The recent COVID-19 crisis forced university students to leave a structured learning environment for an unstructured one. In a structured learning environment, students shape their skills after their instructors’ skills (Dynan et al., 2008). In an unstructured environment, students have to practice their own self-directed skills with less support from the instructor. This change to an unstructured learning environment
Fortune 500 training and development executives strongly recommend information technology (IT) skills for business graduates (Zhao & Alexander, 2002). In an effort to increase students’ IT skills, universities integrate computers into their programs (Davis & Shade, 1994). This can be in two ways, via a computer lab or by placing computers into the classroom. Research shows that IT skill development is more effective in a computer lab than computers placed in a classroom (Rule et al., 2002).
For classes taught in a computer lab, students are expected to acquire IT skills. When the crisis removed the students from the structured computer lab, placing them in an unstructured home environment, it changed the overall learning environment. How this change impacted students is an important question to address. The effect may not be the same for every student due to individual characteristics. During a crisis, individuals who have high levels of self-efficacy (i.e., believe in their abilities and competence) persist during challenging tasks (Avey et al., 2008).
The aim of this essay is to explore if students who completed the spring 2020 semester during the COVID-19 crisis and have high levels of perceived self-efficacy assess their IT skills to be higher. In other words, there may be a direct relationship between self-efficacy and IT skills during a crisis. The COVID-19 crisis sample will be compared to a spring 2018 control sample.
Theoretical Framework on Self-Efficacy and Improved IT Skills During Crisis
The theoretical framework was constructed based on the Venn diagram shown in Figure 1. We suggest that students with higher self-efficacy are more effective learners in an unstructured environment, such as it was created by the COVID-19 crisis. Therefore, higher self-efficacy leads to higher skill development during crisis.
Perceived self-efficacy is defined as people’s beliefs about their capabilities to produce designated levels of performance (Bandura, 1994). Businesses need people with a high sense of self-efficacy (Sun et al., 2016). Self-efficacy operates as an essential contributor to academic development (Bandura, 1993). High levels of self-efficacy help students to master academic activities to determine their aspirations, level of motivation, and academic accomplishments (Schunk, 1991). Furthermore, students with heightened self-efficacy have a higher motivation to acquire skills (Schunk, 1985). According to Bandura (1993), students’ beliefs in their efficacy help them to regulate their own learning and be more self-directed, which is essential in an unstructured learning environment (Dynan et al., 2008). There can be a gender difference concerning self-efficacy (He & Freeman, 2010).
Self-efficacy is seen as a critical resource during a crisis that attenuates the extremity of a crisis (Hannah et al., 2009; Kovoor-Misra, 2020). This may be, in part, because high levels of self-efficacy can lead to deeper self-reflection and facilitate stress, and other emotive responses to fear (Hannah et al., 2009). Furthermore, individuals with high levels of self-efficacy believe that they can positively influence their environment (Hobfoll, 2002). During a crisis, individuals with high levels of self-efficacy improvise and are resilient. Therefore, students with high levels of self-efficacy may have higher skill development during a crisis.
Non-Crisis Situation (Control Sample)
In spring 2018, two undergraduate computer lab courses were surveyed at a small private university located in the southeastern United States: a 100-level course (Excel business applications) and a 400-level finance course. The computer lab consists of 28 computers. At the end of the semester, students voluntarily provided an assessment of their improved IT skills and completed a self-efficacy survey (Schwarzer & Jerusalem, 1995). All measures were on a Likert scale from 1 to 5.
The sample from spring 2018 had 51 participants (64.7% 100-level class, 35.3% 400-level class), and consisted of 74.5% males. All students were in the traditional 18- to 23-year-old range. Data analysis revealed that there was no gender difference for self-efficacy or IT skills. Linear regression was performed (Cohen et al., 2003, R Core Team, 2020) and did not produce a significant relationship between self-efficacy and improved IT skills, indicating that during a non-crisis situation, self-efficacy does not directly affect IT skill development. Previous research established that self-efficacy influences academic performance and the motivation to acquire skills (e.g., Schunk, 1985; Schunk, 1991). Therefore, it can be assumed that another variable mediates the relationship between self-efficacy and IT skills (i.e., that there is an indirect relationship between self-efficacy and IT skills). For instance, Chen (2017) showed that learning engagement mediates the link between self-efficacy and learning performance.
Crisis Situation (COVID-19 Sample)
The spring 2020 courses were virtually identical to the courses in spring 2018 (100-level Excel business applications, and 400-level finance). The same instructors taught them with no change in the class curriculum. However, the courses during the crisis were moved online as of March 23, 2020 (week 12), and students had to use their personal computers in a home setting. The computer lab courses were changed to asynchronous online courses, providing a less structured environment.
The survey used for data collection at the end of the spring 2020 semester was the same as in spring 2018, and the order in which the measures were presented remained the same across all participants. The only change was that in spring 2018, paper surveys were used, whereas online surveys were used in spring 2020.
The crisis sample had 48 participants (58.3% 100-level class, 41.7% 400-level class), and 60.4 % males. All data analysis was performed in R (R Core Team, 2020). No gender difference was identified during data analysis. An independent t-test was performed to compare students’ self-efficacy in spring 2018 versus spring 2020. No statistical mean difference was found. The same was performed for IT skills and there was also no statistical mean difference. Figure 2 shows the mean comparison.
Note. Values shown are the mean.
The sample size needed for linear regression was calculated using a medium to large effect using G*Power, resulting in N = 41 (Faul et al., 2009). With one predictor, the sample size of 48 is sufficient to perform a linear regression. Linear regression was performed according to Cohen et al. (2003). Self-efficacy predicted improved technology skills for the crisis sample (β = .31, F(1, 46) = 4.787, p < .05, R2 = .10) providing evidence that during a crisis there is a direct effect between self-efficacy and IT skills.
Students experienced a change from a structured to an unstructured environment. There was concern that overall skill development would be the same as previously in a structure computer lab setting. The results show that even though their assessment of improving IT skills was lower in 2020 than 2018, the difference was not significant. However, the results show that the effect is not the same for every student. The positive and significant relationship between self-efficacy and IT skills may indicate that students with higher reported self-efficacy assess significantly higher gains in technology skills than their peers with lower self-efficacy during crisis.
According to Dynan et al. (2008), students have a greater opportunity to shape their work in an unstructured environment. Students with higher levels of self-efficacy may be more capable of engaging in self-directed learning (Bandura, 1993). Students with self-directed learning skills do well in an unstructured environment as they are better in problem-solving, creativity, and handling change (Guglielmino & Klatt, 1993). There may be even positive long-term effects on the experience of an unstructured learning environment, as self-directed learning is the basis of lifelong learning (Dynan et al., 2008).
The finding provides various implications on a theoretical and practical level. There is additional evidence that self-efficacy is vital during a crisis, as indicated by Hannah et al. (2009) and Kovoor-Misra (2020). The positive and direct relationship to IT skills is support for the resilience of individuals with high levels of self-efficacy, as stated by Hobfoll (2002). According to Sun et al. (2016) instructors are the primary drivers of learning and the technology related methods (e.g., online learning) they use are still relatively new. During times of crisis, like the COVID-19 pandemic, personal attributes like self-efficacy become even more important for students. Instructors should focus on activities that may help students increase their self-efficacy. For example, giving students a positive outlook about the future will positively influence self-efficacy (Whetten & Cameron, 2011). Furthermore, self-efficacy is increased when students are provided with frequent and immediate positive feedback while working on academic tasks (Schunk, 1983). The feedback needs to be framed that the students attribute this feedback to their own effort (Schunk, 1987). Receiving effort-based feedback will lead students to work harder, experience stronger motivation, and report greater efficacy for further learning.
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