Samantha Ruggiero Remote Learning, Academic Self Efficacy, & Academic Performance of College Students During COVID-19

Remote Learning, Academic Self-Efficacy, & Academic Performance of College Students During COVID-19

Samantha Ruggiero

Since March 2020, the COVID-19 pandemic has upended the day-to-day lives of individuals around the world, particularly students. As a result of the pandemic, remote learning rapidly became commonplace for students of all ages across international borders (Gillis & Krull, 2020; Miller, 2021). While remote learning was considered essential to protect public health during the beginning and middle stages of the pandemic, the widespread transition to this learning system was fraught with challenges for college students, administrators, and professors. Research suggests that many students were dissatisfied with the quality of their remote courses and faced difficulties in maintaining engagement and motivation with course materials (Gillis & Krull, 2020; Miller, 2021), potentially decreasing their levels of academic self-efficacy. As academic self-efficacy is integral to academic performance, the current proposal seeks to investigate the impact of remote learning on college students’ academic self-efficacy and academic performance during the COVID-19 pandemic.

The Challenges of Remote Learning During COVID-19

The abrupt shift to remote learning resulted in a variety of obstacles for college students; remote learning led to a collective decline in students’ course satisfaction levels dropping from 87% pre-pandemic to 59% after the transition to remote learning (Miller, 2021). Findings of one study suggest that only 55% of students found live-Zoom lectures to be effective, with only 36% reporting the lectures to be enjoyable (Gillis & Krull, 2020). Moreover, 42% of college students cited staying motivated while taking classes remotely as a challenge (Miller, 2021). Furthermore, half of college students reported facing technical difficulties that disrupted their access to remote learning resources, which further compounded the obstacles associated with remote learning (Gillis & Krull, 2020). In another study, a majority of college students reported concerns about their academic performance during COVID-19 (Son et al., 2020). These findings demonstrate that many students did not feel able to perform at their full academic potential during the COVID-19 pandemic.

Due to the rapid nature of the emergency switch to remote learning during the pandemic, universities struggled to deliver high-quality education to college students (Johnson et al., 2020). For many professors, teaching remotely was an uncharted endeavor; in one national study, over half of college administrators and professors reported that increasing support for students during the transition to remote learning was a high priority to them, indicating a demand for resources to have better-supported college students during the pandemic (Johnson et al., 2020). Yet, 64% of professors reported having no previous remote teaching experience before the transition to remote learning (Johnson et al., 2020). Furthermore, as of June 2020, only 47% of college and university presidents felt confident in their attempts to train less-experienced faculty members in using remote teaching technologies, and only 55% believed that they were able to uphold high academic standards through remote learning (Miller, 2021). It is, thus, evident that not only have students felt as if the quality of their education was compromised due to the transition to remote learning, but that university administrators and professors did not feel entirely equipped to prepare students to succeed while engaging with remote learning.

Academic Self-Efficacy and Academic Performance

When exploring online academic learning, it is important to consider the ways in which academic self-efficacy impacts academic performance. Academic self-efficacy, or one’s confidence in their ability to achieve desired academic outcomes (Sharma & Nasa, 2014), has been reported to have a significant and positive relation with academic performance (Honicke & Broadbent, 2016; Sharma & Nasa, 2014; Zajacova et al., 2005). Much of the extant literature in this area emphasizes the importance of academic self-efficacy in the learning process and in predicting academic performance (Honicke & Broadbent, 2016; Schunk & Pajares, 2002; Sharma & Nasa, 2014; Zajacova et al., 2005). In particular, academic self-efficacy has been observed to sustain the initiation and completion of academic goals (Schunk & Pajares, 2002). Research supports that high academic self-efficacy is associated with increased motivation towards academic goal persistence, which is an essential component of academic success (Schunk & Pajares, 2002).

The relation between academic self-efficacy and academic performance might be cyclical, such that mastery experiences (i.e., experiences in which the desired outcome is achieved) serve as a source of academic self-efficacy (Bandura, 1994). This means that experiences of academic success, such as getting exceptional grades, may reinforce high levels of academic self-efficacy in students (Bandura, 1994). This hypothesis has been supported by the finding that academic self-efficacy and academic performance are more strongly correlated in new college students at the end of the semester compared to the beginning of the semester, as students accumulate mastery experiences over the course of the semester (Gore Jr., 2006). In another study (Chemers et al., 2001), past academic achievement (i.e., high school GPA) was shown to influence levels of academic self-efficacy in first-year college students, further exhibiting how

academic mastery experiences can impact levels of academic self-efficacy.

The COVID-19 pandemic and the widespread shift to remote learning resulted in a decrease in student motivation and a widespread negative outlook on remote learning (Miller, 2021), which might negatively impact students’ levels of academic self-efficacy. In turn, the influence of remote learning during the COVID-19 pandemic on academic self-efficacy levels might negatively impact students’ academic performance (Miller, 2021).

Proposed Study

The reliance on remote learning during the COVID-19 pandemic has led to concerns about a potential decrease in academic self-efficacy (Alemany-Arrebola et al., 2020; Rohmani & Andriani, 2021). While several studies have explored the challenges of remote learning during the COVID-19 pandemic (e.g., Gillis & Krull, 2020; Johnson et al., 2020; Miller et al., 2020; Son et al., 2020), there has been a lack of literature investigating the relation between the remote learning, academic self-efficacy, and the academic performance of college students during the COVID-19 pandemic. The current proposal thus seeks to address the following research question: How does remote learning impact the academic self-efficacy and academic performance of college students during the COVID-19 pandemic?

Proposed Method

Participants

Random sampling will be used to obtain a sampling pool of 1,000 undergraduate students from across all Title IV degreegranting institutions in the United States to produce a diverse and generalizable sample. To qualify for this proposed study, participants must be between the ages of 18 and 24 years old, speak fluent English, and have been enrolled in remote classes for at least one semester during the pandemic. In addition, participants must have finalized their choice of major to be eligible for this proposed study, so that academic performance can be measured in relation to each participant’s major area of study. The study will aim to recruit a gender diverse sample of participants.

Procedure

Participants who qualify and consent to participate in the study will complete a Qualtrics form regarding their participation in remote courses (e.g., semesters enrolled in remote classes; fully remote or hybrid; number of courses completed). Participants will then complete the Academic SelfEfficacy Scale (Kunnathodi & Ashraf, 2007). The scale includes 20 positive (e.g., “Irrespective of the subject, I am competent in learning”) and 20 negative (e.g., “Often I fail to comprehend the actual meaning of what I study”) statements relating to academic self-efficacy, which participants respond on a 5-point Likert scale (Kunnathodi & Ashraf, 2007). Following the measure guidelines, composite scores ranging from 40 to 200 will be calculated and included in the analyses. The scale has high validity and reliability, with a test-retest coefficient of .85 and split-half reliability of .90 (Gafoor & Ashraf, 2007).

Participants will then be asked to provide information regarding their academic performance, including 1) the number of classes they took related to their major area of study in during the semester(s) they were enrolled in remote courses, 2) an objective report of their grades in these courses, and 3) responses to an adjusted version of the Pearlin Mastery Scale (Pearlin & Schooler, 1978), to capture their academic performance from the participants’ perspectives. The Pearlin Mastery Scale is typically used to measure the level of control participants feel that they have over important life outcomes (Pearlin & Schooler, 1978). The scale has seven items to measure a sense of mastery, to which participants respond on a 4-point Likert scale (Pearlin & Schooler, 1978), and has a high Cronbach’s alpha (i.e., .89), and statistically significant convergent, predictive, and discriminant validity (Edwards et al., 2000). The scale is scored on a range between 7 and 28, with higher scores indicating a higher sense of mastery (Pearlin & Schooler, 1978). For this study, participants will be asked to frame their responses to the scale in terms of the classes related to their major. For instance, the item “There is really no way I can solve some of the problems I have” (Pearlin & Schooler, 1978) will be changed to “There was really no way I could solve some of the problems I had in online classes related to my major,” (Pearlin & Schooler, 1978) and so on. Items in the original scale using the term “life” will be replaced with the phrase “classes related to my major.” Item analysis will be run to ensure that the adapted measure has strong internal reliability.

Discussion

This proposed study has implications for college students, administrators, and professors at universities across the United States. This proposed study can serve as a platform to reflect the attitudes college students have had towards remote learning during the COVID-19 pandemic, as well as their levels of academic self-efficacy during this time. In turn, such insights can inform university administrators and professors of students’ academic performance and levels of academic self-efficacy during what was a prolonged period of remote learning. University administrators and professors can serve as an important resource and source of support for students during difficult times, especially amid a pandemic (Zhai & Du, 2020). Thus, the proposed study can serve as an important resource to prompt university administrators and professors to monitor the academic self-efficacy of students and provide a space in which high academic self-efficacy and success are attainable if a transition to remote learning returns. Thus, this study could be essential in determining the teaching styles, resources, and levels of support provided to students by universities during any future experiences with remote learning.

Further research might explore how remote learning impacts a diverse population of college students across the United States. One way to expand on the current proposal

is to explore how academic self-efficacy and remote learning, as well as academic self-efficacy and academic performance during remote learning, differ amongst types of Title IV degreegranting institutions attended. Furthermore, future research might explore how the relationships between the variables differ by demographics such as gender, sexuality, socioeconomic status, race, and ethnicity. COVID-19 has impacted certain racial and ethnic minority groups disproportionately in terms of health and quality-of-life (CDC, 2020), and students with lower socioeconomic statuses may lack access to the technological resources needed to succeed in a remote learning environment, with the procurement and distribution of resources for students varying across universities. In addition, college students with mental health disorders and learning difficulties may be more susceptible to difficulties in remote learning than other students. Therefore, research regarding the disparities between samples that vary in demographics and neurodiversity could provide more insight into which students require the most support from their professors and administrators to maintain academic success.

The COVID-19 pandemic disrupted the lives of many individuals across the world. This unparalleled time proved to be particularly difficult for college students who were required to attend classes virtually and balance other life activities. Thus, this proposed study can provide insight into how the pandemic and subsequent transition to remote learning impacted the past academic self-efficacy and academic performance of college students. By gaining valuable metrics regarding the relationships between these variables, university administrators and professors can be more equipped to provide college students with engaging and supportive learning environments over remote platforms in the future. Such insight may allow universities to provide more opportunities for all students to succeed during stable and turbulent times alike.

References

Alemany-Arrebola, I., Rojas-Ruiz, G., Granda-Vera, J., & Mingorance-Estrada, A.C. (2020). Influence of COVID-19 on the perception of academic self-efficacy, state anxiety, and trait anxiety in college students. Frontiers in Psychology. Advance online publication. Centers for Disease Control and Prevention (CDC). (2020, July). Health equity considerations and racial and ethnic minority groups. Centers for Disease Control and Prevention. Retrieved from https://www.cdc. gov/coronavirus/2019-ncov/community/healthequity/race-ethnicity.html Chemers, M.M., Hu, L., & Garcia, B.F. (2001). Academic selfefficacy and first-year college student performance and adjustment. Journal of Educational Psychology, 93(1), 55-64. College Board. (n.d.). How to convert your GPA to a 4.0 scale. College Board. Retrieved from https://pages. collegeboard.org/how-to-convert-gpa-4.0-scale Edwards, R. R., Telfair, J., Cecil, H., & Lenoci, J. (2000). Reliability and validity of a self-efficacy instrument specific to sickle cell disease. Behaviour Research and Therapy, 38(9), 951-963. Gillis, A., & Krull, L.M. (2020). COVID-19 Remote learning transition in spring 2020: Class structures, student perceptions, and inequality in college courses. Teaching Sociology, 48(4), 283-299. Gore Jr., P.A. (2006). Academic self-efficacy as a predictor of college outcomes: Two incremental validity studies. Journal of Career Assessment, 14(1), 92-115. Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17, 63-84. Johnson, N., Veletsianos, G., & Seaman, J. (2020). U.S. faculty and administrators’ experiences and approaches in the early weeks of the COVID-19 pandemic. Online Learning, 24(2), 6-21. Kunnathodi, A. G., & Ashraf, P. M. (2007). Academic selfefficacy scale. Pearlin, L. I., & Schooler, C. (1978). The structure of coping. Journal of Health and Social Behavior, 19, 2-21. Miller, C. (2021, March 14). Distance learning statistics (2021): Online education trends. Education Data. Retrieved from https://educationdata.org/online-educationstatistics Rohmani, N., & Andriani, R. (2021). Correlation between academic self-efficacy and burnout originating from distance learning among nursing students in Indonesia during the coronavirus disease 2019 pandemic. Journal of Educational Evaluation for Health Professions. Advance online publication. Schunk, D. H., & Pajares, F. (2002). The development of academic self-efficacy. In A. Wigfield & J. S. Eccles (Eds.), Development of Achievement Motivation (pp. 15–31). Academic Press. Sharma, H., & Nasa, G. (2014). Academic self efficacy: A reliable predictor of educational performances. British Journal of Education, 2(3), 57-64. ISSN: 2054-6351 Son, C., Hegde, S., Smith, A., Wang, X., & Sasangohar, F. (2020). Effects of COVID-19 on college students’ mental health in the United States: interview survey study. Journal of Medical Internet Research, 22(9), 1-22. Zajacova, A., Lynch, S. M., & Espenshade, T. J. (2005). Selfefficacy, stress, and academic success in college. Research in Higher Education, 46, 677–706. Zhai, Y. & Du, X. (2020). Addressing collegiate mental health amid COVID-19 pandemic. Psychiatry Research, 288.

CBT-based Mental Health App Treatment Outcomes for Heterosexual and LGBTQ+ College Students With Depression

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The percent of college students in the United States suffering from depression has increased, rising from 25% in 2007 to 30% in 2017 (Lipson et al., 2019). Among college students, LGBTQ+ individuals experience even higher levels of depression than their heterosexual peers, suggesting that they may be more in need of mental health services (Backhaus et al., 2019; Dunbar et al., 2017). Despite their increased need for mental health services, LGBTQ+ young people often encounter unique barriers that prevent them from accessing or benefiting from such services, including concerns about cost and confidentiality (Brown et al., 2015; Sanchez et al., 2009), fear of harassment (Brown et al., 2015), fear of letting their family members know about their gender or sexual orientation (Williams & Chapman, 2011), and skepticism about the inclusivity of services (Williams & Chapman, 2011).

Recent studies suggest that some of these barriers might be addressed via mental health mobile applications, interventions delivered through smartphones to promote users’ mental health (Rozbroj et al., 2015). Such health tools consist of psychoeducation and therapeutic exercises designed for different psychological disorders, such as depression (Hollis et al., 2016). Although these applications have not been specifically tested on LGBTQ+ students, their efficacy in reducing depressive symptoms in other populations is supported by research (e.g., Firth et al., 2017; Mohr et al., 2017). Compared to in-person services, mobile mental health apps are less expensive, easier to use, and often self-directed, which provide individuals with more control over their treatment (Rozbroj et al., 2015). Among the many types of mental health apps, those using cognitivebehavioral therapy (CBT) are the most common (PorrasSegovia et al., 2020). CBT is a type of psychotherapy based on the premise that mental illness stems from maladaptive cognitive and behavioral patterns, and it treats psychological problems by changing these patterns (Beck, 1970; Ellis, 1962). There is a large body of literature supporting the efficacy of CBT in treating depression because it identifies negative beliefs that lead to depression and replaces them with positive ways of thinking (Charkhandeh et al., 2016; Driessen & Hollon, 2010; Hofmann et al., 2012). When administered through mobile apps, CBT treats depression by guiding people to identify and challenge their negative thinking patterns, track thoughts and feelings, and engage in mood-improving exercises (Stawarz et al., 2018). Users complete homework activities, practice different coping skills, and work towards greater wellbeing (Stawarz et al., 2018). Many CBT-based apps have yielded positive results on reducing depressive symptoms, especially among the college student population (Broglia et al., 2019; Bruehlman-Senecal et al., 2020; McCloud et al., 2020).

Despite the encouraging results of CBT-based apps, little is known about whether LGBTQ+ college students will benefit from them as much as their heterosexual peers. As sexual minorities, LGBTQ+ people often experience unique stressors related to their sexual identity, such as coming out, discrimination, and stigma, which have been associated with higher levels of depression (Meyer, 2003). As such, depression apps designed for the general population might not be inclusive enough to effectively address these LGBTQ-related stressors. Specifically, previous research has found that the majority of digital interventions tend to assume users to be heterosexual (e.g., use pictures of heterosexual relationships to describe marriage; Rozbroj et al., 2014). By adopting these assumptions, they overlook that LGBTQ+ people are experiencing unique stress because of their identity, thereby failing to address relevant topics (e.g., coming out) in the intervention (Rozbroj et al., 2014). This exclusion can lead to inappropriate treatment of LGBTQ+ youth, which contributes to a poor experience and feelings of alienation and distrust of services (Rozbroj et al., 2014). As a result, LGBTQ+ youth may delay seeking help in the future, which allows symptoms to worsen and leads to poorer health outcomes (Elliott et al., 2015; Kilicaslan & Petrakis, 2019). In short, if CBT-based health apps fail to account for LGBTQ+ experiences and stressors, they may not be efficacious, and even be detrimental, for LGBTQ+ people with mental health problems. Nonetheless, if CBT-based apps are found to be effective for LGBTQ+ youth, they have the potential to facilitate easier access to mental health care for this population (Rozbroj et al., 2015). Because LGBTQ+ youth already have a tendency to seek mental health information and support online, they may be more receptive to and benefit from the apps (Lucassen et al., 2018; Rozbroj et al., 2015). In order to assess these concerns, this study proposes to answer the following question: How do CBTbased mental health apps affect depression treatment outcomes in LGBTQ+ college students compared with heterosexual students?

Proposed Method

Participants

The study will recruit 180 U.S. college students diagnosed with depression, evenly distributed across racial and ethnic backgrounds. Half of them will be individuals who identify as