeled as at-risk the more likely he or she would experience life-long problematic behaviors (Hickman & Wright, 2011).
Student dropout is not a sudden event; student disconnection from school is a long-term process (Hynes, 2014). There is no single, underlying risk factor that leads to student dropout, yet dropout risks are higher when multiple risk factors are present (Hammond et al., 2007; Wilkins 2011). Research showed that 63% of non-graduates eventually obtain a high school credential within eight years of their original graduation date (Bloom et al., 2010). Approximately 10% of non-graduates are persistently
disconnected; these individuals never re-engage with education or workforce development programs (Balfanz et al., 2013; Wilkins, 2011).
In a phenomenological study exploring student experiences of high school dropout, researchers found that individuals who leave school do not want to be called dropouts, they prefer to be called ‘non-graduates’ and their decision to discontinue formal education as, ‘leaving school’ (Hynes, 2014). The term ‘dropout’ has negative
‘losers’ (Hynes, 2014). Researchers advised educational stakeholders to reframe the term student dropout as ‘interrupted enrollment’ as this conveys a more positive outlook for student return to school (Hynes, 2014). It is important to see beyond the data and understand that non-graduates are not statistics, but unique individuals with hopes, dreams and stories that deserve to be honored (Hynes, 2014).
Individuals that leave high school often show considerable resilience and are able to recover from difficult situations, yet this resilience may not be sufficient to help them re-engage with school (Hynes, 2014). These individuals need authentic connections with adults and peers that care about them, support and guide them, and provide them with access to educational programs and social services (Cavanaugh et al., 2013; Drysdale, Graham, & Borup, 2014; Hynes, 2014). An individual’s personal resilience, in
conjunction with social and institutional supports, can enhance the academic reentry path (Hynes, 2014).
Created by the U.S. Department of Education's Institute of Education Sciences in 2002, the “What Works Clearinghouse” serves as an open-access, evidence-based database for practices that work in education (Burzichelli et al., 2011). The Clearinghouse contains information on dropout prevention and allows educational stakeholders to research dropout prevention strategies, review program effectiveness, identify programs that address special needs students, and make decisions as to which strategies may generate success (Burzichelli et al., 2011). Proven effective dropout prevention programs include those that make educational quality a top priority, report accurate data, utilize early warning and intervention systems, have high expectations and standards, promote teacher effectiveness and encourage parental engagement (Balfanz et
al., 2013). Educational stakeholders are encouraged to integrate student voices and ideas into the development of student dropout prevention and reentry strategies (Hynes, 2014).
Researchers with America's Promise Alliance, Civic Enterprises and the Everyone Graduates Center at Johns Hopkins University explained that a national, strategic plan, like the “Civic Marshall Plan” can alleviate the dropout crisis (Balfanz et al., 2013). When developing dropout recovery strategies, educational stakeholders are advised to provide multiple, targeted pathways to education success, as there is no universal method to re-engage students (Balfanz et al., 2013; Hickman & Wright, 2011). Recovery
programs and interventions should operate simultaneously to meet the needs of students (Balfanz et al., 2013).
Other researchers believed that the problem of high school dropout should be reframed as a public health issue as this action would bring other organizations, such as health institutions and civil rights groups, into the dropout conversation (Miller et al., 2010). Low adult literacy levels also pose a public health challenge as today’s increased literacy demands can limit workplace opportunities and restrict access to healthcare resources (Miller et al., 2010). Despite the fact that 40 million Americans have only the most basic literacy, there is a lack of research focused on adult learners to inform remediation efforts (Miller et al., 2010).
Researchers agreed that dropout factory schools, where only 60% of freshman students make it to their senior year, should be the first targets of educational reform efforts (Balfanz et al., 2013). There is no single, underlying factor that contributes to student dropout and there is no single type of intervention that can end the dropout crisis (Balfanz et al., 2013; Hammond et al., 2007; Hickman & Wright, 2011). Despite positive
