




































Humanitas: Indonesian Psychological Journal  

Vol. 20 (1), February 2023, 40-52 
ISSN:  2598-6368(online); 1693-7236(print)                                                                 

       humanitas@psy.uad.ac.id             http://journal1.uad.ac.id/index.php/Humanitas           https://doi.org/10.26555/humantias.v20i1.73  

Psychological distress: The role of self-regulated learning in online 

learning during the COVID-19 pandemic 

Honey Wahyuni Sugiharto Elgeka, Jatie K. Pujibudojo 

Faculty of Psychology, Universitas Surabaya, Indonesia 
Corresponding author: honeywahyuni@staff.ubaya.ac.id 
 

 

Introduction 

The COVID-19 pandemic has significantly impacted the health, economic, and education 
sectors. Schools and universities worldwide, including in Indonesia, have had to change their 
education and learning process from an in-class to an online system. Online learning is 
organized to comply with the government’s efforts to mitigate the spread of COVID-19 in 
Indonesia (Sadikin & Hamidah, 2020). In particular, changing university learning and 
instruction systems has required an extended adaptation period. From March 2020 to March 
2021, many educators and students still reported difficulties in self-adjustment due to 
technological factors or overwhelming workloads (Anthony, 2019; Atmojo et al., 2020; 
Irawati & Jonatan, 2020; Martin et al., 2019; Rasmitadila et al., 2020). 

The online learning situation has begun to cause psychological impacts on students, 
stemming from network connection problems, limitations in working on group projects, 
delivery of materials deemed not as clear as in-class instructions, changes in academic 
scheduling, and an overwhelming amount of coursework. These problems have caused 
psychological distress among students (Fauziyyah et al., 2021; Kartika, 2020) and might lead 
to mental health problems (Chen & Lucock, 2022; Holmes et al., 2020). Mental health is a 
state of wellbeing where individuals are aware of their abilities, work productively, and 
contribute to society when facing problems (Johan et al., 2022). The main factors that cause 
low levels of wellbeing among students are social isolation and distancing (Li et al., 2021), 

ART ICLE  INFO  

 

AB ST R ACT  

 

Article history 

Received May 10, 2022 

Revised December 16, 2022 

Accepted January 5, 2023 

 Online learning has become a solution for the world of education, 
including universities, during the COVID-19 pandemic. Every 
student behaves differently in addressing online learning in their lives. 
This research aimed to explore the role of self-regulated learning on 
psychological distress among university students in the online 
learning process during the COVID-19 pandemic. Four hundred 
sixteen students participated online survey and completed Depression 
Anxiety Stress Scale – 21 and the Online Self-Regulated 
Questionnaire. The correlation results show that self-regulated 
learning negatively correlates with depression, although the level of 
depression is mild to moderate. Besides that, students in the third and 
fourth years of study found that they had a higher score of depression 
on online learning than the first and second years of study. Therefore, 
the capacity to motivate and identify the direction of self-regulated 
learning will make students actively participate in online learning and 
could adapt to online learning during the COVID-19 pandemic. Thus, 
more self-regulate learning relates to lower depression among 
students. 

 

    

 
Keywords 

COVID-19; 

depression; 

online learning; 

self-regulated learning; 

social support. 

 

 

mailto:humanitas@psy.uad.ac.id
http://journal1.uad.ac.id/index.php/Humanitas
https://doi.org/10.26555/humantias.v20i1.73
mailto:honeywahyuni@staff.ubaya.ac.id


Humanitas: Indonesian Psychological Journal 41 

 

        Elgeka & Pujibujoyo (Psychological distress: The role of self-regulated learning…)   

wherein students feel unable to connect with friends and families, lose a sense of autonomy, 
experience doubt, and become more sensitive (Meo et al., 2020). 

A previous study found that 29% of students have depression, 70% have anxiety, and 
46% have stress during online learning (Maulana, 2021). Anxiety is a negative emotional 
state that emerges from intuition and somatic tension, indicated by increased heart rate (Ewell 
et al., 2022). Anxiety could adversely impact students’ learning outcomes, related to reduced 
capacity to concentrate on learning, decreased memory functions, and even reduced capacity 
to analyze problems (Hasanah et al., 2020).  

Stress is a relationship between an individual and the environment that an individual 
evaluates to exceed their resources (von Keyserlingk et al., 2022). When various problems 
emerge and are not handled promptly, this could cause cognitive disruptions and create 
tensions that could lead to stress (Biggs et al., 2017). Students face various forms of physical 
irritation in their online learning process, including disruption in sleep patterns, headaches, 
restlessness, irritability, and physical fatigue. All these symptoms emerge as a response to the 
stress faced by the students (Muslim, 2020; Wahyuni, 2018). 

If the various disruptions the students face are not handled promptly, another 
psychological problem could occur, i.e., depression. Depression is an invisible disease in 
which individuals do not realize they have a problem (Sulistyorini & Sabarisman, 2017). For 
some, depression is seen as a problem related to an individual’s faith, and not requiring 
professional help (i.e., from a psychologist or psychiatrist) causes 80% of depression cases 
not to get the right and adequate help (Babicka-Wirkus et al., 2021; Singh et al., 2020).  

During the COVID-19 pandemic, the depression levels faced by university students 
have increased compared to normal conditions (Hasanah et al., 2020). Many depression cases 
among students are not adequately identified because universities are not conducting the 
correct measures for depression for their students (Lopes & Nihei, 2021). A study found 
several reactions that may emerge when students face depression, including continually 
crying, skipping class, and self-isolating without knowing why they feel depressed (Kamble 
& Minchekar, 2018). Additionally, individuals facing depression tend to display feelings of 
sadness, failure, and worthlessness, as well as the tendency to retract themselves from others 
and their environment (Sulistyorini & Sabarisman, 2017). 

Online learning has been found to create an uncomfortable situation among students. 
Moreover, it also has the potential to trigger psychological distress due to the pressure related 
to academic performance and achievement. Several external factors could trigger stress in 
student learning, including the pressure to achieve, non-interactive teaching methods, and an 
overwhelming amount of homework and assignments (Qalbu, 2018). In addition to these 
external factors, internal factors could trigger stress in learning, including self-efficacy, 
hardiness, optimism, achievement motivation, procrastination, and personality types (Sutjiato 
et al., 2015; Yusuf & Yusuf, 2020). Several studies suggested that students’ success in the 
education process is determined, such as self-regulated learning (Fasikhah & Fatimah, 2013; 
Kristiyani, 2016; Latipah, 2010; Sutikno, 2016). Self-regulated learning is the main 
determining factor for success in online learning, wherein independence is the main trait 
demanded by online learning (Barnard et al., 2009). Furthermore, previous studies also found 
that self-regulated learning could affect students’ emotional states, which could help them 
find solutions for their academic problems in their effort to improve academic performance 
(Latipah, 2010; Qalbu, 2018). 

Self-regulated learning uses metacognition, motivation, emotions, and behavior in the 
learning process (Panadero, 2017). Students still strive to perform and achieve optimal results 
during online learning, which could be influenced by self-regulated learning to achieve their 
life goals despite continually adapting to the online learning process. For students, self-
regulated learning may inculcate the capacity to determine their directions for learning, the 
ability to recognize interests and talents according to the learning materials, the skills to make 



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Elgeka & Pujibudojo (Psychological distress: The role of self-regulated learnung…) 

learning more interesting and fun, as well as the capacity to face uncertain, scary, and 
confusing, or disappointing situations (Nadhif & Rohmatika, 2020).  

In various learning contexts (e.g., offline, online, or blended), self-regulated learning 
among students is needed as a proactive process in the ever-changing learning environment 
(Zimmerman, 2008). When students demonstrate low levels of self-regulated learning, failure 
in completing assignments in online learning may occur frequently. Nevertheless, students' 
ability in regulated learning will have positive impacts and has high performance in learning 
(Nadhif & Rohmatika, 2020). However, psychological distress could emerge when the 
assignment burden is high, and the students fail to complete their tasks. Psychological distress 
issues mostly happen from personal and environmental pressures in online learning and could 
affect students' wellbeing, which may worsen procrastination and make students lose their 
focus on learning (Li et al., 2021; Shostak et al., 2021). Therefore, this study aimed to explain 
the role of self-regulated learning when psychological distress occurs among university 
students in their online learning process during the COVID-19 pandemic, as different results 
are found from previous studies in different countries (Shostak et al., 2021). So, the study 
hypothesizes that there is a negative relationship between self-regulated learning and 
psychological distress. Besides that, this study also analyzes the different classes of 
psychological distress, whereas several students had experienced in-class learning and 
changed to the online system. 

Method 

Research Design 

This quantitative correlational study was conducted using the survey technique for collecting 
data. Two variables were observed in the study, i.e., self-regulated learning as the independent 
variable and psychological distress as the dependent variable, which consists of anxiety, 
stress, and depression. 

Participants 

The study involved 416 participants (138 males and 278 females), with ages ranging from 17 
to 21 years (M=19.90, SD=.99). The determination of the total participants was obtained by 
Slovin’s sample formula (α error probability = .05) with minimum participants 385 people. 
The research was conducted between June and September 2021 on active students of 
Universitas Surabaya from seven faculties (i.e., Pharmacy, Law, Business and Economics, 
Psychology, Engineering, Biotechnology, Creative Industry, and Medicine). The sampling 
technique utilized was nonrandom sampling – accidental sampling, i.e., each student who met 
the research criteria could participate in the study. All participants completed informed 
consent forms and expressed willingness to participate in this study. All data were collected 
through online questionnaires (Google Forms) distributed to undergraduate students. 

Table 1 shows that most of the students in this study came from business and 

economics, law, and psychology faculties, mainly from the 2018 to 2020 cohorts. 

Additionally, most students (92%) reported stress in online learning for 0 - 7 months after 

being engaged in online learning. 50.7% of the participants are still motivated by online 

learning, mainly from the desire to reach one’s dreams and the desire for achievement. For 

most of the students, their strategies to cope with stress included resting or doing something 

that they enjoyed. 
 
 
 
 
 



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        Elgeka & Pujibujoyo (Psychological distress: The role of self-regulated learning…)   

Table 1 

Demographic Data of Participants 

Characteristics  N % 

Sex Male 138 33.17 

 Female 278 66.83 

Age 17 1 .2 

 18 36 8.7 

 19 110 26.4 

 20 124 29.8 

 21 145 34.9 

Faculty Pharmacy 36 8.7 

 Law 82 19.7 

 Business and Economics 92 22.1 

 Psychology 80 19.2 

 Engineering 50 12.0 

 Biotechnology 35 8.4 

 Creative Industry 34 8.2 

 Medicine 7 1.7 

Year of study 1st year 119 28.6 

 2nd year 128 30.8 

 3rd year 143 34.4 

 4th year 26 6.3 

Experiencing 

stress in online 

learning 

Yes 386 92.8 

No 30 7.2 

Duration of 

students’ stress 

while engaging 

in online 

learning 

0-3 months 114 27.4 

4-7 months 158 38.0 

8-11 months 69 16.6 

12-15 months 45 10.8 

Not experiencing stress 30 7.2 

Desire to learn 

while in online 

learning 

Yes 211 50.7 

No 41 9.9 

Maybe 164 39.4 

Reasons 

motivating 

students to 

learn online 

(may choose 

more than one) 

Desire to reach one’s dream 313 39.08 

Desire for achievement 236 29.46 

Demands from family 101 12.61 

Formality 82 10.24 

Unknown 8 1.00 

Other 61 7.61 

Stress coping 

strategies (may 

choose more 

than one) 

Doing something that one enjoys (playing games, 

watching film/ drama, sports) 
492 36.36 

Rest (sleep, leaving the tasks/ learning for a while, 

engaging in social media) 
597 44.12 

“Me” time (eating, chatting with friends or families, 

taking care of one’s own needs) 
220 16.26 

Giving up (leaving the tasks without completing them, 

copying from friends) 
39 2.89 

Expressing emotions (crying, contemplating) 5 .37 

 

Instruments 

The first instrument used in this study was the Depression Anxiety Stress Scale – 21 

items (DASS-21) adapted in a previous study (Muttaqin & Ripa, 2021). This instrument 

measures levels of anxiety, stress, and depression in individuals within one week before 

completing the survey. The scale consists of 21 items measured with a four-point Likert 

Scale (1 = has not occurred to me; 4 = very often occurs to me). An item example included: 



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“I feel difficulty in relaxing.” The discriminating index of items ranges from .435 – .779, 

with alpha Cronbach = .923. DASS-21 is a good psychometric scale for measuring 

depression, anxiety, and stress, whereas each subscale has a specific meaning than others. 

Depression measures the situation, whereas individual experiences self-esteem loss and 

inadequacy to achieve the goal. Anxiety measures the response of individuals when facing 

a situation that makes them feel anxious. Stress measures frustration when an individual 

keeps under tension tolerance conditions (Muttaqin & Ripa, 2021). The score of every 

subscale was calculated by summing up the scores of all items in each subscale. 

The second instrument used to measure the self-regulated learning variable was the 

Online Self-Regulated Questionnaire (OSLQ) developed by a previous researcher (Barnard 

et al., 2009). OSQL was developed for online or blended learning, which has different 

characteristics from online learning. This instrument consisted of six aspects and 24 items, 

using a five-point Likert scale (1=strongly disagree; 5 = strongly agree). An item example 

included: “I set clear goals to help me arrange my learning time during online learning.” The 

discriminating index of items ranges from .310 – .726 with alpha Cronbach = .876. 

Data Analysis 

Before conducting hypothesis testing, the authors conducted tests of assumption, i.e., 

normality and linearity. This study employed Spearman nonparametric correlation tests for 

analyzing the data, using the SPSS application version 21.0 for Windows, as the data were 

not distributed normally. Chi-Square was conducted to explore the difference of each 

subscale based on the study cohort. 

Results 

Table 2 shows the category of each variable. It can be seen all variables are in the moderate 
category, with slightly different trends. 
 

Table 2  
Frequency of Each Variable 

Variable Category N % Variable Category N % 

Self-

regulated 

learning 

Very high 27 6.5 Stress Extremely severe 26 6.3 

High 98 23.6 Severe 118 28.4 

Moderate 182 43.8 Moderate 154 37.0 

Low  74 17.8 Mild  78 18.8 

Very low 35 8.4 Normal 40 9.6 

Anxiety Extremely severe 35 8.4 Depression Extremely severe 46 11.1 

Severe 96 23.1 Severe 78 18.8 

Moderate 137 32.9 Moderate 128 30.8 

Mild  118 28.4 Mild  149 35.8 

Normal 30 7.2 Normal 15 3.6 

 

The average value of students’ self-regulated learning was in the moderate range 
(43.8%), leaning towards high (23.6%). While students’ anxiety level was in the moderate 
range (32.9%), leaning towards mild (28.4%). Moreover, students’ stress level was in the 
moderate range (37.0%), leaning towards severe (28.4%). In comparison, depression was in 
the mild range (35.8%), leaning towards moderate (30.8%). 

The normality test conducted using the Kolmogorov-Smirnov test indicated that all 
variables were not normally distributed (p < .05). The linearity test using curve fit estimates 
yielded p < .05, explaining that both variables have a linear relationship. However, since the 
normality test was not met, a nonparametric – Spearman rank correlation was conducted to 



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        Elgeka & Pujibujoyo (Psychological distress: The role of self-regulated learning…)   

test the hypothesis. The result showed that self-regulating learning only correlated with 
depression (rho = -.30, p < 0.05). In comparison, self-regulating learning was not correlated 
with the other two dimensions (see Table 3). 
 
Table 3 
The Results of Spearman Rank Order Correlation 

Variable Self-

regulated 

learning 

Anxiety Stress Depression 

Self-regulated learning     

Anxiety        .01    

Stress       -.05 .72**   

Depression  -.30** .56** .68**  
**p < .01 

 

Table 4 shows the chi-square analysis on each subscale of psychological distress. It 

can be seen only depression has differences (χ2 = 8.75, p < .05), in which the third-year 

students have the highest score of depression than others (M = 228.86). 

 

Table 4 

The Results of Chi-Square 

Characteristics N Mean χ2 p 

Anxiety Year of 

study 

1st year 119 216.06 1.41 .704 

2nd year 128 205.78 

3rd year 143 208.63 

4th year 26 186.58 

Stress Year of 

study 

1st year 119 195.35 4.73 .193 

2nd year 128 207.78 

3rd year 143 224.10 

4th year 26 186.44 

Depression Year of 

study 

1st year 119 186.04 8.75* .033 

2nd year 128 204.04 

3rd year 143 228.86 

4th year 26 221.29 
*p < .05 

Discussion 

The results of this study indicated that self-regulated learning is negatively correlated with 
depression, which means the hypothesis was accepted. Self-regulation learning which is 
students’ capacity to use metacognition, have motivation, and actively participate in the 
learning process (Panadero, 2017), could lead to lower levels of depression. At the same time, 
the result shows that self-regulated learning among students is at a moderate to high level. At 
the same time, depression is at a mild to moderate level. Several studies in various countries 
have found that the prevalence of depression in college students continues to increase (Eller 
et al., 2006; Ibrahim et al., 2012; Reavley & Jorm, 2010), particularly during the COVID-19 
pandemic (Hasanah et al., 2020; Lopes & Nihei, 2021; Sahu, 2020; Sifat, 2021). Students 
with a high level of self-regulated learning tend to demonstrate better performance in learning 
and can better adapt to various changes in learning (Barnard-Brak et al., 2010; Biwer et al., 
2021; Broadbent & Fuller-Tyszkiewicz, 2018; Dörrenbächer & Perels, 2016; Kitsantas et al., 
2008).  



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The adaptation of students supports the assertion that self-regulated learning is one of 
the key predictors of students’ success in learning (Fasikhah & Fatimah, 2013; Kristiyani, 
2016; Latipah, 2010; Sutikno, 2016), particularly in online learning that demands students’ 
autonomy (Barnard et al., 2009). Self-regulated learning allows students to identify their 
direction for learning, understand their talents and interests through the learning materials, 
make the learning more interesting, challenging, and fun, and avoid uncertainty, confusion, 
and disappointment (Latipah, 2010; Nadhif & Rohmatika, 2020). Students with low self-
regulated learning tend to face problems related to the pressure for achievement, lack of 
interactivity in instruction, increasing burden of coursework (Fawaz & Samaha, 2021; Qalbu, 
2018), low levels of support from friends and family (Fawaz & Samaha, 2021; van Harmelen 
et al., 2016), lack of self-efficacy, hardiness, optimism, achievement motivation, 
procrastination, and personality types (Sutjiato et al., 2015; Yusuf & Yusuf, 2020). All these 
problems make students experience anxiety and depression more easily (Islam et al., 2020). 

It is unavoidable that even though students demonstrated adequate levels of self-
regulated learning, most participants reported that they have experienced stress (92.1%) 
related to online learning, particularly in the first 0 to 7 months of online learning (65.4%). 
Besides that, the previous research found that mental health problems commonly happen at 
17-29 years old (Beiter et al., 2015; Fauziyyah et al., 2021; Romadhona et al., 2021), whereas 
participants in this research aged 19-21 years old. Each individual has self-regulated learning, 
which results in different responses to changes in learning and instructions; some students 
would have difficulty concentrating or exert great effort in the new learning environment self-
adjustment (Li et al., 2021; Schunk & Greene, 2018). As the foundation, the social cognitive 
framework asserted that individuals should interact adequately between personal, behavioral, 
and environmental factors to optimize self-regulated learning. However, in reality, students 
often feel socially isolated and distanced in online learning (Li et al., 2021), unable to connect 
with friends and family, and become more sensitive (Meo et al., 2020). A previous study 
established that students able to adapt to change and choose to learn independently by 
reducing interaction with classmates and instructors have higher self-regulated learning than 
their counterparts (Broadbent & Fuller-Tyszkiewicz, 2018). While online learning during the 
COVID-19 pandemic is not by choice, using self-regulated learning strategies has become a 
crucial matter to consider by all educational institutions (Barak et al., 2016; Broadbent & 
Fuller-Tyszkiewicz, 2018; Delen et al., 2014; Lin & Tsai, 2016) 

Data demographics showed a difference in depression in cohort years, especially in the 
third year of study (M = 228.86), which has the highest mean rank of depression. In 2018, 
students had face-to-face learning, which is teacher-centered learning (focusing exclusively 
on instructions and guidelines from teachers). However, in 2020 the condition changed. 
Online learning is student-centered, whereas tools can carry out student evaluations, and 
students can access information from various documents. Besides that, the quality of learning 
depends on the teachers' level of digital training and teaching style (Gherheș et al., 2021). In 
online learning, students might focus less and miss deadlines for different tasks (Nazarlou, 
2013). 

Students with solid and clear learning objectives, e.g., having a strong desire to reach 
their goals or dreams and desire for achievement (68.54%), tended to have greater resilience 
in online learning. Students with the skills and strategies for self-regulated learning could 
focus on reaching their academic performance (Barnard-Brak et al., 2010). However, 
problems would often occur throughout the online learning process. Network connectivity, 
limitations in completing group assignments, delivery of materials not as straightforward as 
in offline instructions, changes in academic calendar and scheduling, and an overwhelming 
amount of assignments may cause anxiety, stress, and depression for students (Fauziyyah et 
al., 2021; Kartika, 2020).  

Students employ various strategies to reduce their stress levels in online learning, 
including rest, doing things they enjoy, and engaging in “me time” activities. The students 



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        Elgeka & Pujibujoyo (Psychological distress: The role of self-regulated learning…)   

prefer to leave the tasks or activities related to learning and do hobbies and sports, giving 
them a chance for oneself to relax and communicate with friends or families. The strategy 
that focuses on problems is usually used by individuals when dealing with various events that 
cause stress and can be controlled (Basith et al., 2021). On the contrary, an individual might 
use emotion-focused coping to reduce the emotional distress associated with a stressful 
situation. In emotion-focused coping, an individual cannot control the situation that is the 
source of the stressor (Bakhtiar & Asriani, 2015).  

The limitation of this research is that researchers only analyze the impact of self-
regulated learning on psychological distress without using other antecedents to give a 
comprehensive perspective of students' psychological distress during online learning and the 
difficulties when adapting. Besides, the data demographic of students' coping strategies 
during the COVID-19 pandemic is not analyzed profoundly. Further research involves life 
satisfaction, general health, teaching technique, and coping strategies during the transition 
period between online learning to face-to-face learning that will impact college students' lives. 

Conclusion 

Online learning during the COVID-19 pandemic impact college students' lives, including 
psychological distress (depression, anxiety, and stress). Depression in a different year of study 
happened, especially for the third year, and mostly had mild to moderate depression. Students' 
capacity to be motivated and actively participate in the learning process impact their 
performance in learning and their ability to adapt to various changes in learning. Self-
regulated learning allows the student to identify the direction for learning, talents, and 
interests through the learning materials, making them easy to adapt to every circumstance. In 
turn, the higher the self-regulated learning, the lower depression among students, and vice 
versa. 

Acknowledgment 

The authors would like to thank the University of Surabaya for the internal grants obtained, 

all students of the University of Surabaya who have been willing to assist in data collection, 

and all the University of Surabaya lecturers who have been willing to distribute the 

questionnaire to their students.  

Declarations  

Author contribution. HWSE designed the research, collected and analyzed the data, and 
wrote the article. JKP conducted the data collection process. 
Funding statement. This research was funded by the Research and Community Service 
Institution (LPPM) of the University of Surabaya. 
Conflict of interest. The authors declare no conflict of interest. 
Additional information. No additional information is available for this paper. 
 

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