essay about online learning during covid 19

The COVID-19 pandemic has changed education forever. This is how 

With schools shut across the world, millions of children have had to adapt to new types of learning. Image:  REUTERS/Gonzalo Fuentes

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Stay up to date:, education, gender and work.

• The COVID-19 has resulted in schools shut all across the world. Globally, over 1.2 billion children are out of the classroom.
• As a result, education has changed dramatically, with the distinctive rise of e-learning, whereby teaching is undertaken remotely and on digital platforms.
• Research suggests that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay.

While countries are at different points in their COVID-19 infection rates, worldwide there are currently more than 1.2 billion children in 186 countries affected by school closures due to the pandemic. In Denmark, children up to the age of 11 are returning to nurseries and schools after initially closing on 12 March , but in South Korea students are responding to roll calls from their teachers online .

With this sudden shift away from the classroom in many parts of the globe, some are wondering whether the adoption of online learning will continue to persist post-pandemic, and how such a shift would impact the worldwide education market.

Even before COVID-19, there was already high growth and adoption in education technology, with global edtech investments reaching US$18.66 billion in 2019 and the overall market for online education projected to reach $350 Billion by 2025 . Whether it is language apps , virtual tutoring , video conferencing tools, or online learning software , there has been a significant surge in usage since COVID-19.

How is the education sector responding to COVID-19?

In response to significant demand, many online learning platforms are offering free access to their services, including platforms like BYJU’S , a Bangalore-based educational technology and online tutoring firm founded in 2011, which is now the world’s most highly valued edtech company . Since announcing free live classes on its Think and Learn app, BYJU’s has seen a 200% increase in the number of new students using its product, according to Mrinal Mohit, the company's Chief Operating Officer.

Tencent classroom, meanwhile, has been used extensively since mid-February after the Chinese government instructed a quarter of a billion full-time students to resume their studies through online platforms. This resulted in the largest “online movement” in the history of education with approximately 730,000 , or 81% of K-12 students, attending classes via the Tencent K-12 Online School in Wuhan.

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Other companies are bolstering capabilities to provide a one-stop shop for teachers and students. For example, Lark, a Singapore-based collaboration suite initially developed by ByteDance as an internal tool to meet its own exponential growth, began offering teachers and students unlimited video conferencing time, auto-translation capabilities, real-time co-editing of project work, and smart calendar scheduling, amongst other features. To do so quickly and in a time of crisis, Lark ramped up its global server infrastructure and engineering capabilities to ensure reliable connectivity.

Alibaba’s distance learning solution, DingTalk, had to prepare for a similar influx: “To support large-scale remote work, the platform tapped Alibaba Cloud to deploy more than 100,000 new cloud servers in just two hours last month – setting a new record for rapid capacity expansion,” according to DingTalk CEO, Chen Hang.

Some school districts are forming unique partnerships, like the one between The Los Angeles Unified School District and PBS SoCal/KCET to offer local educational broadcasts, with separate channels focused on different ages, and a range of digital options. Media organizations such as the BBC are also powering virtual learning; Bitesize Daily , launched on 20 April, is offering 14 weeks of curriculum-based learning for kids across the UK with celebrities like Manchester City footballer Sergio Aguero teaching some of the content.

What does this mean for the future of learning?

While some believe that the unplanned and rapid move to online learning – with no training, insufficient bandwidth, and little preparation – will result in a poor user experience that is unconducive to sustained growth, others believe that a new hybrid model of education will emerge, with significant benefits. “I believe that the integration of information technology in education will be further accelerated and that online education will eventually become an integral component of school education,“ says Wang Tao, Vice President of Tencent Cloud and Vice President of Tencent Education.

There have already been successful transitions amongst many universities. For example, Zhejiang University managed to get more than 5,000 courses online just two weeks into the transition using “DingTalk ZJU”. The Imperial College London started offering a course on the science of coronavirus, which is now the most enrolled class launched in 2020 on Coursera .

Many are already touting the benefits: Dr Amjad, a Professor at The University of Jordan who has been using Lark to teach his students says, “It has changed the way of teaching. It enables me to reach out to my students more efficiently and effectively through chat groups, video meetings, voting and also document sharing, especially during this pandemic. My students also find it is easier to communicate on Lark. I will stick to Lark even after coronavirus, I believe traditional offline learning and e-learning can go hand by hand."

These 3 charts show the global growth in online learning

The challenges of online learning.

There are, however, challenges to overcome. Some students without reliable internet access and/or technology struggle to participate in digital learning; this gap is seen across countries and between income brackets within countries. For example, whilst 95% of students in Switzerland, Norway, and Austria have a computer to use for their schoolwork, only 34% in Indonesia do, according to OECD data .

In the US, there is a significant gap between those from privileged and disadvantaged backgrounds: whilst virtually all 15-year-olds from a privileged background said they had a computer to work on, nearly 25% of those from disadvantaged backgrounds did not. While some schools and governments have been providing digital equipment to students in need, such as in New South Wales , Australia, many are still concerned that the pandemic will widenthe digital divide .

Is learning online as effective?

For those who do have access to the right technology, there is evidence that learning online can be more effective in a number of ways. Some research shows that on average, students retain 25-60% more material when learning online compared to only 8-10% in a classroom. This is mostly due to the students being able to learn faster online; e-learning requires 40-60% less time to learn than in a traditional classroom setting because students can learn at their own pace, going back and re-reading, skipping, or accelerating through concepts as they choose.

Nevertheless, the effectiveness of online learning varies amongst age groups. The general consensus on children, especially younger ones, is that a structured environment is required , because kids are more easily distracted. To get the full benefit of online learning, there needs to be a concerted effort to provide this structure and go beyond replicating a physical class/lecture through video capabilities, instead, using a range of collaboration tools and engagement methods that promote “inclusion, personalization and intelligence”, according to Dowson Tong, Senior Executive Vice President of Tencent and President of its Cloud and Smart Industries Group.

Since studies have shown that children extensively use their senses to learn, making learning fun and effective through use of technology is crucial, according to BYJU's Mrinal Mohit. “Over a period, we have observed that clever integration of games has demonstrated higher engagement and increased motivation towards learning especially among younger students, making them truly fall in love with learning”, he says.

A changing education imperative

It is clear that this pandemic has utterly disrupted an education system that many assert was already losing its relevance . In his book, 21 Lessons for the 21st Century , scholar Yuval Noah Harari outlines how schools continue to focus on traditional academic skills and rote learning , rather than on skills such as critical thinking and adaptability, which will be more important for success in the future. Could the move to online learning be the catalyst to create a new, more effective method of educating students? While some worry that the hasty nature of the transition online may have hindered this goal, others plan to make e-learning part of their ‘new normal’ after experiencing the benefits first-hand.

The importance of disseminating knowledge is highlighted through COVID-19

Major world events are often an inflection point for rapid innovation – a clear example is the rise of e-commerce post-SARS . While we have yet to see whether this will apply to e-learning post-COVID-19, it is one of the few sectors where investment has not dried up . What has been made clear through this pandemic is the importance of disseminating knowledge across borders, companies, and all parts of society. If online learning technology can play a role here, it is incumbent upon all of us to explore its full potential.

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• Published: 25 January 2021

Online education in the post-COVID era

• Barbara B. Lockee 1  

Nature Electronics volume  4 ,  pages 5–6 ( 2021 ) Cite this article

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The coronavirus pandemic has forced students and educators across all levels of education to rapidly adapt to online learning. The impact of this — and the developments required to make it work — could permanently change how education is delivered.

The COVID-19 pandemic has forced the world to engage in the ubiquitous use of virtual learning. And while online and distance learning has been used before to maintain continuity in education, such as in the aftermath of earthquakes 1 , the scale of the current crisis is unprecedented. Speculation has now also begun about what the lasting effects of this will be and what education may look like in the post-COVID era. For some, an immediate retreat to the traditions of the physical classroom is required. But for others, the forced shift to online education is a moment of change and a time to reimagine how education could be delivered 2 .

Looking back

Online education has traditionally been viewed as an alternative pathway, one that is particularly well suited to adult learners seeking higher education opportunities. However, the emergence of the COVID-19 pandemic has required educators and students across all levels of education to adapt quickly to virtual courses. (The term ‘emergency remote teaching’ was coined in the early stages of the pandemic to describe the temporary nature of this transition 3 .) In some cases, instruction shifted online, then returned to the physical classroom, and then shifted back online due to further surges in the rate of infection. In other cases, instruction was offered using a combination of remote delivery and face-to-face: that is, students can attend online or in person (referred to as the HyFlex model 4 ). In either case, instructors just had to figure out how to make it work, considering the affordances and constraints of the specific learning environment to create learning experiences that were feasible and effective.

The use of varied delivery modes does, in fact, have a long history in education. Mechanical (and then later electronic) teaching machines have provided individualized learning programmes since the 1950s and the work of B. F. Skinner 5 , who proposed using technology to walk individual learners through carefully designed sequences of instruction with immediate feedback indicating the accuracy of their response. Skinner’s notions formed the first formalized representations of programmed learning, or ‘designed’ learning experiences. Then, in the 1960s, Fred Keller developed a personalized system of instruction 6 , in which students first read assigned course materials on their own, followed by one-on-one assessment sessions with a tutor, gaining permission to move ahead only after demonstrating mastery of the instructional material. Occasional class meetings were held to discuss concepts, answer questions and provide opportunities for social interaction. A personalized system of instruction was designed on the premise that initial engagement with content could be done independently, then discussed and applied in the social context of a classroom.

These predecessors to contemporary online education leveraged key principles of instructional design — the systematic process of applying psychological principles of human learning to the creation of effective instructional solutions — to consider which methods (and their corresponding learning environments) would effectively engage students to attain the targeted learning outcomes. In other words, they considered what choices about the planning and implementation of the learning experience can lead to student success. Such early educational innovations laid the groundwork for contemporary virtual learning, which itself incorporates a variety of instructional approaches and combinations of delivery modes.

Online learning and the pandemic

Fast forward to 2020, and various further educational innovations have occurred to make the universal adoption of remote learning a possibility. One key challenge is access. Here, extensive problems remain, including the lack of Internet connectivity in some locations, especially rural ones, and the competing needs among family members for the use of home technology. However, creative solutions have emerged to provide students and families with the facilities and resources needed to engage in and successfully complete coursework 7 . For example, school buses have been used to provide mobile hotspots, and class packets have been sent by mail and instructional presentations aired on local public broadcasting stations. The year 2020 has also seen increased availability and adoption of electronic resources and activities that can now be integrated into online learning experiences. Synchronous online conferencing systems, such as Zoom and Google Meet, have allowed experts from anywhere in the world to join online classrooms 8 and have allowed presentations to be recorded for individual learners to watch at a time most convenient for them. Furthermore, the importance of hands-on, experiential learning has led to innovations such as virtual field trips and virtual labs 9 . A capacity to serve learners of all ages has thus now been effectively established, and the next generation of online education can move from an enterprise that largely serves adult learners and higher education to one that increasingly serves younger learners, in primary and secondary education and from ages 5 to 18.

The COVID-19 pandemic is also likely to have a lasting effect on lesson design. The constraints of the pandemic provided an opportunity for educators to consider new strategies to teach targeted concepts. Though rethinking of instructional approaches was forced and hurried, the experience has served as a rare chance to reconsider strategies that best facilitate learning within the affordances and constraints of the online context. In particular, greater variance in teaching and learning activities will continue to question the importance of ‘seat time’ as the standard on which educational credits are based 10 — lengthy Zoom sessions are seldom instructionally necessary and are not aligned with the psychological principles of how humans learn. Interaction is important for learning but forced interactions among students for the sake of interaction is neither motivating nor beneficial.

While the blurring of the lines between traditional and distance education has been noted for several decades 11 , the pandemic has quickly advanced the erasure of these boundaries. Less single mode, more multi-mode (and thus more educator choices) is becoming the norm due to enhanced infrastructure and developed skill sets that allow people to move across different delivery systems 12 . The well-established best practices of hybrid or blended teaching and learning 13 have served as a guide for new combinations of instructional delivery that have developed in response to the shift to virtual learning. The use of multiple delivery modes is likely to remain, and will be a feature employed with learners of all ages 14 , 15 . Future iterations of online education will no longer be bound to the traditions of single teaching modes, as educators can support pedagogical approaches from a menu of instructional delivery options, a mix that has been supported by previous generations of online educators 16 .

Also significant are the changes to how learning outcomes are determined in online settings. Many educators have altered the ways in which student achievement is measured, eliminating assignments and changing assessment strategies altogether 17 . Such alterations include determining learning through strategies that leverage the online delivery mode, such as interactive discussions, student-led teaching and the use of games to increase motivation and attention. Specific changes that are likely to continue include flexible or extended deadlines for assignment completion 18 , more student choice regarding measures of learning, and more authentic experiences that involve the meaningful application of newly learned skills and knowledge 19 , for example, team-based projects that involve multiple creative and social media tools in support of collaborative problem solving.

In response to the COVID-19 pandemic, technological and administrative systems for implementing online learning, and the infrastructure that supports its access and delivery, had to adapt quickly. While access remains a significant issue for many, extensive resources have been allocated and processes developed to connect learners with course activities and materials, to facilitate communication between instructors and students, and to manage the administration of online learning. Paths for greater access and opportunities to online education have now been forged, and there is a clear route for the next generation of adopters of online education.

Before the pandemic, the primary purpose of distance and online education was providing access to instruction for those otherwise unable to participate in a traditional, place-based academic programme. As its purpose has shifted to supporting continuity of instruction, its audience, as well as the wider learning ecosystem, has changed. It will be interesting to see which aspects of emergency remote teaching remain in the next generation of education, when the threat of COVID-19 is no longer a factor. But online education will undoubtedly find new audiences. And the flexibility and learning possibilities that have emerged from necessity are likely to shift the expectations of students and educators, diminishing further the line between classroom-based instruction and virtual learning.

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A Systematic Review of the Research Topics in Online Learning During COVID-19: Documenting the Sudden Shift

• Min Young Doo Kangwon National University http://orcid.org/0000-0003-3565-2159
• Meina Zhu Wayne State University
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Research Article

Validation of depression, anxiety, and stress scales (DASS-21) among Thai nursing students in an online learning environment during the COVID-19 outbreak: A multi-center study

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

Affiliations Movement Science and Exercise Research Center-Walailak University (MoveSE-WU), Walailak University, Nakhon Si Thammarat, Thailand, School of Allied Health Sciences, Walailak University, Nakhon Si Thammarat, Thailand

Roles Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Faculty of Nursing, Roi Et Rajabhat University, Roi Et, Thailand

Roles Investigation, Writing – review & editing

Affiliation Faculty of Physical Therapy, Huachiew Chalermprakiet University, Bangkok, Thailand

Roles Writing – review & editing

Affiliation School of Medicine, Walailak University, Nakhon Si Thammarat, Thailand

Roles Investigation, Writing – original draft, Writing – review & editing

Affiliation Faculty of Nursing, Ratchathani University, Udonthani Campus, Udonthani, Thailand

Roles Investigation, Writing – original draft

Affiliation Faculty of Nursing, Chalermkarnchana University, Srisaket Campus, Srisaket, Thailand

Affiliation Faculty of Nursing, University of Jember, Jember, Indonesia

• Yuwadee Wittayapun, 
• Ueamporn Summart, 
• Panicha Polpanadham, 
• Thanyaporn Direksunthorn, 
• Raweewan Paokanha, 
• Naruk Judabood, 
• Muhamad Zulfatul A’la

• Published: June 30, 2023
• https://doi.org/10.1371/journal.pone.0288041
• Peer Review
• Reader Comments

The Depression, Anxiety and Stress Scale (DASS-21), an introductory scale used to identify common mental disorders (CMDs) among adults, was validated across cultures in Asian populations; nevertheless, its capacity for screening these disorders may be limited for some specified groups, including nursing students. This study attempted to investigate the psychometric scale’s unique features of DASS-21 for Thai nursing students in an online learning environment during the COVID-19 outbreak. A cross-sectional study using the multistage sampling technique recruited 3,705 nursing students from 18 universities located in south and northeast Thailand. The data were gathered using an online web-based survey, and then all respondents were divided into 2 groups (group 1, n = 2,000, group 2, n = 1,705). After using the statistical methods to reduce items, exploratory factor analysis (EFA) using group 1 was performed to investigate the factor structure of the DASS-21. Finally, group 2 used confirmatory factor analysis to verify the modified structure proposed by the EFA and assess the construct validity of the DASS-21. A total of 3,705 Thai nursing students were enrolled. For the factorial construct validity, a three-factor model was initially suggested containing 18 items (DASS-18) spread across 3 components: anxiety (7 items), depression (7 items) and stress (4 items). The internal consistency reliability was acceptable with Cronbach’s alpha in the range of 0.73–0.92for either the total or its subscales. For convergent validity, average variance extracted (AVE) showed that all the DASS-18 subscales achieved convergence effect with AVE in the range of 0.50–0.67. The psychometric features of the DASS-18 will support Thai psychologists and researchers to screen CMDs more easily among undergraduate nursing students in tertiary institutions who enrolled in an online learning environment during the COVID-19 outbreak.

Citation: Wittayapun Y, Summart U, Polpanadham P, Direksunthorn T, Paokanha R, Judabood N, et al. (2023) Validation of depression, anxiety, and stress scales (DASS-21) among Thai nursing students in an online learning environment during the COVID-19 outbreak: A multi-center study. PLoS ONE 18(6): e0288041. https://doi.org/10.1371/journal.pone.0288041

Editor: Omar M. Khraisat, Al-Ahliyya Amman University, JORDAN

Received: November 11, 2022; Accepted: June 18, 2023; Published: June 30, 2023

Copyright: © 2023 Wittayapun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files. We have already generated our minimal data set 2,000 datasets out of the total (3,705) (D1.XLSX).

Funding: This study was supported by The Walailak University's Individual Research Grants provided funding for the study (Grant Number WU-IRG-65-015). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Depression and anxiety are common mental health disorders (CMDs), leading causes of disability and have gained prominence due to their growing global burden [ 1 ]. Individually, these disorders contribute to poor psychological wellbeing, which interferes with learning and inhibits students’ academic performance [ 2 ]. Early screening for anxiety and depression in primary care and academic settings necessitates an assessment strategy that is rapid and easy to apply and has proven psychometric properties [ 2 ].

During the COVID-19 pandemic, undergraduate students also reported their anxiety or stress [ 3 , 4 ]. According to a network study, 932 nursing students were included. More than one third of these students reported at least moderate symptoms of worry or stress, and almost one half of these students presented at least mild symptoms of depression [ 5 ]. Due to a quick shift from face-to-face to an online learning environment during university lock downs, undergraduate nursing and midwifery curricula had trouble adjusting to "remote learning" that relies on the use of electronic technologies and media sources to conduct learning outside of the traditional classroom [ 6 ]. Thus, being unable to participate in extensive training such as clinical settings make students feel as though they are passing up a good opportunity to learn these abilities that may have decreased students’ mental health [ 5 , 7 ]. In addition, anxiety and depression may occur more commonly among low experienced apprentices including nursing students [ 8 ]. Moreover, this current study also discovered that the levels of anxiety and depression were higher among nursing students than among those students from other disciplines, regarding their probably high risk of infection exposure and fear of communicable diseases [ 8 ]. Currently, rare evidence is available concerning the mental health of Thai nursing students encountering an instant psychological response in relation to COVID-19. To provide psychiatric interventions to people experiencing these negative emotional states, early diagnosis of these diseases is essential [ 2 ]. Early assessment, using an effective screening instrument (such as a rating scale), provides a rapid indicator of the client’s emotional well-being and is helpful for further clinical judgment and early treatment. Likewise, self-reported questionnaires and clinician-rated scales are two commonly used methods to assess CMDs [ 9 ].

The Depression, Anxiety, and Stress Scale (DASS) is a common scale frequently used to detect CMDs [ 10 ]. Both the DASS-42 and its shortened version, the DASS-21, are frequently used to assess depression, anxiety, and stress among adults, and are considered superior to other psychometric tools to identify these CMDs and screen for psychological abnormalities [ 11 ]. Moreover, DASS-21 has several advantages over the original 42-item version (DASS-42), such as fewer items, cleaner factor structure and smaller interfactor correlations [ 12 ]. Data analyses among adults using this measure produced consistent results regarding its psychometric properties [ 2 , 7 , 12 – 14 ]. Findings regarding the DASS-21’s factor structure are contradictory, ranging from one to four factors structures [ 15 – 17 ]. Results from a prior study in Asia, conducted among nursing students in Brunei, have validated the DASS-21 used the final model, including a nine-item scale across three components [ 18 ]. However, this study encountered limitations because this representative sample (n = 126) was the smallest compared with other studies.

In the Thai context, DASS-21 has been validated across cultures among Asian residents from various projects and research objectives such as assessing the work-related stress and coping strategies among employees in the education and health care sectors [ 19 ], so its ability in detecting these mental health problems may be limited for specific groups including undergraduate nursing students. Another study enrolled preclinical medical students to explore psychometric properties of DASS-21, but this study also used this tool as dependent variable and did not report the constructed validity or the Cronbach alpha coefficient [ 20 ]. Insufficient data are available to validate the DASS-21’s psychometric properties in specific Thai populations, which could constitute considerable variation concerning sociocultural backgrounds and political differences among groups.

To date, regarding the context of online learning, no research, concerning factor structures and convergent validity of DASS-21, has been conducted among Thai nursing students. Applying the original subscales scoring for all adulthood categories to only young adult age groups (approximately ages 18 to 26 years) without comprehending the instrument’s psychometric properties could lead to inaccurate conclusions [ 21 ]. This study aimed to examine the psychometric scale‐specific features of DASS-21 for Thai nursing students concerning an online learning situation during the COVID-19 outbreak.

Materials and methods

Study design and population.

This constituted cross-sectional research, obtaining data from one part of the larger multi-center study, aiming to assess the effects of online learning on the prevalence and factors associated with musculoskeletal disorders among Thai, Indonesian, Vietnamese, and Lao faculty members and students during the COVID-19 outbreak. After this study is completed, they recruit some samples and send them the survey of DASS-21. The target population of this study comprised Thai nursing students nationwide undertaken using a multistage sampling technique. Altogether, 96 nursing institutes in Thailand are spread across five regions. Using a simple random sampling technique, two of the five regions, the south and the northeast, were chosen in the first stage. In these areas, 37 nursing institutes are located. Then using a nonproportional stratified sampling technique, 15 nursing institutes were chosen. In addition, three nursing faculties from the Central Region were conveniently sampled, providing a total of 18 institutes. Totally, 5395 students were able to receive participant information sheets online from us. Prior to collecting data, administrators’ approval was obtained after a thorough description of the study’s objective. Before the survey began, a statement of consent was obtained from all participants by permitting only those who pressed "I consent" to go to the questionnaires. A total of 5136 participants indicated their willingness to join the study. Code numbers were created to protect students’ privacy. Finally, nonproportional stratified sampling was used on 4,618 undergraduate students, and 3705 became eligible respondents. Individual student data is only accessible to the authors of this study. These samples were employed to access study participants using an online web-based survey. Recruiting participants and collecting data occurred from April 2022 to June 2022.

Study instruments

The DASS-21 assesses depression, anxiety, and stress symptoms [ 10 ], and is divided into three subscales, each with seven items including depression (DASS 21-D), anxiety (DASS 21-A) and stress (DASS 21-S) (DASS 21S). The translation of this tool from English to Thai was carried out during the cross-culture translation procedure [ 19 ]. Each item is graded on a four-point Likert scale ranging from 0 (“did not apply to me at all”) to 3 (“applied to me a lot”). Because the DASS-21 is a shortened version of the DASS (42 items), the final score of each scale was multiplied by two before being compared with the original DASS scale. Higher scores and response values reflect greater levels of the condition being evaluated. In this study we used the Thai version of the DASS-21 with the original author’s permission [ 19 ]. The Cronbach’s alpha coefficients of depression, anxiety, and stress for The Thai version are 0.82, 0.78, and 0.69, respectively [ 19 ].

The Visual Analog Scale to Evaluate Fatigue Severity (VAS-F) [ 22 ] has 18 components all related to one’s perception of exhaustion. Each question asks respondents to place an “X" along a VAS line that runs between two extremes, such as "not at all fatigued" and "very tired," to identify what they are feeling right now. The score goes from 0 to 100 and is recorded using a vertical line of 10 cm. The line from "No fatigue" to the subject’s stated point indicating their level of fatigue, was measured to obtain the score; the higher the VAFS score, the greater the level of fatigue [ 23 ]. The Cronbach’s alpha for the fatigue subscale was 0.91 and the value of energy subscale was 0.94, respectively [ 22 ]. In addition, questions about general information of the participants, i.e., gender, age, study year, online learning, were included.

Sample size calculation

The sample size was calculated using the formula "sample size = number of items X number of participants," which is an extensively used formula in survey development research. We estimated the minimum sample size based on one item to ten participants [ 24 ]. Therefore, the minimum acceptable sample size, based on 21 items of DASS-21, was 210 respondents. However, our research enrolled 3,705 nursing students from 18 universities mainly located in south and northeast Thailand. Hence, larger sample size could provide more meaningful factor loadings and yield more generalizable results. The inclusion criteria for the participants were age at least 17 years, a nursing student at the institute during the study period more than six months and engaged in the online learning. Individuals who do not fill the administered questionnaire or submit an incompletely filled questionnaire such as responding to only a part of general information in the Thai Version of DASS-21, and nursing students with existing CMDs were excluded from this study. To avoid model overfitting, the exploratory (EFA) and confirmatory factor analyses (CFA) were organized on a random split of the total 3705 subjects in two group samples (group 1, n = 2000, and group 2, n = 1705).

Statistical analysis

All statistical analysis in this study was conducted using IBM SPSS and AMOS version 20. Descriptive statistics with means and standard deviation for continuous variables and counts and percentages for categorical data were used to describe the participant’s demographic characteristics.

To investigate the number of components in the EFA for the DASS-21 measuring model, parallel analysis (based on principle component analysis) was undertaken using sample group 1. Then the structure of factors was investigated using principal axis factoring with varimax rotation. Factor loadings less than 0.5 were suppressed, and item cross loadings more than 0.2 were removed one at a time. Furthermore, factor loadings were used to calculate average variance extracted (AVE) and composite reliability (CR). Regarding the findings of the principal axis factoring, a CFA was applied to the remaining held-out participants. The measurement model was fitted using an unweighted least square estimate CFA, and model fit was evaluated using the cumulative fit index (CFI), adjusted goodness of fit index (AGFI), root-mean-square error of approximation (RMSEA), and Tucker-Lewis’s index (TLI) [ 19 , 25 , 26 ]. Likewise, Root Mean Square Error of Approximation (RMSEA) with a p-value less than 0.08 was considered to indicate a good model fit, so it was reported and used in this investigation for the sake of convention. Along with the CFA, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and the Bartlett’s test of sphericity were developed to provide additional construct validity evidence [ 27 ].

CR and Cronbach’s alpha were used to assess reliability. CR is acceptable when the values for the three subscales are greater than 0.6 [ 28 ]. Cronbach’s alpha was used to assess internal consistency reliability, and Cronbach’s alpha above 0.7 for all the subscales was considered to be an acceptable reliability [ 28 ]. In addition, the relationship between each of the DASS items and its own DASS subscale with that item removed is known as the corrected item-total correlations of the three subscales.

We investigated the convergent validity of DASS-18 using the AVE. To indicate convergent validity, the AVE must be equal to or greater than 0.50, indicating that the construct’s variance accounts for more than 50% of its variation [ 26 ]. Furthermore, the discriminant validity determined whether the three indicators of depression, anxiety and stress domains were distinct factors from one another. Pearson’s correlation (r) lower than 0.85 among variables verified their discriminant validity [ 26 ] Pearson correlations were calculated to investigate the intercorrelations matrix, the temporal stability of DASS-18 subscale scores and the relationship between DASS-18 and VAS-F.

Ethics considerations

This study was examined and authorized by Walailak University’s institutional review board (Ref. No. WUEC-22-007-01) and the Center for Ethics in Human Research, Khon Kaen University (Ref. No. HE652094).

General information of the participants

The details of participant characteristics are described in Table 1 . Most completing the questionnaire were females (94.2%) with a mean age of 20 (SD = 1.26) years. Almost two thirds of these participants (67.2%) were experiencing some semesters of online learning at the time of collecting data.

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(n = 3705).

https://doi.org/10.1371/journal.pone.0288041.t001

Exploratory factor analysis

After randomizing the 2000 participants for EFA, firstly, parallel analysis of the matrix indicated that a three-factor solution could be extracted. Secondly, the rotational factor loading matrix was statistically significant. Three factors having eigenvalues over one were created by the initial analyses of the Group 1 sample. To ascertain the factorial structure of DASS-21 and the underlying dimensions comprising its 21 items. The initial analysis revealed a three-factor structure that explained 69.31% of the original data’s variance. Three items (S8, S11, S12) from the stress scale were found to be loading on multiple factors; therefore, these items were removed from this analysis. The three factors resembled the original structure (9) with a reduced factor in stress component; however, the three-factor component (eigenvalues = 9.82; 1.74; and 1.23) was revealed by the scree plot and the eigenvalues higher than one requirement, and this model accounted for 71.31% of the variance. The result of the KMO test was 0.965 (χ2 = 30932; p<0.001), showing that the model was highly adequate. The factor loadings for each DASS- 18 item are shown in Table 2 , with factor loadings >0.50 indicating acceptable loading ( Table 2 ).

https://doi.org/10.1371/journal.pone.0288041.t002

Confirmatory factor analysis

Three items from the stress scale were eliminated (the remaining 18 items of DASS, thus DASS-18). The DASS-18 measuring model, which included 18 items distributed across three components including DASS-18-A (7 items), DASS-18-D (7 items) and DASS-18-S (4 items) was fitted using an unweighted least square CFA. Based on the five specified fit criteria, the model demonstrated an acceptable fit to the data (CMIN/df = 3.082; p = 0.001; CFI = 0.98; RMSEA = 0.032; GFI = 0.98 and NFI = 0.99. The effect of the large sample size may have prevented the chi-square tests from providing acceptable assessments of model fit, whereas other indices indicated that these models remained well-fitted for the data. In addition, except for each factor-constraint item, so that no significant test could be archived, all model items were significantly loaded a long with their concurrent factors (all p-values <0.05) ( Fig 1 ).

https://doi.org/10.1371/journal.pone.0288041.g001

In all cases, the Pearson’s correlation coefficients between DASS-18-D, DASS-18-A and DASS-18-S presented moderate to remarkably elevated levels (0.52 to 0.92) indicating that these scales were moderately to highly discriminatory.

Convergent validity

AVE calculations showed all the DASS-18 subscales achieved a convergence effect (with the AVE of depression = 0.504; the AVE of anxiety = 0.674 and the AVE of stress = 0.551).

Discriminant validity

The magnitude of the correlations among depression, anxiety and stress domains determined the discriminant validity of the variables ( Fig 1 ). The variables showed correlations (r) lower than 0.85 except the correlation between depression and stress domains (r = 0.91).

Association of the DASS-18 scores among demographic characteristic and VAS-F

The total scale of DASS-18 showed a statistically significant positive and fair to moderate association with the VAS-F total score, sex and online learning. Additionally, the DASS-18 total score showed a moderately positive significance correlated to VAS-F in the anticipated direction, confirming the association between higher levels of fatigue and higher levels of depression, anxiety, and stress ( Table 3 ).

https://doi.org/10.1371/journal.pone.0288041.t003

Reliability analysis

The CR of the three domains of DASS-18 ranging from 0.830 to 0.935 indicated evidence of acceptable reliability. Regarding Cronbach alpha values of 0.92 for the overall scale, 0.87 for depression, 0.79 for anxiety and 0.73 for stress, the DASS-18 exhibited adequate internal consistency reliability. Similarly, the internal consistency of this scale was good, as evidenced by the item-rest correlations for all three subscales being better than 0.3 and the corrected item-rest correlation for the entire scale ranging from 0.53 to 0.91 ( Table 4 ).

https://doi.org/10.1371/journal.pone.0288041.t004

Product moment intercorrelations matrix values were determined between the three domains of DASS-18 and VAS-F. These intercorrelations values were found to be moderately strong the subscales of depression and anxiety showed the strongest intercorrelation among the three (r = 0.735), which was also statistically significant. These results could imply that the stress domain of DASS-18 moderately and positively correlated to VAS-F (r = 0.445) ( Table 5 ).

https://doi.org/10.1371/journal.pone.0288041.t005

The purpose of this study was to evaluate the psychometric properties of DASS-21 among Thai nursing students experiencing online classes during the COVID-19 outbreak. For the results of the factorial construct validity of the DASS-18, a three-factor model showed satisfactory conformity to the psychometric construct of the DASS-21 original version [ 10 ], and these results support the fact that the DASS-18 instrument for this cohort contained 18 items spread across three components as follows: anxiety (seven items), depression (seven items) and stress (four items). The three factors were comparable to the structure found by prior studies exploring the psychometric features and generalization of the DASS-21 for use in Asian nations [ 19 ]. This investigation showed that the DASS-18 is a promising and psychometrically sound tool, ideally suited for determining the frequency and intensity of symptoms associated with negative affective states for these participants. Furthermore, the two-week temporal stability was good for all DASS-18 scale scores; in particular, the DASS-18 stress subscale showed the highest correlation values across time and they had a great internal consistency reliability, agreeing with our hypotheses. The consequences of reducing three items from the stress scale are the reasons for the lower Cronbach’s alpha coefficient of this scale than that of Lovibond and Lovibond’ s original version [ 10 ]. These differences might have resulted from the DASS-18 having fewer items because the quantity of items creates an impact on how Cronbach’s alpha is calculated [ 19 ].

According to our results, only minimal changes were observed between the original DASS-stress (seven items) and our DASS-stress (four items) scales. These disparities might be explained by how diverse culture’s view perception of some items that could be interpreted as besides the stress context cultural factors and the response of the participants may influence how individuals understand item of the DASS-stress scales, but not on the DASS-depression and DASS-anxiety scale as we found no significant cultural problem with these two scales and no concerns were noted regarding the EFA findings as demonstrated by the statistical results of this study. Therefore, the DASS-18 factor structure clearly demonstrated three factors, as in the original DASS-21 scale [ 10 ]. Likewise, one study reported that no invariances were discovered in their multi-group analysis across the six countries [ 19 ]. In addition, findings from this previous study on the correlations of the three subscales with those of other psychiatric instruments measuring similar constructs offered support for the validity of the DASS-18 subscales and were generally favorable [ 19 ]. Moreover, the depression and anxiety subscales of the DASS-18 exhibited specific relationships with the relevant measures of these disorders, indicating that using these constructs was appropriate.

Cronbach’s alpha coefficient from our study revealed that total DASS-18 scores and its subscales exhibited good internal consistency. This coefficient ranged from good to excellent in prior studies comprising both nonclinical and clinical adult samples [ 2 , 9 , 10 , 19 , 21 , 29 – 31 ]. Hence, the data collectively proved that the DASS-18 demonstrated strong internal consistency across a variety of demographics and languages. Moreover, the results of the item analysis indicated that the items in each scale had good discrimination indices (corrected item-total correction). These indices suggested that the DASS-18 Thai version items would be effective at distinguishing between high and low scorers on this scale. Related research has also revealed that this assessment tool provides a good item discrimination index [ 13 , 18 ].

The relationships between demographic characteristics and DASS-18 scale scores were also investigated. This study found a weak positive statistically significant relationship between DASS-18 and sex. Despite the concerns about future endurance and competency aspect, female participants expressed more depression, anxiety, and stress than males. This may be because female nursing students usually have commitments outside of the classroom, such as taking care of their family members and performing chores [ 7 , 32 ]. Our results indicated that online learning moderately, positively correlated to the total DASS-18 score because high standards for performance, learning habits, and training may negative impact students’ mental health [ 7 ]. Similarly, clinical courses in nursing programs call for specialized cognitive, emotional, and psychomotor abilities typically following specialized theoretical courses. Because of being unable to take part in clinical settings, these students felt as though they were missing out on a great opportunity to acquire these abilities [ 6 , 20 ]. Thus, these students may have felt unprepared for learning in a clinical setting due to the extremely brief on campus learning time before lockdown, and the pandemic made it more difficult for nursing students to advance in their practical training [ 6 ]. When lockdowns ended, nursing students had greater opportunity to contract an illness by themselves or face patients with COVID-19 experiencing significant effects [ 5 ].

The internal consistency of the DASS-18 was adequate and consistent with the related Asian research [ 7 , 19 ]. The Thai version’s convergent validity is supported by favorable correlations with the Beck Depression Inventory (BDI), the Beck Anxiety Inventory (BAI); correlations in this direction were anticipated to measure the same construct [ 19 ]. These results demonstrated the validity and reliability of the Thai DASS-18 version as a tool for measuring negative emotional states. This indicated that this scale could prove beneficial for screening CMDs among clinical undergraduate students including nursing students.

The convergent validity of the DASS-18 was examined using the AVE calculation of all three subscales. The results revealed that all sub-scales’ AVE were greater than 0.50, corresponding to the convergent validity acceptance criteria. These findings were also compatible with the findings of a study that aimed to validate the DASS-21 among Vietnam students in an e-learning environment [ 7 ]. Regarding the discriminant validity, factors of this stress subscale also highly correlated to each other, which were higher than the values suggested by Hu and Bentler [ 26 ]. These higher correlations indicated significant overlapping in the content of the DASS-18 scales, indicating a general construct, such as affective distress. One related study also reported a higher correlation among these subscales [ 13 ].

When comparing depression, anxiety and stress scales, anxiety items had higher factor loadings, eigenvalues, and percentage of variation than the other domains. Depression and anxiety continued to have the highest inter-correlations, with a value of 0.708 indicating significant overlap between the two domains. Despite the overlap between domains, they could still be separated. An extraordinarily strong and positive association was noted between these domains. The Thai nursing students’ symptoms of stress, anxiety and depression were all positively connected, according to these positive correlation values. The DASS-18’s correlation coefficients showed beneficial correlations between the two instruments in this regard. Likewise, these coefficients also showed that the subjects’ anxiety and depression were present at the same time. The DASS-18 was thus shown to measure depression and anxiety among the responders in a simultaneous and unique manner. These findings were in line with studies in other countries [ 21 , 29 ].

Strengths and limitations

The strength of this study is the fact that the structure and psychometric features of the DASS-21 were examined for the first time in a large sample of undergraduate nursing students in Thailand. Because the participant-to-questionnaire-item ratio was satisfactory, the prerequisites for component analysis were met, and bias resulting from the number of observations was reduced.

Our study encountered limitations regarding the nursing students comprising our subjects. They could not accurately be generalized to other nonclinical undergraduate students due to their diverse qualities because they may be privileged in terms of socioeconomic status, freedom, and health. The study was cross-sectional; hence, the data were unable to show test-retest reliability over time. The study was limited in terms of criterion validity because we did not test any other parameters besides VAS-F.

The study ‘s main findings demonstrate that the DASS-18 is a valid instrument for detecting CMDs among Thai nursing students enrolled in online courses during the COVID-19 outbreak. The three-factor structure with 18 items proposed in the initial study was supported by the findings. Therefore, the availability of the DASS-18’s psychometric features will enhance performance of Thai psychologists and researchers in effectively screening the population of undergraduate nursing students for CMDs at tertiary institutions.

Supporting information

https://doi.org/10.1371/journal.pone.0288041.s001

Acknowledgments

The authors thank all nursing institutions for supporting students’ data collection. The research collaborators at all involved nursing institutes are thanked by the authors for helping with sample recruitment and data gathering. All nursing students joining this study are gratefully acknowledged.

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ORIGINAL RESEARCH article

Expectations and experiences with online education during the covid-19 pandemic in university students.

• 1 Laboratorio de Investigación e Innovación educativa Dirección de Docencia, Universidad de Concepción, Concepción, Chile
• 2 Programa de Doctorado Educación en Consorcio, Universidad de Católica de la Santísima Concepción, Concepción, Chile
• 3 Departamento de Física, Facultad Ciencias Físicas y Matemáticas, Universidad de Concepción, Concepción, Chile
• 4 Departamento Currículum e Instrucción, Facultad de Educación, Universidad de Concepción, Concepción, Chile
• 5 Departamento de Bioquímica y Biología Molecular, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción, Chile

Due to COVID-19, university students continued their academic training remotely. To assess the effects of emergency remote teaching (ERT), we evaluated the expectations and, subsequently, the experiences of university students about online education. This study employed a simple prospective design as its method. We assessed the expectations of 1,904 students from different discipline areas (1,106 women and 798 men; age M = 21.56; SD = 3.07) during the beginning of the first semester, March 2020 (T1), and their experiences at the end of the same academic period, September 2020 (T2). We used convenience non-probability sampling. Participants responded to the questionnaire on Expectations toward virtual education in higher education for students and the questionnaire on virtual education experiences in higher education. The results showed that students’ responses reflected low expectations regarding peer relationships and comparison with face-to-face education (T1). This perception was maintained during the evaluation of experiences (T2). Students reported positive experiences regarding online teaching and learning, online assessment, and their self-efficacy beliefs at T2. Statistically significant differences between measurements were found, with the expertise presenting higher averages than expectations. Furthermore, differences by gender were identified, reporting a positive change in the scores of women. In addition, results reflected differences according to the disciplinary area, showing Social Sciences and Medical and Health Sciences students a more significant size effect. Findings regarding the empirical evidence and the implications for future teaching scenarios in Higher Education are discussed.

Introduction

Higher education institutions had to face the challenge of providing continuity to the educational process remotely due to the COVID-19 pandemic. This scenario implied a drastic transformation without the possibility of preparation, having both teachers and students quickly develop online education competencies ( Hattar et al., 2021 ). Emergency remote teaching (ERT) is the name given to this instructional response ( Bustamante, 2020 ; Hodges et al., 2020 ). ERT applies to any unexpected and urgent transition to online instruction due to a disaster. Given its nature, one of the characteristics of ERT is the lack of time and skills of instructors to adequately prepare and implement their course syllabus in a virtual format ( Hodges et al., 2020 ). Thus, ERT differs significantly from online teaching, in which the focus is on delivering a quality learning experience following a predefined instructional design ( Miramontes Arteaga et al., 2019 ).

Currently, online courses are created using an instructional design, such as ADDIE, and implemented through Learning Management Systems (LMS), like Canvas. In these courses, designers and teachers apply technological and pedagogical innovations to obtain high-quality standards. In this teaching modality, educational experiences occur synchronously and asynchronously using multiple devices to access the internet. Therefore, students can interact with teachers, content, and peers from wherever they are ( Singh and Thurman, 2019 ). It requires stable digital infrastructure and platforms. Thus, its implementation demands many resources and a carefully designed plan to deliver a quality experience ( Mousa et al., 2020 ). As necessary and valuable as ERT is, its design does not necessarily consider the critical elements of quality online education ( Hodges et al., 2020 ). Despite the advances in online education in many higher education institutions worldwide, universities, in general, were not prepared for the necessary, mandatory, and abrupt change at the onset of the COVID-19 pandemic ( Maier et al., 2020 ).

Quality online teaching considers evaluating course characteristics, including the design of learning materials, the virtual environment, and the alignment of curricular components with learning outcomes. It also considers aspects related to the interaction experience of students with their peers and teachers ( Rodrigues et al., 2019 ).

Literature Review

Due to the COVID-19 pandemic, students’ expectations about how their academic year would unfold were rapidly modified and adjusted. This is relevant due to empirical evidence that supports that student expectations are predictors of academic success ( Paechter et al., 2010 ; Alhabeeb and Rowley, 2018 ; Wei and Chou, 2020 ). Student expectations can be defined as the beliefs that students hold about successfully coping with academic responsibilities. From the perspective of the expectancy-value theory ( Wigfield and Eccles, 2000 ), students have beliefs about their ability and success in meeting academic demands. These beliefs can be impacted by the subjective perception of the value of the academic activity to the student ( Valle et al., 2015 ). The expectancy-value theory is widely used to understand how psychological and contextual factors enhance student engagement and learning outcomes ( Chiu et al., 2021 ). Furthermore, expectations also impact student attitudes about the ways of learning (Fernández Jiménez et al., 2017 ). It has been reported that students’ perceptions regarding online learning modalities are related to their learning success ( Nur Agung et al., 2020 ). Therefore, expectations and experiences of university students regarding online learning courses during the pandemic could translate into opportunities or obstacles in the sense of moving closer or further away from a practical online education experience in the future ( Rodrigues et al., 2019 ; Pham and Ho, 2020 ).

Several studies have reported a variety of results regarding the expectations and subsequent experience of university students. For example, descriptive research conducted with 1612 undergraduates from 59 on-site Spanish universities says that students consider that the institutions did not adapt adequately to the ERT scenario (84%), especially regarding teaching methods and the implementation of assessments (64.5%). Furthermore, they state that the adopted institutional measures were not sufficient, affecting their academic performance (88.5%) during this period. In terms of experience, in the same research, students were not satisfied with virtual education, especially regarding courses’ assessment ( Villa et al., 2020 ). These results relate to another study that reported that students would not repeat this experience due to the absence of interaction with teachers, excess of tasks, and the accelerated pace for learning ( Imsa-ard, 2020 ; Suárez et al., 2021 ).

Consistent with the above, another study indicates that students perceived an overload in their academic responsibilities due to excessive activities and assignments, which made the process more exhausting ( Rahiem, 2020 ). Moreover, another research from the pandemic experience indicates that young people reported a low perception of quality and quantity in their learning during ERT regarding the strategies implemented by their universities, which did not meet their expectations (31.3%) ( Almomani et al., 2021 ). Additionally, researchers found that, unlike men, women perceived greater satisfaction with the strategies implemented by universities (66%), were more committed to delivering their assignments (70.6%), and were more optimistic about the assessment process implemented by teachers in their courses (70.2%) ( Almomani et al., 2021 ). Another research concludes that online teaching during the COVID-19 pandemic was only possible when online learning had a robust digital infrastructure and a learning system designed for that purpose; otherwise, it was an attempt to replicate face-to-face teaching in the virtual environment ( Abdulrahim and Mabrouk, 2020 ).

Despite the emergency scenario caused by the pandemic, not all studies reported negative experiences ( Abdulrahim and Mabrouk, 2020 ; Sepulveda-Escobar and Morrison, 2020 ). During ERT training, students from various institutions worldwide ( N = 30,383) claimed to be satisfied with the support provided by their instructors and institutions. In this case, specific sociodemographic characteristics such as gender, academic area, and other elements of the students favorably impacted these beliefs ( Aristovnik et al., 2020 ). Students positively assessed the actions implemented by the universities’ Information and Communication Technologies Departments ( Shehzadi et al., 2020 ). In addition, they thoroughly evaluated the online platforms used since they allowed them to perform their tasks efficiently and quickly, having fun while studying ( Maier et al., 2020 ). It is important to note that some authors report differences in experiences according to the scientific disciplines to which students belong ( Vladova et al., 2021a ).

Regarding social aspects, it seems that students were not satisfied with the preparation of teachers during the ERT modality due to difficulties in the interaction with their teachers and peers ( Alqurshi, 2020 ; Hamdan et al., 2021 ). This aspect is consistent with other research highlighting the importance of interaction between instructors and students in the online education experience ( Sun, 2016 ; Bao, 2020 ).

Due to ERT, a negative effect on students’ self-efficacy beliefs about online education has been reported at the individual level ( Aldhahi et al., 2021 ), while others found no changes ( Talsma et al., 2021 ). Self-efficacy is a relevant element regarding students’ academic satisfaction and performance ( Cervantes Arreola et al., 2018 ; Hamdan et al., 2021 ). When students believe in successfully facing the challenges of online education, they display a series of mechanisms to favor a more efficient and effective coping of their learning process. Consequently, beliefs conversion during the ERT may play an essential role in post-pandemic online learning.

In the context of the COVID-19, the academic, social, and individual experiences during ERT affect the perception of online education, which could impact the implementation of this modality in Higher Education in the future.

The Present Study

The empirical evidence described highlights the importance of assessing students’ experience during the ERT, especially the quality of the learning experience, the integration of teaching approaches, the design, the application of assessment tools, and how the relationship between students and their teachers is fostered ( Sun, 2016 ; Alqurshi, 2020 ; Aristovnik et al., 2020 ; Bao, 2020 ; Rahiem, 2020 ; Van Heuvelen et al., 2020 ; Villa et al., 2020 ; Almomani et al., 2021 ; Suárez et al., 2021 ). These aspects will provide vital information for the design and implementation of effective online learning processes that respond to the needs of students and universities in this context in the future.

This study focuses on the importance of learning about students’ expectations and experiences during the implementation of the ERT for the COVID-19 pandemic. Specifically, we inquire on how students’ expectations and experiences can affect their academic, social, and personal aspects to provide evidence to support actions for the transition to face-to-face and blended learning. In this context, this research aims to analyze the expectations and experiences of students in a traditional university in the south of Chile at a general level and in consideration of the participants’ gender and disciplinary area.

Based on the above and the heterogeneity of students’ experiences reported in the literature, we describe the following hypotheses:

H 1 . There will be changes in the experiences to the expectations of university students during ERT due to the COVID-19 pandemic.

H 2 . Differences will be found between men and women regarding university students’ expectations and experience scores during the ERT due to the COVID-19 pandemic.

H 3 . Differences in university students’ expectations and experience scores will be observed according to disciplinary areas during ERT due to the COVID-19 pandemic.

Materials and Methods

A simple ex post facto longitudinal quantitative research design was used. Researchers find it impossible to manipulate the independent variable in ex post facto studies, describing the associations between variables. It is simply longitudinal since two measurements were performed, starting by measuring the expectation (March 2020; T1) and then the experience (September 2020; T2) of the students with online education during the ERT, to subsequently study the relationships found between the variables ( Montero and León, 2007 ).

Participants

A total of 1,904 students belonging to a traditional Chilean university participated, of which 1106 (58.1%) were women, and 798 (41.9%) were men, with mean age M = 21.56 ( SD = 3.07). On the other hand, 635 (33.35%) of the participants were in their first academic year. According to their undergraduate program, students’ classification according to the areas of the Organization for Economic Co-operation and Development (OECD) is presented in Table 1 .

Table 1. Distribution of students by gender and disciplinary area.

Instruments

Expectations toward virtual education.

The Expectations toward Virtual Education in Higher Education for Students (CEEVESE) questionnaire aims to know higher education students’ expectations about virtual education during ERT. It consists of 28 items distributed in six dimensions about virtual education. The items were elaborated based on available scientific literature and evaluated employing expert judgment ( Lobos et al., 2022 ). Table 2 describes the dimensions that constitute the scale.

Table 2. Description of the dimensions of the CEEVESE questionnaire.

A Likert scale with five response options (1 = Strongly disagree to 5 = Strongly agree) was employed. The average of each dimension and the full scale was analyzed, in which scores higher than 3 indicate positive expectations. Previous studies have examined the factorial structure of the scale, finding an adequate adjustment of the 6 factors [X 2 (335) = 5354.88, p < 0.001, CFI:0.961; TLI:0.956; SRMR:0.041; RMSEA:0.06]. The reliability analysis of the responses was: peer relationship α = 0.894, online learning α = 0.922; online teaching α = 0.907; self-efficacy for online learning α = 0.882, online assessment α = 0.787; comparison with face-to-face education α = 0.779; full scale: α = 0.954 ( Lobos et al., 2022 ).

Experience in Virtual Education

The Virtual Education Experiences in Higher Education for Students (EEEL) questionnaire adapts the CEEVESE (Lobos et al., under review 1 ). Its purpose is to learn about the experiences of higher education students with virtual education during ERT. It consists of the same 28 items of the CEEVESE but presented in the past tense, using again a Likert scale of 5 response options (1 = Strongly disagree to 5 = Strongly agree). For their interpretation, the averages of each dimension and the full scale were analyzed. In both cases, the presence of scores above 3 points reflects a positive student experience. The items’ distribution corresponds with the six original dimensions.

The factorial structure of this version confirmed an adequate adjustment of the 6 factors [ X 2 (333) = 3599.92, p < 0.001, CFI: 0.966; TLI: 0.961; SRMR: 0.036; RMSEA: 0.059]. Reliability analysis of the responses by dimensions was as follows: peer relationship α = 0.869, online learning α = 0.883; online teaching α = 0.876; self-efficacy for online learning α = 0.872, online assessment α = 0.753; comparison with face-to-face education α = 0.671; full scale: α = 0.931 (Lobos et al., under review, see text footnote 1).

This research was endorsed by the Ethics Committee of the participating university, corroborating the ethical criteria for research with human beings. The expectations and experience instruments were applied in digital format and sent to the students’ institutional emails on a single occasion. For the two measurement moments (T1 and T2), the questionnaires were available for 3 weeks at the beginning of March 2020 and at the end of September 2020. Students responded after reading and signing an informed consent form. A convenience non-probability sampling was used. The participants were students who were enrolled in a course during the first semester of 2020. To track the students, the enrollment number and e-mail address of each participant were compared. Only students presenting both measurements were included.

Analysis Plan

We performed a descriptive analysis of the variables. Verification of the assumption of normality for the dimensions and total scales in both measurements (T1 and T2) was made using the Kolmogorov-Smirnov test with the Lilliefors modification ( Thode, 2002 ). Analyzed data did not have a normal distribution ( p < 0.001). Despite this, the Student’s t -test for paired samples was performed to evaluate the differences in the T1 and T2 scores due to the sample size.

The assumptions were verified using the mixed ANOVA tests to assess the effects between groups on gender and OECD areas versus the intra-group effect (changes between expectations and experience). No extreme outliers were found. Levene’s test was analyzed, finding no significant result ( p > 0.05). The homogeneity of covariance of the between-subjects factor (gender-OECD area) using Box’s M test was also evaluated, with a not statistically significant result ( p > 0.001). Therefore, no violation of the homogeneity of covariances assumption is assumed. Verification of the sphericity assumption was automatic since the Greenhouse-Geisser sphericity correction was applied to violating assumption factors during the ANOVA test calculation.

The size effect was analyzed considering the cutoffs by Cárdenas Castro and Arancibia Martini (2014) , in which scores >0.14 are considered large, 0.06 medium, and 0.01 small. The data analysis was performed with R Studio software version 4.0.3 (2020-10-10) ( R Core Team, 2020 ).

The present research aims to analyze the students’ expectations and experiences, considering the gender and disciplinary area of the participants. We presented the results in the context of the research hypotheses described in section “The Present Study.”

Differences Between University Students’ Expectations and Experiences During Emergency Remote Teaching During the COVID-19 Pandemic

Hypotheses H 1 sought to answer the existence of changes between the expectations and experiences of university students produced by ERT during the COVID-19 pandemic. In the first measurement (T1), the general students’ expectations presented an average below 3 points, identifying them as low ( M = 2.92, SD = 0.65). The dimension that presented the highest score was self-efficacy for online education ( M = 3.42; SD = 0.84), whereas the dimensions that showed the lowest scores were peer relationship ( M = 2.1; SD = 0.83) and comparison with face-to-face teaching ( M = 1.91; SD = 1.07).

Regarding the measurement of the students’ experiences with the ERT after the academic semester (T2), the perception was positive since the score was higher than 3 points ( M = 3.18, SD = 0.66). Furthermore, the analysis by dimensions, identify that dimensions’ averages of the experiences (T2) were higher than its corresponding dimensions of the questionnaire of expectations (T1). However, despite having improved, the dimensions of peer relationship ( M = 2.26, SD = 0.95) and comparison with face-to-face education ( M = 2.71, SD = 1.24) remain as negative perceptions, since scores were still lower than 3. Table 3 shows dimensions’ averages and deviations of the scales applied and the assessment of the differences between them.

Table 3. Descriptive and inferential statistics on students’ expectations and experiences during the ERT.

When performing the comparative analysis between the general expectations of the students (T1) and the experience after the end of the semester (T2), statistically significant differences [ t (1903) = 19, p < 0.001] were found. Hence, students’ experience with ERT at the end of the academic period exceeded their expectations. In this sense, results respond positively to the proposed hypothesis, identifying differences in the scores between T1 and T2.

Gender Differences in University Students’ Expectations and Experiences of Online Learning During Emergency Remote Teaching

To analyze differences between expectations and experiences considering gender and OECD area, the presence of statistically significant bidirectional interactions was assessed. Subsequently, we performed post hoc tests to determine the main effects of gender and OECD area, considering the Bonferroni adjusted p -value.

We examined each dimension independently to answer the hypothesis regarding the existence of differences between expectations and experiences related to undergraduate students’ gender during ERT (H 2 ). The results showed statistically significant bidirectional interactions among gender and the change in scores between expectations and experiences in the following five dimensions: online learning [ F (1,1902) = 19.09, p < 0.001, GES.002]; comparison with face-to-face education [ F (1,1902) = 25.23, p < 0.001, GES.004]; online teaching [ F (1,1902) = 5.31, p < 0.001, GES.0006]; peer relationship [ F (1,1902) = 6.79, p < 0.01]; and self-efficacy for online learning [ F (1,1902) = 4.836, p < 0.05, GES.0006]. In the case of the online assessment dimension, no statistically significant results were observed.

Regarding the main effect of gender, a significant effect for experience, but not for expectations in the following four dimensions was observed online learning: experience [ F (1,1902) = 10.64, p < 0.01, GES.006]. Online teaching: experience [ F (1,1902) = 8.54, p < 0.01, GES.004]. Peer relationship: experience [ F (1,1902) = 6.55, p < 0.05, GES = 0.003] and Self-efficacy for online learning: experience [ F (1,1902) = 5.37, p < 0.05, GES.003].

On the other hand, in the case of comparison with face-to-face education, the results were significant for expectation [ F (1,1902) = 13.06, p < 0.001, GES.007], but not for experience ( p = 0.06).

The simple main effect of the differences between expectations and experience were also analyzed, observing statistically significant results for women and men in four of the dimensions: online learning, women [ F (1,1106) = 203, p < 0.001 GES = 0.046] and men [ F (1,796) = 42.1, p < 0.001 GES = 0.011]. In the Comparison with face-to-face education, women [ F (1,1106) = 589.63, p < 0.001 GES = 0.15] and men [ F (1,796) = 169.09, p < 0.001 GES = 0.06]. In the Online teaching, women [ F (1,1106) = 264, p < 0.001 GES = 0.06] and the men [ F (1,796) = 117, p < 0.001 GES = 0.03]. In the peer relationship, women [ F (1,1106) = 57.5, p < 0.001 GES = 0.014] and men [ F (1,796) = 10.1, p < 0.01 GES = 0.003].

In the self-efficacy for online learning dimension, statistically significant results were identified only for women [ F (1,1106) = 13.4, p < 0.001 GES = 0.003]. Even though men and women presented higher scores at T2, women showed the most significant change reflecting a positive experience with online education (see Table 4 ).

Table 4. Descriptive data on the expectations and experience of university students considering gender.

Figure 1 shows the size effect identified in the measurements considering gender. In the case of women, we found a large-size effect in the dimension of comparison with face-to-face education and a medium-size effect in the online teaching dimension. In the case of men, the analysis outcomes determine only a medium effect size in the dimension of comparison with face-to-face education and a small size effect in the rest of the dimensions.

Figure 1. The effect size of the change between expectations and experience according to the gender of the participating student.

Differences by Disciplinary Area in the Measurement of Undergraduate Students’ Expectations and Experiences of Online Learning During Emergency Remote Teaching

Regarding differences between the scores from expectations and experience of university students during ERT during the COVID-19 pandemic according to disciplinary areas (H 3 ), the results by dimension are presented below.

For all six dimensions a statistically significant bidirectional interactions among the OECD area and the differences between T1 and T2 scores was found. The results by dimensions are the following: Comparison to face-to-face education [ F (5,1898) = 3.54, p < 0.01, GES = 0.003], online teaching [ F (5,1898) = 6.053, p < 0.001, GES = 0.004], online assessment [ F (5,1898) = 7.33, p < 0.001, GES = 0.006], online learning [ F (5,1898) = 8.686, p < 0.001, GES = 0.006], peer relationship [ F (5,1898) = 3.86, p < 0.01, GES.003], and self-efficacy for online learning [ F (5,1898) = 6.99, p < 0.001, GES = 0.005].

Regarding the main effect of OECD area, a significant effect for experience and for expectations was observed in the following four dimensions: Comparison to face-to-face education: experience [ F (5,1898) = 4.43, p < 0.01, GES.012] and the expectations [ F (5,1898) = 9.26, p < 0.001, GES.024]: online assessment: experience [ F (5,1898) = 4.71, p < 0.001, GES.012] and expectations [ F (5,1898) = 3.52, p < 0.01, GES.01]; online learning: experience [ F (5,1898) = 7.4, p < 0.001, GES.02] and expectations [ F (5,1898) = 9.57, p < 0.001, GES.03]; self-efficacy for online learning: experience [ F (5,1898) = 6.22, p < 0.001, GES.02] and expectations [ F (5,1898) = 5.52, p < 0.001, GES.01].

Regarding online teaching, a significant effect was observed in expectation [ F (5,1898) = 4.65, p < 0.001, GES.01], but not in experience ( p = 1). On the other hand, for peer relationship, a significant effect was shown for experience [ F (5,1898) = 3.67, p < 0.01, GES.01] but not for expectations ( p = 1).

We performed Tukey’s test to assess the differences between OECD areas in expectations and experience. Concerning expectations, the following dimensions presented significant differences (see Table 5 ). Comparison to face-to-face: Engineering and Technology - Agricultural Sciences p < 0.01, Engineering and Technology - Medical and Health Sciences p < 0.001, Engineering and Technology - Natural Sciences p < 0.05, Engineering and Technology - Social Sciences p < 0.001, and Engineering and Technology - Humanities p < 0.01. Online teaching: Engineering and Technology - Medical and Health Sciences p < 0.01 and Engineering and Technology - Social Sciences p < 0.001. Online assessment: Engineering and Technology - Medical and Health Sciences p < 0.05. Online learning: Engineering and Technology - Agricultural Sciences p < 0.01, Humanities - Medical and Health Sciences p < 0.01, Humanities - Natural Sciences p < 0.05, Engineering and Technology - Natural Sciences p < 0.01, Engineering and Technology - Social Sciences p < 0.001, and Engineering and Technology - Humanities p < 0.001. Self-efficacy for online learning: Engineering and Technology - Social Sciences p < 0.001 and Engineering and Technology - Humanities p < 0.01.

Table 5. Descriptive statistics on students’ expectations and experience during the ERT according to the disciplinary area.

In the case of experience, the dimensions that showed significant differences are listed below: Comparison to face-to-face education: Humanities - Agricultural Sciences p < 0.01, Humanities - Medical and Health Sciences p < 0.01, Humanities - Natural Sciences p < 0.05, Humanities - Social Sciences p < 0.05, and Engineering and Technology - Humanities p < 0.001. Online assessment: Medical and Health Sciences - Agricultural Sciences p < 0.001 and Social Sciences - Agricultural Sciences p < 0.05. Online learning: Medical and Health Sciences - Agricultural Sciences p < 0.001, Social Sciences - Agricultural Sciences p < 0.01, Engineering and Technology - Agricultural Sciences p < 0.01, Natural Sciences - Medical and Health Sciences p < 0.05 Humanities - Medical and Health Sciences p < 0.001, Humanities - Social Sciences p < 0.05, and Engineering and Technology - Humanities p < 0.05. Peer relationship: Humanities - Medical and Health Sciences p < 0.01 and Humanities - Social Sciences p < 0.05. Self-efficacy for online learning: Medical and Health Sciences - Agricultural Sciences p < 0.01, Social Sciences - Agricultural Sciences p < 0.01, Engineering and Technology - Agricultural Sciences p < 0.05, Humanities - Medical and Health Sciences p < 0.01, Humanities - Social Sciences p < 0.01, and Engineering and Technology - Humanities p < 0.01.

Finally, the simple main effect of the differences between expectations and experience for each dimension was analyzed, observing in some cases statistically significant effects for all six OECD areas, while in others only for one (see Table 5 ). The results reflected by the analysis are listed by dimension: Comparison to face-to-face education: Agricultural Sciences [ F (1,140) = 71.71, p < 0.001, GES = 0.16], Medical and Health Sciences [ F (1,415) = 227.33, p < 0.001, GES = 0.14], Natural Sciences [ F (1,311) = 93.81, p < 0.001, GES.08], Social Sciences [ F (1,508) = 247.639, p < 0.001, GES = 0.14], Humanities [ F (1,60) = 11.93, p < 0.01, GES = 0.06], and Engineering and Technology [ F (1,464) = 97.77, p < 0.001, GES = 0.06]. Online teaching: Agricultural Sciences [ F (1,140) = 8.14, p < 0.05, GES = 0.01], Medical and Health Sciences [ F (1,415) = 126, p < 0.001, GES = 0.07], Natural Sciences [ F (1,311) = 58.2, p < 0.001, GES.04], Social Sciences [ F (1,508) = 124, p < 0.001, GES = 0.06], Humanities [ F (1,60) = 23.8, p < 0.001, GES = 0.09], and Engineering and Technology [ F (1,464) = 51.6, p < 0.001, GES = 0.02].

The following differences in the dimension of online assessment between discipline areas were found: Medical and Health Sciences [ F (1,415) = 70.57, p < 0.001, GES = 0.05] and Social Sciences [ F (1,508) = 37.89, p < 0.001, GES = 0.02]. Online learning: Medical and Health Sciences [ F (1,415) = 86.1, p < 0.001, GES = 0.05], Natural Sciences [ F (1,311) = 26.4, p < 0.001, GES.02], Social Sciences [ F (1,508) = 131, p < 0.001, GES = 0.06], Humanities [ F (1,60) = 9.94, p < 0.05, GES = 0.5], and Engineering and Technology [ F (1,464) = 10.8, p < 0.01, GES = 0.006]. Peer relationship: Medical and Health Sciences [ F (1,415) = 42.1, p < 0.001, GES = 0.024], Social Sciences [ F (1,508) = 27, p < 0.001, GES = 0.014], and Engineering and Technology [ F (1,464) = 9.88, p < 0.05, GES = 0.005]. Self-efficacy for online learning: Social Sciences [ F (1,508) = 29.4, p < 0.001, GES = 0.02].

Figure 2 shows the size effect identified considering the OECD area. In Agricultural Sciences, we found a large-size effect in the dimension of comparison with face-to-face education and a small effect size in the dimensions of online learning, self-efficacy for online learning, online teaching, and the full scale. There were no effects detected in the rest of the dimensions. In Medical and Health Sciences, the analysis outcomes reflected a large-size effect in comparison with face-to-face education and a medium-size effect in the dimensions of online teaching and full scale. In addition, we found a small effect in the dimensions of peer relationship, online learning, and online assessment. In Natural Sciences, we found a medium-size effect in the size of comparison with face-to-face education and a small-size effect in online teaching, online learning, and full scale. No effects on the remaining dimensions were found. In the case of Social Sciences, we found a large-size effect for comparison with face-to-face education, a medium-size effect in the dimensions of online learning, online teaching, and the full scale, and a small-size effect in the rest of the dimensions. The Humanities area presented a medium-size effect in online teaching and comparison with face-to-face education dimensions and a small-size effect in online learning, peer relationship, online evaluation, and full scale. Finally, in Engineering and Technology, a medium-size effect in the dimension of comparison with face-to-face education and a small-size effect in the online teaching, online learning, peer relationship dimensions, and full scale were identified. In the rest of the dimensions, there were no effects detected.

Figure 2. Size effect regarding the change between expectations and experience according to the disciplinary area.

Due to the COVID-19 pandemic, the transition to ERT impacted students’ expectations and experiences during their professional training. This research aimed to analyze students’ expectations and experiences considering the gender and disciplinary area of the participants. Findings are analyzed and discussed in terms of the hypotheses raised in section “The Present Study.”

Differences Among University Students’ Expectations and Experiences During the Emergency Remote Teaching Produced by the COVID-19 Pandemic

Changes between students’ expectations and experiences during ERT were found. Students’ expectations at T1 about online education were negative. However, at the end of the academic period, students indicated having a positive experience in most studied dimensions. They only showed a negative experience regarding the relationship with their peers and the comparison with face-to-face.

Several studies during the pandemic point out the lack of confidence toward the different educational actors and online education opportunities. This mistrust is associated with a lack of knowledge of the modality and its advantages ( Villa et al., 2020 ) and little awareness of the available virtual educational tools ( Rahiem, 2020 ; Almomani et al., 2021 ). In addition, the unexpectedness of the transition was a challenge for teachers and students, generating a problematic, improvised, and intuitive confrontation ( Barbour et al., 2020 ; Hodges et al., 2020 ).

Students’ perception of the limited opportunities virtual classrooms and other technological tools provided them to interact and work collaboratively with peers is particularly noteworthy. Several reports emphasize the benefits of cooperative work versus a competitive or individualistic methodology in higher education. The former generates better learning and significant commitment and involvement in academic tasks ( León del Barco et al., 2017 ; Guerra Santana et al., 2019 ; Hamdan et al., 2021 ). Also, collaborative work is closely related to desired competencies in the profession’s exercise, an aspect that is not present in this study. In this context, the literature describes technological mediation in education to provide significant possibilities of simultaneous sociability, of connection between communities and people, subscription, and asynchronous communication that generates network effects that tend to accelerate individuals and group learning ( De Haro, 2010 ; Anthony et al., 2019 ). Therefore, it is crucial to understand why peer interaction during ERT was negatively perceived, especially considering that the LMS had the functionalities for such activities. We believe that it is partly a product of the little knowledge of these tools by both teachers and students.

The observation that students face online education with a high sense of self-efficacy, believing that they have the skills to respond to the learning challenges that this modality presents, could be explained by the lack of knowledge and experience, as well as underestimating the necessary skills. Consequently, students perceive a lower complexity than the real one, as described by the “Durning Kruger effect” ( Dunning, 2011 ). It is possible that by the regular use of technology, social media, phones, and computers, they initially self-perceived as more competent.

The perception of a better experience concerning the initial expectation suggests that the implementation of ERT, although not devoid of difficulties, responded to students’ needs. Hence, higher education institutions’ response and the teachers’ and students’ adaptation adequately provided a well-perceived learning environment. Furthermore, the above is consistent with other research during the pandemic that reported positive experiences by teachers and students in terms of having been able to face the educational process despite the adversities of the confinement and its urgency ( Sepulveda-Escobar and Morrison, 2020 ).

We can conclude that the educational community and higher education authorities have learned greatly during ERT. Therefore, it will be interesting to study how to translate these lessons into explicit guidelines and practices when returning to normality post-pandemic.

When evaluating changes in expectation and experience scores considering the sex of the participants, at the beginning of ERT, men and women presented similar levels of expectations about online education. However, experiences showed differences according to gender. Although both perceived the educational experience as positive, women gave higher values than men, in the dimension with lower punctuation in the experience compared with a face-to-face modality and peer relationship.

These results are consistent with the study reported by Almomani et al. (2021) , conducted during the COVID-19 pandemic, and reports that women students were more optimistic, satisfied, and committed to the online learning experience than men students during this period. Furthermore, a 62-country study on the impact of the pandemic on higher education ( Aristovnik et al., 2020 ) reports a minor negative impact of confinement on women students’ learning, adaptation, and relationship with the teachers. In this study, a similar result was obtained regarding the perception of online teaching. Women students presented a higher value of the teacher’s commitment to ERT. Women considered that instructors were available and attentive to their learning needs, complied with the course syllabus, and made good use of the available virtual classroom tools.

In another study on online university education in the context of COVID-19 ( Shahzad et al., 2021 ), the authors were able to identify differences between men and women regarding the perception of usefulness, ease, and satisfaction with the use of the learning management systems provided by the institution. This finding suggests that adaptation processes to university life in electronic learning environments may be different for men and women. Therefore, this information could be valuable for university authorities to strengthen and improve the university system support.

Differences in Students’ Expectations and Experiences by Disciplinary Area of Online Learning During Emergency Remote Teaching

Research on the effects of the COVID-19 pandemic in the context of higher education has identified significant challenges for implementing online education, such as inequality, funding, and ways to develop learning in general ( Aristovnik et al., 2020 ; Funk, 2021 ). In this context, it is essential to identify if these challenges and opportunities are specific to a particular disciplinary area or apply to the general community. Thus, differences during ERT between disciplinary areas were analyzed.

Differences in the expectations and experiences of university students in the six disciplinary areas classified according to their undergraduate programs were found. Unfortunately, there is little literature on the influence of the disciplinary area to which students’ undergraduate programs belong regarding experience with online education in ERT. Knowing about students’ experience in each disciplinary area will allow teachers and educational authorities to identify weaknesses and good practices that will otherwise not be detected to design and develop monitoring plans and improve the quality of online education in the future.

We found differences within expectations in the online teaching dimension for all disciplinary areas. On the other hand, Students from Engineering and Technology and Medical and Health Sciences areas reported higher experience scores in this dimension, which implies that these students felt more confident about the actions performed by their instructors. This result could be related to the use of technology by Engineering and Technology teachers and the teacher training in the medical education area, often advanced.

Despite the improvement between student expectations and experiences of the online assessment dimension, changes presented null (Agricultural Sciences, Natural Sciences, and Engineering and Technology) or small (Social Sciences, Humanities, and Medical and Health Sciences) size effect. The assessment processes continue to be an area of concern. Other reports support this statement. For example, Jordanian university students perceived that assessment during the pandemic allowed them to obtain higher grades than face-to-face assessments. Nonetheless, most students perceived that the evaluative processes were unfair and learned more minor than the quality reflected ( Almomani et al., 2021 ). Consistently, a study conducted with 8265 Chilean university students ( Lobos et al., 2022 ) reported that students perceived a bad experience regarding the assessment process during the pandemic. Again, researchers observed a greater expectation of obtaining a good grade rather than of achieving learning. As a result, students considered that they failed to achieve good quality training. Despite these findings, a study carried out in Chile indicates that students’ academic performance improved compared to the previous academic period ( Franco et al., 2021 ). Therefore, the guidelines and strategies used by teachers regarding assessment continue to be an essential element to consider in the design of quality online education.

An interesting finding is a large-size effect obtained in the differences between the scores of expectations and experience of students of Agricultural Sciences and Medical and Health Sciences, for the comparison with face-to-face education dimension. Further research is required to identify good practices teachers and students implement in undergraduate programs classified in these two OCDE discipline areas.

We believe that the differences in the results of the students’ expectations and experience according to the disciplinary area are due to the different challenges encountered in the adaptation of the courses (efficient ones). Accordingly, strategies used, for example, in Health Sciences, can be used in realistic training scenarios that relate to people (Social Sciences and Humanities). One of these strategies can be using remote standardized patients who have meetings with students through the Internet. These activities allow teachers and standardized students to have spaces for evaluation and feedback ( Langenau et al., 2014 ; Bączek et al., 2021 ). This technique could be adapted to other teaching contexts using work situations in the training of other professionals.

Concerning the dimension of self-efficacy for online learning, no significant changes in four of the six knowledge evaluated areas were observed. Agricultural Sciences and Social Sciences displayed differences with small-size effect. Thus, ERT did not increase students’ confidence beliefs toward taking classes in the online teaching modality.

Despite valuable information that has been obtained for this study, some limitations are identified. First, the results presented correspond to university students’ responses from a single educational institution, so the interventions of university authorities could bias expectations and subsequent experiences in the context of ERT. Second, it was not part of this study to evaluate access gaps and other student variables that could affect the results. Finally, variables associated with the teacher or course characteristics that may influence the outcomes could not be controlled. Therefore, the results aim to study changes between students’ expectations and experience in an exploratory way. Other studies must consider the assessment of student (e.g., difficulties in accessing online classes), professor (e.g., profession), or course (e.g., type, time commitment) variables that may affect undergraduate expectations and experiences.

Study Implications

In this research, we found that students’ experiences with online education during the ERT were more optimistic than their expectations at the beginning of the semester. For this reason, the results found, together with other sources of institutional information such as learning analytics and institutional indicators, will allow authorities and teachers to develop guidelines to promote quality online education. It is also possible that university authorities could consider these preferences to design and create online courses for their students ( Zapata-Cuervo et al., 2021 ).

The relationship with peers and professors is still considered a weak point of online education. This is a crucial aspect to be addressed by university professors. In the context of virtuality, professors need to maintain communication channels that allow them to provide students with timely feedback from online video tutorials or email guides after class ( Bao, 2020 ; Vladova et al., 2021b ). We identified statistically significant differences in the experiences of men and women. This represents an opportunity to investigate how the characteristics of each student improve academic performance and decrease the probability of dropping out of college.

We found differences in the students’ experiences according to the scientific areas. These results translate into a challenge to identify the strategies and actions that facilitated a positive experience to replicate them in similar formative contexts. Further, studies can be performed to identify good practices applied in general contexts and those appropriate for each discipline. Higher education institutions are expected to accompany teachers and students in the different scientific areas during the post-pandemic academic continuity. Exceptional support is scheduled in aspects such as planning and prioritization of practical classes, promoting a combined approach of virtual and face-to-face education ( Pham and Ho, 2020 ; Vladova et al., 2021b ).

Future research could assess how students’ variables (e.g., internet access, type of device used to study), courses’ factors (e.g., number of hours of dedication, learning goals, instructional design, type of materials, or shared resources), teachers’ aspects (e.g., technological acceptance, use of strategies, training) or the institution’s elements (e.g., promotion of teaching through technology, support for students and teachers, use of online learning platforms, technological campuses) impact the expectations and subsequent experience of students during the development of online courses., especially regarding strength and weaknesses according to discipline areas.

The findings of this work contribute to identifying dimensions and areas that require special attention to establish preventive and corrective actions by university authorities for the near future and propose the opportunity of further studying good practices of better-perceived experiences of discipline areas.

The students’ experiences during ERT due to the COVID-19 pandemic exceeded expectations. Students reported high expectations about their self-efficacy to cope with this new scenario, even though low expectations regarding peer relationships, online teaching, and comparison with face-to-face education were observed concerning the experience after the semester. Students indicated positive experiences with online learning and teaching. They felt that the professor provided adequate support in terms of education, instruction, and assessment. Negative experiences persisted regarding peer relationships and the overall experience compared to face-to-face teaching. Additionally, men and women presented similar expectations at the beginning of the semester regardless of their discipline, while women were more optimistic about educational experiences during ERT. Finally, concerning the disciplinary area, differences in most of the assessed dimensions were observed, representing an opportunity to study further and identify good practices in those dimensions and disciplines that presented positive perception and effect.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by Institutional Ethics Committee of University of Concepción. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

KL and RC-R: conceptualization. KL, RC-R, and CB: methodology. JM-N: formal analysis and visualization. KL, RC-R, and AM-T: research and writing—preparing the original draft. AM-T, CB, and CF: resources, project management, and fundraising. JM-N and RC-R: data curation. CB and CF: writing—revising and editing. KL, CB, and CF: monitoring.

This research reported in this publication was supported by Unidad de Fortalecimiento Institucional of the Ministerio de Educación Chile, project InES 2018 UCO1808 Laboratorio de Innovación educativa basada en investigación para el fortalecimiento de los aprendizajes de ciencias básicas en la Universidad de Concepción.

Conflict of Interest

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

Publisher’s Note

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

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Keywords : COVID-19, higher education, university student, online teaching and learning, student self-efficacy

Citation: Lobos K, Cobo-Rendón R, Mella-Norambuena J, Maldonado-Trapp A, Fernández Branada C and Bruna Jofré C (2022) Expectations and Experiences With Online Education During the COVID-19 Pandemic in University Students. Front. Psychol. 12:815564. doi: 10.3389/fpsyg.2021.815564

Received: 15 November 2021; Accepted: 02 December 2021; Published: 05 January 2022.

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Copyright © 2022 Lobos, Cobo-Rendón, Mella-Norambuena, Maldonado-Trapp, Fernández Branada and Bruna Jofré. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Carola Bruna Jofré, [email protected]

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