Virtual Teachers’ Understanding of Students’ Self-Efficacious Behaviors in Digital Learning Environments

Framework

Introduction
Emerging from the pandemic, the majority of parents with K-12 learners expect digital learning offerings from schools (Stride, 2021). According to Stride (2021), many parents see the value in technology-enabled learning resources and the ability for schools to support shifts between face-to-face and online learning. In response, there has been an emerging focus on quality online learning rather than the emergency remote teaching that took place during the height of the pandemic (Hodges et al. 2020). This focus on quality online learning is important to ensure efficacy for learners as the demand for digital learning grows. Specific components of quality online learning include learning experience design, a focus on social-emotional learning, and teacher training (Hodges et al., 2020; Kamei & Harriett, 2021; Yang, 2021). Additionally, learners need higher levels of motivation, self-regulation, and organization to be successful as digital learners, so developing these skills and dispositions in digital learners is imperative (Darling-Aduana et al., 2019; Pettyjohn & LaFrance, 2014).
Digital learning includes several challenges related to non-academic factors, including motivation and self-efficacy. According to the National Center of Education Statistics (2019), 58% of learners in online courses earn credit in online courses, as compared to 93% of learners in analog courses. This disparity raises the question as to why online learners are less likely to successfully complete a course. Researchers have been studying motivational factors in digital learning for over a decade. Artino (2008) found that students are likely to be more motivated when expectations are clear, instructors provide frequent feedback, and there are opportunities for collaborative learning. Ensuring that students are satisfied with their digital learning experience is critical to their motivation and persistence (Joo et al., 2011; Kao et al., 2014). Researchers have also noted that students need to be supported in organization, self-advocacy, and self-regulatory skills to be successful in digital learning (Nourse, 2019; Darling-Aduana et al., 2019; Oliver & Kellogg, 2015).
There are myriad factors that contribute to digital learners’ success, including expectations, instructor support, platform design, course design, and other factors, and it is critical that digital learning continues to make progress in these areas. However, self-efficacy by definition is foundational to success, and how digital learners develop self-efficacy during learning tasks may be undertheorized in the current literature. Bandura (1997) noted that self-efficacy is developed through mastery experiences, vicarious experiences, verbal persuasion, and affective states. These methods for developing self-efficacy were studied in face-to-face, rather than digital, environments. It is important that digital learning be designed in such a way that learners have the opportunity to develop self-efficacy. Additionally, there may be a need to return to Bandura’s work to understand if the four methods of developing self-efficacy in face-to-face environments are applicable in digital learning environments.
While K-12 learners develop skills, habits, and dispositions that support their future learning, it is important that digital experiences specifically support the development of their self-efficacy. The current theory about self-efficacy development dates back to Bandura’s Social Cognitive Theory (SCT), which was developed before digital learning was widely available and without consideration to the unique processes and needs of digital learners. Following the height of the COVID-19 pandemic, more people were learning online across the world (UNESCO, 2020). This study begins to explore self-efficacy in digital learning so that we may support the success of digital learners both now and in the future.

Perspective or Theoretical Framework
Self-efficacy, or one’s perception of their own ability to use their skills and knowledge to produce a desired result on a task, has been a topic of concern to social scientists since the 1970s. Self-efficacy has been shown to affect people’s effectiveness in approaching tasks, their dispositions while attempting tasks, and even be predictive of their outcomes (Bandura, 1997; Zimmerman, 2107; Schunk & DiBenedetto, 2020). While self-efficacy is a widely applicable construct, it is of particular importance to learners in their acquisition of knowledge and skills. When facing a task such as a challenging exam or complicated topics, efficacious learners are more likely to exhibit a host of behaviors and mindsets conducive to academic success. However, a vast amount of accepted self-efficacy theory and knowledge was developed between 1977 and the mid-1990s in face-to-face settings. Beginning in the late 1990s, access to the Internet and the ubiquity of technology began to impact education on a larger scale than in the previous two decades. As new technology and connectivity emerged, social scientists began to study learners’ self-efficacy in technological endeavors, but generally predicated their work on the assumption that the foundational theories and knowledge of self-efficacy as studied in face-to-face learning remained applicable in these newly formed digital spaces.
While the enterprise of learning online is quite different from learning in face-to-face environments, self-efficacy remains necessary for online learning, along with other skills such as self-regulation (Bradley, 2017). Despite differences in the nature of online learning, recent literature concerning self-efficacy generally holds to the theory of the late 20th century, and social scientists are only now in recent years beginning to question if the foundational elements of self-efficacy studied in face-to-face environments hold true in digital learning.
Framing Self-Efficacy
Bandura’s (1997) Social Cognitive Theory proposed that human action was motivated by three types of factors: personal influences, behavioral, and environmental; each of these factors had the ability to impact the others. Social Cognition Theory is unique because of its focus on these three factors, whereas other motivational theories at the time focused on intrinsic and extrinsic factors. As part of Social Cognitive Theory, Bandura (1977) identified self-efficacy, or one’s belief that they will be able to achieve a certain level of performance, as a personal influence. Self-efficacy is task-specific and one’s perceived self-efficacy is the product of cognitive processes, such as task analysis and self-reflection. Self-efficacy is developed through mastery experiences, vicarious experiences, verbal persuasion, and affective states; these development mechanisms have been cited in literature across decades, including in recent studies measuring self-efficacy in digital environments (Bandura, 1997; Schunk, 2020).
Behaviors of Efficacious Learners
High self-efficacy has been correlated with behaviors in learners that promote success. As previously mentioned, Bandura (1997) found at least seven behaviors linked to high self-efficacy that would improve learner outcomes, including higher levels of effort and perseverance through obstacles. Self-efficacy was hypothesized by Schunk (1996) “to influence choice of activities, effort, persistence, and achievement… [and cause learners to] participate more readily, work harder, persist longer when they encounter difficulties, and achieve at a higher level” (p. 5). Zeiser et al. (2018) found a high correlation between self-efficacy and perseverance, metacognition, and learners experiencing control over their work.
In addition to these behaviors, self-regulation is often cited as being a critical characteristic for digital learning. While the present study is not focused on the behaviors related to self-regulation, self-efficacy is a prerequisite to self-regulation (Bandura, 1997; Greene et al., 2015; Bradley et al., 2017). Self-regulation is linked to a host of positive behaviors that can only be accessed by efficacious learners (Bradley et al., 2017). Some of these behaviors include self-monitoring, setting goals, self-organization, and self-motivation, which are critical for digital learning (Bradley et al., 2017). Clearly, self-regulation is a desirable characteristic of digital learners, and, because self-efficacy is a prerequisite for self-regulation, it yields multiple benefits to learners (Bandura, 1997; Schunk, 1997; Villavicencio & Bernardo, 2016; Bradley et al., 2017). However, given the number of behaviors directly or indirectly impacted by self-efficacy, this study will narrow the behaviors under examination. Across the decades, the literature has repeatedly confirmed that “self-efficacy affects choice of activities, effort, and persistence” (Schunk, 1996, p.3). While it is clear that all the behaviors encouraged by high self-efficacy and self-regulation would benefit learners in digital contexts, this study narrows based on Schunk’s (1996a) statement and investigates these three behaviors as being the product of self-efficacy throughout the literature: effort, persistence, and goal-setting (Bandura, 1977, 1997; Carpenter & Clayton, 2014; Bradley et al., 2017; Zilka et al., 2019; Schunk & DiBenedetto, 2020).
Effort 
Learners who have high self-efficacy generally apply more effort to learning experiences. Self-efficacy is consistently cited in the literature as being positively correlated with effort (Bandura, 1977, 1997; Schunk, 1984, 1996; Carpenter & Clayton, 2014; Zilka et al, 2019).
The level of effort that people apply in various situations is correlated to certain conditions, such as the difficulty level of the task (Locke & Latham, 2002). Learners in digital environments tend to exhibit more effort when they feel connected to others in the learning experience (Zilka et al., 2019). High self-efficacy has been linked to higher levels of effort as learners engage in tasks with increased levels of difficulty (Chen & Zimmerman, 2007). Effort has also been shown to be linked to self-regulation, for which self-efficacy is required (Lee et al., 2020). Self-regulation includes learners evaluating their progress toward goals, and efficacious learners have been found to strive to outperform their previous results (Burns et al., 2020). In summary, high levels of effort improve outcomes, improve chances of success in challenging tasks, and can lead to increased abilities and increasing learners’ mastery in all domains.
Persistence
High self-efficacy has been correlated to learner persistence in challenging learning activities. Bandura (1977) found that self-efficacy was, in fact, predictive of persistence. Conley (2017) named perseverance as a key behavior needed for success in post-secondary education and the workforce. Given the predictive link between self-efficacy and persistence and the key role persistence plays in success, it is worth examining the development of persistence through self-efficacy in digital learning environments. Several studies have demonstrated the ways in which self-efficacy and persistence are linked. In digital learning environments, self-efficacy has been related to persistence through learners using strategies to overcome challenges specifically related to online learning (Zilka et al., 2019). Zilka et al., (2019) also found that digital learners increased persistence as they were successful in learning tasks, which is consistent with Bandura’s (1997) explanation of mastery experiences. According to the literature, developing the critical skill of persistence is also closely linked to the development of self-efficacy.
Goal-Setting
Goal-setting improves learner outcomes, and self-efficacy has been correlated with learners setting more challenging goals in the literature for decades. Goal-Setting Theory outlines four rationales for creating goals: 1) goals help focus attention 2) goals are energizing, 3) goals increase persistence, and 4) setting goals in the zone of proximal development can increase skill level (Locke & Latham, 2002). Goals can increase motivation, support self-regulated learning, improve outcomes, and even increase self-efficacy. (Schunk & Swartz, 1993; Schunk, 1997, Schunk & Ertmer, 1998; Sides & Cuevas, 2020; Burns et al., 2021). While there is an extensive body of literature around effective goal-setting processes and the types of goals that yield the best results, these topics are not the subject of this study. Rather, this study focuses on goal-setting as an efficacious behavior, as the connection between self-efficacy and effective goal setting has been noted by many researchers. Self-efficacy has a direct impact on goal-setting, and those with higher self-efficacy tend to set more challenging goals (Lent et al., 1994; Locke & Latham, 2002; Lüftenegger et al., 2012; Sides & Cuevas, 2020). While pursuing challenging goals, learners will certainly encounter negative feedback, so self-efficacy is required to maintain motivation and high self-efficacy in the future. When learners achieve challenging goals, they increase their self-efficacy, leading to selecting more challenging goals in the future (Sides & Cuevas, 2020). The connection between goal-setting and self-efficacy is well-established, and learners with high self-efficacy set more challenging goals and continue to do so, meaning that high self-efficacy coupled with goal-setting has a compounding positive effect.
The Study of Self-Efficacy in Digital Learning
Many studies examining the development of learner self-efficacy were conducted in face-to-face environments. While the Internet began as a government project in the 1960s, popular web browsers were not adopted at scale by the public until the mid-to-late 1990s (The Science and Media Museum, 2020). It was not until 1969 that learning was delivered online through the University of Illinois’s Programmed Logic for Automatic Teaching Operations (PLATO), but eLearning was not adopted on a broad scale until the 1990s (Etherington, 2017).
Given the timelines leading to the ubiquity of online access and digital learning in the 1990s juxtaposed with Bandura’s studies of self-efficacy beginning in the late 1970s, it is clear why the majority of studies concerned with the theory of self-efficacy relative to learning have been conducted in face-to-face, rather than digital, environments. In relation to digital learning, researchers have generally devoted their time to topics other than the development of self-efficacy, such as the effectiveness of online learning (Rickles et al., 2018). The development of the technology that supports digital learning was not ubiquitous until a significant body of research existed on self-efficacy as studied in face-to-face environments. To understand how learners actually generate self-efficacy in digital learning, additional exploratory studies need to be conducted to understand how mastery experiences, vicarious experiences, and verbal persuasion happen in digital environments, particularly where the learning is asynchronous. There have, however, been many studies related to self-efficacy in digital learning that are often concerned with correlation, focusing on the usage of technology, and reveal a quantitative bias (Sur & Ates, 2022; Muhtadi et al., 2022).
The Need for Research on Self-Efficacy in Digital Environments
Because the behaviors of applying effort, persisting through challenges, and setting effective goals will benefit learners, it is important that digital learning experiences are designed to promote the development of self-efficacy. Many of the literature reviews of recent studies of self-efficacy in digital learning continue to cite Bandura and Schunk’s theories on how learners develop self-efficacy, which originated before digital learning was mainstream. Indeed, Schunk and DiBenedetto (2020) continue to posit the theories of the last several decades, but now include a cautionary note:
Although the theory’s principles are intended to be generic and apply across different contexts, some theoretical adaptation may be needed. Online and asynchronous media do not function in the same fashion as do face-to-face contexts. It should not be assumed that social cognitive motivational processes will operate in the same fashion in the latter contexts as they do in the former...Testing social cognitive motivational principles in technological environments requires newer types of methodologies (p. 8).
Schunk, as an eminent scholar of self-efficacy and the progenitor of self-efficacy for learning, recognizes that the digital learning revolution of the last two decades may have implications for self-efficacy research moving forward.
Other scholars are also beginning to note that the well-established body of knowledge around self-efficacy should be interrogated for its applicability in digital learning. Bradley et al. (2017) note the increasing presence of digital learning and explicitly state the need for more research into the impacts of learning online versus a face-to-face environment. According to Chiu et al. (2021, p. 187):
Although a substantial number of studies have encompassed these relevant issues in the field of educational technology, research evidence on how to appropriately adapt pertinent learning and motivational theories to design effective and sustainable online pedagogy in a complex, multifaceted, and even situational online learning environments are still relatively under-investigated.
W. A. Zimmerman (2016) adds that the majority of self-efficacy research related to digital learning has been focused on digital literacy, rather than the development of self-efficacy. Without significant exploratory study of the development of self-efficacy in digital learning, it would be unwise to hypothesize that the theories related to self-efficacy operate in the same way in digital learning as they do in face-to face environments. For these reasons, researchers must continue to explore the concept of self-efficacy in digital learning.

Methods

Research Methods
The purpose of this study was to explore teachers’ knowledge of self-efficacy and how students demonstrate self-efficacious behaviors in virtual classrooms. The problem of practice being addressed is that, while schools have many initiatives and expectations of virtual students that require self-efficacy, teachers’ knowledge of self-efficacy, and, therefore, their ability to support students in increasing their self-efficacy may be limited. Additionally, how students develop self-efficacy in digital learning environments as compared to face-to-face environments may be undertheorized and further inquiry into the matter could be helpful (Schunk & DiBenedetto, 2020). This study addressed this problem by creating recommendations for curriculum developers to support teachers in helping students increase their self-efficacy. To accomplish this, this study explored teachers’ level of knowledge regarding self-efficacy to develop recommendations for professional learning through curricular materials. Secondly, this study examined how students exhibit behaviors associated with self-efficacy. Self-efficacious learners are known to display specific behaviors, including goal setting, effort, and persistence (Bandura, 1997; Carpenter & Clayton, 2014, Bradley et al., 2017; Zilka et al., 2019; Schunk & DiBenedetto, 2020). By understanding how virtual students demonstrate efficacious behaviors while learning, recommendations for content design can support creating activities that allow for students to demonstrate those behaviors.
Outline of the Solution
High-quality curricular materials support both student achievement and teacher development (Darling-Hammond, 2010). According to Barrow and Hirsch (2022), curricular materials are central to teacher development and learning. There is increasing evidence that curricular materials are an effective conduit for teacher growth (Chu et al, 2022; Barrow & Hirsch, 2022; Foster, 2022). Curriculum influences how teachers engage with students in the classroom and directly has impact on student outcomes (Opfer et al., 2018; EdReports, 2023). Given the impact of curriculum on teacher learning and practice and the importance of self-efficacy, curriculum designers should consider how to support teachers in increasing their knowledge of the topic. This study explored what teachers know about self-efficacy and how virtual students demonstrate efficacious behaviors in order to create a set of recommendations for curriculum designers to support teacher learning about self-efficacy and to design opportunities for students to demonstrate efficacy.
This study used semi-structured interviews to explore what virtual teachers know about self-efficacy and how virtual students demonstrate the behaviors of goal-setting, effort, and persistence. By exploring these items, recommendations for further research and curriculum design could support teachers in increasing virtual students’ self-efficacy. This was a case study design, which is appropriate because of the exploratory purpose, and the case was defined as four virtual middle school teachers at a virtual school in Texas (Creswell & Creswell, 2018).
Participants were recruited from a Texas virtual school in which students are taught asynchronously; an asynchronous instructional model was important so that participants’ responses reflect only digital learning. The structure of the interview was as follows:
• Collect demographic information about the participant.
• Seek to understand what initiatives, if any, within the school context that require self-efficacy.
• Seek to understand expectations of learners that require self-efficacy.
• Explore what participants know about self-efficacy and what misconceptions they have.
• Explore participants’ perceptions of goal setting, effort, and persistence in digital learning.
Participants were recruited from a single school to provide a more in-depth understanding of initiatives related to self-efficacy within the case, and to ensure participants have a shared experience (Bhattacharya, 2017). Interviews followed a script of open-ended questions, but also allowed for probes to more deeply explore participants’ perceptions (Creswell & Creswell, 2018).
Study Context and Participants
 This study took place as many countries emerged from the COVID-19 pandemic in 2023. This study was conducted after the Biden administration announced the end of the national emergency related to the Pandemic and after most schools returned to in-person learning (U.S. News & World Report, 2023). While the national response to the pandemic has ended, COVID-19 pushed millions of American students online and drastically accelerated the use of digital learning in the classroom (UNESCO, 2020; DLAC, 2023). While schools have reopened for in-person learning, the majority of parents of K-12 students desire that digital learning options for their students be provided by schools (Stride, 2021).
Texas Online Virtual School
 The case for this study is defined as four virtual middle school teachers from Texas Online Virtual School. Texas Online Virtual School (TOVS) operates an online elementary school (grades 3-5), a middle school, (grades 3-8), a high school (grades 9-12), and a career readiness program. Participants for this study were teachers at TOVS in the middle grades. TOVS began operating in the 2016-2017 school year. “As a college-prep magnet school authorized by [redacted] school district, TOVS offers an academically rigorous program geared towards college-bound, self-motivated students seeking challenging courses, collaborative instruction and high academic expectations” (PR Newswire, 2016). TOVS offers core subject areas, as well as AP and elective courses; students in 11th or 12th grade can also enroll in college courses at no cost to earn college credit while in high school (PR Newswire).
TOVS is accredited by a school district in southeast Texas; however, enrollment is open to all Texas residents in grades 3-12. TOVS enrolled approximately 9,400 students during the 2021-2022 school year, and 3,202 of those students were in grades 6-8; approximately 73% of students were non-white and over 50% were free lunch eligible (NCES, 2023).
U.S. News and World Report (2022) ranked TOVS 642 out of 2113 middle schools in Texas. On the State of Texas Assessment of Academic Readiness (STAAR) exams, TOVS outperformed the district average in reading and was slightly above the district average in Math. In reading, 58% of TOVS students scored proficient or above as compared to 29% in the district, placing 235th in the state for reading scores (U.S. News & World Report, 2022). In Math, 31% of TOVS students scored at or above proficient as compared to 25% of district students, placing 1010th in the state. In reading, TOVS students scored 19% higher than the state average on the STAAR exam, and in Math they scored 3% below the state average.
As an online, virtual model, TOVS students attend school entirely online and are not required to attend activities in a physical building. This online model has been the method of instructional delivery since the school’s inception. Additionally, the instructional model offers synchronous live sessions as well as asynchronous work, with some students completing their work entirely asynchronously. According to the participants in this study, students must demonstrate their ability to be successful in the program before they are able to opt-in to the completely asynchronous model.
TOVS utilizes a curriculum authored by K12. K12 curriculum utilizes digital delivery methods to support gamification, connection to popular video games and other media, and the curriculum has won awards, such as the Courseware Solution Provider of the Year from the Ed-Tech Breakthrough Awards (K12, 2023). The middle school curriculum focuses on student interactions, including discussions and virtual social activities (K12, 2023). Lessons include downloadable/printable resources for students, multimedia, textbooks, practice exercises, assessments, and extension activities.
Study Participants
Creswell & Creswell (2018) recommend between 3-10 participants for a case study design. Furthermore, Bhattacharya (2017) recommends that the number of participants be correlated to the length of the study so that the researcher can engage with participants as deeply as needed to collect sufficient quality data. Consistent with this, four virtual teachers were selected for this study as a criterion-based sample. Participants who were selected for this study met the following criteria:
• Employed by TOVS as virtual teachers
• Taught grades 6-8
• Have at least 1 year of virtual teaching experience
• Taught students through asynchronous methods
• Were recommended by their assistant principal as high-performing teachers
Participants were recruited through email. Participants taught multiple middle school (grades 6-8) subjects including ELA, Math, Science, and Social Studies.
Research Paradigm
This was a qualitative case study research paradigm. This methodology was appropriate for this study because the purpose was to explore teachers’ perceptions of self-efficacy. In a study of perceptions, the context of the study and the values of the participants are highly important to the design and results of the study, which is a characteristic of naturalistic inquiry (Guba, 1981; Lincoln & Guba, 1985). Additionally, the fact that the researcher was a data collection device would indicate the naturalistic paradigm of this study, and this paradigm is generally associated with qualitative studies (Lincoln & Guba, 1985). Case studies are an appropriate methodology when the research problem centers on understanding participants’ perceptions (Reeves, 2022; Reynoso, 2018; Shamir-Inbal & Blau, 2021). Additionally, case studies are appropriate for research questions that are exploratory, using “how” questions, do not necessitate control of participant behavior, and focus on present events (Yin, 2003). Case studies are also an excellent methodology for presenting information that is highly dependent on the context (Lincoln & Guba, 1985). Cases within case studies share characteristics and data collection (Creswell & Creswell, 2018). In this study, the case was defined as four virtual middle school teachers.
This study adopted a constructivist paradigm because meaning was being made from the participants’ experiences and that meaning was interpreted between the researcher and participants (Creswell & Creswell, 2018; Bhattacharya, 2017). While this study was not meant to generalize, the data from multiple participants within the case allowed for increased validity and a more robust discussion of the findings than if there was a single participant in the case. Data analysis procedures across participants produced themes for discussion in the study.
Data Collection Methods
The semi-structured interviews were recorded with participant agreement, and automatic transcription software was used. The researcher took notes during the interview (Creswell & Creswell, 2018). Creswell & Creswell (2018) indicate that interviews “elicit views and opinions from the participants,” which is appropriate for an exploratory study about teachers’ perceptions (p. 187).
Prior to conducting interviews, a semi-structured interview protocol was developed for use in data collection (Creswell & Creswell, 2018; Reynoso, 2018). This protocol included prompts aligned to the research questions:
1. What do grade 6-8 virtual teachers know about the construct of self-efficacy?
2. What misconceptions do grade 6-8 virtual teachers have about the construct of self-efficacy?
3.  How do grade 6-8 virtual teachers believe virtual students exhibit the behaviors of effort, persistence, and goal setting in digital learning environments?
Data Analysis Strategy
After the completion of the semi-structured interviews, the researcher’s notes, interview recordings, and interview transcripts were gathered. The researcher reviewed the data from each interview in its entirety prior to beginning analysis procedures; once the data had been reviewed, the researcher coded the data, organized codes into categories, and then drew themes across participant data as is consistent with inductive qualitative data analysis (Creswell & Creswell, 2018; Bhattacharya, 2017). Additionally, a recursive approach supported by reflexive writing at each stage was used, which provided deeper analysis of the data (Bhattacharya, 2017).
Data Analysis Procedure
Data were analyzed using the following procedure:
1. Researcher notes, interview transcripts, and interview recordings were gathered and reviewed. Video transcripts were reviewed for accuracy (Creswell & Creswell, 2018).
2.  Open coding was applied separately to interview recording/transcripts and researcher notes by selecting phrases in the data that relate to the research questions and then applying a code to those phrases; phrases could be associated with multiple codes (Shamir-Inbal & Blau, 2021; Kumi-Yeboah & Amponsah, 2023; Bhattacharya, 2017; Creswell & Creswell, 2018). Interview transcripts and researcher notes were coded separately to avoid confirmation bias.
3. During the coding process, the researcher winnowed the data to that which was relevant to the research questions (Reeves, 2022; Creswell & Creswell, 2018).
4. After open coding was applied to the data, a recursive round of a priori coding related to research questions 1 and 2 was applied (Kumi-Yeboah & Amponsah, 2023). The two a priori codes used in this second round of coding were know and misconception, related to research questions 1 and 2, respectively.
5. The researcher wrote in a reflexive journal after coding to record questions, observations, and any other notes (Bhattacharya, 2017).
6. Codes for each participant were then reviewed across interview recordings/transcripts and the researcher’s notes. Codes present in both the interviews and notes were recorded, and those codes were then attributed to direct quotes from the interview recordings. Only codes attributable to a direct quote from the participant were ultimately recorded as a code for the participant (Shamir-Inbal & Blau, 2021; Reynoso, 2018).
7. Once each participant’s data were coded, the researcher highlighted common codes across two or more participants and organized those common codes into categories (Reynoso, 2018; Creswell & Creswell, 2018).
8. Once categories were determined, the researcher determined themes across participants. Themes were determined by reviewing the categories and codes that were highly related between at least two participants and supported answering the research questions; themes were grouped according to the research questions to which they corresponded (Reeves, 2022; Reynoso, 2018; Shamir-Inbal & Blau, 2021). The researcher reviewed the accuracy of themes by considering the repetition between participants’ codes and the content of the quotes to which those codes were attributed between participants (Reeves, 2022). Codes were for thematic analysis if they were present in the data for at least 3 of 4 participants or if at least two participants used the same language in relation to the code (Sarigoz & Deveci, 2023).
1. Themes for research question 1 centered around participants’ responses that correctly demonstrated knowledge of self-efficacy. This includes correctly defining self-efficacy or partially defining it; correctly identifying constructs related to self-efficacy such as achievement, persistence, effort, and self-regulation; and correctly identifying behaviors that might be associated with self-efficacy, such as self-advocacy or attempting challenging tasks (Bandura, 1997; Schunk 1982, 1984, 1996a, 1996b).
2. Themes around research question 2 addressed misconceptions teachers have about self-efficacy; for example, mistaking self-efficacy for another construct or identifying behaviors that are unrelated to self-efficacy.
3. Themes for research question 3 identified observable behaviors exhibited by virtual students that teachers believed demonstrated goal-setting, effort, and persistence.
9. The researcher wrote in the reflexive journal to record analytical questions and other observations that arose during the process of creating themes (Bhattacharya, 2017).
10. To increase trustworthiness, a second researcher coded 25% of the data. The second coder was provided brief training on the theoretical frameworks of the study, a thematic codebook developed in steps 2-8 of this process, and a clean copy of the data (O’Connor & Joffe, 2020; Cheung & Thai, 2023).
11. Once themes were determined, the researcher conducted member checks with each participant by providing each participant their codes as well as the themes identified in the analysis process via email (Guba, 1981; Kumi-Yeboah & Amponsah, 2023). Participants had the opportunity to provide feedback to both the recorded codes and the themes. Participants were asked to provide an overall assessment of the accuracy of the researcher’s interpretations of the data.
12. Themes were corroborated with sample lessons from K12 to provide insight into how data provided by participants might be implemented through the instructional materials. Digital lessons were reviewed from the student experience. This review included reading all written content, viewing all media, and examining all supplemental materials, such as graphic organizers or study guides.

Results

Results
Cross-Participant Analysis for Research Question 1
 One interview question asked participants to define self-efficacy. Jesse was correctly able to define self-efficacy as “How a student is able to practice, perform, and meet the expectations. How they are able to complete a task or achieve a goal they have set, whether it is their own expectation or somebody else’s expectation.” This definition alludes to students producing desired results by “meeting expectations.” Nevaeh, Madison, and Dee were not able to correctly define self-efficacy, meaning three of the four participants did not provide a correct, clear definition of self-efficacy. When I asked Jesse how they learned the definition of self-efficacy, they stated that they learned about self-efficacy in a graduate school program.
Data coded for research question 1 illustrated knowledge participants had related to the construct of self-efficacy. The data indicated that participants knew that self-efficacy is related to motivation, that self-efficacy is related to the behaviors of self-regulation, and that they were aware of some tactics to build self-efficacy in students. Three participants made the connection between self-efficacy and motivation. All four participants made the connection to self-regulation and cited correct methods for building self-efficacy. Codes related to self-regulatory behaviors were the most frequent. Table 7 summarizes thematic codes relative to research question 1 that met the criteria for thematic analysis as outlined in chapter 3. These codes emerged through an open coding process.
Three themes related to participants’ knowledge of self-efficacy emerged from the data:
● Teachers are aware that self-efficacy and motivation are related.
● Teachers have an awareness of self-regulatory behaviors that are linked to self-efficacy.
● Teachers currently exercise methods of building self-efficacy in their work, even if they are not aware that those methods develop self-efficacy.

Cross-Participant Analysis of Research Question 2
 During the interviews, participants incorrectly described relationships between self-efficacy and other constructs. The first type of misconception was describing causal relationships between self-efficacy and other constructs that do not exist in the literature. Secondly, virtual teachers had misconceptions about which constructs were directly related to self-efficacy; participants identified some constructs, which were unrelated to self-efficacy or Social Cognitive Theory, as being the same or only having slight differences. These misconceptions arose as participants were defining self-efficacy, describing how they support students in building their self-efficacy, and identifying why self-efficacy is important to digital learners.
Data coded for research question 2 illustrated misconceptions participants had about self-efficacy. The data indicated that participants had misconceptions around what causes self-efficacy and the relationship between self-efficacy and other constructs. Misconceptions about the relationship between self-efficacy and other constructs fell into two types: naming relationships that do not necessarily exist and suggesting that self-efficacy was synonymous with other constructs. Three of the four participants revealed each of these misconceptions.
Two themes related to research question 2 arose from the data:
● Virtual teachers have some misconceptions about what causes self-efficacy.
● Teachers have some misconceptions about the relationship between other constructs and self-efficacy

Cross-Participant Analysis of Research Question 3
 Research question 3 was: how do grade 6-8 virtual teachers believe virtual students exhibit the behaviors of effort, persistence, and goal setting in digital learning environments? These three behaviors have been cited extensively throughout the literature as being enabled by self-efficacy (Bandura, 1977, 1997; Carpenter & Clayton, 2014, Bradley et al., 2017; Zilka et al., 2019; Schunk & DiBenedetto, 2020). In face-to-face environments, teachers might visibly observe these behaviors in students through actions, such as sustained writing on a project or reworking a math problem. However, in digital environments, where teachers cannot observe all the actions of students, students may have their cameras turned off, or when work may be altogether asynchronous, it is not as obvious when students are demonstrating these behaviors.
Codes related to research question 3 identified observable ways in which students demonstrated the efficacious behaviors of effort, persistence, and goal-setting. Participants were explicitly asked how students demonstrated these behaviors. Codes indicated that effort was demonstrated by students completing work, spending time on task, and accessing materials in the LMS. Behaviors of persistence included students revising work, including implementing feedback, or advocating for themselves. Finally, no codes related to student-initiated behaviors of goal-setting met the criteria for thematic analysis. Rather, the data revealed that students were not independently engaged in goal-setting, and that teachers led any goal-setting processes with little follow-up. Therefore, the two thematic codes related to goal-setting were Teacher-led/created and Lack of Monitoring.
This study took a first step in this investigation through the lens of self-efficacy. Self-efficacy supports learner success during any stage of life, so it is critical that those who develop learning materials keep an eye toward research-based methods by which learners might increase their self-efficacy. Additionally, in K-12, it is important that practitioners are equipped with knowledge about self-efficacy and practices that support students in developing it. The findings of this study demonstrated that teachers often do not have formal learning around this topic. To remedy this, knowledge of self-efficacy can be imparted by teaching materials used each day in the classroom.

Importance

Educational Importance
This study took a first step in this investigation through the lens of self-efficacy. Self-efficacy supports learner success during any stage of life, so it is critical that those who develop learning materials keep an eye toward research-based methods by which learners might increase their self-efficacy. Additionally, in K-12, it is important that practitioners are equipped with knowledge about self-efficacy and practices that support students in developing it. The findings of this study demonstrated that teachers often do not have formal learning around this topic. To remedy this, knowledge of self-efficacy can be imparted by teaching materials used each day in the classroom.

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