Does Empathy Matter? An Exploratory Study of Class-Transition Satisfaction in Unplanned Course Interruptions

Abstract

The sudden transition to online course delivery necessitated by the COVID-19 pandemic led to a significant service interruption in the academic lives of college students. Some challenges posed were immediately present such as to how to deliver course material and handle student concerns about classes and life in a new, unexpected, and abrupt “normal.” One aspect that arguably can generate a much-improved recovery is the pivotal role that a professor’s empathy can provide. This study captures the role of empathy regarding professorial behaviors directly related to the course, those not directly tied to the class, and how they all merge to influence student perceptions of the course. Student’s level of perceived stressors related to their life and ultimately their stress level are impacted as well. Findings show empathy plays a key role in positively impacting student satisfaction and well-being while reducing students’ sources of concerns such as household conflict, lack of Wi-Fi reliability, lack of access to a constant computer, and financial and food insecurity.

Keywords

pandemic pedagogy teaching crisis online/distance education level/type of education marketing education issues student motivation learning approaches and issues student satisfaction surveys methodology partial least squares undergraduate education classroom management education administration issues

The path to teaching and learning was abruptly disrupted when the COVID-19 pandemic hit. In early 2020, the World Health Organization (WHO) declared a public health emergency of the highest level, and in a rushed effort, students and professors were “sent home” to move to virtual environments that provided the much needed social distancing to keep everyone safe. Home quarantine, isolation, and concerns regarding the virus and its effects on oneself and family weighed on people from all walks of life. Families spent much time together, and all members had different roles: school children stayed home, parents worked remotely, and college students often returned home (Campbell et al., 2020; Hamaideh et al., 2021).

Some argue that the effects of the sudden transition were most pronounced on college students who, in large part, experienced the pandemic more acutely as they were forced to either isolate at home (Hamaideh et al., 2021) or attend classrooms in dorms or apartments with roommates present. This issue purportedly affected Black, indigenous, people of color, and international students disproportionately (Blake et al., 2021). These challenging circumstances were exacerbated by the sudden lack of socialization and development that organically occurs on college campuses (Campbell et al., 2020; Hamaideh et al., 2021; Krishnamurthy, 2020). Needless to say, the sudden departure from the norm required adaptation from everyone.

Lockdowns and policies that restricted socialization in colleges and universities clearly changed “normal” behaviors and routines (Shridhar, 2021). Professors quickly adapted to online technologies, and phrases such as “you are on mute” and “yes, we can see it” became commonplace. Professors and students who were not highly involved with, or had a low affinity for, online education were expected to adapt immediately. Never before had entire student bodies been moved to online pedagogy in unplanned ways, particularly so swiftly (Krishnamurthy, 2020). Not surprisingly, many educators struggled with mastering technological tools, such as Zoom and Microsoft Teams, while maintaining the “human” touch and managing their classes (Crittenden, 2021).

One unfortunate result of the transition process was the increased stress foisted upon students. Outside of class time, students faced environmental, social, and political issues, along with financial difficulties, job losses, and economic hardships (Shridhar, 2021). All of these issues led to an increase in insecurity, fear, and uncertainty. For some, this may have been a time to search for inner balance and happiness, but for others, the unplanned transitions only added to their already heavy stress loads (Hurst et al., 2013). Regarding classroom time and college life, the literature clearly indicates that student stressors include relationships, lack of resources, academics, expectations, diversity, transitions, and environmental pressures (Blake et al., 2021; Hurst et al., 2013). Not surprisingly, as perceptions of financial anxiety and debt increased, so did the likelihood that students would discontinue their education (Britt et al., 2017). Further stress emerged as students faced other challenges such as ensuring reliable Wi-Fi, computer accessibility, and quiet places to attend classes, often while managing the day-to-day dynamics of learning at home (Hamaideh et al., 2021). All of these concerns and unknowns negatively impacted students’ stress levels, mental well-being, and satisfaction with their courses.

Given the unparalleled challenges of the COVID-19 pandemic and the unplanned transitions to online learning, a review of behaviors enacted by professors and their impact on student satisfaction and stress is warranted. Not only such an exploration is timely, it is also a priority, given the emergent variations in pedagogical knowledge that ultimately challenged conventional wisdom regarding teaching effectiveness. Furthermore, from students’ perspectives, the humble goal of earning good grades became less relevant when stressors such as experiencing health concerns, income, and changing quality of life became more salient (Campbell et al., 2020). Ultimately, in these conditions, a survival mentality was shown to take priority over a growth mindset.

Given the myriad of issues that emerged during the pandemic, the purpose of the current work is to explore professors’ efforts and behaviors during these unplanned transitions and how they encouraged student satisfaction and mitigated stress. Accordingly, the work presents a review of these issues, discusses best practices that emerged, and details the findings of an exploratory study that explores professor empathy, class and nonclass faculty behaviors, and student satisfaction and stress. A key construct in the work is empathy. It is emphasized that a professor’s empathy offers the potential to improve the delivery of academic topics and to nurture supportive academic relationships (Meyers et al., 2019). Empathy, as a means for gaining awareness and understanding, provides for sensitivity to what struggling individuals are experiencing. This important construct has been shown to reduce implicit bias in preservice teachers (Whitford & Emerson, 2019) and to improve student learning in nursing school environments (Mikkonen et al., 2015). The lack of empathy in business courses has also been noted as a target for pedagogical improvement (Katz-Buonincontro, 2015).

As noted, the work also explores class- and nonclass-related behaviors exhibited by professors and how they influence student satisfaction in a post-transition environment. Class-related behaviors are conceptualized as the actions undertaken by instructors to deliver their subject matter content successfully. By focusing on these behaviors, they can create a sense of immediacy and intimacy that promotes student engagement (Top Hat, 2020). Nonclass-related behaviors are defined as class maintenance actions to break the ice as class meetings start, get a read of the online class, focus on personal emotion and day-to-day actions (Top Hat, 2020), and alleviate the sense of isolation that can exist in online environments. By examining empathy along with these types of behaviors, the article explores whether a professor’s empathy mediates relationships between class- and nonclass-related behaviors and student satisfaction and sources of stress. As such, the work highlights the role of educators as change agents beyond the COVID-19 pandemic.

Literature Review

Student Stress and Faculty Empathy

As noted, student stress increased markedly during the COVID-19 pandemic. An examination of the effects of stress on student success is therefore necessary, as well as how faculty empathy helps to alleviate its detrimental effects. This section reviews the roles of student stress and faculty empathy in the context of unplanned course transitions.

Student Stress

Stress takes on many different connotations in various work and academic settings, and there is little agreement on its conceptualization (Beehr, 1995). Romano (1992) noted in his research on stress management in school curricula that stress can be conceptualized in three ways: stimulus, response, and interactional. Given this research looks at how students are impacted by stressors, the conceptualizations of stress through stimulus and response are most relevant.

For students, time in school with the social elements of the academic environment mirror that of a workplace, and, given that Beehr (1995, p. 11) describes occupational stress as “a situation in which some characteristics of the work situation are thought to cause poor psychological or physical health, or to cause risk factors making poor health more likely,” student stress is conceptualized as school situations that are thought to cause poor psychological or physical health, or to cause risk factors making poor health more likely. Anxiety and stress are increasingly prevalent among college students (Jones et al., 2018) and the unexpected transition to online learning due to the COVD-19 pandemic only served to increase their detrimental effects.

It is critical that educators understand the stressors that influence a student’s health and well-being. Stressors are “events or conditions (stimuli) that demand adjustments beyond the normal wear and tear of daily living” (Gadzella, 1994, p. 396). While some students adjusted well during the rapid pandemic transition, others encountered numerous difficulties and questioned if they should continue with their education. In fact, a study by J. Lee et al. (2021) on the effects of the COVID-19 pandemic revealed that “88% of students experienced moderate to severe stress, with 44% of students showing moderate to severe anxiety and 36% of students having moderate to severe depression” (p. 1). The difficulties faced during this time were related to a variety of stressors.

Other research has examined the factors that contribute to increasing levels of stress among college students (e.g., Britt et al., 2017; Hurst et al., 2013; Jones et al., 2018; Tollefson et al., 2018). For example, in their review of 40 qualitative studies relating to college student stressors, Hurst et al. (2013) examined a variety of stressor themes including relationships, lack of resources, academics, the environment, expectations, diversity, and transitions. Their work revealed that relationship stressors were the most commonly reported theme, which covered areas such as stress associated with family, romantic, peer, and faculty relationships. Notably, the researchers observed that class time served to lower the stress felt by the students back at home. It seems that knowing class time was held offered much needed consistency in an otherwise uncertain environment. Student comments in a chat function of the study revealed that phrases such as “love class time” or “I like that we are one big family” were commonplace. According to Perrine (1998, 2001), assessment and management of stress can also help reduce student attrition. As the level of stress felt by college students increased due to the uncertainties of the pandemic, the ability of instructors to be compassionate and empathetic to struggling students became critical.

Empathy

As noted above, another key driver of student stress and their perceptions of course outcomes is empathy. The current work follows the conceptualization of empathy by Batson (2011) as an emotion oriented toward others caused by, and congruent with, the perceived well-being of someone in need. Put simply, empathy is an emotion that motivates one to act as they observe another in distress. Such a conceptualization also signifies empathic concern or compassion (DeSteno, 2015; Xu et al., 2021).

It is important to note that empathy is not synonymous with sympathy. Sympathy calls for understanding from one’s perspective and is commonly expressed in adverse scenarios to convey sorrow and compassion. Sympathy is largely about observing and acceptance with a degree of detachment from the situation (Radhakrishnan, 2021). Empathy is defined more broadly and involves a more intense reaction as it entails listening, asking questions, and proposing solutions. To illustrate, a sympathetic person experiences a fraction of the other’s feelings, while an empathic person visualizes and mentally inserts themselves in the situation. It is also more likely that an individual will feel empathy for another if they have undergone or struggled with a similar situation in the past (Radhakrishnan, 2021).

Empathy has been linked with prosocial behaviors such as bolstering consumer charity appeals (Batson & Powell, 2003), increasing concern for other’s distress (Batson & Powell, 2003; Schoreder et al., 1988), remediating unfair word of mouth (Allard et al., 2020), and rousing selfless acts to benefit those with whom they empathize (S. Lee et al., 2014). Empathy plays a critical role in the current environment as it has become clear that by doing so, instructors build flexibility into their course policies and temper expectations in a compassionate and empathetic manner. This is especially true considering that professor behaviors account for as much as 44% of students’ motivators and demotivators (Gorham & Christophel, 1992).

Professor empathy, like the more general construct “empathy,” is conceptualized as existing along a continuum. As such, it is not a binary characteristic that a professor “has” or “does not have.” As Meyers et al. (2019) assert

professor empathy is the degree to which an instructor works to deeply understand students’ personal and social situations, to feel care and concern in response to students’ positive and negative emotions, and to respond compassionately without losing the focus on student learning. (p. 160)

This definition acknowledges the existence of interpersonal and social empathy. Interpersonal empathy allows an individual to recognize how another individual is doing and to respond with sensitive care (Batson, 2009). Social empathy acknowledges that group membership such as economic status is related to student success. Thus, social empathy reflects one’s ability to understand individuals by realizing or experiencing the situations they live in and, consequently, to acquire insights regarding any potential inequalities and disparities (Segal, 2011).

Faculty who demonstrate high levels of empathy can reduce the unnecessary challenges that students encounter, which work to threaten a general sense of belonging (Reeves et al., 2016). With that said, empathic professors also set boundaries to avoid becoming overwhelmed with the intensity of student’s experiences and to avoid compassion fatigue. Professors who exhibit strong degrees of empathy also prioritize student learning. That may mean, for example, allowing students to have time extensions on work, if necessary (Meyers et al., 2019). However, to have a strong degree of empathy does not mean that one is reducing academic standards (Meyers et al., 2019). Some researchers recommend what can be referred to as “empathy-based solutions” to support students, such as providing timely feedback, encouraging students to find different ways to meet their goals, and dividing large projects into smaller deliverables (e.g., Verschelden, 2017). In addition, encouraging a growth mindset wherein professors teach students that intelligence can be cultivated can positively impact both student’s motivation to learn and actual learning outcomes. Also, focusing on a reward-oriented, prosocial approach allows for both increased student learning and motivation (Gorham & Christophel, 1992).

COVID-19 Classroom Challenges and Best Practices

With the onset of COVID-19, nearly all colleges and universities adjusted how class content was delivered. The ongoing pandemic led to a widespread need for instruction to move online and for instructors to hold synchronous class meetings using videoconferencing software to maintain social distancing and to help minimize the spread of infection (The Chronicle of Higher Education, 2020; Yuan, 2020). It is noted that while the shift to online learning was already underway before COVID-19, the pandemic served to greatly accelerate this trend.

While a necessary remedy to encourage social distancing and reduce the spread of the virus, the abrupt change in delivery mode led to new challenges for faculty and students alike. For faculty, challenges were found in developing engaging content, ensuring technology accessibility, providing access to materials for students with disabilities, and with replicating face-to-face class experiences in an online environment. These challenges, along with best practices that emerged, are discussed below.

Developing Engaging Content

One of the biggest challenges faced by faculty members in this transition was how to make the coursework engaging in a virtual format. According to Top Hat (2020), engaging and challenging students is crucial in virtual classrooms where oversight is minimal and the chances for distraction constant. For example, the level and quality of energy and feedback in a face-to-face classroom are difficult to achieve in a virtual environment as the components of spontaneity and intensity are markedly reduced. Companies such as Top Hat, Teachable, and Turning Technologies that had been offering interactive cloud-based teaching platforms, learning engagement, and best practices to help instructors design and develop highly engaging courses stepped in by offering white papers and social media posts to guide professors through these challenges.

To overcome the engagement challenge, a best practice was found in giving students a larger say in the development and facilitation of the learning process. Giving students a more active role in learning not only increases participation and engagement but also affords a greater sense of ownership in the learning process itself (Top Hat, 2020). Another best practice, regardless of delivery medium, proposes that professors focus even more strongly on the dimensions of engagement, development, and content. In the engagement and development dimension, professors address critical issues such as critical thinking, skill development, flexibility in learning, linkage to innovation, self-reflective practices, motivation, interdisciplinary integration, and problem-solving capabilities. With regard to content, a keen focus on learning objectives, knowledge integration, content quality, and teaching content and strategies better enables a learner-centered curriculum (Varouchas et al., 2016).

A third best practice to improve engagement is to ensure the adoption of active learning strategies. Active learning engages students in classroom activities and encourages them to elaborate on what they are doing. As Adler (1982, p. 49) states, “All genuine learning is active, not passive. It is a process of discovery in which the student is the main agent, not the professor.” Adler’s stance on active learning emphasizes the importance of incorporating active learning into any course. Given the challenges that are present with online teaching, incorporating active learning to further engage and involve students is paramount. Although incorporating active learning strategies contains some level of risk, instructors should select the strategies that they are most comfortable with to maximize the likelihood of success (Bonwell & Eison, 1991).

Undoubtedly, in online courses, instructional style and a learner-centered approach are critical. Students immersed in an online synchronous class tend to value a “guide-on-the-side” approach wherein professors encourage students to discover and to share information from their readings and experiences. Live workgroup time is also valued as it provides access to peer learners and socialization (Bonnici et al., 2016) as well as activities that enable career exploration and skill development (Munoz et al., 2016a). Additional methods of practicing active learning in the virtual classroom are brainstorming, online discussions, problem-based learning, video assignments, and collaboration via breakout rooms (Top Hat, 2020). It is also important to acknowledge the varying levels of desire for being an active member among students. Previous studies have found that students can be motivated advocates, unconvinced talent, or indifferent agents when opting to involve themselves in college (Munoz et al., 2016b). Hence, the need for teaching practices that can motivate the myriad of clusters evident in a college class.

Ensuring Technology Accessibility

Another challenge with unplanned transitions, especially to online delivery, is students’ technology access and use. For example, instructors face the issue of not being able to see students during synchronous class meetings held via video conferencing software because students do not have their video cameras turned on (Castelli & Sarvary, 2021). While instructors can better interact with students if their cameras are on, there is also a need to understand the multiple reasons why a student may choose not to have their camera on during a synchronous session. First, instructors are urged to remember that not every student has high-speed internet availability in their homes. Students may simply not possess the high bandwidth and strong internet connections that online courses require, thus making their learning experience problematic. In a recent study by Castelli and Sarvary (2021), a weak internet connection was a main reason why students reported not wanting to have their cameras on. Second, online students may feel as though their teachers expect them to look at the screen for the entire class and stay focused on the video feeds of their classmates. This can result in prolonged eye contact and resultant fatigue, which can feel threatening and uncomfortable. Feeling as though everyone is watching can be distracting as students focus on how they may appear to others. In fact, students have noted concerns about personal appearance, and even concerns about safety when their physical location is exposed in the background (Castelli & Sarvary, 2021). A clear understanding of these student concerns and limitations helps instructors set realistic expectations for the use of video cameras during class sessions.

A best practice in this regard is for instructors to gauge and understand the technological challenges faced by students. By understanding that not every student has the necessary internet access at home, instructors can aid students by guiding them to locations where they can access the internet effectively. In addition, the instructor can ensure that students are provided clear guidance on how and where to obtain technical assistance during the course. Another best practice is for faculty members to be mindful of student concerns and obstacles when designing requirements for the use of video cameras, and therefore, set expectations that promote learning and inclusion.

Providing Access to Material for Students With Disabilities

Another challenge is to ensure equal access to course materials and support for students with learning disabilities. These disabilities fall into several categories that inhibit the student’s ability to fully access materials or participate in coursework. For example, a dyslexic student may understand course content intellectually but struggle to spell or organize words. Similarly, a student with an oral/written language disorder may recognize individual words perfectly but have difficulty processing a paragraph.

A best practice for addressing this is to ensure the accessibility of course materials in the Learning Management System (LMS). Another best practice is to ensure that a course adheres to the standards for accessibility set forth in the Quality Matters (QM) rubric. The QM rubric provides guidelines and standards for online courses which are consistent with educational research literature regarding factors that improve student learning (QM, 2021). Another best practice is for instructors to ensure that there is clear language included in syllabi that explains available student resources. Each university can be expected to have an office that coordinates academic and residential accommodations for students with physical, neurological, and psychological needs. Instructors should make every effort to communicate this information to their students and be sure to comply with accommodation guidance.

Replicating Face-to-Face Experiences

Another challenge for instructors is replicating the existing materials and teaching methods that are utilized in face-to-face courses. The rapid transition to online teaching due to the COVID-19 pandemic forced instructors to scramble to adapt course materials. In many cases, this transition was completed with little or no thought of how to replicate traditional face-to-face activities. While the technology used to deliver the course material may impact students’ perceptions of the course and the effectiveness of the instructor, the teaching methods used are even more critical to a successful online course. Some teaching methods that worked well in a face-to-face setting may not be as impactful to students when used in an online course. For example, many instructors have a hard time translating the creative and interactive activities they have built for their face-to-face classes to an online learning context. Lacking a creative way to make the transition, some instructors simply substitute these interactive learning activities with additional readings and formative assessments. This can create additional stress on students that may reduce engagement.

A best practice in this regard is to reexamine the structure and content of the course when making the transition to online delivery. As Nilson and Goodson (2018) purport, a “good design leads students to a destination.” As educators plan their online course designs, it is strongly recommended they consider chunking content into meaningful segments with clear directions that provide pathways to progress and promotes learning (R. M. Smith, 2014). Organizing online courses into digestible modules or units that include a sequence of course content, activities, and assessments helps lead students toward learning goal achievement.

While it seems intuitive to assume that one can simply transition to a synchronous online course format quickly, the challenges previously discussed may make this a less than ideal solution. The blending of synchronous and asynchronous teaching may improve student participation and engagement. Another best practice is to choose technology solutions that better support the redefined course objectives, learning outcomes, and format. Each LMS has multiple tools that support the achievement of student outcomes. Many of these tools enable professors to provide more meaningful student interactions and to more closely replicate the activities that would normally occur in face-to-face settings.

Method

Sample

To explore the interrelationships between faculty class and nonclass behaviors, empathy, and student satisfaction/stress, an exploratory study was conducted which utilized a sample of 240 students from two AACSB-accredited universities (one small, private school located in the southern United States and one mid-sized public in the Midwestern United States). The initial, exploratory main effects model is presented in Figure 1.

Figure 1.

Initial Main Effects Model.

The sampling frame included students who either took face-to-face courses in Spring 2020 and/or who enrolled in marketing classes in Fall 2020 at the two universities. The response rate for the survey is approximated, given that students may have been enrolled in more than one class that was included in the study but could only participate once. Also, enrollment numbers fail to account for students who withdrew from a class after the drop/add period. Ultimately, these situations would lead to a reduction in the calculated response rate. That said, the response rate based on the enrollments in the courses at the end of the drop/add period for the semester was 64.5% (240/372). This final sample size exceeds the minimum recommended by Hair et al. (2011) of 10 times the number of paths associated with the latent variable that has the largest number of paths. In this study, the class-related behaviors construct had 15 associated paths, indicating a minimum sample size requirement of 150.

Demographic information was not collected due to institutional review board (IRB) concerns, given the small class sizes and the possibility of identifying individual students. Given this limitation, the overall demographic compositions for the business programs included are presented in Table 1. Respondents were either junior or senior level and were either marketing majors or minors. The survey was administered via Qualtrics midway through the Fall 2020 semester, and all students had experienced the transition to online teaching during the initial COVID-19 outbreak the preceding Spring. The survey timing was as close to the event of interest as allowed by the IRB.

Table 1.

Sample Frame Demographics.

School	Public Midwestern (%)	Private southern (%)
Ethnicity
American Indian or Alaska Native	0.32	0.22
Asian	2.03	6.04
Black or African American	14.50	1.94
Hispanics of any race	4.48	24.30
Native Hawaiian/Pacific Islander	0.11	0.00
Nonresident alien	2.88	2.37
Two or more races	3.20	3.31
White	71.86	59.60
Unknown	0.64	1.58
Gender
Female	43.60	55.00
Male	56.40	45.00
Residence
U.S. citizen	96.38	98.42
Not U.S. citizen	3.62	1.58

Measures

Students were asked to reflect on a course they took during the Spring semester of 2020 that had been transitioned from in-person to online format due to COVID-19 that was perceived as either the best or the worst course at making the transition. Slightly over 62% (150) of the students reported on the class they thought did the best job, while 37.5% (90) reported on the worst class. They were then asked to respond to the survey as pertaining to the selected class.

All survey items were assessed on 6-point Likert-type scales. The items related to instructor behaviors were drawn from a review of the literature (e.g., Bonwell & Eison, 1991; Britt et al., 2017; Meyers et al., 2019), and the Top Hat (2020) report. The initial pool included 31 items that represented professorial behaviors needed for effective online teaching. It is noted that this approach has been used previously to address motivating and demotivating professor’s behaviors in college courses (Gorham & Christophel, 1992). The pool of items is presented in Table 2. Given the exploratory nature of the study, the items were purified as discussed in a later section. Ultimately, the purification resulted in a 15-item measure of behaviors that were deemed to be class related and a 14-item measure of behaviors that were nonclass related.

Table 2.

ANOVA Results of Item Comparisons by Class Type Chosen.

Item	Best M	Worst M	M difference	SE difference	t value	df	p value
The class started with the professor greeting the class	5.480	4.253	1.227	0.184	6.682	239	.000
The professor opened time during each to talk about how everyone was doing	4.580	3.176	1.404	0.225	6.239	239	.000
The professor shared sometimes that he or she was struggling	3.733	2.890	0.843	0.219	3.853	239	.000
The professor seemed relaxed while teaching	5.093	3.989	1.104	0.171	6.443	239	.000
In live sessions, the professor was adaptive if someone in the class had a sudden camera visitor	5.073	3.429	1.645	0.186	8.841	239	.000
In live sessions, the professor was adaptive if someone from his/her household suddenly appeared on camera	4.953	3.505	1.448	0.189	7.662	239	.000
The professor offered or mentioned free resources for food, Wi-Fi, etc.	2.980	2.187	0.793	0.218	3.640	239	.000
Interactions with your professors made you feel connected to your class	4.753	2.890	1.863	0.192	9.687	239	.000
The professor seemed to genuinely care	5.487	3.956	1.531	0.150	10.226	239	.000
The professor talked about time management	4.787	3.231	1.556	0.192	8.101	239	.000
The professor talked about how to handle stress or anxiety	3.933	2.582	1.351	0.213	6.336	239	.000
The professor talked about the importance of positive thoughts	4.313	2.835	1.478	0.217	6.826	239	.000
The professor talked about emotions	3.767	2.670	1.096	0.216	5.086	239	.000
The professor asked consistently if people were understanding during live class sessions	5.000	3.308	1.692	0.183	9.268	239	.000
Students knew they could use the chat function or unmute their mic and ask questions at any time during live class sessions	5.487	4.154	1.333	0.161	8.297	239	.000
The professor mentioned how questions were always welcome	5.473	3.945	1.528	0.159	9.613	239	.000
The professor answered all questions students asked	5.607	4.132	1.475	0.149	9.870	239	.000
The professor would get annoyed if people were having too many questions	1.840	2.198	−0.358	0.211	−1.696	239	.091
Professor hosts regular online hours	4.647	3.571	1.075	0.193	5.569	239	.000
Professor informed everyone about how to contact him/her and availability	5.453	4.209	1.245	0.149	8.357	239	.000
Professor informed how long to expect an email response or call back would take	5.173	3.703	1.470	0.173	8.504	239	.000
The professor had a decent microphone to ensure students can hear clearly	5.507	4.198	1.309	0.162	8.072	239	.000
The professor kept track of time	5.347	4.000	1.347	0.161	8.375	239	.000
The professor communicated a class agenda, or a sense of how the time was going to be divided in a lecture block	5.193	3.538	1.655	0.171	9.651	239	.000
You looked forward to live sessions because they gave you a sense of consistency	4.613	2.857	1.756	0.204	8.598	239	.000
You looked forward to live sessions because they gave you a sense of normalcy	4.540	2.967	1.573	0.207	7.611	239	.000
You looked to online sessions because you were focused on class time only	4.127	2.978	1.149	0.218	5.280	239	.000
You looked forward to feedback from your professor	4.953	3.604	1.349	0.179	7.530	239	.000
Students knew they could honestly answer how their day was going	4.840	3.549	1.291	0.188	6.877	239	.000
Students knew they could talk about their class conflicts	4.780	3.516	1.264	0.187	6.763	239	.000
Students knew they could talk about their thoughts or emotions	4.553	3.286	1.268	0.197	6.421	239	.000

Note. ANOVA = analysis of variance.

The professor empathy scale consisted of 13 items from industry practice on service recovery that were tailored to the context of the online transition. The development of the items largely followed the authors’ personal experiences with dealing with course transitions during the COVID-19 pandemic. To guide these efforts, the L.E.A.D. model process was followed. This process, which is widely adopted across industries as a service recovery mechanism, underscores the role of empathy after a service mishap (NRC Health, 2017). The model consists of four steps: L—Listen: to show concern and gain an understanding of the situation, E—Empathize: to acknowledge an individual’s feelings and validate them, A—Apologize: to seize the opportunity to express regret for the problem or service interruption, and D—Deliver: to overcome and compensate for service mishaps (NRC Health, 2017). The process afforded an invaluable method for developing the scale. The initial items were purified and subjected to a subsequent reliability analysis. One item was removed due to a very low item-to-total correlation.

Pretransition student satisfaction and post-transition student satisfaction were each measured by a single item that asked students to evaluate their satisfaction with the class at the time of transition (pretransition) and after the forced transition (post-transition satisfaction). While there is debate in the literature regarding the use of single-item measures, such scales have shown to be valid when the construct is concrete and univariate in nature (Wanous & Reichers, 1996). Such is the case when student evaluations are assessed at two points in time (Rossiter, 2002).

The sources of student concerns scale was developed through conversations with students during the transition to online education due to COVID-19. The five items included were the most frequently mentioned sources of concerns for students as they attempted to complete courses that were originally scheduled as face to face. These items were subjected to a reliability analysis, and all were retained. Student stress was measured on an 11-item scale from Feldt (2008).

Analyses and Results

Analysis of Variance (ANOVA)

The first step in the analysis process was to determine which of the items from the literature review on effective online teaching behaviors discriminated between best and worst rated courses. To accomplish this, the responses to each item were compared using a one-way ANOVA with a Bonferroni post hoc that had family-wise error set to 0.05. From this analysis, one item (“the professor was annoyed if people asked too many questions”) was removed as it did not differentiate between best and worst classes (see Table 2).

Factor Analysis

The next step in the analysis process was to subject the 30 remaining items related to optimal professorial behavior in online settings to an exploratory factor analysis. A maximum likelihood extraction with an oblique (Direct Oblimin) rotation was utilized as the extraction and rotation, respectively. Maximum likelihood was chosen as the extraction technique as it was the most sensitive to deviations from normality as well as the most appropriate for identifying reflective scales in this context. Oblimin rotation was chosen as it was assumed that any distinct factors would be correlated, given that they are all behaviors related to effective online teaching. The analysis revealed that there were four factors with eigenvalues greater than 1.0, so, using the Kaiser–Guttman rule, this was the starting point for creating the pattern matrix. However, the analysis did not result in a meaningful or useful solution. A subsequent examination of the scree plot (see Figure 2) revealed that a two-factor solution was more appropriate, given the way the eigenvalues flattened after the first two factors. This led to a very clean solution presented in Table 3 where all items, except one, clearly loaded on one of the two factors above a 0.5 level. This solution was also easily interpretable as (a) class-related behaviors and (b) nonclass-related behaviors.

Figure 2.

Factor Analysis Scree Plot.

Table 3.

Factor Analysis Results.

Item					Factor 1	Factor 2
The class started with the professor greeting the class					0.644
The professor opened time during each to talk about how everyone was doing						0.551
The professor shared sometimes that he or she was struggling						0.543
The professor seemed relaxed while teaching					0.556
In live sessions, the professor was adaptive if someone in the class had a sudden camera visitor					0.545
In live sessions, the professor was adaptive if someone from his/her household suddenly appeared on camera					0.512
The professor offered or mentioned free resources for food, Wi-Fi, etc.						0.563
Interactions with your professors made you feel connected to your class					0.443	0.465
The professor seemed to genuinely care					0.703
The professor talked about time management						0.504
The professor talked about how to handle stress or anxiety						0.714
The professor talked about the importance of positive thoughts						0.807
The professor talked about emotions						0.792
The professor asked consistently if people were understanding during live class sessions					0.55
Students knew they could use the chat function or unmute their mic and ask questions at any time during live class sessions					0.827
The professor mentioned how questions were always welcome					0.858
The professor answered all questions students asked					0.972
Professor hosts regular online hours					0.59
Professor informed everyone about how to contact him/her and availability					0.81
Professor informed how long to expect an email response or call back would take					0.651
The professor had a decent microphone to ensure students can hear clearly					0.76
The professor kept track of time					0.748
The professor communicated a class agenda, or a sense of how the time was going to be divided in a lecture block					0.631
You looked forward to live sessions because they gave you a sense of consistency						0.691
You looked forward to live sessions because they gave you a sense of normalcy						0.76
You looked to online sessions because you were focused on class time only						0.71
You looked forward to feedback from your professor						0.545
Students knew they could honestly answer how their day was going						0.805
Students knew they could talk about their class conflicts						0.824
Students knew they could talk about their thoughts or emotions						0.921
Factor 1 = class-related behaviors, Factor 2 = nonclass-related behaviors
Goodness-of-fit test			Factor correlation matrix
Chi-square	df	Significance	Factor	Class related	Nonclass related
1,640.697	376	0.000	Class related	1	0.691
Extraction: maximum likelihood			Nonclass related	0.691	1
			Rotation: Oblimin with Kaiser normalization

While the chi-square goodness of fit indicated that more factors would better explain the data, this test is sensitive to minor deviations from normality and large sample sizes. The final items for each factor can be seen in Table 3. Finally, and as presented in Table 3, the factor correlation matrix shows a sizable, but reasonable correlation between the constructs, given the nomological connection.

The final reliability statistics, including average variance extracted (AVE), composite reliability, r², and Cronbach’s alpha, for each multi-item scale can be found in Table 4, and the final versions of all scales are in Appendix.

Table 4.

PLS-SEM Latent Variable Information.

Construct quality criteria
Scale		AVE	Composite reliability		R ²		Cronbach’s α
Nonclass-related behaviors		0.573	.949		N/A		.942
Class-related behaviors		0.596	.957		N/A		.951
Empathy		0.716	.954		.719		.947
Post-transition satisfaction		N/A	N/A		.323		N/A
Sources of student concern		0.553	.858		.033		.793
Student stress		0.558	.932		.177		.922
Construct correlations
	Nonclass-related behaviors	Class-related behaviors	Empathy	Post-transition satisfaction	Pretransition satisfaction	Sources of student concern	Student stress
Nonclass-related behaviors	.757
Class-related behaviors	.737	.772
Empathy	.795	.785	.846
Post-transition satisfaction	.452	.514	.507	N/A
Pretransition satisfaction	.425	.517	.455	.428	N/A
Sources of student concern	.059	−.021	−.049	−.053	−.067	.744
Student stress	.002	−.030	−.062	−.086	.009	.421	.747

Note. Diagonal elements are the square root of the AVE. PLS-SEM = partial least squares-structural equation modeling; AVE = average variance extracted.

Smart Partial Least Squares (PLS)

The structural relationships between the constructs were assessed via PLS-structural equation modeling (SEM) using a two-step process to assess the role of empathy. Initially, the path model was constructed as a main effects model without empathy, and then, the model was tested with empathy as a full mediator of the links between professorial behavior and student satisfaction with the class after the transition and with student sources of concerns. Finally, a partially mediated model was tested to identify the best way to represent the data.

The first step of this process was to ensure latent variable quality, as presented in Table 4. Each of the quality criteria related to construct reliability met the minimum values as noted by Hair et al. (2011) with two exceptions. Those exceptions are that the correlations between empathy and class-related behaviors and empathy and nonclass-related behaviors are greater than the square root of the AVE for class-related behavior and nonclass-related behavior. However, they are close enough that the issue may be due to restricted ranges of the loading in each construct as noted in Henseler et al. (2015).

As presented in Figure 1, in the main effects model, class-related behaviors and nonclass-related behaviors were set as exogenous predictors of both post-transition satisfaction with the class and with sources of student stress. Also, student stress was modeled as an endogenous outcome of sources of student concerns. The analysis revealed that class-related behaviors by the professor significantly influenced student evaluations of the class after the transition to online delivery, thereby accounting for 27.9% of the variance in the construct after controlling for pretransition satisfaction with the class. However, class-related behaviors did not directly impact students’ ratings of sources of concerns, even when controlling for student satisfaction with the course before the unplanned transition. Conversely, nonclass-related behaviors did not directly impact the students’ post-transition satisfaction with online delivery but did significantly influence students’ evaluations of their sources of concerns albeit only accounting for 0.3% of variance. Of particular note in this relationship was that instead of nonclass-related behaviors reducing these sources of concerns; they appear to actually increase students’ rating of these issues as prevalent in the minds of the respondents. This counterintuitive finding is addressed in the “Discussion” section. Finally, sources of student concerns accounted for 17.7% of the variance in student-reported stress.

In the fully mediated model (see Figure 3), both class-related and nonclass-related behaviors were significant predictors of empathy and accounted for 72% of the variance in empathy. Empathy was a significant predictor of post-transition class satisfaction, accounting for 25.8% of the variance after controlling for pretransition satisfaction. Empathy was not significantly related to sources of student concerns, but the cardinality of the path was as expected (empathy lowered student sources of concerns). This will also be covered in more detail in the “Discussion” section. Again, sources of student concerns accounted for 17.7% of the variance in student-reported stress.

Figure 3.

Fully Mediated Model.

In the partially mediated model (see Figure 4), the effects were largely as expected. Class-related behaviors impacted post-transition class satisfaction and empathy. Nonclass-related behaviors directly impacted both sources of student concerns and empathy. Class-related and nonclass-related behaviors combined to account for 71.9% of the variance in professor empathy. Empathy directly impacted both post-transition satisfaction and sources of student concerns. The combination of class-related behaviors and empathy accounted for 29.2% of the variance in post-transition class satisfaction after accounting for pretransition class satisfaction, and the structural paths from each predictor to post-transition class satisfaction were significant and in the expected direction. The partial mediation effect of empathy is also shown as the direct structural path from class-related behaviors to post-transition class satisfaction dropped from 0.295 (t = 3.77) in the main effects model to 0.220 (t = 1.87) in the partially mediated model, thus satisfying the Baron and Kenny (1986) criteria for partial mediation. The combined impact of nonclass-related behaviors and professorial empathy combined to account for 2.9% of the variance in sources of student concerns. Even more surprising than the large relative increase in r² was that both structural paths leading to sources of student concerns were now statically significant (empathy to sources of student concerns was nonsignificant in the fully mediated model). In addition, the level of significance increased for the main effects of nonclass-related behaviors to sources of student concerns. However, the cardinalities of both paths did not change from the previous models indicating that nonclass-related behaviors increase sources of student concern, whereas empathy reduces them. Once again, this unique outcome related to the predictors of sources of student concerns will be addressed in the “Discussion” section. As with the prior two models, sources of student concerns accounted for 17.7% of the variance in student-reported stress.

Figure 4.

Partially Mediated Model.

Discussion

This exploratory analysis reveals several noteworthy findings regarding class- and nonclass-related behaviors, professor empathy, and students’ satisfaction and concerns during unplanned course transitions. First, as can be seen in the main effects model, class-related behaviors significantly impact student ratings of class satisfaction. This result shows the importance of professors exhibiting behaviors such as being adaptive, greeting the class, asking students if they are understanding and following the lecture, keeping track of time, and communicating the class agenda. Students’ expectations are met by professors who proactively manage their class so students can focus on paying attention when unplanned delivery transitions occur. Second, and unexpectedly, nonclass-related behaviors do not significantly impact the student’s post-transition satisfaction. It appears that nonclass-related behaviors such as breaking the ice and getting a read of online meetings by asking how students are doing, sharing if the professor is also struggling, talking about time and stress management, as well as asking students how they are doing, the conflicts they are having, and their thoughts and emotions is crucial for success. However, discussing these issues without perceived empathy can increase the sources of student concerns. It seems that nonclass-related behaviors, when mentioned just as part of the class, are not positively impacting student’s lives.

When a professor exhibits empathy while engaging with a class through both class- and nonclass-related behaviors, a positive all-around effect is achieved. In both the fully mediated and partially mediated models, class- and nonclass-related behaviors significantly improved student satisfaction when behaviors were exhibited in an empathic manner. The professor’s efforts toward listening when students talked about stress, exhibiting care in a genuine manner, validating feelings while also offering apologies that the semester was interrupted, were valued. When empathy was conveyed, both models reveal how student satisfaction increases and concerns decrease. Clearly, it matters not only what one says, but how one says it.

By showing empathy while talking about these topics, professors can help students see that they are not alone in experiencing these challenges. The partially mediated model allows for aspects of empathy to manifest, thereby reducing sources of students’ concerns that naturally emerge during unplanned transitions. Therefore, if faculty want to help students deal with their key sources of concerns, it is not enough to simply discuss them, which leads to more worry, fears, and ultimately stress, but they must also react empathetically to help calm those fears and reduce stress in the end. The importance of exhibiting empathy will likely improve student evaluations in times of uncertainty where both the best practices of classroom management and basic aspects of human interaction combine to satisfy students.

Practical Implications

The results of this study provide insights into how to remain effective during unplanned course transitions and how empathy plays a vital role. COVID-19 obviously challenged everyone. Faculty would be remiss not to see the silver lining and do our best to advance pedagogy by examining and applying the best practices emerging from this unique situation. This study provides initial evidence of the impact of faculty behaviors and empathy.

Class-related and nonclass-related behaviors clearly matter to students. When both are proactively managed by the professor via empathy, student’s satisfaction increases, and sources of concern decrease, thereby reducing student stress. This means that it matters not only what professors say or do, it matters whether they exhibit genuine care for students through speech and actions. Focusing on being empathetic in the long term can improve professor–student relationships and positively impact student evaluations. Class-related behaviors call for greeting the class, remaining calm while teaching, and being adaptive to what students are experiencing under duress. Flexibility is vital, as is humor. It is not uncommon to experience unexpected visitors, including pets, during synchronous course delivery. A professor’s calming presence provides a sense of normalcy and consistency where students can remain safe to learn. Hence, it is important for faculty to host regular online office hours, set expectations regarding how long to wait to get a response to an inquiry, keep track of time, and set agendas for each class meeting. Nonclass-related behaviors demonstrate additional levels of care that a professor can provide for their students. Faculty may also open class time to talk about anything the class prefers, even if off subject such as sharing struggles, discussing time management, and the importance of positive thinking.

So, what does empathy look like in a classroom? Regardless of whether the instruction takes place face to face or in online delivery, professors can start by seeing empathy as a skill that can be learned, practiced, and improved over time. To begin, a professor mindfully accepts and states in their welcoming words to the class that unforeseen situations do happen, and that they are there to support the students. To do so, statements such as

I am here to support and empower your learning. With that said, I can only assist you if you keep me informed of your situation. That does not mean there are no deadlines or deliverables. It means I will work with you if something unexpected happens or your personal circumstances become very stressful.

Another variation can come from stating “If you do not tell me, I cannot know what is happening in your life. I am here to help you figure out as much as I can in order for you to successfully complete this class.”

Clearly, it benefits all involved to engage in active listening and ask questions before passing judgment or making blanket statements such as “it is on the syllabus.” Professors may also choose to add an anonymous “suggestion box” where students are encouraged to provide suggestions for improving the learning experience. These efforts can greatly increase goodwill as well. As an additional bonus, improvements in class evaluations may also be observed as issues will not fester throughout the semester. In addition, when under pressure, students might show a tendency to withdrawal rather than ask for help. Rather than informing students that they have omitted or incomplete work, an empathetic response would be to ask questions regarding the omissions. Asking questions and offering empathy is therefore strongly suggested. Students will begin to recognize that if they do not communicate problems and issues, then they cannot receive the assistance that they need. Perhaps an extension with penalty might be warranted if the professor deems the circumstances are extenuating.

Empathic thinking can also help to incorporate active learning in the classroom. For example, by considering the challenges that students face, professors may see the value of breaking major projects into smaller deliverables. This would allow for student practice and professor feedback where low- and high-risk assignments are included. Additional methods for integrating active learning include using breakout rooms, collaboration tools (e.g., Google Docs, Flipgrid, Screencast-O-Matic, Flipgrid, or EdPuzzle), polls and quizzes, and group work.

Faculty are encouraged to also consider the affective and cognitive components of empathy. Affective empathy resonates with a person’s emotion, such as with statements of “I am feeling stressed too,” or, it can take the individual’s emotional perspective with statements such as “I hear you. I would be mad too.” Cognitive empathy takes into consideration how the person thinks. Responses such as “If I were in your shoes, I would have thought that too,” “I can see why you are frustrated,” or “I think you seem uneasy, what is going on?” can be helpful. In addition, empathic behaviors that support affective or cognitive empathy can be expressed in terms of directly following up with a student and communicating empathy such as “I understand how you feel” or “I would have felt the same way.” Ultimately, the current research reveals that professor empathy encourages relationships that are based on trust and proactiveness and the student’s sources of concern and their stress level become significantly lowered. Class and nonclass-related behaviors displayed with empathy can turn otherwise from disappointing delivery modes into opportunities for students to thrive.

Limitations and Future Research

There are a number of limitations with the current research that should be acknowledged. First and foremost, the study is exploratory in nature. Several of the scales employed could benefit from further study and validation. In addition, the lack of specific student demographic data limits a number of potential insights. The generalizability of the data may also be questioned. Finally, measures were collected during a crisis period. While this is obviously due to the nature of the study, further replication is warranted that enables a comparison of current results to more “normal” time frames.

Future research is encouraged that includes a more holistic approach to student concerns and challenges. This would include an integration of pedagogical, social, psychological, and environmental variables. Answering this call where more inclusive and varied perspectives are welcomed promises to yield rich and helpful results. We must remember that student learning does not exist in a vacuum. Stressors and relaxation responses clearly matter. How does common societal stressors such as the environment (being disruptive or hostile, being in a different country), diversity (being part of a racial minority, first generation, disability status, and sexual orientation), and resources (money, time, sleep, support, technology) impact students under taxing and nontaxing circumstances? Also, the university environment (e.g., private/public and faith-based college) can provide unique outcomes as well. Well-being, calmness, positive approaches, and student adaptive behaviors merit investigation. To date, the literature has offered limited support as to what behaviors or structures, whether face to face, hybrid, or online, can positively impact a student’s learning outcomes and well-being. Every student population deserves attention regarding this matter. As such, it is important to explore whether there are larger themes or behaviors such as synchronous and asynchronous modalities that encompass a superior learning environment in the virtual classroom.

Furthermore, several questions about pedagogical and psychological choices can be explored. Future studies can study whether professorial behaviors toward topics such as adopting a growth mindset, normalizing discomfort of learning, and redefining that failure is lack of trying and the impact of such choices on outcomes such as student satisfaction, engagement, and class effectiveness. Additional topics lie in studying resilient behaviors such as grace, grit, and vulnerability to empower students to be leaders of their academic lives. The pandemic seems to have made people more open and welcoming to online technologies, have student expectations and drivers changed?

The service recovery literature offers a plethora of topics and behaviors that may guide future inquiry. Displaying empathy, as a service recovery behavior, needs to be further studied along with many other service recovery behaviors such as displaying promptness. Furthermore, given that empathy is also a dimension of service quality perceptions along with (tangibles, reliability, responsiveness, and assurance), how do these dimensions interact to affect student perceptions?

Equally important, how does the professor’s role evolve or it pertain to remaining a supportive educational figure under stressed or challenging circumstances? Are professors supported or hindered in their efforts and, in turn, how does this impact a professor’s likelihood to stay at an institution or remain a committed teaching, research, and service-focused individual? Is there the perception among faculty that high-performing colleagues are judged in a more forgiven manner and thus, less is expected of them in the classroom? How does this impact commitment, satisfaction, and engagement among the rest of the faculty members?

Conclusion

Regardless of the delivery mode, college students expect professors to provide an engaging, motivating, and effective educational experience. One of the key notions that many professors may have learned during the unplanned transition of course delivery mode is that the changes must go beyond stating a class is now “virtual” if an effective educational experience is to be delivered. While technology certainly helps, it is not a substitute for planning and adapting to a live virtual environment. Excellence in teaching and learning calls for embracing adaptation, collaboration, exploration, and experiential learning while maintaining accountability. With that said, the manner in which professors interact greatly matters.

In times of fear, anxiety, and uncertainty, as during the COVID-19 pandemic, the findings of this study show the critical role that empathy plays. Professors must obviously continue to perform regular class-related behaviors such as teaching relevant content and managing the classroom, but they must embrace nonclass-related behaviors such as discussing how to handle anxiety, the importance of positive thoughts, and the impact of emotions. Such empathy can then be communicated to students through course policies as well as the instructor’s behavior toward students. When the professor genuinely cares and takes the time to understand student concerns, all the other issues such as student satisfaction and stress fall into place.

Interruptions and disruptions will continue to occur and, unfortunately, it seems the COVID-19 pandemic not only reminded us of this fact but also tested academia’s resilience, adaptation, and innovation. Future research should account for other types, and sources, of disruptions and their impact on student’s learning, well-being, and satisfaction. Because the types of and length of interruptions can be varied, studies exploring these variations will become invaluable in preparing and guiding professors for future environments and possibilities. Revising the effectiveness of learning environments is paramount as unforeseen events occur.

Footnotes

Appendix

Final Scales.

Nonclass-related behaviors (never = 1, always = 6); α = .94

The professor opened time during each to talk about how everyone was doing

The professor shared sometimes that he or she was struggling

The professor offered or mentioned free resources for food, Wi-Fi, etc.

The professor talked about time management

The professor talked about how to handle stress or anxiety

The professor talked about the importance of positive thoughts

The professor talked about emotions

You looked forward to live sessions because they gave you a sense of consistency

You looked forward to live sessions because they gave you a sense of normalcy

You looked to online sessions because you were focused on class time only

You looked forward to feedback from your professor

Students knew they could honestly answer how their day was going

Students knew they could talk about their class conflicts

Students knew they could talk about their thoughts or emotions

Class-related behaviors (never = 1, always = 6); α = .95

The class started with the professor greeting the class

The professor seemed relaxed while teaching

In live sessions, the professor was adaptive if someone in the class had a sudden camera visitor

In live sessions, the professor was adaptive if someone from his/her household suddenly appeared on camera

The professor seemed to genuinely care

The professor asked consistently if people were understanding during live class sessions

Students knew they could use the chat function or unmute their mic and ask questions at any time during live class sessions

The professor mentioned how questions were always welcome

The professor answered all questions students asked

Professor hosts regular online hours

Professor informed everyone about how to contact him/her and availability

Professor informed how long to expect an email response or call back would take

The professor had a decent microphone to ensure students can hear clearly

The professor kept track of time

The professor communicated a class agenda, or a sense of how the time was going to be divided in a lecture block

Empathy (strongly disagree = 1, strongly agree = 6); α = .94

For the class you selected, rate your agreement with the following statements:

The professor seemed to listen when someone talked about stress or anxiety

The professor seemed to genuinely care about people sharing their realities

The professor showed empathy

The professor talked about how everyone’s concerns and fears were valid

It was reasonable to share that someone was just “hanging in there” or “stressed”

The professor said how he/she was sorry that this pandemic happened

The professor said he/she was sorry we had to move to an all online delivery

The professor expressed apologies that the regular semester was interrupted

The class had several ways to receive lectures once it became an online class

The class had several days as a window to submit assignments

The class knew if someone was having technical problems (Wi-Fi) the professor would be understanding

The class knew if someone was having personal problems (anxiety, family situation) the professor would be supportive

Sources of student concerns (never = 1, always = 6); α = .79

How often did you experience the following concerns during the transition to online course delivery?

Household conflict (roommates or family)

Lack of Wi-Fi reliability

Lack of access to computer

Financial insecurity

Food insecurity

Post-transition satisfaction (not at all close to idea = 1, extremely close to ideal = 6)

After the transition to online teaching, this course was . . .

Pretransition satisfaction (not at all close to idea = 1, extremely close to ideal = 6)

Rate your satisfaction with the course before the transition to online teaching

Student stress (strongly disagree = 1, strongly agree = 6); α = .92

Please rate your agreement with the following statements. I am often anxious about . . .

Personal relationships

Family matters

Financial matters

Academic matters

Housing matters

Being away from home

My ability to handle difficulties in my life

My ability to attain my personal goals

Events not going as planned

Not having control over my life

Being overwhelmed by the difficulties in my life

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 iDs

Laura Munoz

J. Ricky Fergurson

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