Undergraduate Surveys Reveal That Instructors Are Key in Students Overcoming Classroom Struggles During the COVID-19 Pandemic

Introduction

Many undergraduate students encounter struggle as they navigate academic, financial, personal-emotional, and social contexts of higher education. To date, much of the research on college struggle has focused on student adjustment to college, how this adjustment is affected by antecedents and correlates, and how adjustment affects grades and retention (Credé and Niehorster, 2012). Students arrive at college with differing levels of preparedness and adaptability (Aspinwall and Taylor, 1992; Trimble, 2019); many students lack the knowledge or understanding to navigate the collegiate environment and may lack access to adequate resources, social support, and mentors (Horn and Nunez, 2000; Hickman and Andrews, 2003; Schneider and Ward, 2003; Mehta et al., 2011). Institutions often use correlates like SAT/ACT scores, socioeconomic status, first-generation college student status, transfer student status, and student commuter status to predict student persistence or to identify students that may need extra support while navigating campus life (Horn and Nunez, 2000; Longwell-Grice and Longwell-Grice, 2008; Mehta et al., 2011; Swecker et al., 2013). However, we know that college students are constantly adjusting to new social, institutional, and classroom environments throughout their college experience. Each semester, undergraduates must adjust to new instructors, courses, content, and course schedules, while also shouldering shifting external pressures and responsibilities (Li and Carroll, 2020). Balancing these ever-changing learning and social environments can be difficult for students, and struggling in one aspect may cascade to impact other aspects of the student’s life (Napoli and Wortman, 1998; Credé and Niehorster, 2012). Students that cannot navigate and adjust to the class format (Akiha et al., 2018), adjust their study habits (Lammers et al., 2001; Proctor et al., 2006), find effective study methods (Patel et al., 2015), and balance external pressures (Rytkönen et al., 2012; Li and Carroll, 2020), will struggle to succeed. The on-going COVID-19 pandemic has placed additional challenges on the adjustment to new learning and social environments with the uncertainties surrounding viral spread and mitigation strategies as well as shifts to emergency remote instruction.

Over the last 18 months, the COVID-19 pandemic has disrupted university class structure, campus life, and the personal lives of college students worldwide, which has heightened student struggles and stress (Wang et al., 2020). This abrupt change in learning environments has increased student classroom struggles during March and April of 2020, as most students, instructors, and institutions were ill-prepared for the transition to virtual learning (Taylor et al., 2020; Jaggars et al., 2021; Shin and Hickey, 2021). The transition to remote learning reduced student sense of community, perceptions of learning, and perceptions of career preparation (Supriya et al., 2021). These impacts may have been felt especially hard within STEM disciplines like biology, chemistry, physics, and engineering, where much of the learning and career preparation is done during interactive, hands-on laboratory/field research opportunities or internships. The loss of internship opportunities, hands-on learning, and research experiences increased student concern for post-graduation job prospects and future success (Aucejo et al., 2020; Lederman, 2020; Camfield et al., 2021; Gemelas et al., 2021).

The transition to remote learning heightened many of the already existing social and economic inequalities that students experience, including disparities in the accessibility of technology, secure employment, housing, social networks, and a dedicated workspace (Gillis and Krull, 2020; Lederman, 2020; Suprianto et al., 2020). The pandemic also caused major disruptions in students’ lives outside the classroom, affecting overall mental health and wellbeing [e.g., sleeping habits, nutrition, and exercise (Copeland et al., 2021)], and student stress level (Son et al., 2020; Charles et al., 2021; Correia et al., in press). Thus, the pandemic’s disruption of the academic and personal lives of students—in conjunction with the effect of external pressures on academic performance—may have created a feedback of student stress and anxiety that took a toll on success in the classroom (Grubic et al., 2020; Son et al., 2020; Copeland et al., 2021; Correia et al., in press).

While an abundance of literature has identified best practices for teaching online (Bailey and Card, 2009; Price et al., 2016; Woodley et al., 2017; Gillis and Krull, 2020), evaluated various technologies for teaching online (Persell, 2004), and measured learning outcomes of online learning for diverse student populations (Driscoll et al., 2012; Martin et al., 2019), the COVID-19 transition was marked by faculty-specific decision-making, which lead to the modification of content and resources and implementation of new learning technology in real-time (Westwick and Morreale, 2021). Thus, instructors may serve as critical front-line institutional agents that serve as a bridge to student success if they communicate expectations, communicate changes in class structure, demonstrate effective study methods and habits for class structure, and provide flexibility for students navigating changes in external pressures (Findley and Varble, 2006; Tanner, 2013). During the transition to emergency instruction, many students reported a reduction in mental health, they lacked motivation, felt distracted, reported feelings of uncertainty and anxiety, and worried about their ability to enroll or stay in college due to COVID-19 (Gillis and Krull, 2020; Murphy et al., 2020; Zhai and Du, 2020). Additionally, student perceptions of instructor preparedness, organization, and classroom learning environments were linked to student learning and emotional support and student wellbeing during the COVID-19 transition (Butler Samuels et al., 2021). Inversely, instructors can serve as barriers to student success if they fail to provide this type of learning environment. Students that report classroom struggles are more likely to withdraw from college relative to the general withdrawal rate of 40% nationally (Porter, 2003; Cortiella and Horowitz, 2014). However, the withdrawl rate increases two to three-fold when those students come from historically excluded racial/ethnic groups and/or first-generation college student households (Swail, 2003; Green and Wright, 2017).

The transition to online learning during the Spring of 2020 due to the COVID-19 pandemic provided an interesting opportunity to understand student struggle and persistence in the classroom. While many larger land-grant institutions, as well as smaller well-funded liberal arts colleges, may have had extra resources to support student wellbeing and access to technology, smaller state institutions and community colleges simply lacked the resources to support students during the transition to online learning. To explore student struggle associated with the transition to emergency remote teaching at a medium-sized, R2 institution, we: (1) measured the prevalence of self-reported student struggle at the University of South Alabama in Mobile, Alabama, (2) tested whether struggle varied across demographic factors such as race/ethnicity, gender, first-generation college students, transfer students, and commuter students, and (3) determined what factors contributed to student struggle and the ability to overcome those struggles. To accomplish this, we surveyed undergraduate students enrolled in biology courses during spring 2020 and asked whether they experienced struggle and, if so, if they were able to overcome it. We define student struggle as any self-perceived barrier that a student may encounter navigating the college learning experience and may be internal or external to the student and/or the college classroom. Based on their responses, open-ended questions further assessed how or why they succeeded or failed in overcoming their struggle, or what they attributed their lack of struggle to. We did not provide students with any pre-defined definition of struggle because we were interested in student perceptions and experiences in biology courses during the transition to remote learning. We then used thematic coding to group student responses to evaluate why students struggled, how students were able to persist in the face of the transition to online classes, or what barriers they experienced that did not allow them to overcome their struggle.

Materials and Methods

Student Participant Pool

Our participant sample included 238 undergraduate students enrolled across multiple undergraduate biology courses at the University of South Alabama, a public, R2 research institution in Mobile, Alabama. The student body of the University of South Alabama consists of 63% White, 20.6% African American/Black, 4.1% Latinx/Hispanic, and 3.7% Asian/Asian American students and consists of 67% female students, 32% male, and ∼1% gender expressive students (45). Our pool of students included both biology majors (120 students) and non-majors (104 students) across 16 courses. Of these 16 courses, seven were introductory biology courses with students being a mix of biology majors and non-majors (130 students total) and nine of the courses classified as upper-level courses with students enrolled as biology majors or minors (108 students total).

Study Design and Questionnaire Development

Students enrolled in biology courses were emailed a link to the Qualtrics survey two times during the final week of courses as part of a larger post-course data collection during May 2020. The post-course survey measured student perceptions of their learning environments and how they were impacted by the transition to remote instruction. The full survey consisted of questions to understand which biology courses students were enrolled in, any struggles they experienced in their biology courses, how they accessed departmental and campus resources during the transition to remote learning, external commitments like course load and job commitments, questions to understand how their hidden and visible identities shaped their in-class experiences, science identity formation, intrinsic motivation, demographic information, and questions on perceptions of evolution. Questions aimed at student struggle were collected at the beginning of the survey, after questions asking students to list which biology courses they were enrolled in. The full survey can be found in Appendix 1. Student participation in the study was completely voluntary and no monetary or class incentives were provided. The survey was emailed to 2,234 students that were enrolled in a biology course during the Spring 2020 semester for a response rate of 10.6% and took the average student 25.4 min to complete. Survey items and methodology were granted an exemption from full review by the University of South Alabama IRB, # 1544421-1 to JH.

Student Struggle

Our survey instrument consisted of two yes/no questions to explore (1) whether students struggled in their biology courses and (2) whether students were able to overcome that struggle (Figure 1). We did not explicitly define struggle for students on our survey; our goal was to allow students to define struggle through their lived experiences over the course of the semester, which undoubtedly included in-class and external factors of struggle. To get a more nuanced understanding of how students interpreted their struggle, we asked students an open-ended question which was dependent upon how students answered the yes/no questions. For instance, a student that responded that they did not encounter struggle in their biology course received the question: “Your response suggests that you did not encounter struggle in biology courses this semester. Please explain why this is or is not an accurate description of your experience,” termed “No struggle.” However, a student that responded they struggled, were able to overcome their struggle was prompted with: “Your response suggests that you encountered struggle and successfully overcame this struggle in biology courses this semester. Please explain why this is or is not an accurate description of your experience,” termed “Overcame struggle.” Finally, a student that reported struggle in biology courses and indicated they were not able to overcome those struggles were prompted with “Your response suggests that you encountered struggle and could not overcome this struggle in biology courses this semester. Please explain why this is or is not an accurate description of your experience,” termed “Never overcame.” The open-ended responses allowed students to communicate the various reasons they felt they may have struggled during the transition to remote learning and how they were able to overcome their struggles (Figure 2 and Table 1). We were able to qualitatively codify student responses to understand how in-class and external factors shape student struggles during the transition to remote learning during the Spring 2020 semester.

FIGURE 1

Figure 1. Progression of survey questions based on student responses.

FIGURE 2

Figure 2. Mosaic plots depicting qualitative responses of why students struggled during the Spring 2020 semester grouped by students were able to overcome that struggle (Overcame struggle) vs. students that were never able to overcome their struggles (Never overcame). See Table 4 for percentages in each category.

TABLE 1

Table 1. Example responses from students that did not struggle (No struggle), those that experienced struggle but overcame it (Overcame struggle), and those that experienced struggle but did not overcome it (Never overcame).

TABLE 2

Table 2. Identity facets and their categories.

TABLE 3

Table 3. Example responses from students that explain why they did or did not struggle, and how they were able or unable to overcome that struggle.

TABLE 4

Table 4. Percentage of responses that attributed students’ ability (Overcame) or inability (Not overcome) to overcome struggle, or why they did not struggle (No struggle) to each subcategory.

Student Identities

In addition to the struggle questions, we also asked students to self-report their race/ethnicity identity, gender identity, if they are the first person in their family to attend college (i.e., first-generation college student), if they were a transfer student, and how far they commute to campus (Table 2). Additionally, we used data on which courses students were enrolled in to determine if they were enrolled in introductory biology or upper-level biology courses and determined whether students were biology majors.

Statistical Analysis

To understand how student identities impacted self-reported struggle and ability to overcome struggle, we used Chi-Square analyses and summed dichotomous responses across student identities to determine differences in the percentages of students across race/ethnicity, gender identity, first-generation college students, biology major, whether students were enrolled in introductory- or upper-level biology courses, and transfer and commuter students that (a) reported struggle and (b) were able to overcome that struggle. This allowed us to test whether there were demographic differences in student struggle and ability to overcome that struggle during the transition to online learning.

To analyze our open-ended survey questions, responses were first categorized into three major areas: students who experienced no struggles, students who experienced struggles and overcame them, and students who experienced struggles but were not able to overcome them. Responses within those three major categories were then coded into subcategories using open and thematic coding for each survey item (Saldaña, 2021), based on why students struggled or how students were able to overcome the struggle (Table 1). Coding was completed in a series of predetermined steps to ensure consistency between coders (RLC and LRC). First, RLC and LRC examined the student responses, and designed a proposed coding rubric, then met to create a tentative rubric. Then, the first 30–50 responses for each survey item were independently binned into coding themes. Coders then met and revised the rubric based on discrepancies. All student responses were then coded using the finalized rubric. Coders then met and determined the percent agreement among coders and coded to consensus (100% agreement). In Appendix 2, we have included the initial coding results among our coders which matched 92–100% of their codes. We were able to organize our student responses into seven subcategories (Table 3), which included: instructors, self-effort or confidence, study habits, external factors, time management, class format, anxiety, and general difficulty with college not covered by the other categories. In some instances, students’ responses were grouped to more than one code, as responses and identities cannot always be defined by a single descriptor. Responses that did not fully answer the questions asked or were left blank were not coded and were excluded from our analysis. Out of the 350 students that were surveyed, 238 responses were analyzed. Once the data was organized and coded, a Pearson’s Chi-squared test was run to determine if there was a significant difference among the reasons why students struggled or how students overcame their struggles based on whether they were categorized as never struggled, struggled but overcame, or struggled and not overcome (Table 4). We calculated the frequency of responses within each theme by dividing the number of responses for a specific category and dividing it by the total data points gathered for one coding category.

Results

With the transition to online classes during March of 2020, 66.4% (n = 158) of the 238 students we surveyed reported that they encountered struggle. Fortunately, 83.5% (132 of 158) of the students that reported struggling with the transition indicated they were able to successfully overcome their struggle (Table 5). When we compared across our demographic groupings, we found no difference in reported struggle among racial/ethnic identities (χ² = 7.24, df = 4, p = 0.12), gender identities (χ² = 0.911, df = 1, p = 0.34), first-generation college students (χ² = 2.394, df = 2, p = 0.30), transfer students (χ² = 0.84, df = 1, p = 0.36), or commuter status (χ² = 2.53, df = 5, p = 0.77). Similarly, we found no demographic differences in how students were able to overcome that struggle across racial/ethnic identities (χ² = 1.24, df = 4, p = 0.87), gender identities (χ² = 0.175, df = 1, p = 0.68), first-generation college students (χ² = 1.88, df = 4, p = 0.76), commuter students (χ² = 1.72, df = 5, p = 0.89), and transfer students (χ² = 0.06, df = 1, p = 0.81). Additionally, we found no difference in introductory and upper-level biology courses (χ² = 1.75, df = 1, p = 0.19), although biology majors reported higher struggle in their biology course relative to non-majors (χ² = 6.07, df = 1, p = 0.014). However, whether students were biology majors or non-majors (χ² = 0.99, df = 1, p = 0.32) or introductory- vs. upper-level biology courses (χ² = 1.92, df = 1, p = 0.17) had any impact on whether students overcame their struggle. Thus, the transition to online learning due to the COVID-19 pandemic impacted our students broadly. No student groups were spared by the challenges of emergency remote instruction, although it appears our majors seemed to experience higher struggle within biology courses compared to non-majors. As a follow up, we coded our open-ended responses based on how students described the cause of their struggles and how they were able to overcome them.

TABLE 5

Table 5. Percentage of students that struggled (Struggle) but overcame their struggles (Overcame struggle), students did not struggle (No struggle), and students that could not overcome their struggles (Not overcome struggle).

Based on the open-ended responses that measured why students struggled during the transition to emergency remote learning, we found that students struggled for a variety of reasons. Of the 158 students who reported struggles, we found that 26% struggled with the class format, 22% reported the difficulty with the rigor of the course, 18% struggled because of poor or incorrect study habits, 15% reported that they struggled with both time management, and 14% struggled with external pressures (Figure 2). We found that 8% reported that their struggle was associated with lower self-confidence, 7% reported that the instructor was the reason they struggled, and 3% reported that high anxiety associated with the transition to online learning were the main reasons for their struggle (Figure 2). We found no significant differences in the reasons why students struggled when we compared the students that overcame their struggle to the students that did not struggle (χ² = 13.42, df = 7, p = 0.06), although the instructor was slightly higher in students that did not overcome their struggle. Thus, our results indicate that instructors can be a barrier for students navigating and attempting to overcome course struggles.

When we explored how students were able to overcome struggle or reasons why they did not struggle in class, we found significant differences between the “Overcame struggle” and “No struggle” groups (χ² = 55.17, df = 4, p < 0.0001, Figure 3). Of the 81 students who reported not struggling in the course, over 60% of the students that did not struggle in biology courses (49 of 81) and 13% of students that overcame their struggle, mentioned positive interactions with instructors as one of the main reasons for their success throughout the transition (Table 4). Students that attributed their success to instructors also tended to report higher self-confidence and belief in themselves to succeed (Figure 3). We found that 35% (28 of 81) of the students that did not struggle and 20% (26 of 132) of the students that overcame their struggle reported belief in themselves or self-confidence as a contributing factor. Changing their study habits after a poor performance on an early exam allowed 34% (45 of 132) of students to overcome their struggles. Furthermore, over 70% (32 of 45) of those students indicated that they improved their study habits with assistance from their instructors. Demographic groupings had no influence on why students struggled with remote learning or how they were able to overcome their struggles (data not shown).

FIGURE 3

Figure 3. Mosaic plots depicting qualitative responses of how students were able to persist above the challenges of the Spring 2021 semester (No struggle) or students that were able to persevere and overcome their struggles (Overcame struggle). See Table 4 for percentages in each category.

Discussion

The transition to emergency remote instruction during the Spring of 2020 because of COVID-19 provided an interesting case study to better understand college student struggle and how students overcome their struggle. Overall, we found ∼66% of our students struggled during the transition and this struggle was ubiquitous across all measured demographic identities. Additionally, we found that student struggle did not change between introductory- or upper-level biology courses, however, students enrolled as biology majors experienced more struggle relative to non-majors, although this did not impact their ability to overcome their struggle. We found that the transitional class format, poor study habits, difficulties with instructors, shifting external pressures, and time management were the main reasons why students struggled during the transition to remote instruction. Fortunately, most of our struggling students (∼84%) were able to overcome that struggle. Students reported that improved or altered study habits and improved self-confidence were the main reasons they were able to overcome the struggles. Students who could not overcome their struggles mostly attributed this to the abrupt transition to remote learning, while some also attributed struggle to poor study habits and their difficulties communicating and understanding the expectations of instructors. Students that did not struggle attributed their success to effective instructors and high self-confidence or belief in their abilities to navigate course content. In general, four main attributes (instructors, study habits, self-confidence, and class format) explained most of the student struggles, or lack thereof, at our university during the transition to remote instruction. Thus, instructors directly and indirectly impact student persistence and ability to overcome struggles within the classroom through their ability to boost self-confidence and self-efficacy, provide organized and well-thought-out course formats, and their ability to instruct students on study habits best suited for their course.

Our data demonstrate that instructors play key roles in preventing students from struggling in the classroom. Several students (n = 32) reported an effective change in study habits and exam performance after directly seeking help from their instructors. During difficult times, such as the ongoing COVID-19 pandemic, it is especially important for instructors to not only support students who need help, but also to create a virtual course that sets students up for success (Camfield et al., 2021; Supriya et al., 2021). According to our survey results, effective professors also assisted their students by making the transition to online learning as seamless as possible, while ineffective instructors were cited as a reason that students could not overcome their struggle. For example, students cited effective instructors as those who provided structure, clearly communicated course expectations (especially with exam material), and were accessible and approachable during the transition, with a willingness to help students as best they could. On the other hand, students cited ineffective instructors as those that did not provide structure, did not clearly communicate expectations, and even drastically altered the planned content and material. Several students also noted the increased workload during the transition to remote learning. Our results emphasize how essential it is for instructors to adjust their teaching style in a way that benefits students during this emergency transition, reinforcing the findings of many recent studies (Gillis and Krull, 2020; Kamble et al., 2021; Orlov et al., 2021; Tsang et al., 2021). Because instructors can be so influential, those that support their students as much as possible likely have positive influences on their students’ success, while those who do not may contribute to their students’ adversity (Gillis and Krull, 2020). A recent study highlighted how cognitive and social interventions contributed positively to student engagement, sense of belonging, and resilience during the transition to remote instruction, which positively improved student self-efficacy during remote instruction (Camfield et al., 2021). Instructors can ultimately function as a bridge or barrier for student success.

Our students indicated that efficiency of study habits and higher self-confidence played a large role in their successes in the classroom during the transition. Study habits were the main reason that students were able to overcome their struggles or not. Past studies support that it’s not just the time that students devote to studying, but how they study that affects academic performance (Lammers et al., 2001), including parameters such as motivation and concentration (Nonis and Hudson, 2010). During COVID-19, students that were able to adjust their study habits and adjust to the new instruction methods were able to maintain their academic performance (Limniou et al., 2021). However, students that struggled to adjust to learning style had reduced course performance and satisfaction in learning (Tsang et al., 2021). This ultimately leads to difficulty with development of effective study habits and assessment of their own learning (Bjork et al., 2013; Dunlosky et al., 2013). Because of this, instructors have been encouraged to include study strategies as part of their lessons (Hora and Oleson, 2017), to positively impact student study habits and learning outcomes (Brown-Kramer, 2020). A recent study demonstrated during the COVID-19 transition to online learning that instructor-student dialogue was a significant predictor of student initiative (i.e., motivation) which ultimately shaped student satisfaction with online learning (Tsang et al., 2021). Instructors play a critical role in encouraging students and demonstrating a positive-growth mindset, which constructively benefits student self-confidence, self-efficacy, and motivation (Zimmerman, 2000; Yeager and Dweck, 2012). Self-confidence can be linked to academic performance (Hansford and Hattie, 1982; Ugwuanyi et al., 2020) and can help reduce student anxiety and increase participation in class (Akbari and Sahibzada, 2020), which is vital during remote learning in a format that increased anxiety and separated students from each other and their classmates. Therefore, active instructor participation and explicit demonstration of best study habits is a necessary aspect during times with abrupt changes in class format/instruction.

Our results indicate that class format played the biggest role in students not being able to overcome their struggles. Nearly half of the students that could not overcome their struggles indicated that they could not adjust to the new class format. These results were not overly surprising, given the fact that our instructors and students were given little time or resources to make the transition from in-person classes to remote learning. Students were also forced to adjust to losses in routine, structure, peer interaction, and instructor interaction, as well as potential losses of on-campus housing, employment, etc. During the early part of the transition to online learning, student mental health declined rapidly; self-reported depressive and anxious symptomology among students increased, reducing motivation, and focus of students (Gillis and Krull, 2020; Grubic et al., 2020; Zhai and Du, 2020). When given the choice, most students prefer in-person course formats as opposed to remote learning (Driscoll et al., 2012), making the transition to online learning even harder and more likely to affect motivation to learn in an online learning environment (Gillis and Krull, 2020). The transition to online learning created additional challenges such as stable access to the internet, implementation of new software and online programs, limited technical support, and a lack of motivation and engagement in classes (Lederbogen et al., 2011; Gillis and Krull, 2020; Lederer et al., 2020; Son et al., 2020; Charles et al., 2021). These added challenges contributed to lower student retention, as seen in increased withdrawal rates or leaves of absence at schools nationally (Hope, 2021).

We did not find any significant relationship between student struggles in the classroom and the ability to overcome them across any student demographic groupings we compared. Regardless of race/ethnicity, gender, first-generation college student status, transfer status and commuter status, 66% of our student population struggled and 85% of them were able to overcome those struggles equally. This was surprising, given the many recent reports of COVID-19 inequality being experienced on many college campuses (Gillis and Krull, 2020; Pellicano and Stears, 2020; Fisher and Ryan, 2021). Given the fact that COVID-19 has exacerbated social and economic disparities across the nation (Fortuna et al., 2020; Hooper et al., 2020; Millett et al., 2020; Panchal et al., 2020; Gemelas et al., 2021), we expected to see first-generation students and students from historically excluded racial/ethnic backgrounds to experience higher levels of struggle associated with external pressures (loss of income, increased family commitments, increased stress, work/life balance, etc.; Swail, 2003; Green and Wright, 2017; Li et al., 2020). However, several recent studies have demonstrated that student struggle and performance was more correlated with how instructors were choosing to teach online courses rather than who they were teaching in those courses (Forakis et al., 2020; Orlov et al., 2021; Supriya et al., 2021) which is in-line with our results. Interpreted a slightly different way, our results highlight that the abrupt transition to remote learning caused struggle for most students, regardless of their demographic background. As COVID-19 continues to spread in waves with subsequent mutations, we suggest instructors follow best practices for remote instruction (recently reviewed in Adedoyin and Soykan, 2020; Gillis and Krull, 2020).

Although we saw no differences in student struggle or their ability to overcome those struggles based on any student demographic variables, we found that students that were biology majors indicated higher struggle compared to students that were non-majors. It is worth noting, our non-majors pool of students includes students that are both fulfilling general education requirements in introductory courses as well as students that are enrolled in upper-level biology courses pursing a minor. While the broadness of our open-ended questions may not have allowed us to understand the nuance of what biology students perceive as struggle compared to non-majors, recent investigations conducted on biology students reported many negative impacts on their learning, sense of community, and biology career preparation during the transition to remote learning in the Spring of 2020 (Driessen et al., 2020; Supriya et al., 2021). The loss of the formal and informal time spent with instructors and peers during a largely hands-on lab learning environments may contribute to negative impacts on student learning (Supriya et al., 2021), as well as loss in motivation and self-efficacy within biology students (Camfield et al., 2021). The reduction of self-efficacy during the transition to remote instruction may have led students to perceive higher struggle during courses that rely heavily on lecture delivery of information while simultaneously removing the opportunity of hands-on learning experiences within the laboratory (Camfield et al., 2021). However, the differences in perceived struggle among majors and non-majors within our study remain unknown.

Many undergraduate students encounter struggle as they navigate academic, financial, and social contexts of higher education. While much of this work has historically focused on student adjustment to starting college, there are many lessons that can be applied to the ongoing COVID-19 pandemic. For instance, the ability of students to adjust and transition to a new learning environment is predictive of college grades and retention (Credé and Niehorster, 2012). Adjustment is driven by a multitude of factors that include individual traits (Aspinwall and Taylor, 1992; Trimble, 2019), social support (Schneider and Ward, 2003), students’ relationship with their parents or guardians (Hickman and Andrews, 2003), demographic variables (McDonald and Vrana, 2007; Gray et al., 2013; Melendez, 2016), prior achievements (Adelman, 1996; Garavalia and Gredler, 2002), coping approaches (Matthews, 1998; Jantzer, 2006), and psychological independence. Once enrolled in college, instructors, along with other university agents like academic advisors, research mentors, and program heads, serve as critical social supports for students and can foster a strong sense of belonging, improve self-confidence and self-efficacy, and promote student growth and development. Our results highlight the direct and indirect role of instructors as focal points for student success while navigating the college experience. The importance of instructors to student success has been heightened during a global pandemic and the transition to remote learning environments.

Limitations

In conducting this research, we came across several limitations within the student sampling pool as well as within the sampling methods. While using an open answer format in the questionnaire allowed for a variety of student responses, it was difficult to gain insight on student experiences during the transition with students choosing not to answer the open-ended questions. It is also important to note that these are student perception surveys, meaning all the information we have of the student’s struggles is from their perspective and lived experience. If a student cited an instructor as the reason they could not overcome struggle, we don’t fully understand the reasoning behind this unless the student chose to divulge more information, nor do we have the experiences and perspectives of the instructors teaching these courses. Additionally, after binning student responses into not struggling, overcoming struggles, or not overcoming struggles, we asked an open-ended question why this is or is not an accurate description of their experience. However, if we asked why student struggled and how they were able to overcome those struggles may have provided us with more straightforward student responses. Furthermore, being a smaller institution that has a predominantly White and female student body, we may not have collected a large enough sample size of students from diverse gender, racial and ethnic backgrounds, and lived experiences to distinguish differences among our students.

Future Directions

Given the resurgence of COVID-19 across the United States with the rapidly spreading Delta and Omicron variants and other potential variants to come, universities may again be forced to transition to online learning. Thus, it is critical to understand the drivers of student struggles, persistence, and ability to overcome struggles during the transition between in-person and online course environments. We advocate for instructors facing a transition to remote learning to familiarize themselves with best practices in teaching online (Bailey and Card, 2009; Price et al., 2016; Woodley et al., 2017; Adedoyin and Soykan, 2020; Gillis and Krull, 2020; Westwick and Morreale, 2021). We also recommend that instructors conduct surveys at the start of the semester to understand differences in success and engagement between the two class formats and to address concerns or questions about a potential transition. These surveys can also be used to determine what resources students need to succeed in both in-person or online formats, informing the instructor about what additional resources need to be made available. We have learned a tremendous amount about what works and what doesn’t during the transition to remote learning; given the reality of facing another spike in COVID-19 cases, instructors should put this gained knowledge into practice during future transitions.

Data Availability Statement

The datasets and R scripts generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by the University of South Alabama IRB, # 1544421-1. The participants provided their written informed consent to participate in this study.

Author Contributions

JH conceived of the idea and put together the questionnaire. JH and MN organized questionnaire and data. RC and LC coded student response data. JH, RC, and LC conducted the statistical analysis. RC and LC coordinated the first draft with writing input by KC, BM, and MN. All authors contributed to the editing of the subsequent drafts of the manuscript.

Funding

This research was conducted using funding to JH from the National Science Foundation (NSF-BCSER-2025162).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We would like to thank two reviewers for their constructive assessment of our manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2022.841060/full#supplementary-material

References

Adedoyin, O. B., and Soykan, E. (2020). Covid-19 pandemic and online learning: the challenges and opportunities. Interact. Learn. Environ. 1–13. doi: 10.1080/10494820.2020.1813180

CrossRef Full Text | Google Scholar

Adelman, H. S. (1996). Restructuring education support services and integrating community resources: beyond the full service school model. Sch. Psych. Rev. 25, 431–445. doi: 10.1080/02796015.1996.12085831

CrossRef Full Text | Google Scholar

Akbari, O., and Sahibzada, J. (2020). Students’ Self-Confidence and Its Impacts on Their Learning Process. Am. Int. J. Soc. Sci. Res. 5, 1–15. doi: 10.46281/AIJSSR.V5I1.462

CrossRef Full Text | Google Scholar

Akiha, K., Brigham, E., Couch, B. A., Lewin, J., Stains, M., Stetzer, M. R., et al. (2018). What Types of Instructional Shifts Do Students Experience? Investigating Active Learning in Science, Technology, Engineering, and Math Classes across Key Transition Points from Middle School to the University Level. Front. Educ. 2:68. doi: 10.3389/feduc.2017.00068

CrossRef Full Text | Google Scholar

Aspinwall, L. G., and Taylor, S. E. (1992). Modeling cognitive adaptation: a longitudinal investigation of the impact of individual differences and coping on college adjustment and performance. J. Pers. Soc. Psychol. 63:989. doi: 10.1037//0022-3514.63.6.989

PubMed Abstract | CrossRef Full Text | Google Scholar

Aucejo, E. M., French, J., Araya, M. P. U., and Zafar, B. (2020). The impact of COVID-19 on student experiences and expectations: evidence from a survey. J. Public Econ. 191:104271. doi: 10.1016/J.JPUBECO.2020.104271

CrossRef Full Text | Google Scholar

Bailey, C. J., and Card, K. A. (2009). Effective pedagogical practices for online teaching: perception of experienced instructors. Internet High. Educ. 12, 152–155. doi: 10.1016/J.IHEDUC.2009.08.002

CrossRef Full Text | Google Scholar

Bjork, R. A., Dunlosky, J., and Kornell, N. (2013). Self-regulated learning: beliefs, techniques, and illusions. Annu. Rev. Psychol. 64, 417–444. doi: 10.1146/annurev-psych-113011-143823

PubMed Abstract | CrossRef Full Text | Google Scholar

Brown-Kramer, C. R. (2020). Improving Students’ Study Habits and Course Performance With a “Learning How to Learn” Assignment. Teach. Psychol. 48, 48–54. doi: 10.1177/0098628320959926

CrossRef Full Text | Google Scholar

Butler Samuels, M. A., Samuels, S. M., and Peditto, K. (2021). Instructor factors predicting student support: psychology course feedback following COVID-19 rapid shift to online learning. Scholarsh. Teach. Learn. Psychol. [Epub online ahead of print].

Google Scholar

Camfield, E. K., Schiller, N. R., and Land, K. M. (2021). Nipped in the bud: COVID-19 reveals the malleability of STEM student self-efficacy. CBE Life Sci. Educ. 20:ar25. doi: 10.1187/cbe.20-09-0206

PubMed Abstract | CrossRef Full Text | Google Scholar

Charles, N. E., Strong, S. J., Burns, L. C., Bullerjahn, M. R., and Serafine, K. M. (2021). Increased mood disorder symptoms, perceived stress, and alcohol use among college students during the COVID-19 pandemic. Psychiatry Res. 296:113706. doi: 10.1016/j.psychres.2021.113706

PubMed Abstract | CrossRef Full Text | Google Scholar

Copeland, W. E., McGinnis, E., Bai, Y., Adams, Z., Nardone, H., Devadanam, V., et al. (2021). Impact of COVID-19 Pandemic on College Student Mental Health and Wellness. J. Am. Acad. Child Adolesc. Psychiatry 60, 134–141.e2. doi: 10.1016/j.jaac.2020.08.466

CrossRef Full Text | Google Scholar

Correia, K. M., Bierma, S. R., Houston, S. D., Nelson, M. T., Pannu, K. S., Tirman, C. M., et al. (in press). Racial and gender inequalities in worry and stress related to the COVID-19 pandemic in an undergraduate student body. J. Microbiol. Biol. Educ. doi: 10.1128/jmbe.00224-21

CrossRef Full Text | Google Scholar

Cortiella, C., and Horowitz, S. H. (2014). The state of Learning Disabilities: Facts, Trends and Emerging Issues. New York: National Center for Learning Disabilities.

Google Scholar

Credé, M., and Niehorster, S. (2012). Adjustment to college as measured by the student adaptation to college questionnaire: A quantitative review of its structure and relationships with correlates and consequences. Educ. Psychol. Rev. 24, 133–165. doi: 10.1007/s10648-011-9184-5

CrossRef Full Text | Google Scholar

Driessen, E., Beatty, A., Stokes, A., Wood, S., and Ballen, C. (2020). Learning principles of evolution during a crisis: an exploratory analysis of student barriers one week and one month into the COVID-19 pandemic. Ecol. Evol. 10, 12431–12436. doi: 10.1002/ece3.6741

PubMed Abstract | CrossRef Full Text | Google Scholar

Driscoll, A., Jicha, K., Hunt, A. N., Tichavsky, L., and Thompson, G. (2012). Can Online Courses Deliver In-class Results?: A Comparison of Student Performance and Satisfaction in an Online versus a Face-to-face Introductory Sociology Course. Teach. Soc. 40, 312–331. doi: 10.1177/0092055X12446624

CrossRef Full Text | Google Scholar

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., and Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: promising directions from cognitive and educational psychology. Psychol. Sci. Public Interes. 14, 4–58. doi: 10.1177/1529100612453266

PubMed Abstract | CrossRef Full Text | Google Scholar

Findley, B., and Varble, D. (2006). Creating a conducive classroom environment: classroom management is the key. Coll. Teach. Methods Styles J. 2, 1–6. doi: 10.19030/ctms.v2i3.5262

CrossRef Full Text | Google Scholar

Fisher, A. N., and Ryan, M. K. (2021). Gender inequalities during COVID-19. J. Index. Metr. 24, 237–245. doi: 10.1177/1368430220984248

CrossRef Full Text | Google Scholar

Forakis, J., March, J. L., and Erdmann, M. (2020). The impact of COVID-19 on the academic plans and career intentions of future STEM professionals. J. Chem. Educ. 97, 3336–3340. doi: 10.1021/acs.jchemed.0c00646

CrossRef Full Text | Google Scholar

Fortuna, L. R., Tolou-Shams, M., Robles-Ramamurthy, B., and Porche, M. V. (2020). Inequity and the disproportionate impact of COVID-19 on communities of color in the United States: the need for a trauma-informed social justice response. Psychol. Trauma 12, 443–445. doi: 10.1037/tra0000889

PubMed Abstract | CrossRef Full Text | Google Scholar

Garavalia, L. S., and Gredler, M. E. (2002). Prior achievement, aptitude, and use of learning strategies as predictors of college student achievement. Coll. Stud. J. 36, 616–626.

Google Scholar

Gemelas, J., Davison, J., and Ing, S. (2021). Inequities in employment by race, ethnicity, and sector during COVID-19. J. Racial Ethn. Heal. Disparities 9, 350–355. doi: 10.1007/s40615-021-00963-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Gillis, A., and Krull, L. M. (2020). COVID-19 Remote Learning Transition in Spring 2020: class Structures, Student Perceptions, and Inequality in College Courses. Teach. Sociol. 48, 283–299. doi: 10.1177/0092055x20954263

CrossRef Full Text | Google Scholar

Gray, R., Vitak, J., Easton, E. W., and Ellison, N. B. (2013). Examining social adjustment to college in the age of social media: factors influencing successful transitions and persistence. Comput. Educ. 67, 193–207. doi: 10.1016/j.compedu.2013.02.021

CrossRef Full Text | Google Scholar

Green, S. L., and Wright, C. F. (2017). Retaining first generation underrepresented minority students: a struggle for higher education. J. Educ. Res. 11, 323–338.

Google Scholar

Grubic, N., Badovinac, S., and Johri, A. M. (2020). Student mental health in the midst of the COVID-19 pandemic: A call for further research and immediate solutions. Int. J. Soc. Psychiatry 66, 517–518. doi: 10.1177/0020764020925108

CrossRef Full Text | Google Scholar

Hansford, B. C., and Hattie, J. A. (1982). The relationship between self and achievement/performance measures. Rev. Educ. Res. 52, 123–142. doi: 10.3102/00346543052001123

CrossRef Full Text | Google Scholar

Hickman, G., and Andrews, D. (2003). Humor and college adjustment: the predictive nature of humor, academic achievement, authoritative parenting styles on the initial adjustment of male and female first-year college students. J. First-Year Exp. Students Transit. 15, 61–81.

Google Scholar

Hooper, M. W., Nápoles, A. M., and Pérez-Stable, E. J. (2020). COVID-19 and racial/ethnic disparities. JAMA 323, 2466–2467.

Google Scholar

Hope, J. (2021). Pandemic presents challenges, inspires innovation. Success. Regist. 21, 6–7. doi: 10.1002/TSR.30832

CrossRef Full Text | Google Scholar

Hora, M. T., and Oleson, A. K. (2017). Examining study habits in undergraduate STEM courses from a situative perspective. Int. J. STEM Educ. 41, 1–19. doi: 10.1186/S40594-017-0055-6

CrossRef Full Text | Google Scholar

Horn, L., and Nunez, A. (2000). Mapping the Road to College: First-Generation Students Math Track, Planning Strategies, and Context of Support NCES 2000 153. Postsecondary Education Descriptive Analysis Reports, Vol. 1. Washington, DC: National Center for Education Statistics. U.S. Department of Education.

Google Scholar

Jaggars, S. S., Motz, B. A., Rivera, M. D., Heckler, A., Quick, J. D., Hance, E. A., et al. (2021). The Digital Divide among College Students: Lessons Learned from the COVID-19 Emergency Transition. Minneapolis: Midwestern Higher Education Compact.

Google Scholar

Jantzer, A. M. (2006). Retrospective Reports of Bullying Victimization, College Adjustment, and the Role of Coping. Carbondale: Southern Illinois University.

Google Scholar

Price, J. M., Whitlatch, J., Maier, C., Burdi, M., and Peacock, J. (2016). Improving Online Teaching by Using Established Best Classroom Teaching Practices. J. Contin. Educ. Nurs. 47, 222–227. doi: 10.3928/00220124-20160419-08

CrossRef Full Text | Google Scholar

Kamble, A., Gauba, R., Desai, S., and Golhar, D. (2021). Learners’ Perception of the Transition to Instructor-Led Online Learning Environments: facilitators and Barriers During the COVID-19 Pandemic. Int. Rev. Res. Open Distrib. Learn. 22, 199–215. doi: 10.19173/irrodl.v22i1.4971

CrossRef Full Text | Google Scholar

Lammers, W. J., Onweugbuzie, A. J., and Slate, J. R. (2001). Academic success as a function of gender, class, age, study habits, and employment of college students. Res. Sch. 8, 71–81.

Google Scholar

Lederbogen, F., Kirsch, P., Haddad, L., Streit, F., Tost, H., Schuch, P., et al. (2011). City living and urban upbringing affect neural social stress processing in humans. Nature 474, 498–501. doi: 10.1038/nature10190

PubMed Abstract | CrossRef Full Text | Google Scholar

Lederer, A. M., Hoban, M. T., Lipson, S. K., Zhou, S., and Eisenberg, D. (2020). More than inconvenienced: the unique needs of US college students during the CoViD-19 pandemic. Heal. Educ. Behav. 48, 14–19. doi: 10.1177/1090198120969372

PubMed Abstract | CrossRef Full Text | Google Scholar

Lederman, D. (2020). How Teaching Changed in the (Forced) Shift to Remote Learning. Washington, DC: Inside Higher Ed.

Google Scholar

Li, I. W., and Carroll, D. R. (2020). Factors influencing dropout and academic performance: an Australian higher education equity perspective. J. High. Educ. Policy Manag. 42, 14–30. doi: 10.1080/1360080X.2019.1649993

CrossRef Full Text | Google Scholar

Li, Y., Cen, X., Cai, X., and Temkin-Greener, H. (2020). Racial and ethnic disparities in COVID-19 infections and deaths across US nursing homes. J. Am. Geriatr. Soc. 68, 2454–2461. doi: 10.1111/jgs.16847

PubMed Abstract | CrossRef Full Text | Google Scholar

Limniou, M., Varga-Atkins, T., Hands, C., and Elshamaa, M. (2021). Learning, student digital capabilities and academic performance over the COVID-19 pandemic. Educ. Sci. 11:361. doi: 10.3390/educsci11070361

CrossRef Full Text | Google Scholar

Longwell-Grice, R., and Longwell-Grice, H. (2008). Testing Tinto: how do retention theories work for first-generation, working-class students? J. Coll. Student Retent. Res. Theory Pract. 9, 407–420. doi: 10.2190/cs.9.4.a

CrossRef Full Text | Google Scholar

Martin, F., Ritzhaupt, A., Kumar, S., and Budhrani, K. (2019). Award-winning faculty online teaching practices: course design, assessment and evaluation, and facilitation. Internet High. Educ. 42, 34–43. doi: 10.1016/j.iheduc.2019.04.001

CrossRef Full Text | Google Scholar

Matthews, T. B. (1998). The Influence of Parental Attachment and Coping Style on the Adjustment to College. New York: Syracuse University.

Google Scholar

McDonald, S. D., and Vrana, S. R. (2007). Interracial social comfort and its relationship to adjustment to college. J. Negro Educ. 76, 130–140.

Google Scholar

Mehta, S. S., Newbold, J. J., and O’Rourke, M. A. (2011). Why Do First-Generation Students Fail? Coll. Stud. J. 45, 20–35.

Google Scholar

Melendez, M. C. (2016). Adjustment to college in an urban commuter setting: the impact of gender, race/ethnicity, and athletic participation. J. Coll. Student Retent. Res. Theory Pract. 18, 31–48. doi: 10.1177/1521025115579671

CrossRef Full Text | Google Scholar

Millett, G. A., Jones, A. T., Benkeser, D., Baral, S., Mercer, L., Beyrer, C., et al. (2020). Assessing differential impacts of COVID-19 on black communities. Ann. Epidemiol. 47, 37–44. doi: 10.1016/j.annepidem.2020.05.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Murphy, L., Eduljee, N. B., and Croteau, K. (2020). College Student Transition to Synchronous Virtual Classes during the COVID-19 Pandemic in Northeastern United States. Pedagog. Res. 5:em0078. doi: 10.29333/pr/8485

CrossRef Full Text | Google Scholar

Napoli, A. R., and Wortman, P. M. (1998). Psychosocial Factors Related to Retention and Early Departure of Two-Year Community College Students. Res. High. Educ. 39, 419–455.

Google Scholar

Nonis, S. A., and Hudson, G. I. (2010). Performance of College Students: impact of Study Time and Study Habits. J. Educ. Bus. 85, 229–238. doi: 10.1080/08832320903449550

CrossRef Full Text | Google Scholar

Orlov, G., McKee, D., Berry, J., Boyle, A., DiCiccio, T., Ransom, T., et al. (2021). Learning during the COVID-19 pandemic: it is not who you teach, but how you teach. Econ. Lett. 202:109812. doi: 10.1016/j.econlet.2021.109812

CrossRef Full Text | Google Scholar

Panchal, N., Kamal, R., Orgera, K., Cox, C., Garfield, R., Hamel, L., et al. (2020). The implications of COVID-19 for mental health and substance use. Kaiser Fam. Found. Availabel online at : https://www.kff.org/report-section/the-implications-of-covid-19-for-mental-health-and-substance-use-issue-brief/

Google Scholar

Patel, R., Tarrant, C., Bonas, S., Yates, J., and Sandars, J. (2015). The struggling student: a thematic analysis from the self-regulated learning perspective. Med. Educ. 49, 417–426. doi: 10.1111/medu.12651

PubMed Abstract | CrossRef Full Text | Google Scholar

Pellicano, E., and Stears, M. (2020). The hidden inequalities of COVID-19. Autism 24, 1309–1310. doi: 10.1177/1362361320927590

CrossRef Full Text | Google Scholar

Persell, C. H. (2004). Using focused web-based discussions to enhance student engagement and deep understanding. Teach. Sociol. 32, 61–78. doi: 10.1177/0092055x0403200107

CrossRef Full Text | Google Scholar

Porter, S. R. (2003). Understanding retention outcomes: using multiple data sources to distinguish between dropouts, stopouts, and transfer-outs. J. Coll. Student Retent. Res. Theory Pract. 5, 53–70. doi: 10.2190/nv6h-55ng-8eyw-ekgp

CrossRef Full Text | Google Scholar

Price, J. M., Whitlatch, J., Maier, C. J., Burdi, M., and Peacock, J. (2016). Improving online teaching by using established best classroom teaching practices. J. Contin. Educ. Nurs. 47, 222–227.

Google Scholar

Proctor, B. E., Prevatt, F., Adams, K., Hurst, A., and Petscher, Y. (2006). Study skills profiles of normal-achieving and academically-struggling college students. J. Coll. Stud. Dev. 47, 37–51. doi: 10.1353/csd.2006.0011

CrossRef Full Text | Google Scholar

Rytkönen, H., Parpala, A., Lindblom-Ylänne, S., Virtanen, V., and Postareff, L. (2012). Factors affecting bioscience students’ academic achievement. Instr. Sci. 40, 241–256. doi: 10.1007/s11251-011-9176-3

CrossRef Full Text | Google Scholar

Schneider, M. E., and Ward, D. J. (2003). The role of ethnic identification and perceived social support in Latinos’ adjustment to college. Hisp. J. Behav. Sci. 25, 539–554. doi: 10.1177/0739986303259306

CrossRef Full Text | Google Scholar

Saldaña, J. (2021). The Coding Manual for Qualitative Researchers. London: Sage.

Google Scholar

Shin, M., and Hickey, K. (2021). Needs a little TLC: examining college students’ emergency remote teaching and learning experiences during COVID-19. J. Furth. High. Educ. 45, 973–986. doi: 10.1080/0309877x.2020.1847261

CrossRef Full Text | Google Scholar

Son, C., Hegde, S., Smith, A., Wang, X., and Sasangohar, F. (2020). Effects of COVID-19 on college students’ mental health in the United States: interview survey study. J. Med. internet Res. 22:e21279. doi: 10.2196/21279

PubMed Abstract | CrossRef Full Text | Google Scholar

Suprianto, S., Arhas, S. H., Mahmuddin, M., and Siagian, A. O. (2020). The Effectiveness of Online Learning Amid the COVID-19 Pandemic. J. Ad’ministrare 7, 321–330.

Google Scholar

Supriya, K., Mead, C., Anbar, A. D., Caulkins, J. L., Collins, J. P., Cooper, K. M., et al. (2021). Undergraduate Biology Students Received Higher Grades During COVID-19 but Perceived Negative Effects on Learning. Front. Educ. 6:759624. doi: 10.3389/feduc.2021.759624

CrossRef Full Text | Google Scholar

Swail, W. S. (2003). Retaining Minority Students in Higher Education: A Framework for Success. ASHE-ERIC Higher Education Report. Jossey-Bass Higher and Adult Education Series. San Francisco: ERIC.

Google Scholar

Swecker, H. K., Fifolt, M., and Searby, L. (2013). Academic advising and first-generation college students: A quantitative study on student retention. NACADA J. 33, 46–53. doi: 10.12930/nacada-13-192

CrossRef Full Text | Google Scholar

Tanner, K. D. (2013). Structure matters: twenty-one teaching strategies to promote student engagement and cultivate classroom equity. CBE Life Sci. Educ. 12, 322–331. doi: 10.1187/cbe.13-06-0115

PubMed Abstract | CrossRef Full Text | Google Scholar

Taylor, S., Landry, C. A., Paluszek, M. M., Rachor, G. S., and Asmundson, G. J. G. (2020). Worry, avoidance, and coping during the COVID-19 pandemic: A comprehensive network analysis. J. Anxiety Disord. 76:102327. doi: 10.1016/j.janxdis.2020.102327

PubMed Abstract | CrossRef Full Text | Google Scholar

Trimble, D. V. (2019). The Right to Be Counted: student Retention and the Washington State LGBTQ Student Success Initiative. New Dir. Community Coll. 2019, 79–94. doi: 10.1002/cc.20380

CrossRef Full Text | Google Scholar

Tsang, J. T. Y., So, M. K. P., Chong, A. C. Y., Lam, B. S. Y., and Chu, A. M. Y. (2021). Higher Education during the Pandemic: the Predictive Factors of Learning Effectiveness in COVID-19 Online Learning. Educ. Sci. 11:446. doi: 10.3390/educsci11080446

CrossRef Full Text | Google Scholar

Ugwuanyi, C. S., Okeke, C. I. O., and Asomugha, C. G. (2020). Prediction of learnersâ€TM mathematics performance by their emotional intelligence, self-esteem and self-efficacy. Cypriot J. Educ. Sci. 15, 492–501. doi: 10.18844/CJES.V15I3.4916

CrossRef Full Text | Google Scholar

Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., McIntyre, R. S., et al. (2020). A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain. Behav. Immun. 87, 40–48. doi: 10.1016/j.bbi.2020.04.028

CrossRef Full Text | Google Scholar

Westwick, J. N., and Morreale, S. P. (2021). Advancing an agenda for instructional preparedness: lessons learned from the transition to remote learning. Commun. Educ. 70, 217–222. doi: 10.1080/03634523.2020.1857416

CrossRef Full Text | Google Scholar

Woodley, X., Hernandez, C., Parra, J., and Negash, B. (2017). Celebrating Difference: best Practices in Culturally Responsive Teaching Online. TechTrends 61, 470–478. doi: 10.1007/S11528-017-0207-Z

CrossRef Full Text | Google Scholar

Yeager, D. S., and Dweck, C. S. (2012). Mindsets That Promote Resilience: when Students Believe That Personal Characteristics Can Be Developed. Educ. Psychol. 47, 302–314. doi: 10.1080/00461520.2012.722805

CrossRef Full Text | Google Scholar

Zhai, Y., and Du, X. (2020). Addressing collegiate mental health amid COVID-19 pandemic. Psychiatry Res. 288:113003. doi: 10.1016/J.PSYCHRES.2020.113003

CrossRef Full Text | Google Scholar

Zimmerman, B. J. (2000). Self-Efficacy: an Essential Motive to Learn. Contemp. Educ. Psychol. 25, 82–91. doi: 10.1006/CEPS.1999.1016

CrossRef Full Text | Google Scholar