The onset of the COVID-19 (coronavirus disease 2019) pandemic in the United States caused abrupt changes to the landscape of higher education. Faced with an indefinite timeframe and health measures such as quarantines, a shift to virtual learning was deemed a necessary measure to balance safety with education needs. Though the shift occurred quickly, ownership of mobile devices was already ubiquitous among the college student population (e.g., Gierdowski, 2019; Sage et al., 2020). Most students today own both smartphones and laptops, with smartphones often being the most popular as students find them convenient and helpful for communication (e.g., Anshari et al., 2017; Sage et al., 2020). Though students already use both technologies for educational purposes (e.g., Callaghan, 2018; Stec et al., 2018), the pandemic created an immediate need to use those digital technologies specifically to attend classes and meet with professors. These technologies vary in their affordances. They differ perceptually (e.g., text size), cognitively (e.g., ability to focus), and physically (e.g., weight or interface). Thus, it stands to reason that different technologies may affect students in distinct ways when used for online coursework.
The present research explored students’ technology choices, experiences, and perceptions while taking an online course necessitated by the COVID-19 pandemic. Their experiences were then related to and discussed in the context of various student outcomes and individual difference measures, such as feelings of isolation, engagement, and perceived stress. Though research has repeatedly explored online course experiences (e.g., Ralston-Berg et al., 2015), little research has focused on the physical technology students use to interact with their courses, and not during a global pandemic. Such research can help predict the challenges encountered by teachers and students adapting to the online educational environment during the COVID-19 pandemic as well as provide recommendations for these online spaces.
Online course quality and frameworks
Online courses have existed for a few decades and have been extensively examined in terms of their quality. One frequently cited set of research-based standards for online courses is Quality Matters (https://www.qualitymatters.org/). By reviewing these standards, educators at higher education institutions can determine if their course design meets a particular quality threshold (Ralston-Berg et al., 2015). The Quality Matters (QM) rubric provides guidance on course introduction, learning objectives, assessment/measurement, instructional materials, learning activities/interaction, course technology, learner support, and accessibility/usability. Perhaps most relevant for the present study, the standards for course technology include specific recommendations on recruiting multiple technologies and using digital tools that support course objectives and promote engagement as well as learning.
Instructors often turn to such frameworks as QM when designing their online courses, to bolster student engagement and success. In a study on instructors’ facilitation strategies, Martin et al. (2018) found that students most appreciated timely feedback and responses while strategies such as interactive syllabi and live sessions were rated lower. Ralston-Berg et al. (2015) analyzed students’ perceptions of online course quality to see if they aligned to QM standards. Student perceptions mostly aligned, though some aspects were rated as less important by students. Students viewed clear instructions, logical navigation, and readily available technology as most important while learning activities that encouraged interaction were not as important. Such research is critical as students’ satisfaction is connected to other educational outcomes, such as to their retention and motivation (Lei & Yin 2020).
Though instructors’ facilitation strategies in online courses are critical for meeting quality standards, the conditions of the COVID-19 pandemic presented a unique challenge. While some instructors may have been aware of frameworks such as Quality Matters, others may have had little to no training in online pedagogy. With limited time to make the move, and sometimes limited institutional support (some colleges may have never offered online courses before), it can be difficult to adhere to best practices.
Online courses and psychological variables
It is possible then that students enrolled in online courses that arose unexpectedly and may not have been designed with as much focus on frameworks such as QM. Furthermore, research during the COVID-19 pandemic has suggested that students are experiencing new situations and stressors that contribute to sleep issues, trouble focusing, fewer social interactions, enhanced concern with academics, and more (Son et al., 2020). Accordingly, the experiences of students in these online courses may vary from typical circumstances and be intertwined with individual differences in unique ways. To highlight these potential relationships, we explored several individual characteristics of student learners: engagement, motivation, procrastination, stress, and self-efficacy.
Research has found that engagement is vital for achieving positive outcomes in online courses (e.g., Czerkawski & Lyman III, 2016). New circumstances and stressors during the pandemic may lead students’ engagement to fluctuate. There are design options that instructors can use to facilitate course engagement (e.g., required meetings with that instructor) but also behaviors that students can perform to enhance their own engagement (e.g., keeping up on their readings or participating in discussions).
Students’ motivation may also waver during these unusual times. Extrinsic motivation (e.g., get a passing grade) often drives practices such as studying, and may be exacerbated during a pandemic as students face new stressors. Research has shown connections between intrinsic motivation and online course participation (Xie et al., 2006), and researchers agree that personal initiative to do well drives success (Mandernach et al., 2006).
Procrastination may also fluctuate under these circumstances. On one hand, students may be at home with fewer activities and social engagements, and thus may complete coursework sooner. On the other hand, students may be facing challenges such as sharing computers and caring for sick family members, which could enhance procrastination. In general, procrastination is a common practice amongst college students, and lower levels contribute to student success (You, 2015).
Additionally, stress has taken on new forms, small and large: financial concerns, cancelled social engagements, health issues, and more. When surveyed, college students reported increased stress and anxiety during the pandemic, with less than half believing they could adequately cope (Wang et al., 2020). Some research has suggested that stress rose in college students during spring semester 2020 but may have returned to pre-pandemic levels during fall semester 2020 (Charles et al., 2021). That said, stress varies by circumstance. For fall 2020, schools adopted varying modalities, though the virtual approach was still most common (Walke et al., 2020). Housing and campus situations were similarly variable, as was the timeline for receiving updates for the semester. In addition to the broader context, daily hassles such as changing deadlines and cancelled events also influence perceived stress for students.
Lastly, self-efficacy, or a person’s belief in their own capacity to successfully execute a behavior, may vary as students encounter different resources than they may be used to, such as no library or computer lab next door. This may influence the confidence they have in themselves to do well. Furthermore, online courses may require more self-efficacy than in-person courses, given the self-guidance required to advance through coursework. Like the other variables, research has shown a connection between high self-efficacy and academic success (e.g., Bradley et al., 2017).
The preceding variables all represent individual differences between students. These factors can be interrelated, such as positive relationships between motivation and self-efficacy (Salazar & Hayward, 2018) or engagement (Martin & Bolliger, 2018) and a negative relationship between self-efficacy and procrastination (Wolters et al., 2017). In addition to this set of variables, we wondered how cognitive load might vary during these times. Though cognitive load often relates to grades (e.g., Sage et al., 2020), cognitive space for both students and teachers may be occupied with pandemic-related stressors. This could influence students’ perceptions of effort and course difficulty as well as actual course difficulty levels.
A final variable we considered in the context of this pandemic, as more of an outcome variable, was the level of isolation that was felt. At various points in recent months, students have experienced stay-at-home orders, quarantined, and self-isolated. In short, they are experiencing a starkly different social landscape than they are probably used to in college where social exchanges abound. Feelings of isolation can influence other outcomes such as dropout rates and persistence in a course (e.g., Yuan & Kim, 2014). Research during the COVID-19 pandemic has been contradictory, suggesting simultaneously that loneliness has not increased (Luchetti et al., 2020) and has increased (Lee et al., 2020) at different times. Online classes could exacerbate these same isolating experiences or potentially provide a social outlet for students.
Smartphones for learning
With the move online, there was a quick need for students to use their personal devices to engage in coursework. As previously mentioned, smartphone ownership is near universal among the college student population (e.g., Sage et al., 2020). Laptop ownership is similarly high at above 90% (Gierdowski, 2019; Sage et al., 2020). In an NPR article written during the pandemic (Nadworny, 2020), it was speculated that computer availability declined for college students. Even if students had their own laptops, the conditions necessitated by the pandemic might have led students to share devices with others in their household working remotely or attending virtual school (Chiner et al., 2021). Furthermore, even if the house had multiple computers, streaming video or accessing large files across multiple computers in the home can be challenging and create bandwidth issues, particularly if all devices are on the same WiFi network. Chiner et al. (2021) reported that 31.5% of students (at a Spanish university during the pandemic) always or often had a poor Internet connection during online learning experiences. This could lead to students being dropped from live class sessions or having trouble up/downloading files. Using smartphones to engage in coursework, particularly if equipped with unlimited data, may be one method to circumvent the need to share a WiFi network that is overburdened. Rahiem (2021) investigated device sharing within a sample of university students in Indonesia during the COVID-19 pandemic, reporting that a majority of students used their phone’s Internet access and data plan to access materials and to tether their laptop to the Internet. Students may not have encountered the same pressure to share smartphones with other household members as well. Rahiem (2021) also reported that, while about 30% of students were sharing laptops with other family members, all students had smartphones.
Mobile devices such as smartphones do have promise for education given their unique features, such as personalized interfaces and social applications. Growing literature has endorsed smartphone use in education. Anshari et al. (2017) surveyed college students, finding that students often used smartphones to retrieve digital course materials and browse the Internet for relevant information. Interestingly, students noted using the Internet more often on their smartphones than laptops. Students mentioned such reasons as convenience and portability, while also noting the benefit of smartphones for instant communication with teachers and classroom peers. Agreeably, Sage et al. (2020) reported that reviewing flashcards on a smartphone was just as effective as reviewing flashcards on a laptop or paper cards. Sage and colleagues (under review) further reported that smartphones were just as effective as laptops for completing basic academic tasks like sending an email to a professor. In that research, students reported believing that smartphones were more effective than laptops for completing polls or surveys in class, receiving assignment reminders, and reviewing communication from peers and professors. They complimented smartphones for their size, convenience, and easy access to resources. Agreeably, Crompton and Burke (2018) have emphasized that humans are increasingly comfortable with small screens given their regular use in everyday life.
On the other hand, many learners and instructors believe that smartphones do not facilitate learning. Smaller screens may challenge learners in new ways. Kim and Kim (2010) noted the possibility of enhanced cognitive load from such traits as small font size. Kim et al. (2019) reported that phones distract students from their classwork every 3–4 min. Tossel et al. (2015) studied smartphone use in college students without prior smartphone experience. Though students had viewed phones positively relative to education prior to the study, they viewed phones as more of a distraction by the end. Like other research, students did label the devices as useful for retrieving course materials and communicating with others. And though Sage et al. (under review) found the aforementioned positive characteristics of smartphones, they also reported that students saw more educational value in laptops over smartphones and believed laptops were superior for interactive activities, research, accessing some course materials, and note-taking. They complimented laptops for their ability to login to class meetings and complete assignments.
When focused specifically on online coursework, the advantages of the bigger screen afforded by the computer may be intuitively clear: more space to view your instructor and class during live sessions, a larger keyboard for typing, and easier navigation with multiple browser windows open. However, smartphones may also have unique functionality to assist in online coursework, as they can provide a constant connection to peers, facilitate groupwork and communication given many apps and notifications, and have an internet connection that relies less on a home WiFi network. Al-Hariri and Al-Hattami (2017) investigated the relationship between students’ use of both phones and laptops with academic achievement. They discovered a positive relationship between these variables, citing such reasons as the role of technology in encouraging independent, self-directed learning and in expediting student collaboration. Thus, both devices may play into students’ experiences with their online courses.
The current study
This study focuses on college students’ technology choices, experiences, and perceptions in an online course during the pandemic. Our work expands current literature in several ways. First, we contribute to the growing literature on the educational experience during the COVID-19 pandemic. Second, we add to the literature on online classes by focusing on the physical technology used. Smartphones are an understudied device in students’ learning (see meta-analyses: Delgado et al., 2018; Sung et al., 2016). Third, we investigate student outcomes as well as various individual difference measures to see where relationships might lie. In addition, we employ both quantitative and qualitative data approaches to provide a richer data set when drawing conclusions.
Research questions and hypotheses
How do student outcomes, individual differences, device, and cognitive load relate?
As our primary analysis, we sought to reveal key relationships between our variables. Student outcomes were operationalized as current grade, satisfaction, and isolation felt in the course. Individual differences included engagement, motivation, procrastination, stress, and self-efficacy. Device was operationalized as the proportion of coursework completed on laptops versus smartphones. Cognitive load included students’ perceptions of course difficulty and effort expelled.
In this analysis, we expected that more smartphone use might be related to poorer student outcomes, more negative scores on the individual difference measures, and higher cognitive load. This seemed possible given certain affordances of the smartphone, such as more difficulty focusing on and reading a smaller screen. The one exception was for isolation felt, where it seemed possible for smartphone use to be a boon since it is a social device. We additionally expected our individual difference measures to correlate to one another as well as meaningfully relate to student outcomes and cognitive load in ways generally consistent with the literature described earlier. Though mean levels of these items might all vary during a pandemic, we believed that the underlying relationships would hold true.
What variables predict student outcomes in these online courses?
As a secondary analysis to look at these data from a different angle, we sought to ascertain whether device and the individual difference measures meaningfully predicted the three outcome variables. We believed that smartphone use might be a negative predictor of course grade and satisfaction while laptop use might be a positive predictor of student outcomes, given the challenges created by using smaller devices for online coursework. It also seemed possible that students’ choice in technology may influence their feelings of isolation. Smartphones may enhance feelings of social connection given the constant notifications from various social apps, and thereby their use might predict lower feelings of isolation. We also believed that the individual difference measures would contribute to the variance in these outcomes, consistent with past research (e.g., Bradley et al., 2017; Czerkawski & Lyman III, 2016).
What are students’ experiences with, and perceptions of, these technologies for online courses?
This section was exploratory, to provide qualitative supporting data on students’ experiences with these technologies and their associated perceptions. Thus, we did not form specific hypotheses. Questions focused on students’ experiences with laptops and smartphones for their online courses, as well as what they liked and disliked about their technology and online courses more generally.