Self-regulation, motivation and quality of life of

Accounting students in remote education

Nágila Giovanna Silva Vilela

PhD in Business Administration from USP.

College of Business Administration and Economic Sciences,

FACEC, Brazil.

nagilavilela@gmail.com

http://lattes.cnpq.br/6442991737466181

https://orcid.org/0000-0002-8279-2763

Beatriz Negrelli da Silva

Master in Accounting by UEM.

College of Business Administration and Economic Sciences,

FACEC, Brazil.

beatriznegrelli@hotmail.com

http://lattes.cnpq.br/6823740730297435

https://orcid.org/0000-0002-0187-0644

Availability: https://doi.org/10.5965/2764747111212022021

Submission date: July 15, 2022.

Approval date: October 3, 2022.

Issue: Vol. 11, No. 21, Dec. 2022

Self-regulation, motivation and quality of life of Accounting students in remote

education

Abstract

Objective: To analyze how the use of self-regulated learning practices, the level of motivation

in learning, and quality of life are correlated. Method: A quantitative and descriptive research

was conducted by applying a questionnaire to 217 accounting undergraduates enrolled in four

higher education institutions (HEIs) at Paraná. Results: Students reported feeling little

motivated to study during remote education due to COVID-19 and experiencing relatively high

levels of negative feelings (such as bad mood, anxiety, and/or depression). As for self-regulated

learning strategies, “self-evaluation” and “self-consequence” were, respectively, the most and

least used. Statistical correlations showed that student motivation is strongly correlated with

quality-of-life factors such as “ability” and “concentration” to study, and these, in turn, were

the items most correlated with self-regulated learning strategies. Contributions: These results

highlight the importance of increasingly stimulating autonomous learning by using teaching

approaches that promote motivation, reducing the levels of negative feelings.

Keywords: Self-Regulated Learning Strategies. COVID-19. Motivation. Quality of Life.

Autorregulação, motivação e qualidade de vida dos discentes de Ciências Contábeis no

ensino remoto

Resumo

Objetivo: Analisar como o uso de práticas de aprendizagem autorregulada, o nível de

motivação em aprender e a qualidade de vida estão correlacionados. Método: Foi realizada

uma pesquisa de abordagem quantitativa e descritiva por meio da aplicação de um questionário

com 217 estudantes do curso de Ciências Contábeis de quatro instituições de ensino superior

(IES) do Paraná. Resultados: As respostas dos estudantes evidenciaram que os respondentes

se sentem pouco motivados a estudar no período de ensino remoto devido à Covid-19, além de

apresentarem níveis relativamente altos de sentimentos negativos (como mau-humor,

ansiedade e/ou depressão). Em relação às estratégias de aprendizagem autorregulada, a mais

utilizada é a “autoavaliação”, e a menos utilizada é a “autoconsequência”. Já as correlações

estatísticas evidenciam que a motivação dos discentes está fortemente correlacionada aos

fatores de qualidade de vida, como “capacidade” e “concentração” para estudar, e esses, por

sua vez, foram os itens mais correlacionados com as estratégias de aprendizagem

autorregulada. Contribuições: Com base nesses resultados, ressalta-se a importância de

estimular cada vez mais uma aprendizagem autônoma do discente, atentando-se em utilizar

abordagens de ensino que os façam sentir-se mais motivados, reduzindo os níveis de

sentimentos negativos.

Palavras-chave: Estratégias de Aprendizagem Autorregulada. Covid-19. Motivação.

Qualidade de Vida.

Autorregulación, motivación y calidad de vida de estudiantes de Contabilidad en

educación a distancia

Resumen

Objetivo: Analizar cómo se correlacionan el uso de prácticas de aprendizaje autorregulado, el

nivel de motivación para aprender y la calidad de vida. Método: Se realizó una investigación

con enfoque cuantitativo y descriptivo mediante la aplicación de un cuestionario con 217

estudiantes del curso de Contabilidad de cuatro instituciones de enseñanza superior (IES) de

Paraná. Resultados: Las respuestas de los estudiantes mostraron que los encuestados se sienten

poco motivados para estudiar en el período de enseñanza a distancia debido al Covid-19,

además de tener niveles relativamente altos de sentimientos negativos (como mal humor,

ansiedad y/o depresión). En cuanto a las estrategias de autorregulación del aprendizaje, la más

utilizada es la “autoevaluación”, y la menos utilizada es la “autoconsecuencia”. Las

correlaciones estadísticas muestran que la motivación de los estudiantes está fuertemente

correlacionada con factores de calidad de vida, como "capacidad" y "concentración" para

estudiar, y estos, a su vez, fueron los ítems más correlacionados con las estrategias de

autorregulación del aprendizaje. Aportes: A partir de estos resultados, es importante fomentar

un aprendizaje cada vez más autónomo por parte de los estudiantes, cuidando de utilizar

enfoques didácticos que los hagan sentir más motivados, reduciendo los niveles de sentimientos

negativos.

Palabras clave: Estrategias de Aprendizaje Autorregulado. COVID-19. Motivación. Calidad

de vida.

Introduction

The COVID-19 pandemic generated a global sense of threat and uncertainty (Schiff et

al., 2021). Challenges resulting from this health crisis affected both higher education

institutions (HEIs) and students, teachers, and staff (Hodges et al., 2020). This is because with

the suspension of in-person teaching activities, HEIs had to move to an “online reality”

(Moreira et al., 2020) which affected their routines and work organization. Alongside this

process, virtual environments and digital technologies have gained prominence as a way to

continue promoting learning (Santos Junior & Monteiro, 2020).

In this new educational context, teachers should guide students towards a more

autonomous learning, in which they learn how to learn (Moreira et al., 2020), a major

educational goal (Pintrich, 1999) required in several areas, including Accounting (Aguiar et

al., 2014). To do so, according to Boruchovitch (2008), self-regulated learning is needed.

Self-regulated learning can be influenced by motivation, personal resources and the

environment (Becker, 2016), as well as external social factors (Aguiar et al., 2018).

Consequently, although remote learning is a temporary option, we must consider that

adaptation to the virtual environment can be difficult for students, because, according to Santos

Junior and Monteiro (2020), their routines are modified, having to share time with other

activities. As a result, emotional impacts can occur. Further, the different socioeconomic

conditions of students interfere in effective learning, impacting, according to Borba et al.

(2020), access to technologies and the time available for their use, the study environment, the

working day, and family income. In the meantime, we can observe the impact of the student’s

quality of life during remote education due to COVID-19.

Moreover, such difficulties can leave many students unmotivated to learn. Perassinoto

et al. (2013) indicate that student motivation can contribute to the adoption of learning

strategies, such as self-regulation. We should also consider that institutions and teachers play

a vital role in student motivation (Reis et al., 2017), and is up to them, especially in this period,

to generate a virtual communication and learning structure in which students feels motivated

(Moreira et al., 2020).

Given the above, this study sought to analyze how the use of self-regulated learning

strategies, the level of motivation in learning and quality of life correlated during the COVID-

19 pandemic. To do so, we analyze the perceptions of accounting undergraduates from four

HEIs (two public and two private) in the state of Paraná.

Its relevance stems from the fact that research on Accounting education is still little

explored, as Santos et al. (2016) point out. Besides, we identified no studies that investigated

these variables in this specific context, considering that the pandemic scenario requires many

reflections, aiming at solutions to help meet the new needs of the academic community (Hodges

et al., 2020) and develop teaching and learning strategies capable of mitigating the teaching

losses due to school closures (Valente et al., 2020). From a theoretical perspective, this study

seeks to complement research on accounting education, and may imply, in a practical context,

the adoption of new pedagogical strategies and practices according to the understanding of

student learning in this context. From a practical point of view, we also highlight the possibility

of presenting alternatives to avoid a possible academic evasion from HEIs, since quality of life

can lead to difficulties in the teaching and learning process (Catunda & Ruiz, 2008). Finally,

motivation is considered a determining factor for students to “continue studying or not”

(Becker, 2016, p. 2).

This article is subdivided into five sections, including this introduction. In the following

section we review the specialized literature on the theme. Next, we describe the methodological

procedures. The fourth section discusses the analysis of the results. Finally, the fifth and last

section presents the final considerations, limitations and possibilities for future studies.

Theoretical framework

Remote Education and Digital Technologies in Mediating Learning During the

Pandemic

With in-person classes suspended due to the COVID-19 pandemic, some educational

institutions, via Ordinance No. 343 of March 17, 2020, which provides for the use of digital

media for the duration of the pandemic, have chosen to continue their activities using digital

technologies (Santos Junior & Monteiro, 2020), such as Skype, Google Hangouts, Zoom and

platforms such as Moodle, Microsoft Teams and Google Classroom (Moreira et al., 2020), in

a model called emergency remote education (ERE).

This new scenario demanded a rapid response from Universities, including a complete

transformation from the traditional classroom teaching model to the online environment

(Novak, Kozjak, & Perić, 2021). Novak et al. (2021) report that, in Europe, problem solving

by technical infrastructure and teacher training were crucial aspects for maintaining quality in

the teaching and learning process.

In Brazil, opinions diverged on the quality of teaching methodologies implemented by

ERE, contributing so that, as Hodges et al. (2020) point out, remote education was seen as a

weak option, given the great “temptation” of comparing it with in-person education. The issue,

however, is not to opt for one model or another; but rather the need to analyze and discuss this

unprecedented context in which it was impossible to project the resumption of in-person classes

(Arruda, 2020).

Importantly, distance education (DE) and ERE differ: DE is a model planned and

designed to be online since its inception, whereas ERE is an alternative—and temporary—

teaching model proposed to provide access to educational supports and content in a “fast, easy

to set up and reliable manner” during the pandemic (Hodges et al., 2020, p. 6). Arruda (2020)

argues that implementation of alternatives for students affected by school closures, even if

conducted through digital technologies, does not constitute a distance education project.

ERE becomes relevant for maintaining the link between HEIs and students, despite

possible limitations (Arruda, 2020), being a time in which, according to Hodges et al. (2020),

one must “think outside the box” to enable solutions that can help meet the new needs of the

academic community.

Self-Regulated Learning in Accounting and the Role of Motivation and Quality of Life

By self-regulated learning strategy we mean the learners’ actions directed at acquiring

information or skills that involve agency, purpose (goals), and instrumentality self-perceptions

(Zimmerman & Pons, 1986). Moreover, self-regulated learning strategies (SRL) “is a process

that establishes the active participation of the individual” (Lima Filho et al., 2015, p. 41).

In this new educational context, a more active stance is required from students, with

Moreira et al. (2020) and Valente et al. (2020) pointing out that teachers should guide students

towards a learning experience in which they learn how to learn. Studies on self-regulated

learning in accounting are of paramount importance. International accounting bodies such as

the Accounting Education Change Commission (AECC) and the American Institute of

Certified Public Accountants (AICPA) recommend forming professionals capable of learning

to learn, whether in in-person education or distance education (Aguiar et al., 2014).

Zimmerman and Pons (1986) developed 14 self-regulated learning strategies,

considering them highly related to academic success (Table 1).

Table 1

Self-regulated learning strategies

Characteristics

1. Self-evaluation

Indicates student-initiated evaluations of the quality

or progress of their work.

2. Organizing and transforming

Indicates student-initiated use of instructional

materials to improve learning.

3. Goal-setting and planning

Indicates planning for sequencing, timing, and

completing activities related to goals set by the

student.

4. Seeking information

Indicates student-initiated efforts to secure further

task information.

5. Keeping records

Indicates student-initiated efforts to record

discussions.

6. Environmental structuring

Indicates student-initiated efforts to arrange the

physical setting to make learning easier.

7. Self-consequences

Indicates student arrangement or imagination of

rewards or punishment for success or failure.

8. Rehearsing and memorizing

Indicates student-initiated efforts to memorize the

material.

9. Seeking assistance from teachers

Indicates student-initiated efforts to solicit help.

10. Seeking assistance from peers

11. Seeking assistance from experts

12. Reviewing notes

Indicates student-initiated efforts to reread notes,

tests, or textbooks.

13. Reviewing tests

14. Reviewing textbooks

Source. Adapted from Zimmerman and Pons (1986).

Previous research on the topic, such as that of Lima Filho et al. (2015), show that self-

evaluation, environmental structuring and seeking assistance are the most used self-regulation

strategies. In a study with DE students, Silva et al. (2017) observed the significant use of self-

regulated learning strategies, especially self-evaluation, environmental structuring, and

reviewing notes, tests, and textbooks. Aguiar and Silva (2017, p. 20), in turn, investigated the

use of self-regulation strategies by Accounting undergraduates enrolled in in-person and DE

classes, pointing out that DE students “tend to use more intensely goal-setting and planning

strategies, while in-person students prefer rehearsing and memorizing.”

Due to the limitations found in these studies, we chose to expand our research sample

to include other locations and universities in other contexts, as well as other variables that may

explain the use of self-regulation strategies (Aguiar & Silva, 2017; Santos Junior & Monteiro,

2020; Silva et al., 2016).

From this perspective, motivation, seen as the essence of the teaching-learning process

(Maehr & Meyer, 1997), emerges as a variable that implies the use of learning strategies,

referring to how much a student gets involved with some activity (Engelmann, 2010). Thus,

“motivation for learning and the ability to regulate are expected characteristics of a self-

regulating student so that they are able to self-monitor and self-manage their learning” (Jones

et al., 2010, as cited in Aguiar et al., 2014, p. 6). Teodorescu et al. (2022) expand on it, noting

that the social distancing imposed by the pandemic highlighted a major challenge—the

students’ loss of motivation.

Another relevant point, especially during the ERE, which can impact the use of self-

regulated learning are the characteristics and conditions to which students are subjected (Silva

et al., 2020), since the students’ well-being and quality of life may be related to the teaching-

learning process (Cerchiari, 2004). A study conducted with university students from Ukraine

and Israel showed that concern for their family’s health status and difficulty with learning tasks

and online learning were the main difficulties faced during the pandemic (Schiff et al., 2021).

Quality of life, according to Tarbone et al. (2018, p. 206), is a broad and complex

concept that “interrelates the environment with physical, psychological aspects, level of

independence, social relations, and personal beliefs.” As such, low quality of life may be

associated with students losing motivation, performing activities simply to meet deadlines, or

even dropping out.

Methodological procedures

As a quantitative study, this research used statistical techniques to analyze the

correlations between the established variables (Raupp & Beuren, 2003; Richardson, 2012). As

an applied study, its interest lies in the use and practical consequences of knowledge (Gil,

2002). As a descriptive research, it seeks to describe “characteristics of a given population or

phenomenon or to establish relations between variables” (Gil, 2002, p. 44), since it analyzes

the perception of Accounting students regarding the use of self-regulated strategies, quality of

life and motivation, as well as the correlation between these factors.

Study sample consisted of Accounting students from two small private universities, in

the municipality of Maringá (PR), and two state public universities, located in Paranavaí (PR)

and Campo Mourão (PR). These universities have students who are enrolled in in-person

education and, at the time of data collection—between July and August 2020—were

performing their activities in the ERE modality. Since the private and public universities had

about 80 and 620 students enrolled in the Accounting program, respectively, totaling a student

population of approximately 700 students, we calculated a sample of at least 183 students for

the sample to be significant, with sampling error of 5% and confidence level of 95%.

Data were collected by a closed-ended questionnaire with answers on an 11-point

interval scale (0 to 10). This type of question “facilitates participant interpretation since, in

general, people are familiar with this reference” (Santos Junior & Costa, 2014, p. 7). We

applied the questionnaire to Accounting undergraduates to identify their perceived level of

motivation to study during the ERE period. The questionnaire comprised 28 questions: 11 on

self-regulated learning, based on the studies by Zimmerman and Pons (1986) and Lima Filho

et al. (2015); eight on quality of life, based on the study by Tarbone et al. (2018); and nine on

sociodemographic characteristics. After elaboration, the questionnaire was either sent directly

to the students by email (one of the public universities) or forwarded by the course coordinator

(both private universities and one of the public universities) via the Google Forms platform.

We obtained a total of 217 valid answers. Importantly, prior to questionnaire application, we

conducted a pre-test with eight individuals (students and teachers in the field), to verify

adequacy and consistency.

Data were analyzed by descriptive statistics and correlations using Excel and IBM-

SPSS software.

Analysis and discussion of results

Regarding gender, 59.4% of the undergraduates were women and 40.6% men. Age

ranged from 17 to 41 years old, and 54% were up to 27 years old.

As for professional occupation, 22.2% declared they were not currently working or

interning. Of the remaining 77.8% who work or do internships, 18% work up to 30 h/week,

48% up to 44 h/week, and the rest declared they work more than 44 h/week, are self-employed

and/or have their own business.

Regarding the devices used to follow remote education, 44% of the students use both

mobile phone and computer, 23% use only mobile phone and 32% only computer, and the

remaining participants use tablets. Importantly, some students reported using their work

computer, while others use only their cell phones because they don’t have a computer.

Educators use platforms such as Moodle and Google Classroom to upload

asynchronous activities and classes, using YouTube and/or Google Drive to make access links

to recorded video lessons available. Synchronous classes are held via platforms such as Zoom,

Google Meet, and Microsoft Teams.

We note that both teachers and students are adapting to the virtual reality, trying to

optimize teaching and learning within their limitations. Moreover, motivation plays a key role

in this process. Figure 1 shows the students’ level of motivation to study during the ERE

imposed by the pandemic.

Figure 1

Level of motivation to study during remote learning

Note. 0 = unmotivated; 10 = very motivated.

Most respondents (68.2%) attributed a value of up to 5 to their level of motivation, with

a mean of 3.97. Such a result is worrisome since values well below the midpoint (5) point to

students who feel little motivated to complete activities and attend classes during this period.

This result corroborates study by Teodorescu et al. (2022), who investigated students at a

Romanian university, pointing out that half of the students could not stay motivated. Thus, one

can see that the loss of motivation is not restricted to the national context.

As discussed in the theoretical background, motivation may be associated with

students’ quality of life (Cerchiari, 2004; Tarbone et al., 2018). According to Figure 2, factors

with the lowest means are related to “satisfaction with ability to study” (4.5), “concentration

on activities to do” (4.9), and “satisfaction with sleep” (6.0). The latter and “ability to study”

corroborate a study by Tarbone et al. (2018).

Figure 2

Mean self-evaluation of students’ quality of life.

Note. 0 = unmotivated; 10 = very motivated.

Another interesting factor regarding quality of life is the frequency with which students

experience “negative feelings, such as: bad mood, despair, anxiety, and/or depression” (mean

of 6.8). About 63% of the respondents attributed a value between 7 and 10 to this aspect. Such

a finding is relevant and indicates that HEIs should provide pedagogical support not only to

fulfill the programmatic content of the disciplines, but also to monitor students’ psychological

issues. When analyzing these results on motivation and quality of life, one should also

remember that students had their routine affected as a whole (personal, professional, and

academic), and the overload of academic activities—which began to be developed in an

environment other than the classroom—can result in low concentration and make students feel

unable to meet academic demands, besides contributing to the emergence of negative feelings.

Given the new methodologies adopted during ERE, we should examine whether

students have also changed their way of studying. When asked if they changed their learning

strategies during ERL, 76% answered yes, 15% said they changed strategies for at least one

discipline, and only 8.8% did not change their way of studying.

We also sought to analyze whether students are adopting self-regulated learning

strategies. We subdivided these strategies into 11 questions (Figure 3), based on research by

Lima Filho et al. (2015), addressing the 14 self-regulated strategies proposed by Zimmerman

and Pons (1986) (Table 2).

Table 2

Statements on self-regulated learning strategies

Statements

Self-regulated learning strategies

1. After finishing an academic paper, I check my work

to make sure I did it right.

1. Self-evaluation

2. I make an outline before starting an activity.

2. Organizing and transforming

3. If I have an exam, I start studying as soon as possible

so I can pace myself on the day.

3. Goal-setting and planning

4. Before starting on a paper, I go the library and/or use

other means of research, whether physical or digital.

4. Seeking information

5. I take as many notes as possible on the contents

studied.

5. Keeping records

6. To better concentrate to my studies, I look for

suitable environments.

6. Environmental structuring

7a. If I do well on a test, I treat myself to a reward.

7b. If I fail a test, I give up something.

7. Self-consequences

8. I use strategies (memorizing points, formulas) to

improve my learning on subjects to be studied.

8. Rehearsing and memorizing

9. If I have problems with an assignment, I seek

external help (teacher, peers, others).

9. Seeking assistance from teachers

10. Seeking assistance from peers

11. Seeking assistance from experts

10. I evaluate my performance, note what I need to

improve, and try to overcome any difficulties detected.

12. Reviewing notes

13. Reviewing tests

14. Reviewing textbooks

Source: Adapted from Lima Filho et al. (2015).

Results show that “self-evaluation” (S1) was the most used strategy, followed by

“seeking external help” (S9/10/11) and “environmental structuring” (S6), corroborating the

study by Lima Filho et al. (2015). Moreover, “environmental structuring” has been widely used

by students, suggesting that most respondents have an adequate physical environment.

Figure 3

Self-regulated learning strategies used by students

Note: 0 = never; 10 = always. S = Strategy

In turn, “self-consequences” (S7a/b), “goal-setting and planning” (S3), and “organizing

and transforming” (S2) were the least used strategies. Such result complements Aguiar and

Silva’s (2017) research, which found that DE students tend to use planning strategies with

greater intensity, while in-person students do not prepare in advance for tests/exams. In our

study with students in ERE, which resembles DE, these perspectives remained unchanged.

Overall, we note a still moderate use of self-regulated learning strategies, with students

attributing a, average adoption of 5.6. We found no significant differences between public and

private universities regarding the most used strategies and average use.

After the descriptive analysis of each study variable, we sought to analyze how they

were correlated. Due to the non-normal statistical distribution of the data, we performed

Spearman’s correlation (Table 3).

Table 3

Correlation between motivation and quality of life

Motivation

Overall quality of life

.508**

Satisfaction with one’s health

.403**

Concentration on activities to do

.742**

Health of the physical environment where one lives

.352**

Enough money to meet one’s needs (in general)

.315**

Sleep satisfaction

.323**

Satisfaction with ability to study

.795**

Presence of negative feelings

.286**

Note: *Correlation is significant at level 0.05; **correlation is significant at level 0.01.

As can be observed, student motivation shows a statistically significant correlation with

all quality-of-life factors. In other words, the higher the quality of life, the more motivated the

student is to learn during the period, and the less negative feelings they experience.

But considering that only values equal or above 0.5 indicate a strong correlation

(Pereira, 2014), motivation is strongly and positively correlated with “ability to study” (by

79.5%), “concentration on activities to do” (74.2%), and “overall quality of life” (50.8%).

We also analyzed the correlation between motivation and use of self-regulated learning

strategies (Table 4).

Table 4

Correlação entre motivação e estratégias de aprendizagem autorregulada

Self-regulated learning strategies

Motivation

E12/13/14

.491**

.515**

.321**

.306**

.347**

.302**

E7a (positive)

.203**

E7b (negative)

.163*

.433**

E9/10/11

.241**

.298**

Note: *Correlation is significant at level 0.05; **correlation is significant at level 0.01.

As can be observed, all correlations were statistically significant and positive. Although

most correlations were low, we found a strong correlation between motivation and self-

regulated learning strategies S3 (Goal-setting and planning, 51.5%) and S12/13/14 (Reviewing

notes; Reviewing tests; Reviewing textbooks, 49.1%). This finding concurs with the study by

Perassinoto et al. (2013), who argued that motivation can contribute to the adoption of learning

strategies.

Finally, we analyzed the correlation between use of self-regulated learning strategies

and quality-of-life factors (Table 5).

Table 5

Correlação estratégias de aprendizagem autorregulada e qualidade de vida

Strategy/QoL

E12/

13/14

E7a

positive

E7b

negative

E9/

10/11

Overall quality of life

.321**

.310**

.271**

.282**

.288**

.377**

.181**

No sig.

.359**

.226**

.241**

Satisfaction with

one’s health

No sig.

.253**

.265**

.217**

.257**

.224**

.155*

No sig.

.271**

.147*

.202**

Concentration on

activities to do

.436**

.457**

.392**

.294**

.366**

.393**

.196**

.137*

.428**

.315**

.345**

Health of the physical

environment where

one lives

.218**

.220**

.204**

.160*

.230**

.322**

.212**

No sig.

.324**

.294**

.268**

Enough money to

meet one’s needs (in

general)

.210**

.281**

.241**

.178**

.179**

.261**

No sig.

.237**

.170*

.253**

Sleep satisfaction

.230**

.159*

.291**

.206**

.225**

.220**

No sig.

.285**

.208**

.254**

Satisfaction with

ability to study

.510**

.488**

.419**

.301**

.399**

.356**

.202**

.159*

.463**

.242**

.342**

No sig.

-.183**

-.138*

No sig.

-.170*

No sig.

Note: *Correlation is significant at level 0.05; **correlation is significant at level 0.01.

Quality of life factors such as “Concentration on activities to do” and “Satisfaction with

ability to study” are significantly correlated with all self-regulated learning strategies.

“Satisfaction with ability to study” and strategies S3 (goal-setting and planning) and

S12/13/14 (reviewing notes; reviewing tests; reviewing textbooks) were strongly correlated.

Thus, the more students feel satisfied and able to study, the more they evaluate the activities

done to optimize their performance and plan their studies. In other words, they feel more

empowered, learn more by reviewing the material and stop studying “at the last minute.”

Conversely, “self-consequences” (S7a/b) showed a low degree of correlation (and even

significance) with every quality-of-life factor. All other self-regulated learning strategies

presented some significant correlations, weakly or moderately, with quality of life.

According to these results, student’s quality of life can affect the use of self-regulated

strategies, given their degree of correlation. Thus, teaching and learning processes must

consider the physical environment available to students (to study and concentrate), and their

mental and physiological aspects, as Cerchiari (2004) and Santos Junior and Monteiro (2020)

point out.

Final considerations

During the remote education period imposed by the COVID-19 pandemic, educational

institutions, which operated in-person and unexpectedly had to adapt to a virtual reality, faced

a number of challenges. In this educational context, the need for student-initiated self-regulated

learning becomes more evident. Moreover, students’ motivation and quality of life interfere

with effective learning.

Given the above, this study sought to analyze how the use of self-regulated learning

strategies, the level of motivation in learning and quality of life correlated during the pandemic.

By applying questionnaires to accounting undergraduates, we observed that students are feeling

unmotivated and presenting relatively high levels of negative feelings (such as bad mood,

anxiety and/or depression), which is worrisome because besides interfering in the students’

learning process, they can cause academic dropout and damage to their mental health.

Regarding self-regulated learning strategies, we noted that students generally use them

moderately, with “self-evaluation” (S1) and seeking external help (S9/10/11) and “self-

consequences” (S7a/b) and “goal-setting and planning” (S3) being the most and least used,

respectively. This last strategy is considered important, especially in a period such as the

pandemic, which requires better organization since classes and asynchronous activities demand

a higher level of discipline on the part of students to be performed within the established

deadlines.

As for the statistical correlations, we found that student motivation is strongly correlated

with the following self-regulated learning strategies: “goal-setting and planning” (S3) (51.5%)

and “reviewing notes; reviewing tests; reviewing textbooks” (S12/13/14) (49.1%). In other

words, the higher the motivation, the more students tend to adopt such strategies.

In conclusion, the study variables are linked to each other, given that motivation is

strongly correlated with quality-of-life factors related to “ability” and “concentration” to study,

which, in turn, were the items most strongly correlated with the self-regulated learning

strategies, except for the S7a/b strategy (“self-consequence”). Self-regulated learning,

motivation, and quality of life are therefore correlated with each other, forming a cycle in which

the higher quality of life, the more motivated the student is and the more self-regulated learning

strategies they use—and vice versa.

Our results also point to the need for teachers to apply methodologies that motivate

students to learn, in order to inhibit negative factors such as anxiety and bad mood. Anxiety,

for example, can worsen due to the overload of academic activities made available in virtual

environments, which can make students feel unable to solve what has been proposed. Educators

should therefore be cautious when proposing some types of classes and activities, since the

different physical and mental states of each student, as discussed, imply in effective learning

processes or not.

Finally, further discussions on Accounting Education are needed to optimize

educational processes in the pandemic and post-pandemic period, since the knowledge

obtained during tertiary education reflects on the student as a professional. Given the limited

number of students surveyed, future research should expand the study sample to investigate

whether the factors presented in other regions and universities differ. Another recommendation

is to analyze post-pandemic student motivation, and whether online didactic practices influence

motivation, as suggested by Teodorescu et al. (2022).

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