Navigating the shift to online learning: student experiences of inclusivity, efficiency, and study efforts in Chile

Abstract

Online learning can broaden access to education; however, it might pose challenges for students at risk of exclusion (i.e., first-generation, low-income, disabled, rural origin, or ethnic minority students) who often lack the minimum learning resources. As online learning continues expanding, this study aims to understand students’ views regarding critical aspects of students’ learning experience, including inclusion, efficiency, grade impacts, and efforts. We employed a mixed methods study based on a survey and focus groups with college students in a large, well-known Latin American university in Chile that delivered online instruction for multiple semesters. Regression analyses of survey responses from undergraduate students indicate that students at risk of exclusion did not perceive the online learning environment to be more challenging than those not at risk. Students at risk of exclusion, however, were more likely than students not at risk to express a need to study more to achieve good grades, potentially to mitigate any challenges associated with the COVID-19 pandemic on academic performance. Focus group evidence indicates that while some students faced mental/emotional health issues negatively affecting learning, other students rapidly adapted their studying practices, which had positive impacts on learning.

Keywords:

• Online learning
• students at risk of exclusion
• mixed methods
• COVID-19 pandemic
• inclusion
• experiential learning

Reviewing Editor:

• Anatoly Oleksiyenko
• The Education University of Hong Kong Faculty of of Education and Human Development
• Hong Kong

Subjects:

• Adult Education and Lifelong Learning
• Higher Education
• Assessment
• Study of Higher Education
• International & Comparative Education
• Teachers & Teacher Education
• Classroom Practice
• Education Policy

Introduction

Creating a supportive and inclusive learning environment is crucial for students at risk of exclusion, including first-generation, low-income, disabled, rural origin, or ethnic minority students (Corrigan, 2003; Ecklund, 2013; Fleming & Grace, 2014; Kranke et al., 2013; Ribeiro, 2014; Richardson, 2008; Rodríguez-Planas, 2022; Weatherton & Schussler, 2021; Yang, 2010). Such an environment provides a space where attention is given to all students, allowing them to be present, participate, and learn (Booth et al., 2002; Kranke et al., 2013; Smirnova et al., 2020). An educational environment fostering a sense of belonging, sustaining student motivation in overcoming academic challenges, and encouraging persistence until course completion is tied to student engagement, retention, and success (Kapasia et al. 2020).

Strategies such as curriculum design, pedagogical reforms, and adequate access to university resources have successfully fostered inclusion and efficiency in face-to-face (FTF; in-person) instruction (Harpell & Andrews, 2010; Peña-Lévano & Melo, 2022). Yet, these strategies might not be effective in online or hybrid learning because students often rely on the physical and nonphysical systems universities provide, for instance, libraries, study spaces, internet access, structured schedules, and interpersonal interaction (Abdur Rehman et al., 2021; Tarus et al., 2015). Additionally, it is worth noting that during the COVID-19 pandemic, students globally faced a shortage of necessary resources to address the potential challenges associated with online learning, such as technology access and physical study spaces (Bao, 2020; Castle, 2020; Deming, 2020.; Haelermans et al., 2022; Melo et al., 2021).

Our understanding of how students feel about inclusion in the online learning environment is limited, despite increasing focus on supporting students’ learning experience amid the pandemic (Almusharraf & Khahro, 2020; Barrot et al., 2021; Gopal et al., 2021; Irawan et al., 2020). We have a limited understanding of students’ experiences in Latin America, particularly regarding students in an expanding economy marked by significant income inequality, as observed in Chile. We have limited understanding regarding: (i) How do students at risk of exclusion (e.g., low-income students with inadequate systems for studying from home) feel about transitioning from FTF instruction to online learning? and (ii) How do they view other aspects of their online learning experience, such as efficiency (resource and time optimization) and study efforts?

The purpose of the present mixed methods study was to explore how undergraduate students at risk of exclusion felt about online learning after the shift from FTF to online instruction in a large private university in Chile. Students from this university participated in online education for at least three semesters since the pandemic started. They, therefore, had time to adjust to the online educational modality. Additionally, nearly 22% of students in this private university face financial needs and receive subsidized education.

Under the online learning modality, external (Selvaraj et al., 2021) and internal (Ferrer et al., 2020) factors can affect students’ learning experience. These factors can encompass aspects like technology accessibility, internet connectivity, access to learning resources, learning style, and personality. Some elements can profoundly impact the experiences of students at risk of exclusion, such as access to learning resources and technology (Papouli et al., 2020).

External factors include various elements unrelated to the individual learner, which include Technological Barriers: Limited access to the necessary technology and internet connectivity can hinder participation and engagement, particularly for low-income and disabled students (Roshid et al., 2022). Peer Interaction: Isolation and difficulties participating in group activities can impact minority and disabled students’ sense of belonging (Arënliu et al., 2021). External Distractions: External responsibilities and distractions, including family and work obligations, can impede learning, particularly for low-income students (Corrigan, 2003). Access to Resources: Affordability and accessibility of learning resources can affect the engagement of low-income students (Jaggars, 2011).

Contrary to external factors, internal factors in online learning refer to individual characteristics and behaviors that influence a student’s success and engagement. Some key internal factors include Prior Knowledge and Skills: At-risk students may have gaps in their prior knowledge and skills, particularly in their first academic period (Soria et al., 2020). Learning Styles: In online learning, reflexive learners may spend more time reflecting on concepts and appreciate opportunities for in-depth discussions and self-assessment (Battalio, 2009). Time Management: Time management challenges can disproportionately affect at-risk students with additional responsibilities outside their studies (Rodríguez-Planas, 2022). Personality Style: Extroverted students thrive on social interaction. They may actively participate in group discussions, collaborative projects, and virtual meetings in online courses, contributing to a more dynamic learning environment. Figure 1 presents a conceptual framework summarizing how these factors can affect learning.

Figure 1. Conceptual framework of factors affecting students’ online learning. Note. Authors elaboration, adapted from the conceptual framework on quality of learning (Entwistle, 2003).

One key aspect of our mixed methods study, in particular the survey component, is that we examined students’ views regarding critical elements of their learning experience (regarding inclusion, efficiency, grade impacts, and efforts) while accounting for several critical external and internal factors that can influence this experience. For students who face greater challenges during online learning, such as first-generation, low-income, disabled, rural origin, or ethnic minority students (hereinafter referred to as students at risk of exclusion), understanding their attitudes and beliefs about online learning is especially relevant to proposed changes in instructional delivery models. The qualitative part exposed students’ positive and negative views, aspects that might have been overlooked if solely relying on survey data. This approach contributes to a more comprehensive understanding of students’ learning experiences during the shift from FTF to online education.

Our study, therefore, contributes relevant information to developing an inclusive educational framework that considers the diverse academic needs of students and the unique challenges faced by those at risk of exclusion. Online and hybrid learning formats have become attractive, convenient, and affordable alternatives for students seeking to pursue a college degree at their own pace (Vaughan, 2007). As more students consider these formats and universities develop options to respond to this evolving demand, understanding how students, in particular those at risk of exclusion, perceive online learning is critical in this transition (Aguilera-Hermida, 2020; Azlan et al., 2020; Chakraborty et al., 2021; Gherheș et al., 2021; Khalil et al., 2020; Sindiani et al., 2020).

Methods

To evaluate students’ views on the online learning environment, we conducted a mixed methods study using an explanatory sequential design (Creswell & Clark, 2018) comprised of two components: an online survey (quantitative component) and focus groups (qualitative component). Herein, we draw on Johnson et al. (2007) definition of mixed-methods research design: ‘the type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches for the broad purposes of breadth and depth of understanding and corroboration’ (p. 123).

The research process is summarized in Figure 2. The data collection process can be divided into two sequential Phases. In Phase 1, we collected data from two online surveys: an exploratory study based on a survey in the first academic semester of online learning during the COVID-19 pandemic and a second survey in the third academic semester of online instruction. This phase aims to understand students’ views on inclusion, efficiency, perceived impacts on grades, and study efforts. In Phase 2, we conducted focus group sessions. This phase aimed to supplement our survey data and explore specific questions that emerged from collected survey data regarding the overall learning experience, instruction quality, copying learning strategies, education inequalities, and positive aspects. Next, we describe in detail how we approach each phase.

Figure 2. Stages of the research process (authors’ elaboration). Notes. The blue arrows indicate that evidence from early stages was used to develop the subsequent stage. In the first survey, students enrolled in Agriculture from two large universities in Latin America participated (n = 141), while in the second survey, students enrolled in experiential learning courses from different majors at one of these two universities participated (n = 217): the university that maintained online learning for at least three semesters. To understand whether students’ experience evolved across academic semesters, students enrolled in Agriculture at this last university who participated in the early-pandemic survey were invited to participate in the focus groups (n = 26).

Phase 1: Survey data

Considering early evidence about the negative impact of online learning on the hands-on activities of students (Melo et al., 2021), this study focuses on understanding the perception regarding online education of students taking courses with an experiential learning component. An online survey was deployed in July 2021 with undergraduate students enrolled in experiential learning courses at the Pontificia Universidad Católica de Chile, which provided online learning for at least three semesters due to the pandemic (Melo et al., 2022). The long exposure to students from this large private university significantly affected students’ experiential learning, as shown by an early exploratory study (Melo et al., 2021). Students were asked their views on inclusion, efficiency, perceived impacts on grades, and study efforts. The survey instrument is presented in Supplementary Appendix B.

Recruitment

Only students enrolled in experiential learning courses, including those in Agriculture, were invited to participate in this second online survey. To explore students’ overall views of the online learning environment, we surveyed students in the third academic semester the university offered online education during the pandemic. In an earlier study with students enrolled in Agriculture from two prominent universities in Latin America, Melo et al. (2021) reported that experiential learning was significantly affected during the pandemic.

Participant recruitment and data collection procedures were conducted as follows (approved by the Human Research Ethics Committee of the Pontificia Universidad Católica de Chile, ID 210430001): Students were invited to participate in the study during online lectures after obtaining instructor consent. Then, students shared their email addresses if interested in participating in the study by registering via Google Forms. Later, registered students received in their email a survey link. Upon providing consent, participants could access the survey questions. After completing the survey (using Typeform), students could provide their email to participate in a drawing for a gift card as compensation for their time.

Questionnaire

We deployed two survey versions containing questions regarding the following aspects: (1) students’ preferences for different assessments, (2) students’ perceptions of their learning experience, and (3) students’ characteristics, including their socio-demographic information and (self-reported) personality type and learning style. Students also reported whether they self-identified with at least one of the following student groups at risk of exclusion: ethnic minorities, low academic performers, first-generation college students, rural communities, low-income households, and/or disabled students (Corrigan, 2003; Ecklund, 2013; Fleming & Grace, 2014; Kranke et al., 2013; Ribeiro, 2014; Richardson, 2008; Rodríguez-Planas, 2022; Weatherton & Schussler, 2021; Yang, 2010).¹ Responses to this question were used to identify students at risk of exclusion (Table 1). We also included a quality check question (i.e., ‘How many days are there in a week?’) to verify students’ attention. None of the students failed to answer this quality check question.

Table 1. Description and summary statistics of variables used in the regression analysis.

Variable	Students not at risk	Students at risk of exclusion	T or X^2 statistic	Unit	Description
Risk of exclusion group	0	1		Binary	Belong to at least one risk of exclusion group (1) or not (0)
Self-reported extroversion	0.1	0.06	0.97	Binary	Extraversion is the personality I identify the most with (1) or otherwise (0)
Self-reported reflexive learning style	0.25	0.22	0.55	Binary	Reflexive is the learning style I identify the most with (1) or otherwise (0)
Female	0.59	0.69	−1.49	Binary	Female student (1) or otherwise (0)
Age	20.94	21.28	−1.08	Years	Age of students surveyed
First year	0.14	0.18	−0.68	Binary	Student belongs to the first academic year (1) or otherwise (0)
Second year	0.28	0.17	2.03	Binary	Student belongs to the second academic year (1) or otherwise (0)
Third year	0.19	0.21	−0.32	Binary	Student belongs to the third academic year (1) or otherwise (0)
Agronomy major	0.24	0.14	1.92	Binary	Agronomy student (1) or other major (0)
Medical major	0.23	0.31	−1.22	Binary	Medical student (1) or other major (0)
Engineering major	0.17	0.19	−0.49	Binary	Engineering student (1) or other major (0)
Psychology major	0.08	0.08	0.05	Binary	Psychology student (1) or other major (0)

Notes. Students at risk of exclusion are those who self-identified in the survey with at least one of the following groups: first-generation, low-income, disabled, rural origin, and/or ethnic minority. T-test was used, except for binary variables; in this case, we used the X² test for differences between the two groups. N = 217 (36% are students at risk of exclusion).

The main difference between the two survey versions was section (1) regarding students’ preferences for different assessments (i.e., we elicited students’ preferences for assessment methods in one version while preferences for assessment attributes in the other version, Melo et al., 2022). The surveys were pretested with ten students to ensure proper technical functioning of the platform and language understanding.

Students’ perceived learning experiences were elicited by asking the following questions: (i) Have you felt you belonged to an inclusive classroom during your online classes? For a baseline, we asked: Have you felt you belonged to an inclusive classroom during your in-person classes (i.e., pre-pandemic)? (ii) Do you consider online learning, including online assessments, to be more efficient (optimizes resources and time) in achieving learning objectives as much as in-person instruction? (iii) Did the assessment methods used in your online classes slightly benefit or benefit your academic grades? and (iv) Do you feel like you have to study harder to achieve good grades in your online assessments?

Statistical analyses

Survey responses were analyzed using STATA, a statistical software for quantitative research. To analyze survey responses, we employed descriptive statistics and regression analyses. We estimated a logit (for binary responses) or ordered logit (for categorical responses) within a regression analysis framework. A regression analysis allows us to account for different factors associated with students’ perceptions (e.g., access to physical resources for studying at home) while exploring the effect of our variable of interest: whether the student self-identifies as a student at risk of exclusion. Finally, we estimate alternative estimations to discard the possibility that the choice of the model specification drives our results. The variables in the main model specification are described after presenting the data in the results section.

Phase 2: Focus group data

To further understand students’ perceptions and evolution, focus group sessions were conducted with a sample of students from the College of Agriculture who participated in an exploratory online survey early in the pandemic—their first semester of online learning (Melo et al., 2021). Experiential learning of these students was particularly affected at the beginning of the pandemic, as revealed by an exploratory survey early in the pandemic conducted with students from the College of Agriculture from two Latin American universities. (Melo et al., 2021).

Student recruitment was as follows: Students from the College of Agronomy at the Chilean university who participated in this early survey and provided their email for a follow-up study were invited to participate. Students were offered a 10% chance to obtain a gift card worth $10 for their participation (as approved by the Human Research Ethics Committee of the Pontificia Universidad Católica de Chile, ID 200528003).

Four focus group sessions with 6-8 students in each group were conducted online (due to quarantine restrictions in Chile). Sessions were conducted during the university’s third academic semester of online learning. The focus group sessions, lasting less than one hour, proceeded as follows. Students met online via Zoom. After providing their consent to participate, the moderator introduced the session and facilitated discussion based on the following questions: (i) How has the pandemic affected learning?, (ii) What measures have faculty adopted to teach online?, (iii) What learning methods have you adopted to improve performance in online classes?, (iv) What inequalities do you perceive in online classes? and (v) What positive practices have you perceived in online learning? Responses to these questions were recorded and then transcribed for analysis using content analysis (Stemler, 2015).

Results

Results: Phase 1

Sample

Two hundred seventeen (n = 217) undergraduate students from a well-known Latin American University participated in our study. As explained earlier, they were surveyed during their third semester of online learning to understand their views on inclusion, efficiency, perceived impacts on grades, and study efforts.

A total of 232 students started the survey, but only 226 students completed it. After eliminating inconsistent responses to assessment preferences and other questions, our final sample consisted of 217 students. In the final sample, about 36% self-identified with at least one of the groups at risk of exclusion. Within this group, 24% of students self-identified as first-generation attending college, 14% belonging to a low-income household, 11% from a rural area, and 4% with a disability or from an ethnic minority (Supplementary Appendix Table 1). Student self-identification information is consistent with related reports (Catalán & Santelices, 2018; Jarpa-Arriagada & Rodríguez-Garcós, 2021).

Statistical analyses

Descriptive statistics

Table 1 presents students’ characteristics (students not at risk of exclusion vs. students at risk). In general, respondents in the sample consist of female students (59 vs. 69%), self-identified extroverts (10 vs. 6%), and self-identified reflexive learners (25 vs. 22%). Students represented different academic years (14–18% were first-year students, 28–17% were second-year students, 19–21% were third-year students, and the rest were fourth-year or higher-year students, 39–45%). Most participants studied Medicine (23 vs. 31%), followed by Agronomy (24 vs. 14%).

Overall, in Table 1, there were no significant differences between the two groups with few exceptions (with statistical differences at the 10% level). Compared with students not at risk, fewer students at risk of exclusion were in the second year (28 vs. 17%) and studying Agronomy (24 vs. 14%). Interestingly, students at risk of exclusion were less likely to report limited class participation as a limiting factor compared to students not at risk (22 vs. 32%).

Figure 3 summarizes student’s responses regarding the factors affecting performance in online assessments; limited attention was the main factor (76 vs. 77%), followed by limited interaction with the professor (45 vs. 38%) and limited classmates’ participation (32 vs. 22%). The last limiting factor was limited learning resources (13 vs 17%). There were no significant differences between the two groups with few exceptions (with statistical differences at the 10% level).

Figure 3. Students’ perceptions of factors limiting their learning experience. Notes. Students at risk of exclusion are those who self-identified in the survey at least with one of the following groups: first-generation, low-income, disabled, rural origin, and/or ethnic minority. There were no statistical differences in responses between the two student groups (p < 0.1). To identify factors affecting the learning experience, students were asked the following question: What factor has affected your performance during online assessments since the onset of the pandemic? (Select all that apply).

Figure 4 reflects students’ responses to the main variables of interest: students’ views on inclusion, efficiency, perceived impacts on grades, and study efforts. Regarding respondents’ opinions on online learning efficiency, there were no differences between students at risk of exclusion and students not at risk. A nontrivial proportion of students at risk of exclusion and students not at risk (42 and 41%) agreed that online learning is either inefficient or very inefficient.

Figure 4. Students’ perceptions of their learning experience. Notes. For the inclusivity variable (online vs. in-person learning), we defined three categories (less inclusive, same, more inclusive) based on students’ responses to the two questions: (1) Have you felt that you belonged to an inclusive classroom during your online classes and (2) Have you felt you belonged to an inclusive classroom during your in-person classes (i.e., pre-pandemic)? Responses were recorded with a 5-point Likert scale (very agreed to very disagree).

Similarly, students’ opinions about belonging to a less inclusive classroom did not differ between the two groups. Most students at risk of exclusion and students not at risk (61 and 65%, respectively) felt that inclusivity did not change after switching to online learning.

Finally, students’ responses regarding impacts on academic performance indicate that most students at risk of exclusion answered that their grades did not change (34%) or slightly increased (30%). Similarly, most students not at risk answered that their grades did not change (32%) or slightly increased (33%). In contrast, students at risk of exclusion stated that they needed to study more to get better grades than students not at risk (69 vs. 52%).

In sum, findings reported in Figure 4 suggest similarities in the responses of students at risk of exclusion and those not at risk. The only notable difference was that self-reported study efforts were higher among students at risk of exclusion compared to their counterpart group.

Regression analyses

Although the results in Figure 4 are informative, student responses can be associated with several factors (e.g., having access to the internet, academic major, and personality). Therefore, we conducted regression analyses to control for different aspects (described in Table 1) that could influence students’ learning experiences and opinions. Regression analyses also allow us to identify potential factors correlating with students’ opinions, such as those negatively affecting learning (reported in Figure 3).

To test whether students at risk of exclusion perceived the learning environment as more challenging than their counterparts, measured by students’ responses shown in Figure 4, while controlling for several factors described in Table 1 and Figure 3, we estimated a logit (for binary responses) or ordered logit (for categorical responses).

In the regressions, we included as an independent variable the variable of interest: an indicator variable of whether a student belongs to at least one of the groups at risk of exclusion (described earlier and decomposed in Supplementary Appendix Table A1). We also included the explanatory variables shown in Table 1 and Figure 3: students’ perceptions, factors affecting learning (e.g., internet access), students’ characteristics such as socio-demographic information (e.g., gender, age, academic year, and major), self-identity (i.e., extrovert) personality, and learning style (i.e., reflexive).

Table 2 shows the estimation results from logistic regression models (Supplementary Appendix Table A2 reports the odds ratios). The results indicate that being at risk of exclusion does not explain students’ perceptions of the online learning environment being more inclusive than FTF instruction. This suggests that this group was not more (or less) likely to report that online learning is more inclusive.

Table 2. Regression analysis.

Variable	Belong to a less inclusive classroom in online than in-person instruction	Online learning is less efficient	Online assessments affected grades	Feel you need to study more for a good grade in online assessments
Belong to at least one risk of exclusion group	0.0586	0.0177	−0.245	0.943**
Extraversion is the personality I identify the most with	0.172	0.311	0.231	−0.587
Reflexive is the learning style I identify the most with	−0.124	0.313	0.369	0.133
Factor affected assessment: limited attention	0.489	1.014**	0.198	0.519
Factor affected assessment: limited interaction professor	−0.0523	0.439	−0.463+	0.565+
Factor affected assessment: limited classmates’ participation	0.0852	0.231	0.147	0.282
Factor affected assessment: limited resources	−0.619	0.399	−0.491	0.370
Female	0.0620	−0.156	−0.0523	0.353
Age	0.156+	0.0547	−0.0278	−0.0129
First year	−0.367	−0.163	−1.326*	−0.412
Second year	0.484	0.223	−0.989*	0.618
Third year	0.197	−0.377	−0.729+	0.291
Major: Agronomy	−0.0339	−0.653+	0.390	0.0523
Major: Medicine	0.226	0.0118	1.055**	0.188
Major: Engineering	0.304	−0.370	0.723+	0.110
Major: Psychology	−1.137*	−0.539	1.713***	−0.737

Notes: +p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001. Cut points (thresholds) are omitted for simplicity. N = 217. Students at risk of exclusion are those who self-identified in the survey with at least one of the following groups: first-generation, low-income, disabled, rural origin, and/or ethnic minority. T-test was used, except for binary variables; in this case, we used the X² test for differences between the two groups. N = 217 (36% are students at risk of exclusion).

We also evaluated students’ general perceptions of the transition from FTF instruction to online learning (i.e., learning efficiency, perceived impact on grades, and study efforts). Regarding this last aspect, we found that students at risk of exclusion feel they need to study more to receive a good grade in online assessments than students not at risk. The odds of agreeing with this statement for students at risk of exclusion are nearly three times that of students not at risk (Supplementary Appendix Table A2).

Contrary to study effort responses, the results indicate that students at risk of exclusion do not perceive online learning efficiency and impacts on grades differently than students who are not at risk. Overall regression results conditional on covariates (i.e., independent variables reported in Table 1 and Figure 3) confirm the results reported the results from the unconditional analyses reported in Figure 4.

Finally, consistent with previous results and a priori expectations, we found that other factors are associated with students’ responses. For instance, students reporting limited attention as a factor affecting assessments are more likely to indicate that online learning is more inefficient (Mukhtar et al., 2020).

Furthermore, the number of years in college affected students’ opinions regarding the educational transition. For instance, first- and second-year students are more likely to agree (compared to students in the fourth year or higher) that online learning negatively impacted their grades. These results are possible considering that these students have less familiarity with the college environment and its challenges (Clark, 2005; Kantanis, 2000; McCracken et al., 2001; Rayle & Chung, 2007).

Students’ major is also associated with students’ opinions regarding the educational transition. For instance, students in Medicine and Psychology perceived that online learning positively impacted their grades compared to students pursuing a degree in other careers (e.g., Arts, Education). These findings suggest that instructor demands from students during the transition varied by career, positively or negatively affecting grades (Iglesias-Pradas et al., 2021; Karadag, 2021; Kim et al., 2021; Motz et al., 2021).

Overall, the above results indicate several factors that explain students’ opinions that are consistent with expectations, suggesting that students’ low engagement with the survey should not be a concern. Interestingly, students from Agriculture are less likely to perceive that online learning is less efficient. This result is consistent with previous evidence indicating that students in this major are comfortable taking online classes and using virtual meeting software (Moorberg et al., 2021). Some students (enrolled in Agriculture) appreciated the new instruction methods and more time available (as they did not have to travel to the university). Qualitative evidence from focus groups (as discussed later) provides a finer-grained understanding of these findings.

Robustness

We tested whether our results were mainly driven by students’ exposure to the online learning system (rather than caused by the shock of the educational transition from FTF instruction to online) by comparing students’ responses enrolled in Agriculture at the Chilean university across time. We posed the question below.

How did students’ responses from a study early in the pandemic compare to responses from the present study? Results indicated that students were generally better prepared to learn online than early in the pandemic. Using data from an exploratory survey conducted early in the pandemic (i.e., in the first semester of online learning at the Chilean university; Melo et al., 2021), we found that a nontrivial proportion of undergraduate students reported having internet connection problems (41%) and a lack of proper space to study (13%). In contrast, results from this study’s survey (conducted during the third semester of online learning at the same Chilean university) indicated fewer students had these problems. For instance, a smaller proportion reported having limited resources (i.e., technology, infrastructure; 13%), and very few declared that their internet was ‘bad’ or ‘very bad’ for delivering assessments (only 8%; see Supplementary Appendix Table A3).

Results from these two surveys, contrasting students’ readiness to study online early in the pandemic vs. three semesters later, align with Google trends data regarding students’ responses to online learning in Chile. Specifically, trends indicate that as students navigate online learning and are exposed to teaching adaptations and technology (e.g., Zoom and CANVAS), top searched words (e.g., Google Classroom and Virtual Classroom in the pandemic) become less popular with time (Supplementary Appendix Figure A1). As expected, the frequency of searching these words increased whenever the semester was about to start (i.e., in March of each year).

We also tested the consistency of our regression results by considering two robustness checks. First, our main specifications used self-reported responses regarding belonging to at least one group at risk of exclusion, as in previous work (House et al., 2020). The main reason for defining a general category rather than individual categories is that there were categories with lower responses (e.g., ethnic minorities and students with disabilities). Having very low responses in one category can create false positives (Austin & Brunner, 2004; Heckathorn, 1997). As a robustness check, we repeated the regression analyses but used indicator variables for each group at risk of exclusion instead of one indicator variable. The risk of exclusion categories included low-income, first-generation students, rural area origin, and others (i.e., have a disability or are from a cultural/race minority). Regression results for students’ views on inclusion, efficiency, and effects on grades remained robust (Supplementary Appendix Table A4). Further, results indicated that regarding students’ opinions of study efforts, students from rural origin are more likely to report they needed to study more than students coming from urban areas.

Second, the social stigma associated with income status can lead to students misreporting their low-income status (Hamilton, 2012). As a robustness check, we used an alternative measure (whether the household received financial aid from the government) to identify low-income students. About 40% of students responded that their families received financial assistance. In contrast, only 14% of students self-identified with a low-income family. This alternative measure’s results were qualitatively similar to the main results but less efficient, as some responses to the financial aid questions were missing (Supplementary Appendix Table A5).

Results: Phase 2

Twenty-six (n = 26) undergraduate students from the College of Agriculture participated in the focus group sessions. The focus groups comprised 50% female, 19% second-year, 54% third-year, 8% fourth-year, and 19% fifth-year students. Supplementary Appendix Table A6 lists participants by group.

In this section, we present a summary of students’ comments about online learning based on the five themes discussed during the sessions regarding students’ experience in online learning during the pandemic. Themes comprised the following: learning impact, learning strategies, family adaptations, inequality, and positive aspects. As in related studies (Altwaijry et al., 2021; Devkota, 2021), for each team, we also present selected relevant students’ comments. Students’ comments were considered relevant whenever authors familiar with the university environment agreed that comments represented the theme and could inform (complement or contrast) the survey evidence presented.

Learning impact

How has the pandemic affected learning?

Students described, based on their experience, several negative factors that influenced their online learning experience. For instance, students indicated some negative aspects regarding faculty, administration, and university response (or lack of response) to the new learning environment:

We have tried to talk to the professors, and still, they do not adapt to the complex reality that we are living in (Third-year female student).

I think there is not a good adaptation of the university; there is no consideration of the stress of being at home, the confinement, the coexistence, which is super important (Fifth-year male student).

The online environment during the pandemic was also associated with adverse students’ mental/physical health and future career opportunities. Study participants highlighted that the online learning modality led to the manifestation of both physical harm (such as back pain and damage to life) and psychological harm (through stress and anxiety related to their academic and work prospects). These effects were intensified by the lack of interaction with peers and inadequate time management.

…. I am usually a very extroverted person, I always participate in everything, and the pandemic generated a lot of social anxiety to the point that I was practically panicked to turn on the microphone; I could not do anything, and the first semester was terrible; the second semester was even more so and I was practically the whole second semester with a psychologist trying to improve everything (Second-year female student).

It generates me a lot of anxiety to think about the future; in some years, I will present myself as an agronomist in a company that requires specific skills such as technical control of fruits and because of the years I studied, they are going to visualize that most of my academic years were done in online mode, and I feel that this will subtract points, it will hurt me (Third-year male student).

I feel that online classes have affected me physically, for example, I use glasses permanently and it has been horrible to be all day in front of a computer since the hours are extensive. Also, every day I end up with a horrible headache, even my eye (optic nerve) tingles and it causes me back pains, since I am sitting all day (Third-year female student).

Students also commented on their poor academic performance and learning quality during online learning.

The online assessments that we perform do not allow to review the questions already answered, therefore it also does not allow to change the answers, which generates a lot of anxiety, since it is a disadvantageous situation compared to performing in-person assessments, since in the physical classrooms one can see the tests, these actions go against the mental health of the students. (Third-year male student).

The relationship with my classmates is fundamental, add from studying in the classes it is important to form those bonds with friends, sharing after class, studying together, that motivates one much more to study, what you do not know you learn from the other (Third-year female student).

Some students also shared the struggles they faced studying from home.

I have felt a disorder in everything related to studying, I feel that when I was in-person classes, I had an order of going to the university and studying and then coming home and resting. Now no longer, you have to do all the housework … and worrying about the family, everything started to get mixed up. (Fifth-year female student).

This mode of studying limits me to share with my family, when the classes were in-person I was almost all day at the university, and I would arrive and I could have dinner with my family, and now I finish my classes and I am in my room all the time, so when do I really rest? I usually go to bed for 10 minutes and then again, I have to sit at my desk again. (Third-year female student).

Learning strategies

What learning methods have you adopted to improve your performance in online classes?

Students commented about strategies regarding the environment and habits students adopted to make their online learning more efficient. Students adopted improvised strategies to optimize their learning experience. These initiatives included the creation of favorable study environments characterized by the presence of plants or music. Likewise, some students developed self-taught skills in response to the demands inherent to their Agriculture field.

I always apply the method of changing my place of study. For example, one week studying in my room, then another week I would study in the living room (Fifth-year female student).

In the second semester, I started to take breaks; for example, after class, I would go out to the courtyard or to the surrounding squares to disconnect a bit, and so my concentration started to improve (Fifth-year female student).

It was very helpful to have plants in my room, visually, it motivated me. (Third-year female student).

Students also shared other nonphysical related factors that supported their learning (e.g., self-learning):

I tried to make it a bit playful, for example, with music. To each course, I assigned a playlist of an album by a different artist, then for example, when I studied Calculus, I listened to an album by a certain artist (Third-year female student).

… what has helped me adapt has been partly the support of the family I live with because my brother is also studying, and we share our experiences. Also, in the economic aspect, to adapt, I have had to buy a bigger desk and screen, among others. (Third-year female student).

I feel that our career is quite practical, and as I am about to culminate, I have been more active in social networks (such as LinkedIn or YouTube), where they show videos of activities and practices of my career in the field or laboratory, all on my own (Fifth-year male student).

Faculty adaptations

What measures have faculty adopted to teach online?

Despite pointing out the educational institution’s limitations in adapting to online instruction, the students highlighted the teachers’ efforts to improve the effectiveness of classes by implementing various pedagogical methodologies. Students commented on the positive aspects of teaching adaptations adopted by faculty to support students learning:

…this year there have been more open book assessments … and I feel that this has forced me to have little less orthodox methods like studying and memorizing and has forced me to understand the subject more (fifth-year male student).

I think one of the methods that helped me the most was that the professors would generate short video capsules, providing concrete information on a topic and then I would answer a mini-questionnaire and the professor would give a bonus for that exercise as additional points, which motivated me a lot (Third-year female student).

Recording the classes on a drive has been a very good tool so that we can access the information whenever we want (Fifth-year male student).

I had ‘virtual’ field trips; in my case, they were quite useful because, for example, we had the pig production course, which in classroom format we could not enter the plant and in the online format we could, where a drone went through the entire plant, so at least that was more beneficial (Third-year female student).

They always brought guest professors to the class who were specialists in the topic they were going to expose, and I feel that motivated me a lot to go to class, to pay attention, since it was an opportunity that, at least when they brought international guests, an authority that in non-pandemic context, we could not have. I feel that it is a good method to motivate and also to give other points of view of topics that may be the professor is not so expert (Fifth-year male student).

However, some students expressed concerns regarding changes in some faculty’s teaching.

The distancing with the faculty has complicated relationships. I perceive that now the communication is very distant, and the answers are not immediate; for example, you send an email, and it takes a long time for them [referring to faculty] to reply, the relationship with them over time has been lost (Fourth-year female student).

I feel a lack of support from the faculty, I know that in careers such as Medicine or Dentistry, they have provided more support to students, I know students of Dentistry, even in quarantine, were doing dental implants in their college and we [student from another career] have limited support (Third-year male student).

The assessments methods used are deficient, I perceived that they measured the speed in answering the tests and sought that the students knew the subjects by heart; they do not measure the comprehension of the subject (Fifth-year male student).

Inequality

What inequalities have you perceived in online classes?

Students emphasized that the university lacked the adequate infrastructure to serve those students who belong to low economic strata effectively, have disabilities, or come from rural environments, where accessibility to internet services could be problematic.

… I have seen differences, but mainly with respect to internet connectivity. The quality of the internet is not the same for a student who lives in the city as a student who lives in a rural area. This is even worse when teachers decide not to record their classes; in those cases, I have perceived intolerance and lack of understanding from teachers (Third-year male student).

One of my best friends has fibromyalgia, a disease that causes pain and sensitivity throughout the body. She has applied to PISNE (Program for the Inclusion of Students with Special Needs), but they told her there were no openings. At the beginning of the pandemic, the faculty provided her with a ‘note taker’ (because of her illness), but later the same faculty withdrew the help, arguing that they no longer had the resources. This situation is aggravated by the fact that she lives in the countryside, where the quality of the internet is very poor (Third-year female student).

Positive aspects

What positive practices have you perceived in online learning?

Students expressed their appreciation for the time efficiency offered by the virtual learning modality by avoiding the need to physically travel to the classrooms and the advantages provided by technological tools, such as the possibility of recording classes, which would facilitate greater concentration on attention to academic content. Some of the advantages of studying online during the pandemic were mentioned:

The positive thing that I rescue is that the online classes can be recorded, and I can write down all the subject [referring to the lectures] without missing it. This could not happen in in-person classes. (Second-year female student).

I have met several colleagues who we used to talk in-person, but in the online modality we were able to have a friendship in another way, supporting each other in our mental health. (Third-year male student).

Discussion

The present study evaluated whether students at risk of exclusion (i.e., students who self-identified as first-generation, low-income, disabled, rural, and/or ethnic minority students) perceived the online learning environment during the pandemic as more challenging than students who were not at risk. Student responses to an online survey with questions about inclusion, efficiency, perceived impact on academic performance, and effort in the online learning environment indicate that students at risk do not consider the online learning environment more challenging than students who are not at risk. One exception was that students at risk of exclusion were more likely to state that they needed to study more to get good grades than students who were not at risk.

Our findings suggesting online learning did not significantly affect students at risk of exclusion align with an early study. Pre-pandemic research indicates that ethnic minority students perform as well as other students in FTF instruction and online courses (Richardson, 2012). A related study revealed no significant differences in college student grades between in-person and online classes, suggesting that the rapid and unplanned move to online education did not result in a bad learning experience (El Said, 2021). In our study, the university offered online courses for at least three semesters; therefore, it is unsurprising that students and instructors gradually adapted to the shift from in-person to online instruction (Rad et al., 2021).

Results from our study contrast with recent studies indicating that students at risk of exclusion were more likely to perceive the rapid shift from in-person to online instruction as very impactful on their learning than those who were not at risk. Research conducted in six countries revealed that students who faced more adverse living conditions, such as lack of a suitable place to study, noisy environment, and health problems, reported more significant disruption in their learning process compared to their peers who lived in less challenging circumstances (Bartolic et al., 2022). These adverse circumstances could have negatively impacted academic performance. Another study with students in business-related disciplines found that those who are first-generation college students performed significantly worse in online courses compared to those who included at least some FTF interaction (Engelhardt et al., 2022), suggesting that universities and instructors may need to dedicate additional academic support and resources to students at risk of exclusions (Soria et al., 2020). On the other hand, Cameron et al. (2021) identified that students with limited financial resources expressed lower satisfaction and academic performance than those with higher income during online learning. The main difference between our study and these related studies is that in the latter, students experienced online learning for a shorter period than the students in our sample, who have more than one semester to adapt.

Further analyses of our sample confirmed that students from rural areas believed they needed to study more to obtain good grades. These results hold even after controlling for other factors affecting learning (e.g., internet access) (Supplementary Appendix Table A4). This finding infers that rural students might overcome challenges associated with virtual instruction by studying more (Morton et al., 2018). A study with academically exceptional students from rural areas also revealed students’ concerns about getting good grades and the difficulty of online education (Swan et al., 2015).

When contrasting survey responses with comments from focus groups, we found that in the survey, fewer students reported having significant challenges with online learning (e.g., technical problems). Results from focus groups with agronomy students, however, highlighted other problems. For instance, (i) factors associated with a lack of understanding and support from faculty and the university authorities, (ii) problems associated with physical and mental stress from online learning, and (iii) concerns about the absence of practical training. The last is essential, considering the nature of their career path.

Overall, focus group findings are consistent with other studies on agronomy students (Melo et al., 2021). For instance, Moorberg et al. (2021) noted that agronomy students had to study more during online learning. Yet, instructors did not recognize this additional effort. Another significant challenge pointed out by Melo et al. (2021) was that students had negative impressions of the theoretical and experimental courses. For instance, referring to the question of learning impact, a female student in her fifth year mentioned: ‘I am in the last semester of the career. The pandemic was the time when the career was going to have more experimental activities in the field, where students were going to internalize their profession more, and I think it was demotivating because, from the beginning, we expect to perform these activities’.

Focus group responses also highlight the positive aspects of online learning. These aspects were associated with time efficiency, such as reduced commuting to school, which increased time for other activities. For instance, a male student in his fifth year mentioned: ‘I usually take three hours of transportation to go to the university now because of online learning; I can use the time to do sports, to be able to rest here at home, to share with my family, among others’ Another factor was time and learning efficiency. By offering different learning alternatives, students could review recorded lectures several times. This feature advantages FTF classes, where students only rely on lecture notes (Davis et al., 2009). A study with exceptional students from rural areas indicates that they value having control over their online learning objectives and the content of the courses (Swan et al., 2015).

Overall, evidence from a mixed-method approach provides a better snapshot of important issues in higher education. For instance, survey evidence points out that first-year students were more likely to report the negative influence of online learning on their grades than upper-level students. Conversely, evidence from focus groups suggests that online learning was more difficult for upper-level than first-year students. This might be expected as the last years of college are critical for fieldwork and experiential learning. Therefore, it is crucial to explore the learning needs of students across different academic years and professional careers based on a mixed methods approach.

Implications

Our results indicate that online learning might positively and negatively affect all students, both at risk and not at risk of exclusion, regarding impacts on grades, inclusion, and efficiency. Since at-risk students reported higher studying efforts, special attention is recommended for these students during crises or disasters (Kuran et al., 2020). The following factors might be employed to overcome online learning challenges: behavioral support for increasing self-motivation and resilient attitudes toward new challenges and goals; and social support for increasing the positive influence of social groups, family, and friends (De Souza et al., 2020; Mitchall & Jaeger, 2018; Rahiem, 2021; Zhao et al., 2022).

Additional results from our study highlight that online instruction has the potential to provide benefits that FTF instruction does not for all students. To ensure this, a series of requirements are necessary. For instance, our findings highlight students’ need for additional support beyond physical conditions (e.g., internet and study space). Support can include pedagogical tools to facilitate learning (e.g., innovative teaching and assessment techniques) and mechanisms supporting students’ psychological and emotional needs. Similarly, universities must provide adequate infrastructure and professional development for instructors as well as feedback mechanisms to instructors and students that improve the quality of the online learning experience, especially for populations at risk of exclusion (Drane et al., 2021; Kohli, Wampole & Kohli, 2021).

As a final point, although traditional in-person teaching and learning are still the most widely used in education, it is essential to explore further the benefits that online learning might provide undergraduate students in expanding Latin American economies characterized by significant inequality, as exemplified in the case of Chile. For instance, reusing and sharing educational resources can decrease education costs and generate networks that support students’ academic development (Concannon et al., 2005; Ochieng & Gyasi, 2021; Ren, 2019). Moreover, the application of emerging technologies such as virtual reality, artificial intelligence, and gamification in online or hybrid classrooms can enrich the learning experience (Alam, 2022; Tlili et al., 2021; Woo et al., 2021), while online communities can facilitate student collaboration and peer-based learning for all students (Coleman & Money, 2020). Ensuring equitable access to these technologies is crucial in addressing potential learning disparities between students at risk and those not at risk.

Contributions and limitations

The present study findings contribute to the literature on undergraduate students’ experience transitioning from FTF to online learning environments. By providing insights into the perspectives and reported behaviors of students from a large university in Chile providing online instruction for multiple academic periods during the pandemic, this study explores students’ experiences during the transition. Using a cross-sectional design, we provide a snapshot of the preparation of pre-vocational students in university from initial introductory courses (year 1) to practicum (year 5). Additionally, we analyzed the student data by investigating commonalities and differences between at-risk students (i.e., first-generation, low-income, disabled, rural origin, and/or ethnic minority students) and students who are not at risk of exclusion.

Although students at risk of exclusion (i.e., first-generation, low-income, disabled, rural origin, and/or ethnic minority students) did not perceive the online learning environment during the educational shift during the COVID-19 pandemic as more challenging compared to students not at risk, as revealed by a survey, focus groups with agronomy students, however, highlighted other problems regarding well-being and limited practical training and faculty support. These contrasting findings underscore the importance of adopting a mixed-methods approach to support students’ learning. Practitioners should not solely rely on quantitative assessments but also consider qualitative insights, ensuring students’ specific needs are addressed in considering pedagogy approaches.

Two key limitations should be considered: memory or recall bias and accuracy (given the response constraints of the survey instrument). To address these limitations, we used a mixed-methods approach where student volunteers participated in focus groups with trained facilitators. The study, however, has other limitations. First, participation of eligible students in focus groups and surveys was conditional on students being able to have access to technologies and the internet; therefore, responses to the survey and focus group evidence cannot reflect the opinions of those who might be struggling the most during online learning. Second, this study was carried out in the context of a large private university in Chile. Future research would benefit from studying other teaching and learning contexts to obtain a complete and more representative outlook of students’ online learning experience.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Grace Melo

Dr. Grace Melo Guerrero is an Assistant Professor in the Department of Agricultural Economics at Texas A&M University. Her research focuses on the intersection of agricultural and environmental policy, with a particular emphasis on the role of behavioral insights in policy design. Some of her publications focus on the relationship between food assistance programs and food security and how these programs can be designed to improve food access and promote healthy dietary choices among vulnerable populations.

Diego Monteza

Diego Monteza is a researcher in Applied Economics with experience in quantitative and qualitative research applied to the improvement of food and education policy and the management of innovation and scientific entrepreneurship programs. He obtained an MSc in Environmental and Agricultural Economics from the Pontificia Universidad Católica de Chile.

Sandra Acosta

Sandra Acosta is an Associate Professor in the Department of Educational Psychology at Texas A&M University. Her research, grounded in transformative learning theory, focuses on three areas: biliteracy discourse development in adults and children, professional identity development and mentoring for early career professionals. Her methodological focus is collaborative analytical autoethnography, cultural historical activity theory, and systematic review. She developed the Methodological Quality Questionnaire (MQQ) for systematic reviews. Her most recent publication is “A Collaborative and Poetic Self-Study of Transformative Learning, Professional Identity, and Teaching in Academe”.

Notes

1 Students were also asked whether they self-identified with LGTBIQ+.