This chapter describes the research method for this qualitative, phenomenological study to understand student experiences of Internet-Based Learning Self-Efficacy
(IBLSE) and online high school persistence. Chapter sections include: the research method and design, the study population and sampling method, materials and instruments, data collection, processing and analysis processes, study assumptions, limitations and delimitations, as well as information on ethical assurances.
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). 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). 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). The purpose of this qualitative phenomenological study was to provide educational stakeholders with this necessary information.
The intent of the research questions was to generate data that revealed lived student experiences of IBLSE and online high school persistence. Research Question 1 addressed the problem and data reflective of this question indicated connections between IBLSE and learning outcomes. Research Question 2 addressed the study’s purpose and data reflective of this question indicated ways educational stakeholders can leverage student experiences of IBLSE and online high school persistence to reduce student attrition and inform programming decisions.
Q1. What are the student experiences of IBLSE and persistence within online high schools?
Q2. How can educational stakeholders leverage student experiences of IBLSE and persistence to reduce student attrition and inform online high school programming decisions?
Research Methods and Design(s)
This qualitative phenomenological study described the phenomenon of student experiences of IBLSE and persistence within an online high school. Qualitative efficacy studies are more effective than quantitative studies in understanding the self-efficacy sources students deem to have a greater impact on their academic success (Bandura, 1997). A qualitative approach allowed for the exploration of the phenomenon from the student’s point of view and indicated the meaning students attribute to this experience (Marshall & Rossman, 2011). This approach generated rich data as verbatim
participant interview transcription ensured for a comprehensive interview portrayal (Moustakas, 1994). A qualitative design aligned with theoretical contribution as this approach generated information on student self-efficacy in online learning environments (Usher & Pajares, 2008).
A phenomenological, modified van Kaam research methodology was optimal for the study as researchers utilized this methodology to understand the essence of shared experiences and to convey fundamental meanings (Moustakas, 1994). Phenomenological methods allow researchers to adopt holistic and inductive approaches to the exploration of how others perceive events (Moustakas, 1994). Edmund Husserl (1859-1938), a German mathematician and philosopher, developed phenomenology and Adrian van Kaam, a Catholic priest and psychologist, further operationalized the methodology (Bailey, 2013; Moustakas, 1997). American psychologist Clark Moustakas (1923 –2012) adapted Husserl’s phenomenological approach to include modifications to van Kaam’s method of analysis (Blau, 2013; Moustakas, 1994).
Husserl used the words ‘transcendental’ and ‘phenomenology’ interchangeably to explain the research methods used to describe phenomena (Moustakas, 1994). Within phenomenology, researchers used the process of epoché, which is a Greek term meaning
‘suspension of judgment,’ to remove themselves from the experience being studied and reach a “transcendental state of freshness and openness, a readiness to see in an
unfettered way” (Moustakas, 1994, p. 41). Another phenomenology method is researcher use of imaginative variation, which involves researcher use of imagination and reflection to identify underlying themes, or units of meaning, within participant transcripts
(Moustakas, 1994).
Another phenomenological approach is hermeneutical or interpretive
phenomenology, which comes from the work of German philosopher Martin Heidegger (Moustakas, 1994). Hermeneutical phenomenology involves researcher use of his or her own experiences to interpret participant experiences and the researcher acts as an insider versus a distant observer (Moustakas, 1994). This approach was not recommended for this study, as the researcher did not have experience with the phenomenon under review.
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 high school non-graduates (Hynes, 2014). It is important to see beyond statistics and understand that data on student dropout consists of unique individuals with hopes, dreams and stories that deserve to be honored (Hynes, 2014). Phenomenological research is an effective method that allows others to hear the voices of online high school students. A phenomenological approach addressed literature gaps in IBLSE and
achievement in online high schools (Hammond et al., 2007; Tsai et al., 2011; Tucker, 2007; Usher & Pajares, 2008; Wilkins, 2011).
The researcher considered other methodologies for the study, but they were not appropriate based upon study goals. Quantitative methodologies use objective data collection and analysis methods that did not align with the study’s self-efficacy theory framework or the study’s goal to understand the phenomenon from the experience of study participants (Maxwell, 2013). Quantitative methods do not generate 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 individuals 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 would not fit as this method involves the development of theory and this study was framed by self-efficacy theory (Maxwell, 2013). Narrative inquiry was a less desirable approach as this method involved studying individuals through the stories they tell about their lives and would not be applicable to understanding core essences of the
phenomenon under review (Maxwell, 2013).
Ethnography is similar to phenomenology and was under consideration as a research design. Ethnography involves holistic descriptions and interpretations of
cultural behaviors and cultural groups over a prolonged period while a phenomenological study focuses on understanding meaning through the lens of the individual participant (Maxwell, 2013). The study may have ethnographic elements due to the nature of the research, but the study’s focus is on the unique lived experiences of the phenomenon and their relation to theory, making the phenomenological study the preferred design route.
Bandura’s (1977a) self-efficacy theory served as the study’s framework and lens to evaluate findings. This theory was appropriate as application indicated foundational information about student belief systems and what students perceived themselves to be capable of accomplishing in an online learning environment (Tsai et al., 2011). Self-efficacy correlates with academic achievement and study data indicated relationships between student self-efficacy and academic success in the targeted domain (Artino &
Stephens, 2009; Caprara et al., 2008; Tsai et al., 2011; Usher & Pajares, 2008).
Population
The setting for the study was Career Online High School (COHS), which is a private school within the world’s first accredited online, private school district, Smart Horizons Career Online Education (“About Us,” 2015). The district, located in
Pensacola, Florida, was founded by educator Dr. Howard Liebman in 2009 and COHS was launched in 2010 (“About Us,” 2015). The school is a division of ed2go, which is an online learning provider for adults and ed2go is owned by Cengage, an international education services company (“About Us,” 2015). COHS is accredited by the AdvancED Accreditation Commission, which is the national commission that confers the Southern Association of Colleges and Schools Council on Accreditation and School Improvement accreditation seal (“Accreditation,” 2015).
Smart Horizons Career Online Education program contacts for this study included Dr. Howard Liebman, Superintendent and Chief Executive Officer, Wendy Kauffman, Chief Operating Officer, Teresa Salafrio, Director of Academics and Principal of Schools, and Dr. John H. Padgett Jr., Director of Strategic Partnerships. The researcher did not have a prior relationship with the school and received permission to conduct a study with COHS and publish the name of the school, district, and program managers.
Permission documentation is located in Appendix A.
The Smart Horizons Career Online Education district has partnerships with career colleges (e.g., Brown Mackie, City College), major corporations (e.g., Walmart,
McDonald’s, Taco Bell), public libraries (e.g., Los Angeles, Sacramento, Cincinnati, Fountaindale, State of New Jersey), correctional facilities (e.g., Florida), workforce boards (e.g., Chicago, Seattle), and non-profit organizations (e.g., National Urban
League, Clinton Global Initiative) (“Leadership,” 2015). There is also an international partnership in South Africa (“Announcements,” 2015). As of August 2015, COHS had 2,766 active students enrolled and 1,498 graduates (W. Kauffman, personal
communication, June 29, 2015).
Approximately 20% of COHS students self-enroll (private pay), while others enroll through partnerships with career colleges (20%), correctional facilities (12%), school districts (10%), public libraries (20%), and corporations/workforce boards (18%) (W. Kauffman, personal communication, June 29, 2015). The average
program-completion time is 10-12 months and 72% of students matriculate into post-secondary programs (W. Kauffman, personal communication, June 29, 2015). Approximately 42%
of COHS students are employed, 68% are African American or Hispanic, 71% are female, and the average student is 27 years old (W. Kauffman, personal communication, June 29, 2015).
COHS offers complete, 18-credit, career-based high school diplomas geared towards helping adults prepare for the workplace (“About Us,” 2015). The school’s mission focuses on ensuring non-graduates receive the education they need to enter today’s workforce, with diplomas and credentialed career certificates available in the following areas: General Career Preparation, Childcare and Education, Certified Protection Officer, Certified Transportation Services, Homeland Security, Office Management, Retail Customer Service Skills, and Food and Customer Service Skills (“About Us,” 2015).
Students must officially withdraw from traditional high school in order to enroll into COHS. The minimum age requirement to self-enroll is 16, but different partner
programs may have different age restrictions (W. Kauffman, personal communication, June 29, 2015). The English proficiency level for COHS coursework is grade 5-6 for core academic courses and grade 8 or higher for career courses (W. Kauffman, personal communication, June 29, 2015).
New COHS students receive a New Student Checklist and complete an online orientation within the school’s Learning Management System (LMS) (W. Kauffman & T.
Salafrio, personal communication, June 27, 2014). COHS students start with career course electives, which creates early engagement and is intrinsically motivating as content is of personal student interest (Hartnett et al., 2011; Malinovski et al., 2014 ;W.
Kauffman & T. Salafrio, personal communication, June 27, 2014). The COHS program uses a mastery model where students can retake failed coursework or exams until they are passed (W. Kauffman & T. Salafrio, personal communication, June 27, 2014).
COHS program software includes remediation activities, as well as audio options for students that prefer to have content read aloud (W. Kauffman & T. Salafrio, personal communication, June 27, 2014). Struggling students can take advantage of one-on-one certified academic instructor support and technology support is always available (W.
Kauffman & T. Salafrio, personal communication, June 27, 2014). In locations where COHS has partnerships with career colleges, there are on-site computer labs and facilitators to support program participation (W. Kauffman & T. Salafrio, personal communication, June 27, 2014).
COHS academic coaches play a critical role in establishing a trust-based
relationship with students and are essential to tracking student progress, communicating with students on a regular basis and in maintaining student motivation (W. Kauffman &
T. Salafrio, personal communication, June 27, 2014). Prior to enrollment, academic coaches discuss school readiness factors with students, including motivation to return to school, commitment/ time available to dedicate to school work, access to relevant technology and the Internet, and relevant technology skills. Partner programs with required on-site participation may assess for student readiness to attend regular, in-person sessions (W. Kauffman, personal communication, June 29, 2015).
Academic coaches spend the majority of their time engaged in student follow-up;
their primary responsibility is student motivation and ensuring students are on-pace to graduate within 18 months of program commencement (W. Kauffman & T. Salafrio, personal communication, June 27, 2014). For a fee, students can receive extensions beyond the 18 months, but this practice is strongly discouraged (W. Kauffman & T.
Salafrio, personal communication, June 27, 2014). LMS software with tracking and reporting capabilities allows academic coaches to monitor student activities and extended student inactivity results in a phone call from the student’s academic coach (W.
Kauffman & T. Salafrio, personal communication, June 27, 2014). Although there are many mechanisms in place to mitigate student dropout, the COHS attrition rate mirrors other similar online schools, with student attrition around 60% (Barbour & Reeves, 2009;
Roblyer, 2006).
Sample
Study participants included a purposive sample of five participants, ages 16 and over, who 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). Purposive, or purposeful sampling, is a
non-probability sampling method that involves the selection of individuals from a predefined group (Maxwell, 2013). The study excluded individuals that completed fewer than three courses, as they may not have had the breadth of experience required to speak about the phenomenon under investigation. Also excluded were individuals that graduated over a year ago, as they may not have been able to recall experiences in as vivid detail as more recent graduates. COHS enrollment is only open to individuals ages 16 and older that have completed eighth grade and have officially withdrawn from traditional high school.
Thus, the population was reflective of this program requirement. Study participants were working towards, or received, a COHS diploma and career certificate in any of the aforementioned fields of study.
Participants included those that were solely virtual, meaning they did not have an affiliation with a COHS partner organization and access to site-based support services, as well as those able to take advantage of site-based support. Participants included those that transferred high school credit into COHS, as well as those that started COHS from the first semester of ninth grade. Students with completion extensions were included in the study. The study population was appropriate to the study’s problem as all participants had experience with the phenomenon under review and were able to verbalize their experiences and provide recommendations for students that might be struggling.
COHS program managers assisted in the recruitment of participants. Program managers identified a cross-representation of students and considered the following factors: (a) student demographics, such as gender, age, educational attainment level, and geographical location; (b) the availability and student use of on-site computer labs and on-site program facilitators; and (c) student motivation levels. Use of a criterion
sampling strategy ensured all participants had experience with the phenomenon under investigation (Marshall & Rossman, 2011). As the sample is not random, findings are not generalizable to other populations, programs or school districts. There are no rules regarding sample size in qualitative studies and research often included 5 to 25
participants (Mason, 2010).
As previously mentioned, the COHS model utilizes academic coaches to provide individualized support for students. Coaches regularly communicate with students by telephone and email and foster a trust-based relationship to maintain student motivation and track student progress. COHS program managers worked with the academic coaches to contact study participants, via email or telephone, depending on the participants’
preferred method of communication. Coaches used the Participant Recruitment Email/Script in Appendix B. Once academic coaches and program managers received written or verbal permission from interested participants, they shared participant contact information with the researcher, who followed up with participants.
As the study population is purposive, and the pool of study participants was limited, a general recruitment posting was not an effective way to solicit participants.
Personal solicitation ensured participants met study criteria. The researcher took care to mitigate compromising the voluntariness of the agreement to participate and ensured potential participants did not feel pressured or obligated to volunteer due to relationships with academic coaches. Clear scripting within the Participant Recruitment Email/Script (Appendix B) highlighted that participation was voluntary and not participating had no bearing whatsoever on COHS program success or relationships with academic coaches.
Materials/Instruments
A semi-structured Interview Guide consisting of open-ended questions and supportive scripting aided the researcher in identifying patterns and themes related to self-efficacy in an online learning environment (see Appendix F). Interviews allow researchers to understand objective and subjective aspects of an individual’s self-efficacy beliefs, including how beliefs influence performance (Usher & Pajares, 2008). Other researchers successfully used interview protocols to capture the lived experiences of non-graduates engaged in high school reentry programs (Hynes, 2014). A semi-structured interview process allowed for follow-up questions when clarification was needed (Kvale
& Brinkmann, 2015). The semi-structured format allowed for adaptability as conversation naturally digressed from the original, planned questions (Kvale &
Brinkmann, 2015).
There is no standardized approach to self-efficacy measurement as self-efficacy is relative to specific tasks, environments and situations (Bandura, 2006). Study interview questions were based on Bandura’s (2006) recommendation that researchers develop custom measurement tools to fit the particular domains under review. Self-efficacy research evaluated at domain-specific levels helps improve result validity (Bandura, 1997, 2006). The interview questions were specific to the online learning domain and to the domain-specific construct of IBLSE. Question design aligned with Bandura’s (1977a) four theorized sources of self-efficacy: performance accomplishment, vicarious experience, verbal persuasion, and physiological and emotional states. Bandura’s (2006)
‘Guide for Constructing Self-Efficacy Scales’ aided 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).
To establish protocol credibility and transferability, the researcher used non-leading interview questions to ensure for unbiased responses from the participants’ own experiences (Kvale & Brinkmann, 2015). The questions helped the researcher to understand the context of the participants’ experiences, to construct their experiences, and reveal meanings behind experiences (Flood, 2010). Questions that may have evoked an emotional response, such as those related to online program challenges or past
schooling experiences, were located at the end of the interview to allow for the establishment of interviewer/interviewee rapport.
The researcher emailed experts with extensive experience in the fields of self-efficacy research, online learning and IBLSE, and asked them to review all interview questions. The researcher edited to questions based on feedback from: (a) Dr. Dale Schunk, former Dean and current Professor at the University of North Carolina at
Greensboro; (b) Dr. Ellen Usher, Associate Professor, Director of the P20 Motivation and Learning Lab and Chair of the Educational Psychology Program at the University of Kentucky; and (c) Dr. Chin-Chung Tsai, Chair Professor of the Graduate Institute of Digital Learning and Education at the National Taiwan University of Science and Technology. The aforementioned authors’ research was used extensively in support of this study. Expert feedback helped reduce study risks and ensured for the credibility and dependability of the interview tool (Cozby, 2011).
Data Collection, Processing, and Analysis
Once participants were identified, the researcher emailed students using the
Participant Introductory Email, located in Appendix C. This email included the Participant Informed Consent Form (Appendix D). Once the signed informed consent documentation was received from participants, the researcher sent the Interview
Scheduling Email (Appendix E) to provide background information on self-efficacy and
Scheduling Email (Appendix E) to provide background information on self-efficacy and