Teacher Sense of Efficacy and Capturing the Initial Transition to Remote Instruction

Abstract

Schools abruptly ended face-to-face instruction in March 2020 and transitioned to emergency remote teaching due to the COVID-19 pandemic. We surveyed teachers across the United States between March and April 2020 to understand their experiences during this time in our history (n = 249). Linear regression analysis was used to examine relationships that existed between teacher-level, school-level, and learning modality-related variables and teacher self-efficacy. Teachers in Title 1 schools reported lower levels of efficacy. Elementary teachers, as well as those that initially relied on instructional packets during the transition to remote instruction reported significantly lower levels of efficacy for classroom management.

Keywords

COVID-19 coronavirus teachers emergency remote teaching self-efficacy

Introduction

The COVID-19 viral pandemic disrupted all facets of daily life in early 2020, and K-12 schooling was no exception. By March 23, all but two states ordered their schools closed to slow the spread of the virus (Maranto et al., 2020; Marshall, 2022). With face-to-face instruction no longer an option, schools were forced to transition to remote instruction. Teachers were faced with the impossible job of moving instruction online within a matter of days, many with little to no prior training or experience to do so, and some with very little guidance from their schools or departments. Teaching remotely is challenging under normal circumstances, and these challenges are exacerbated in an emergency situation for which schools had no contingency plan. The purpose of this study was to understand the extent to which teachers felt prepared and able to manage their professional work during the transition to emergency remote instruction. We specifically examined the extent to which teacher characteristics, school contextual variables, and instructional approaches were associated with teachers’ sense of efficacy.

Teacher Sense of Efficacy

Teacher self-efficacy refers to a teacher’s belief about their capabilities to bring about desired student engagement and learning outcomes (Tshannen-Moran and Hoy, 2001). Denham and Michael (1981) defined teacher sense of efficacy as both “the sense of likelihood that the ideal or normative teacher can bring about positive changes in the student; and an assessment of the teachers own ability to bring about such changes” (p. 39). Similarly, Dembo and Gibson (1985) defined teacher sense of efficacy (TSES) as “the extent to which teachers believe they can affect student learning” (p. 173). The construct has been tied to a wide range of important outcomes, including student achievement (Caprara et al., 2006; Guo et al., 2010), student motivation (Midgley et al., 1989), and job satisfaction (Caprara et al., 2006; Skaalvik & Skaalvik, 2010). Teacher self-efficacy has also been associated with classroom management skills and teacher stress and burnout (Herman et al., 2018). Throughout this paper, we will use TSES and teacher self-efficacy synonymously.

Studies have recently demonstrated the validity of Tschannen-Moran and Woolfolk Hoy's (2001) TSES scale in a variety of contexts (i.e., Duffin et al., 2012; Heneman et al., 2006; Klassen et al., 2009; Tsigilis et al., 2010). Heneman and colleagues (2006) surveyed a school district in Nevada and found a factor structure and scale reliabilities consistent with Tschannen-Moran and Woolfolk Hoy (2001). This study indicated that TSES scores had significant direct effect on teacher performance. Results from Duffin and colleagues (2012) also confirmed the factor structure for the TSES in a sample of undergraduate pre-service teachers at the beginning of their program, though they suggested that “pre-service teachers who lack pedagogical knowledge and teaching experience do not differentiate between the different aspects of teaching measured by the TSES” (p. 827). The TSES has also been validated in other countries. For example, Klassen and colleagues (2009) demonstrated its validity across five countries and showed a positive relationship between teacher self-efficacy and job satisfaction. Similarly, Tsiglis and colleagues (2010) found a Greek version of TSES to be valid and reliable in a sample of primary and secondary teachers in Greece. Larson and Goebel (2008) used the TSES to measure the impact of co-teaching on pre-service TSES and found that it increased because of collaboration between elementary teacher and college professor working with pre-service teachers. Additionally, Powers and colleagues (2016) employed the TSES across 15 schools in the same district and found that teacher-led professional development positively impacted teacher self-efficacy.

Teacher sense of efficacy has been explored in terms of its relationship to teacher-level variables. Previous literature on the association between teacher experience and self-efficacy is mixed. Studies have found that greater teaching experience was associated with higher levels of teachers’ sense of efficacy (Ross et al., 1996; Wolters & Daugherty, 2007); however, other studies have not found a positive relationship between teacher experience and TSES (Ghaith & Yaghi, 1997). Consistent with Ross and colleagues’ findings, Fives and Buehl (2009) found that teachers with 10 or more years of teaching experience had significantly higher levels of self-efficacy. Fives and Beuhl also found that elementary teachers reported significantly higher levels of self-efficacy than middle or high school teachers. However, Pressley and Ha (2021) found that elementary teachers who taught virtually during COVID-19 reported lower levels of self-efficacy. For special education teachers, the literature is mixed on predictors of self-efficacy. Leyser et al. (2011) found that special education teacher candidates had significantly higher levels of self-efficacy than general education teachers. Adebomi et al. (2012) studied job satisfaction and self-efficacy in special education teachers and found that teacher self-efficacy was a significant predictor of job commitment.

Finally, teachers who taught subjects other than English, Math, Science, and/or Social Studies/History often had their instructional time cut back during the Spring 2020 transition to remote instruction (Brown-Aliffi, 2022; Marshall et al., 2020). Teachers who taught these elective subjects were often given less live instructional time where instruction was synchronously delivered, or shifted to provide asynchronous instruction only, typically without the ability to require student participation or assign grades. As such, understanding how elective teachers’ sense of professional efficacy during the initial remote instruction phase of the pandemic is important.

Teacher self-efficacy has also been explored in terms of its relationship with school-level/contextual variables. One study found that pre-service teachers felt more prepared and confident to teach at suburban schools compared to urban schools (Siwatu, 2011). Other research suggests that a range of factors mediate the development of teachers’ self-efficacy beliefs and that many teachers placed in an urban setting do not experience a decline in their self-efficacy (Chester & Beaudin, 1996; Knoblauch & Woolfolk Hoy, 2008; Rushton, 2003). Very little research exists on the relationship between teachers’ sense of efficacy and working in a Title 1 school (Carter, 2022). However, Hammack and Ivey (2017) investigated engineering self-efficacy and engineering teacher self-efficacy and looked at schools’ Title 1 status as a factor. Engineering self-efficacy was found to be greater in non-Title 1 schools; however, engineering teacher self-efficacy was not found to be significantly different between settings. During the COVID-19 pandemic, access to resources has been found to be an important consideration, especially during the initial transition to remote instruction (Cash et al., 2022; Marshall et al., 2020; 2022a; 2022b; Marshall & Neugebauer, 2022; in press; Vanourek, 2020). Lee et al. (2011) found that a lack of support and resources for special education teachers negatively impacted their self-efficacy and ultimately their success as special education teachers. School learning modality, especially during the transition to remote instruction, has also been found to have a considerable impact on teacher practice during the pandemic (Love & Marshall, 2022). As such, exploring the impact of high-poverty contexts and school learning modality on teacher self-efficacy is prudent.

Online Teaching Self-Efficacy

As online instruction has become increasingly common, research related to online teaching and learning has become an important area to highlight. Specifically, scholars have begun to explore teacher self-efficacy in online contexts in addition to more traditional settings. In one of the early studies on online teaching self-efficacy, Presno (1998) identified discrete types of teacher anxiety related to using the internet in teaching and found that low self-efficacy played a role in each type. More recently, Lee and Tsai (2010) explored teacher’s self-efficacy related to teaching with the internet by examining correlations between teacher self-efficacy and their TPCK-W (Technological Pedagogical Content Knowledge-Web) score. They found significant correlations between web-based teaching self-efficacy and positive attitudes toward web-based instruction. Teachers with more overall teaching experience had lower levels of self-efficacy related to using the internet while teachers with more experience using the web had higher levels of self-efficacy (Lee & Tsai, 2010). In another recent study, Horvitz and colleagues (2015) used the TSES to measure online teaching self-efficacy and understand challenges professors face in transitioning to teaching online. Results demonstrated that online teaching self-efficacy was high, and that the perception of student learning variables had the greatest impact on self-efficacy. Other significant variables included semesters taught online, future interest in teaching online, gender, satisfaction with teaching online, and academic discipline (Horvitz et al., 2015). Importantly, Corry and Stella (2018) reviewed the literature on teacher self-efficacy in online education and found that much of this research focuses on three areas: (1) the balance between technological and pedagogical knowledge in supporting teacher self-efficacy development; (2) the role of learner self-efficacy in teacher self-efficacy; and (3) whether teacher self-efficacy differs in an online context. The researchers also point out the need to validate the association of teacher self-efficacy to student success in online education (Corry & Stella, 2018).

Novice Online Teaching

In addition to considering the literature related to online teaching self-efficacy, it is also important to understand the research on novice online teaching, particularly in the context of COVID-19 where many teachers began teaching online for the first time. Much of the literature in this area is situated in undergraduate and graduate level instructional settings. However, many of the themes and conclusions drawn from this literature may also be applicable in the K-12 environment.

One of the major challenges faculty face with online education is the shift from a faculty-centered approach to a more student-centered approach (Coppola et al., 2001). According to Conrad (2004), the novice online instructor is one who relies heavily on traditional face-to-face classroom experiences to inform his or her pedagogy, and the strategies used in face-to-face instruction do not always transfer seamlessly to the online environment. According to Tunks (2012), the instructor is the single most important factor in determining student success in an online class. Therefore, the instructor’s ability to communicate, form community, and deliver the appropriate lesson effectively is critical for student learning outcomes. However, novice instructors often demonstrate little awareness of collaborative learning, social presence, or the role of community in online environments and instead focus on their role as deliverers of content (Conrad, 2004).

Another major issue associated with online instructors is the demand on their time. According to Cavanaugh (2005), it takes faculty twice as long to prepare and teach online than face-to-face. Specifically, Conceicao (2006) found that faculty teaching online experienced an increase in workload due to the need to compensate for the lack of physical interaction (i.e., creating and preparing materials, interacting with students individually). Other research has demonstrated that novice teachers found online teaching to be challenging and burdensome (Choi & Park, 2006). In their case study on a novice online instructor, Choi and Park (2006) discuss specific challenges related to communicating and interacting with students, becoming accustomed to technology and multitasking during synchronous online sessions, and difficulties organizing one-hour synchronous sessions and measuring student outcomes.

A lack of interest in and preparation to teach online are also reported as issues related to online instruction. Instructors who have been teaching for many years in a face-to-face setting often do not feel comfortable switching to an online format. Some reasons for this discomfort may be due to a lack of confidence in their ability to connect with students online, their comfort level with technology, or their general perceptions of the value of online education (Osika et al., 2009). Additionally, online education preparation programs may not know how to best prepare instructors to transition from face-to-face to online teaching (Baran et al., 2011). Instructors tend to bring their traditional styles of teaching to online environments, and that does not appear to be working (Coppola et al., 2001). Therefore, it may be necessary to consider new strategies and approaches when preparing instructors for online teaching.

Some research has also found that teaching online can be very rewarding and recommended that instructors work with instructional designers to receive advice on time-saving techniques (Conceicao, 2006). Choi and Park (2006) agreed that it may be easier for the new online instructor to be successful with support in the form of training, instructional design, and development. De Gagne and Walters (2010) also pointed out the rewards, flexibility and convenience of online teaching and found that instructors want administrative support and recognition (i.e., compensation, mentoring, professional development, etc.) for the extra labor required for moving a course online. In a study of six faculty members who had never taught online before, Kammer (2015) found that “teaching online offers new experiences that may help faculty to grow as educators, including rethinking instruction, the needs of students and learning to use new teaching tools” (p. 6), and also discussed the importance of campus resources that support online teaching to guide novice online teachers “through the new world they are experiencing” (p. 6). Thus, the literature demonstrates clear challenges and rewards associated with online teaching as well as a specific need to provide support for novice online teachers that help them navigate these challenges and capitalize on the rewards.

Emergency Remote Teaching

Due to recent events revolving around the COVID-19 pandemic, the literature has only just begun to discuss emergency remote teaching (ERT) and how it differs from standard online instruction. According to Hodges et al., (2020), ERT “is a temporary shift of instructional delivery to an alternate delivery mode due to crisis circumstances” (para. 13). This is a distinct contrast to online learning in normal circumstances where virtual experiences are planned from the beginning and designed to be online (Affouneh et al., 2020; Hodges et al., 2020). Specifically, ERT is described as providing temporary access to instructional materials and instruction in a quick and reliable way during an emergency or crisis. These remote teaching solutions are meant to be temporary and anticipate a return to face-to-face or hybrid formats once the crisis or emergency comes to end (Hodges et al., 2020). Additionally, educators must work under stressful circumstances while having no knowledge of the end of the crisis (Affouneh et al., 2020). According to Marshall et al., (2020), teachers found all facets of their work to be more difficult during the spring 2020 transition to ERT. Specifically, they found that teachers described themselves as lacking the appropriate training and adequate time to do their job well. They also often lacked the ability to communicate with students in real time, hold students accountable for their learning, and faced challenges related to access to technology.

Scholars are finding it critical to distinguish between ERT and online learning due to the significant time many have spent “trying to promote online learning as a viable, sustainable, valuable method of teaching and learning” (Manfuso, 2020, para. 6). Hodges et al. (2020) explain that there will be a great temptation to compare online learning to face-to-face instruction in these circumstances. The authors see this as problematic due to the pre-existing stigma of online learning being of lower quality than face-to face learning and the likelihood that the current emergency shifts online could confirm these negative perceptions of online learning. While researchers in educational technology have carefully defined terms and designed frameworks for effective types of online instruction, the important differences in pedagogy and design strategies have not yet diffused beyond educational technology and instructional design researchers and professionals (Hodges et al., 2020). Tendencies to compare ERT to thoroughly researched strategies for online learning is “a highly problematic suggestion” (para. 12).

Many sources have also discussed strategies for coping with the sudden demands of moving instruction online. Given the extraordinary circumstances teachers faced during the pandemic, some advised instructors to reduce workloads in their courses for both them and their students. Stephen Merrill (2020) wrote that it was necessary to reset our baselines, “we’re all operating in the shadow of a global pandemic, and it is disorienting and limiting… business as usual is unrealistic” (para. 8). Acknowledging that “these are not normal teaching and learning conditions,” Millman (2020), suggested that school leaders and educators prioritize needs by establishing short- and long-term priorities, be flexible with policies and practices, and collaborate in online communities to support one another. Cavanaugh and DeWeese (2020) also found that many educators who transitioned to remote learning sought out online resources to help plan and deliver lessons to their students in this new environment. Their findings indicated that online searches for education content increased significantly from February to March 2020, demonstrating that teachers were looking for pedagogical support online (Cavanaugh & DeWeese, 2020).

Taken together, literature on novice online teaching and emergency remote teaching tell us that even in the best circumstances, teachers who are new to online teaching face challenges and benefit from support systems and resources. Even before considering the additional challenges related to the COVID-19 pandemic, online instructors face challenges in transitioning from a teacher-centered to a student-centered approach, managing the additional time demands that online instruction requires, and finding the resources and support needed to ensure effective pedagogical practice in an online environment.

Teachers prepared their classrooms for the 2019–2020 school year unaware that school doors would be closing by March 2020. In early spring, the COVID-19 pandemic replaced face-to-face instruction with remote instruction, changing the manner in which teachers taught and students learned. As the transition to remote instruction increased, so did the need for teachers to effectively prepare and implement online lessons for their students.

Method

This study aimed to understand teacher preparedness during the transition to remote instruction by centralizing teacher self-efficacy, online teaching self-efficacy, novice online teaching, and emergency remote teaching. We were specifically interested in learning how variables related to teacher experience, the schools they taught in, and how their school’s response to the disruption caused by COVID-19 influenced their sense of efficacy as teachers. We were also interested in learning whether teachers described the challenges they faced during this moment in history differently based on variables that were significant predictors of their sense of efficacy. Our aim was not to generalize beyond this specific point in time, but rather to capture it as a benchmark against which future studies’ findings might be compared. This work was guided by the following three questions:

1. To what extent were teacher-level variables associated with teachers’ sense of efficacy during the transition to remote instruction?

2. To what extent were school/contextual variables associated with teachers’ sense of efficacy during the transition to remote instruction?

3. To what extent was school learning modality associated with teachers’ sense of efficacy during the transition to remote instruction?

Data Sources

To answer our research questions, we surveyed a voluntary sample of teachers nationwide between mid-March and April 2020. After obtaining IRB approval, we distributed an anonymous link to our survey using our personal networks of teachers. The link was also shared on social media networks including Facebook and Reddit. We also asked teachers to share the link with other teachers they knew who might be willing to complete the survey. Despite the limitations associated with convenience and snowball sampling, this approach was used for the sake of time, since we were collecting these data during the early stages of the transition to emergency remote instruction. Participants had to be currently employed as PK-12 teachers and respond to the requested teacher self-efficacy items to be included in this study.

A total of 249 teachers were included in the study. This study’s participants predominantly identified as White (89.3%), female (83.5%) and had an average age of 40 years. The average participant had been teaching at their current school for 7.5 years and had been in the profession for 12.14 in total. More than half of our sample (55.6%) indicated that they teach in a Title 1 school. More than one-third (38.3%) of teachers in our sample teach in a school located in a suburban setting, and about a quarter teach in rural (25.4%) and urban settings (22.6%). About one in seven teachers (13.7%) teach in a small town. Student access to digital resources varied in schools where participants taught. About one-third (35.3%) of participants teach in schools with a 1:1 student to technology ratio and 44.2% of teachers indicated that 75% or fewer of their students had access to broadband internet outside of school. See Table 1 for descriptive statistics for the study’s sample.

Table 1.

Descriptive Statistics for Study Sample (N = 249).

	n (%)	Mean (SD)
Race/ethnicity (n = 224)
African American/Black	12 (5.4%)
Asian American	2 (0.9%)
Hispanic or Latino/a/x	4 (1.8%)
White/Caucasian	200 (89.3%)
Other	6 (2.6%)
Gender (n = 224)
Female	187 (83.5%)
Male	37 (16.5%)
Age as of 1/1/2020 (n = 249)		40.0 (10.43)
Geographic location of school (n = 248)
Rural	63 (25.4%)
Small town	34 (13.7%)
Suburban	95 (38.3%)
Urban	56 (22.6%)
% of students with broadband (n = 249)
76–100%	110 (44.2%)
51–75%	91 (36.5%)
50% or fewer	48 (19.3%)
Teach at title I school (n = 248)	138 (55.6%)
Taught online before COVID-19 (n = 249)	10 (4.0%)

Note. N = 249.

Instrumentation

The survey administered in this study included (1) demographic and contextual items; (2) the Teacher Sense of Efficacy scale (long form); and (3) open-ended items asking teachers to describe their experiences during the transition.

Teacher Sense of Efficacy Scale

The long form of the Teacher Sense of Efficacy scale was used to examine teachers’ self-efficacy in the current study (Tschannen-Moran & Woolfolk Hoy, 2001). It is comprised of 24 items in total and has three subscales. The subscales focus on teacher self-efficacy in relation to student engagement, instruction, and classroom management, each of which is comprised of eight items. The scores for overall sense of efficacy, as well as for the three subscales were calculated by taking unweighted mean scores based on participant responses. Higher scores indicated greater levels of TSES, whereas lower scores indicated lower levels of TSES. (See Tschannen-Moran & Woolfolk Hoy, n.d. for more information on how the items are scored.) Scores from all three TSES subscales were highly correlated with one another. See Table 2 for a correlation matrix of the three subscales.

Table 2.

Correlations Between Teacher Sense of Efficacy Subscales.

Subscale	SE	IS	CM
SE	1
IS	0.675***	1
CM	0.634***	0.674***	1

Note. SE = Student Engagement; IS = Instructional Strategies; CM = Classroom Management; ***p < .001; **p < .01; *p < .05.

We modified the TSES instrument in two ways. First, we amended the prompt for the items. The original prompt reads, “Please respond to each of the questions by considering the combination of your current ability, resources, and opportunity to do each of the following in your present position.” Since we were interested in learning how efficacious teachers felt during the transition to remote instruction, we amended the prompt to read, “Please indicate your opinion about each of the statements below in terms of your ability to teach in the current context with schools physically closed.” Also, we modified the number of response options and opted to use a 7-point scale, maintaining the labels for the two bookends: (1, “None at all” and 7, “A great deal”) and the middle point (4, “Some degree”). The original scale includes nine response options with anchors for all of the odd-numbered response options. Previous scholarship has found that 2–9 point scales yielded similar reliability (e.g., Bendig, 1954). Lissitz and Green (1975) found no substantial increase in reliability when moving beyond five scale points, and Krosnick and Presser (2010) have noted that increases in reliability are negligible beyond 7 points. The 7-point scale that we use is a compromise between five points and the nine points found in the original instrument. The reliability estimates summarized in Table 3 all exceed .75 and approximate those reported by Tschannen-Moran and Woolfolk Hoy (2001).

Table 3.

Comparing Reliability Coefficients Between Tschannen-Moran and Woolfolk Hoy (2001) and Current Study.

Teacher Efficacy Scales	Items	Mean^a (SD)	Cronbach’s Alpha (Current Study)	Cronbach’s Alpha (Tschannen-Moran & Woolfolk Hoy, 2001)
Student engagement	8	4.07 (0.87)	0.77	0.87
Instructional strategies	8	5.02 (1.12)	0	0.91
Classroom management	8	4.72 (1.31)	0.84	0.90
Overall teacher sense of efficacy	24	4.50 (1.00)	0.92	0.94

N = 249.

^a7-point scale (1 = None at all, 4 = Some influence, 7 = A great deal).

Findings

Overall, teachers expressed a moderate amount of self-efficacy with all scales averaging above the scale midpoint of 4. An examination of the specific aspects of self-efficacy revealed that teachers expressed the greatest amount of self-efficacy related to the use of instructional strategies, followed by classroom management and student engagement. The results from a within-subjects analysis of variance revealed that there was a statistically significant difference among the three aspects of self-efficacy (F _1,494 = 135.877, p < .001). Post-hoc tests indicate that teacher self-efficacy related to student engagement was significantly lower than that related to instructional strategies and classroom management, and self-efficacy related to classroom management was significantly lower than that related to instructional strategies.

TSES During the Transition to Remote Instruction

Linear regression analysis was used to examine relationships with TSES. More specifically, the total TSES score, and the three subscales were used as the dependent variables in four separate linear regression analyses—one with the overall TSES score as the dependent variable and three additional analyses, one for each of the three subscales. A total of 15 teacher-level, school-level, and learning modality independent variables were examined to build these regression models and are operationally defined described in Table 4. Teacher-level variables included two dummy variables for teacher experience; a dummy variable indicating whether a teacher taught a subject that was an elective subject; a dummy variable for whether the participant was a special education teacher; a dummy variable for whether the participant taught grades PK-5; and a dummy variable for whether the participant had previous online teaching experience (at any level, including postsecondary). School-level variables included three dummy variables for the geographic location of the school; one for Title 1 status; a dummy variable for school-level technology resources; and a dummy variable for student broadband access. Finally, three dummy variables were included to represent learning modalities used to teach students during the initial phase of COVID-19 when school discontinued in-person instruction. Both types of variables (teacher and school) were important to examine for several reasons. First, teachers must teach within the parameters of school instructional policies and rely on the resources and technology available to them and their students. Second, the teaching and learning process and instructional approaches also vary in relation to different content areas and grade levels (Horvitz, et al., 2015). That is, teaching high school history is not the same as teaching elementary school music. Finally, the amount of experience and especially preparation to teach online will impact teachers’ sense of efficacy (Lee & Tsai, 2010; Powers et al., 2016).

Table 4.

Variables Included in the Models.

Variable	Operationalization
Teacher-level variables
Teacher experience 1–5 yrs	Dummy variable (1 = Yes, 0 = No) indicating that a teacher had 1–5 years of teaching experience (reference = taught 6–9 years)
Teacher experience 10+ yrs	Dummy variable (1 = Yes, 0 = No) indicating that a teacher had 10 or more years of teaching experience (reference = taught 6–9 years)
Taught elective subject	Dummy variables (1 = Yes, 0 = No) used to indicate that the participant taught a subject that was not English, Math, Science, Social Studies/History (reference = taught English, Math, Science, and/or Social Studies/History, or was an elementary classroom teacher)
Special education	Are you a special education teacher? Yes = 1; No = 0
Taught elementary grades	Dummy variable (1 = Yes, 0 = No) indicating that a teacher taught students enrolled in grades PK-5 (reference = taught secondary grades)
Previous online teaching	Have you ever taught online prior to the pandemic? Yes = 1; No = 0
School-level variables
Geographic location	Three dummy variables for rural, small town, and urban (reference = Suburban)
Title 1	Do you teach at a Title 1 school? Yes = 1; No = 0
1:1 devices	Does your school issue a device that can access the internet to each student (e.g., laptop, tablet) Yes = 1; No = 0
Student internet access	Do more than 50% of your students have access to broadband internet at home? Yes = 1; No = 0
Learning modality during COVID
Instructional modality	Three dummy variables indicating the instructional modality that was employed at their school when schools closed for in-person instruction, including (1) online instruction only; (2) paper-based instructional packets only; and (3) online instruction supplemented by instructional packets

For each dependent variable (overall TSES, student engagement, instructional strategies, classroom management), three models were tested which correspond to the study’s three research questions. The first research question sought to learn the extent to which teacher-level variables were associated with TSES. For each of the four regression analyses conducted, the first model includes the six above-described teacher-level variables. The second research question sought to understand the extent to which school-level variables were associated with TSES. The third research question sought to learn how much the teacher’ school’s instructional modality during emergency remote teaching was associated with TSES. For each set of analyses, the third and full model included the same teacher-level and school-level variables and added the three dummy variables related to the participant’s school’s learning modality during the initial transition to remote instruction.

The COVID-19 pandemic represented a unique context in which to explore teacher self-efficacy. Data collected in K-12 school settings are often hierarchical in nature (Raudenbush & Bryk, 2002). Had we known the identities of the schools in which our participants taught, multilevel regression modeling would have been appropriate (Raudenbush & Bryk, 2002), nesting teachers in schools to understand the amount of variance explained at both the individual teacher level and at the school level. This was not possible since we did not collect personally identifiable data in this study and did not know the schools in which teachers participating in this study taught. As such, we tested teacher-level and school-level variables separately to understand their unique influence on the amount of variance explained (Darlington & Hayes, 2017). The third block of variables related to instructional modality were then tested separate from the first two blocks of variables because they were unique to the context in which this study took place—the transition to emergency remote instruction during the COVID-19 pandemic. All requisite assumptions were tested and met for the regression analyses.

Overall Teacher Sense of Efficacy

Overall, the full model regression model was statistically significant and explained approximately 13% of the variance. For the full model, the only variable that reached statistical significance was whether teachers were employed in Title 1 schools (β = −.298, p < .05). Teachers in Title 1 schools reported lower overall teacher self-efficacy compared to teachers who taught in non-Title 1 schools. Teaching students enrolled in elementary school grades (PK-5) was also associated with lower levels of overall self-efficacy for the first two models. This was no longer significant when learning modality predictors were included in the full model. See Table 5 for beta coefficients and standard errors for each of the three models tested.

Table 5.

Regression Findings for Overall Teacher Sense of Efficacy.

Predictor	Model 1	Model 2	Model 3
Predictor	β (SE)	β (SE)	β (SE)
Teacher variables
Teaching experience: 1–5 yrs	−0.051 (0.179)	−0.050 (0.182)	−0.061 (0.177)
Teaching experience: 10+ yrs	−0.015 (0.166)	−0.007 (0.166)	0.000 (0.163)
Taught elective subject	0.022 (0.138)	0.051 (0.140)	−0.006 (0.138)
Taught special education	0.064 (0.174)	0.043 (0.174)	−0.038 (0.171)
Taught elementary grades	−0.308* (0.121)	−0.299* (0.124)	−0.188 (0.126)
Previous online teaching experience	0.310 (0.222)	0.317 (0.221)	0.309 (0.216)
School context variables
Title 1		−0.316* (0.139)	−0.298* (0.136)
Rural		−0.278 (0.174)	−0.167 (0.173)
Small town		0.179 (0.195)	0.220 (0.192)
Urban		−0.217 (0.170)	−0.208 (0.167)
Students had 1:1 devices pre-COVID		0.099 (0.128)	0.011 (0.127)
75%+ students had broadband		−0.054 (0.167)	−0.177 (0.169)
Learning modality during transition
Online instruction only			0.146 (0.504)
Online instruction with packets			−0.200 (0.504)
Instructional packets only			−1.112^a (0.582)
R²	0.036	0.074	0.130
R² Δ	0.036	0.038	0.056*

^ap = .057.

Note. ***p < .001; **p < .01; *p < .05.

Student Engagement

The full model for the student engagement subscale was statistically significant and explained approximately 9.5% of the variance. None of the 15 variables included in the full model reached statistical significance. Teachers reported similar levels of TSES in student engagement regardless of experience, subject or grade level taught, school context, or the learning modality employed during the transition to remote instruction. See Table 6 for beta coefficients and standard errors for each of the three models tested.

Table 6.

Regression Findings for Student Engagement Subscale.

Predictor	Model 1	Model 2	Model 3
Predictor	β (SE)	β (SE)	β (SE)
Teacher variables
Teaching experience: 1–5 yrs	−0.123 (0.163)	−0.009 (0.166)	−0.101 (0.163)
Teaching experience: 10+ yrs	−0.024 (0.151)	−0.019 (0.152)	−0.013 (0.150)
Taught elective subject	0.067 (0.125)	0.080 (0.127)	0.035 (0.126)
Taught special education	−0.017 (0.157)	−0.029 (0.158)	−0.094 (0.156)
Taught elementary grades	−0.091 (0.110)	−0.104 (0.113)	−0.020 (0.115)
Previous online teaching experience	0.288 (0.201)	0.289 (0.200)	0.278 (0.198)
School context variables
Title 1		−0.247^a (0.126)	−0.234 (0.124)
Rural		−0.023 (0.159)	0.066 (0.159)
Small town		0.239 (0.177)	0.277 (0.176)
Urban		−0.219 (0.154)	−0.210 (0.153)
Students had 1:1 devices pre-COVID		−0.010 (0.116)	−0.083 (0.117)
75%+ students had broadband		−0.012 (0.152)	−0.108 (0.156)
Learning modality during transition
Online instruction only			0.191 (0.461)
Online instruction with packets			−0.070 (0.461)
Instructional packets only			−0.823 (0.532)
R²	0.017	0.051	0.095
R² Δ	0.017	0.034	0.044

^ap= .051.

Note. ***p < .001; **p < .01; *p < .05.

Instructional Strategies

The full model for the instructional strategies subscale was statistically significant and explained approximately 10.1% of the variance. For the full model, the only variable that reached statistical significance was whether teachers were employed in Title 1 schools (β = −.367, p < .05). Teachers in Title 1 schools reported lower overall teacher self-efficacy compared to teachers who taught in non-Title 1 schools. Teaching students enrolled in elementary school grades (PK-5) was also associated with lower levels of overall self-efficacy for the first two models. This was no longer significant when learning modality predictors were included in the full model. See Table 7 for beta coefficients and standard errors for each of the three models tested.

Table 7.

Regression Findings for Instructional Strategies Subscale.

Predictor	Model 1	Model 2	Model 3
Predictor	β (SE)	β (SE)	β (SE)
Teacher variables
Teaching experience: 1–5 yrs	−0.018 (0.214)	−0.040 (0.216)	−0.053 (0.212)
Teaching experience: 10+ yrs	0.032 (0.197)	0.057 (0.198)	0.073 (0.196)
Taught elective subject	0.088 (0.164)	0.086 (0.166)	0.032 (0.164)
Taught special education	−0.016 (0.206)	−0.047 (0.205)	−0.130 (0.204)
Taught elementary grades	−0.326* (0.144)	−0.310* (0.147)	−0.212 (0.150)
Previous online teaching experience	0.186 (0.262)	0.207 (0.261)	0.189 (0.258)
School context variables
Title 1		−0.379* (0.164)	−0.367* (0.162)
Rural		−0.386 (0.206)	−0.268 (0.207)
Small town		0.181 (0.230)	0.235 (0.229)
Urban		−0.282 (0.201)	−0.276 (0.199)
Students had 1:1 devices pre-COVID		0.036 (0.152)	−0.062 (0.153)
75%+ students had broadband		−0.135 (0.198)	−0.265 (0.203)
Learning modality during transition
Online instruction only			0.535 (0.601)
Online instruction with packets			0.179 (0.602)
Instructional packets only			−0.658 (0.694)
R²	0.025	0.062	0.101
R² Δ	0.025	0.037	0.039*

Note. ***p < .001; **p < .01; *p < .05.

Classroom Management

The full model for the classroom management subscale was statistically significant and explained the greatest amount of the variance out of the three subscales (14.8%). Two variables were significant in the full model. Teaching students enrolled in elementary school grades (β = −.331, p < .05) was associated with lower levels of self-efficacy for classroom management. Teachers who taught in schools that exclusively relied on paper-based instructional packets (β = −1.731, p < .05) also reported lower levels of self-efficacy for classroom management. Teachers in rural schools reported lower levels of classroom management self-efficacy (β = −.466, p < .05) when teacher-level and school-level variables were tested together. When learning modality predictors were added for the full model, this was no longer significant. See Table 7 for beta coefficients and standard errors for each of the three models tested. Table 8 compares the full model for each of the four dependent variables tested—overall TSES, student engagement, instructional strategies, and classroom management.

Table 8.

Regression Findings for Classroom Management Subscale.

Predictor	Model 1	Model 2	Model 3
Predictor	β (SE)	β (SE)	β (SE)
Teacher variables
Teaching experience: 1–5 yrs	0.047 (0.238)	−0.028 (0.239)	−0.046 (0.234)
Teaching experience: 10+ yrs	−0.128 (0.219)	−0.124 (0.219)	−0.135 (0.216)
Taught elective subject	0.004 (0.182)	0.044 (0.184)	−0.030 (0.181)
Taught special education	0.391 (0.229)	0.336 (0.228)	0.247 (0.225)
Taught elementary grades	−0.528*** (0.160)	−0.480** (0.163)	−0.331* (0.166)
Previous online teaching experience	0.373 (0.291)	0.394 (0.290)	0.400 (0.285)
School context variables
Title 1		−0.367* (0.182)	−0.337 (0.162)
Rural		−0.466* (0.229)	−0.339 (0.229)
Small town		0.114 (0.255)	0.138 (0.253)
Urban		−0.154 (0.223)	−0.153 (0.220)
Students had 1:1 devices pre-COVID		0.275 (0.168)	0.182 (0.169)
75%+ students had broadband		−0.047 (0.220)	−0.199 (0.225)
Learning modality during transition
Online instruction only			−0.291 (0.664)
Online instruction with packets			−0.714 (0.665)
Instructional packets only			−1.731* (0.766)
R²	0.066	0.102	0.148
R² Δ	0.066	0.036*	0.046*

Note. ***p < .001; **p < .01; *p < .05.

Instructional Modality

Further examination of instructional modality revealed teachers in schools using only instructional packets reported lower teacher self-efficacy in comparison to the other approaches. Statistically significant differences pertained to overall teacher self-efficacy (F = 12.17, p < .001, η² = .083), as well as for self-efficacy related to student engagement (F = 5.99, p = .003, η² = .043), instruction (F = 8.35, p < .001, η² = .059), and classroom management (F = 12.13, p < .001, η² = .083). For each modality, the greatest self-efficacy was reported for the instructional strategies scale (online only, M = 5.14, combination of online instruction and packets, M = 4.77, and paper-based packets only, M = 3.81). The lowest self-efficacy was reported related to engagement (M = 4.16 for online only, M = 3.94 for online/packets, M = 3.26 for packets only).

Model Comparison

For each of the four of the regression analyses conducted in this study, the full model was significant. The overall change in variance explained also improved from Model 2 to the full model (Model 3) for each of the analyses conducted; the addition of the instructional learning modality variables improved the amount of the variance explained. The full model for overall TSES (R² = 0.148) and for the Classroom Management subscale (R² = 0.130) had the greatest explanatory power of the four analyses. Only the models that explored predictors of teachers’ self-efficacy for classroom management were significant for all three models tested. Each set of predictor variables added significantly improved the model. See Table 9 for a comparison of findings from the full model for each of the four sets of regression models tested.

Table 9.

Comparing Regression Findings for Teacher Sense of Efficacy.

Model Predictors	Overall	Student Engagement	Instructional Strategies	Classroom Management
Model Predictors	B(SE)	β(SE)	B(SE)	B(SE)
Teacher variables
Teaching experience: 1–5 yrs	−0.061 (0.177)	−0.101 (0.163)	−0.053 (0.212)	−0.046 (0.234)
Teaching experience: 10+ yrs	0.000 (0.163)	−0.013 (0.150)	0.073 (0.196)	−0.135 (0.216)
Taught elective subject	−0.006 (0.138)	0.035 (0.126)	0.032 (0.164)	−0.030 (0.181)
Taught special education	−0.038 (0.171)	−0.094 (0.156)	−0.130 (0.204)	0.247 (0.225)
Taught elementary grades	−0.188 (0.126)	−0.020 (0.115)	−0.212 (0.150)	−0.331*(0.166)
Previous online teaching experience	0.309 (0.216)	0.278 (0.198)	0.189 (0.258)	0.400 (0.285)
School context variables
Title 1	−0.298*(0.136)	−0.234 (0.124)	−0.367*(0.162)	−0.337 (0.162)
Rural	−0.167 (0.173)	0.066 (0.159)	−0.268 (0.207)	−0.339 (0.229)
Small town	0.220 (0.192)	0.277 (0.176)	0.235 (0.229)	0.138 (0.253)
Urban	−0.208 (0.167)	−0.210 (0.153)	−0.276 (0.199)	−0.153 (0.220)
Students 1:1 devices pre-COVID	0.011 (0.127)	−0.083 (0.117)	−0.062 (0.153)	0.182 (0.169)
75%+ students had broadband	−0.177 (0.169)	−0.108 (0.156)	−0.265 (0.203)	−0.199 (0.225)
Learning modality during transition
Online instruction only	0.146 (0.504)	0.191 (0.461)	0.535 (0.601)	−0.291 (0.664)
Online instruction with packets	−0.200 (0.504)	−0.070 (0.461)	0.179 (0.602)	−0.714 (0.665)
Instructional packets only	−1.112 (0.582)	−0.823 (0.532)	−0.658 (0.694)	−1.731^a(0.766)
Model 1 R²	0.036	0.017	0.025	0.066**
Model 2 R²	0.074	0.051	0.062	0.102**^b
Full model R²	0.130***^c	0.095*^c	0.101*^c	0.148***^c

^ap = .057.

^bR² change from Model 1 p < .05.

^cR² change from Model 2 p < .05.

***p < .001; **p < .01; *p < .05.

Discussion

This study sought to understand teachers’ sense of efficacy during the greatest disruption to American public education in history—the closure of schools for in-person instruction due to the COVID-19 pandemic. Specifically, we were interested in whether relationships existed between teacher-level, school-level, and learning modality predictor variables and teachers’ sense of efficacy. Teacher-level variables were consistently nonsignificant in each of the models tested. The lone exception was for teachers who taught students enrolled in grades PK-5. Elementary school teachers experienced lower levels of self-efficacy for instructional strategies and classroom management. Similarly, most school-level predictors were also found to be nonsignificant, with one exception. The most stable predictor of teacher self-efficacy was whether they taught in a Title 1 school. This was either statistically significant or approaching statistical significance in every model. In terms of learning modality, teachers who taught in schools that exclusively relied on paper-based instructional packets for instruction reported lower levels of self-efficacy for classroom management and lower levels of overall self-efficacy that approached statistical significance. The nonsignificant findings are perhaps as noteworthy in this study as those that reached statistical significance. Regardless of how many years of experience a teacher had, the subject(s) they taught, or the urbanicity of the school in which they taught, the teachers we surveyed largely reported experiencing similar levels of self-efficacy.

More than nine in ten (88.2%) of the teachers that we surveyed taught at schools that online learning was a part of their school’s transition to remote instruction in the spring semester of the 2019–20 school year, yet a similar proportion of participants (96.0%) of teachers indicated that they had never taught online prior to the COVID-19 outbreak. Open-ended comments indicate that very few of the teachers that responded received any meaningful training from their school or school district during the transition period. In spite of this inexperience and limited preparation, almost half (49%) of teachers we surveyed felt at least somewhat prepared to deliver instruction remotely at the start of the transition to remote instruction. Teachers also indicated that they felt at least moderately effective at doing their jobs during remote instruction. Perhaps the most interesting takeaway from these findings is that, on average, teachers experienced this transition similarly. Again, the nonsignificant findings are at least as interesting as those that were significant. Regardless of grade level or subject taught, the urbanicity of their school setting, or whether their school had one-to-one devices for their students, teachers felt moderately efficacious at navigating an unprecedented moment in the history of American schooling. As we stated previously, our aim in this work was not to generalize beyond the time frame that is captured here, but rather to establish a benchmark against which future work can be compared.

Challenges Related to Poverty

Extant literature has outlined the challenges that teachers who teach in schools with high concentrations of poverty (e.g., Simon & Johnson, 2015), and our findings are a reminder of the extent of these challenges. Our findings suggest that teachers largely experienced the disruption caused by COVID-19 similarly, with those teaching in Title I schools being the exception to this. Survey participants who taught at Title 1 schools reported lower levels of self-efficacy than their peers who taught at schools that do not have the Title 1 designation, and this was the lone consistent teacher- or school-level predictor of TSES. Our survey included open-ended items, including one that asked teachers about the challenges they faced during this pivotal time. High-poverty schools have a more difficult time recruiting teachers (Lankford et al., 2002), face greater levels of teacher attrition (Borman & Maritza-Dowling, 2008; Glazer, 2020; Gross & DeArmond, 2010), and have more teachers teaching outside of their licensed subject area (Machtinger, 2007) than schools with lower concentrations of poverty. A lack of student access to internet, computing devices, and the resources to do school at home was a common challenge for students, and by extension their teachers, during the transition to remote instruction (Marshall et al., 2020).

Among our participants, 75.6% of teachers from Title 1 schools identified challenges related to poverty in the open-ended items, including access to technology and broadband internet, lack of resources at home, and other equity-related concerns. A middle school English teacher from Massachusetts shared, “My students are not on an even playing ground. My students are not all able to access instruction, or do not have the motivation to do so. Several are homeless, many have no internet, and most struggle with the intrinsic value of education… all of these cause an enormous uphill battle.” Similarly, an elementary special education teacher from New Jersey shared:

Some kids have little to no parent support. A few families are ESL. Some families are struggling with food insecurity, severe illnesses, and emotional crises. It’s hard to teach and learn when basic needs aren’t always being met due to the challenges of life right now. We are trying to make sure we are supporting students’ emotional and social needs as much as their academic growth, connecting families to food resources, and other essentials.

About half (53.6%) of teachers included in our sample who do not teach at a Title 1 school shared similar concerns. However, their concerns more often focused on instructional challenges and student motivation as they did on equity issues. A fifth grade teacher from Idaho shared, “I can’t even teach. I may only provide brief review activities that they should be able to do independently.” Other comments dealt with the difficulty of moving instruction online, adapting curriculum for remote instruction, instruction not being mandatory (and subsequent low participation rates), and student apathy. Although teachers in both Title 1 and non-Title 1 schools described challenges related to poverty, these challenges were exacerbated in high-poverty schools, and our findings suggest that these challenges led teachers to report lower levels of self-efficacy. These challenges likely mediate teachers’ ability to conduct their professional work, including planning for and delivering instruction.

Remote Learning Modalities

The other items that were found to be significant predictors of teacher self-efficacy during remote instruction were related to the remote learning modalities their school was employing. Teachers working at schools that used online instruction alone reported the highest levels of overall TSES as well as TSES related to instructional strategies, classroom management, and student engagement. Teachers who taught in schools that supported online instruction with instructional packets reported the second highest levels of self-efficacy. Teachers that only delivered instruction through packets consistently reported the lowest levels of self-efficacy. Among teachers who taught in schools that solely relied on packets, the greatest concern expressed in open-ended items was the inability to interact with their students and answer questions in real time. A special education teacher from Virginia shared that their biggest challenge was “being able to have back-and-forth communication where students can ask questions on the fly.” This subgroup of teachers was particularly concerned with their students who did not have internet or devices at home. Not only were their students not able to ask their teachers questions, in many cases they also lacked access to the resources they needed to complete the work required in the instructional packets.

Limitations

There are limitations worth noting related to this work. First, it is worth noting that all of our data is self-report data (Fowler, 2014) and that we administered our survey to a voluntary sample. As such, our findings might not be reflective of the larger population of teachers in the United States (Privitera & Ahlgrim-Delzell, 2019). Research endeavors often involve trade-offs. This study relied on a voluntary convenience sample and snowball sampling. This potentially limits the generalizability of our findings, since it is possible that those who elected to respond to the survey differed in some manner from those who did not respond. However, pursuing a more robust sampling approach would have been time-consuming, and time was not something that we had given that we were attempting to collect these data at the start of the transition to remote instruction in spring 2020. It is also reasonable to speculate that we would not have had a fast response from schools and school districts for research requests during the start of the pandemic as they were dealing with a crisis of an unprecedented magnitude.

Another limitation involves the item that we included to understand how our participants’ schools were transitioning to remote instruction; it was a crude measure of what took place in hindsight. As described above, we asked our participants if their schools were solely utilizing online instruction, solely using instructional packets, or using a mix of both. We constructed our survey in early March during the first couple of days when states began announcing that they were closing schools for in-person instruction due to the COVID-19 pandemic, and we began administering it within a week after receiving IRB approval. Since that moment in time, we have come to learn much about remote instruction including what took place after schools closed for the end of the 2019-20 school year, and how schools are delivering instruction to begin the 2020-21 school year (Marshall & Bradley-Dorsey, 2020, 2022). Given the wisdom of the present, we might have asked whether online instruction was delivered synchronously, asynchronously, or a combination of the two modalities. We also might have inquired about participation rates, how grading was handled, or any number of other items that we did not know to ask when we were surveying teachers in the midst of the transition. In fairness, many of these items were things that school districts and policymakers had also not yet considered at that moment in time. Additional work should explore if differences exist in teacher self-efficacy based on their assigned instructional modality and whether these differences change over time.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

David T. Marshall

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