UNIVERSITY OF AMSTERDAM
FACULTY OF ECONOMICS AND BUSINESS
BSc. BUSINESS ADMINISTRATION
Procrastination, academic performance, self-regulation and online participation: Three moderating models for online education
By Nathaly Tapia
Author: NM Tapia Vaca Student number: 11952555
Thesis supervisor: Dr. Wendelien van Eerde Date: 06/2021v erpage
eStatement of Originality
This document is written by Nathaly Michelle Tapia Vaca who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
UvA Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Dedica tio n
I would like to dedicate this thesis to my aunt María Dolores Vaca who always believed in me.
Thank you for your love and support. I will always have you in my heart.
First of all, I would like to thank God for giving me the opportunity to study abroad, keeping my family safe and making my deepest dream come true. Second, I would like to thank my mom Susana Vaca, my dad Milton Tapia, and my sister Lucero Tapia for their unconditional love and support. I also want to thank my friends Cinthya Criollo and Daniel Santos for always being on my side and becoming my second family.
I want to express my gratitude to Gobierno de la República del Ecuador and Secretaría de Educación Superior, Ciencia y Tecnología (SENESCYT) for have allowed me to study in one of the best universities in the world while belonging to a low-income family. Without the monetary support offered for living expenses and education I would not have not been able to culminate my bachelor degree in Business Administration.
In addition, I wish to extend my special thanks to my dear University of Amsterdam and all its support and understanding during my academic journey. Studying in this university has been an honor.
Last but not least I would like to thank Universidad Central del Ecuador Modalidad en línea and Instituto Tecnológico Superior Japón for your support during data collection for the creation of this thesis.
The purpose of the present study had three aims. First, analyze if procrastination levels were different among four regions around the world, then investigate which region procrastinated the most. Second, corroborate findings from past studies about procrastination affecting academic performance, but during Covid-19 lockdown. Third, investigate if the three different moderators, time management, task strategies and online participation, could affect this relationship. A cross- sectional design conformed by 215 students from 39 nationalities was utilized to test the six hypotheses of this research. The first two hypotheses were rejected as the results showed that only South America and Asia were significantly different, being the latter the region with highest procrastination, followed by Western Europe and South America. Eastern Europe was not significant. The third hypothesis which predicted a negative relationship between procrastination and academic performance in e-learning environments was supported. In the same way, task strategies, time management and online participation significantly reduced the effect of procrastination in academic performance.
Key words: Procrastination, task strategies, time management, online participation, Covid-19.
VI Table of Contents
Cover page ... I Statement of Originality ... II Dedication ... III Acknowledgements ... IV Abstract ...V Table of Contents ... VI
SECTION 1: ... 1
Introduction ... 1
SECTION 2: ... 4
Literature Review ... 4
2.1 Geographical Region ... 4
2.2 Academic Procrastination & Academic performance in online environments ... 5
2.3 Relationship between Academic Procrastination and Academic Performance... 6
2.4. The moderating role of self-regulation in online environment ... 7
2.5 The moderating role of Online Participation ... 8
SECTION 3... 9
Methods ... 9
3.1 Design, Sample and Procedure ... 9
3.2 Variables ... 10
3.3 Analytical Plan ... 13
SECTION 4: ... 14
Results: ... 14
4.1. Hypothesis 1 – Krustkal Wallis One Way ANOVA ... 14
4.2. Hypothesis 2 - Linear regression (Categorical IV and Quantitative DP) ... 15
4.3 Hypothesis 3 - Linear regression (Quantitative IV and Quantitative DP) ... 16
4.4 Moderators ... 18
SECTION 5... 20
5. Discussion: ... 20
6. Implications ... 24
7. Limitations and future study ... 25
VII 8. Conclusions ... 26 9. Sources ... 28 10. Appendix ... 35
1 SECTION 1:
In light of the worldwide events of last year, online education has not only been considered as the “new normal” but also as the “next normal” (Bozkurt & Sharma, 2020; Drohan
& Seeling, 2020). The drastic worldwide transition from face-to-face education to online education forced by the pandemic outbreak of Coronavirus (Covid-19), ushered in a new era, the e-learning (Didenko et. al, 2021). Given the demands that online education posed in the last year, researchers have responded to this reality by considering internal factors, such as the tendency of some students to procrastinate, that could overshadow the benefits of online education. Online education has been around for decades, but it has never been as important as it is today. Therefore, it requires further research regarding students’ behavior towards this way of learning.
Previous studies have shown that e-learning can outperform traditional education, especially for university students who experiment with more autonomy and flexibility in time and place (Elfaki et. al, 2019). In other words, they can study anytime, anywhere. However, this freedom comes with more self-regulation skills, which not all the students possess. In fact, Keramidas (2012) revealed that students tend to procrastinate more in online classes than in face- to-face classes. Several studies point to the same causes such as lack of self-regulation skills (Balkis, & Duru, 2016) and class participation (Michinov 2011). The core problem is that these situations prompt university students to burnout (Balkis, 2013) and dropout intentions (Bäulke et.
2 Even though there is much literature about the negative relationship between procrastination and academic performance (Kim & Seo, 2015), few studies have focused on online environments. Therefore, there is a need to understand the complex situation in which students are currently going through. One of the most remarkable examples is online participation. Existing literature has explored online participation through discussion forums, chat rooms, number posts and messages (Michinov, 2011; Cheng & Chau, 2016) while in the last years more sophisticated communication channels have been used for online classes. Since students can choose to not show themselves or participate in class, it is still unknown to what extent these actions can affect their academic performance, especially during Coronavirus lockdown. In the same way, no literature was found about task strategies and time management alone as the majority of the studies ecompasses them in the self-control variable (Zhao et al., 2019).
Unlike other studies that focus on one specific place, this study will contrast developed and developing countries to provide a new perspective of online education in Covid times. This study will also analyze the moderation effects of task strategies, time management and online participation on the negative relationship between procrastination and academic performance.
Besides, this investigation will be developed with a sample from three universities in two countries and will contribute to the literature in the education field in two ways. First, it will corroborate if past literature findings still apply during Covid-19. Secondly, this report will include people from different countries and backgrounds around the world to have a broader perspective of procrastination across cultures, age, and genders. With this in mind, universities in different countries can have a broader perspective of how to develop new strategies to help students to reach their best outcome.
3 This study will examine two questions related to the topic of procrastination. First, which regions procrastinate the most?. Second, do task strategies, time management and online participation counter the negative effects of procrastination on academic performance of university students in an online environment?
Figure 1. Theoretical model hypothesis 1 and 2
Figure 2. Theoretical model hypothesis 3 and 4
4 Figure 3. Theoretical model hypothesis 5
Figure 4. Theoretical model hypothesis 6
2.1 Geographical Region
Procrastination is not a reserved issue to only one geographical place. There are studies about procrastination around the world that have found a significant level of procrastination in their samples, for example, from China, Kuwait, Germany, Switzerland (Hong et. al, 2020; El-
5 Sayed et. al, 2011; Schouwenburg et. al, 1995; Kim et. al, 2017). However, some think demographic factors such as country or a region of origin could change the perception of a person regarding work and management of time based on stereotypes. For example, Durante and Fiske (2017) found that poor countries are considered incompetent while rich countries are considered competent. In fact, Western European and Asian countries are considered to be more organized with their time and more hardworking compared to Eastern European and South American countries, which are stereotyped with lateness (Poppe & Linssen, 1999; Enesco, et. al, 2005; Hu, et. al, 2014; Svartdal et. al, 2016). Cross-cultural studies regarding procrastination in academic performance are rare (e.g., Klassen et al., 2010). Therefore, a contrast study between rich and poor countries will illustrate how stereotypes may differ from reality.
H1. There is a difference in the level of procrastination among the four regions (Western Europe, Eastern Europe, South America and Asia).
H2. South America procrastinates more than Western Europe, Eastern Europe and Asia
2.2 Academic Procrastination & Academic performance in online environments
Shraw et, al (2007) defined academic procrastination as “intentionally delaying or deferring work that must be completed” (p.12). One of the first studies in the field was brought to the literature by Ellis & Knaus (1979), who found that up to 95% of university students could be procrastinators. Since then, many other researchers have investigated how procrastination negatively affects long term learning (Schouwenburg, 1995), and academic performance (Kim &
Seo, 2015) in traditional face-to-face education. However, in online environments procrastination
6 seems to be even higher (Elvers et. al, 2003). Given that during face-to-face education students are forced to follow a schedule of regular classes, they learn the materials in a structured manner. In contrast, with online education they can wait until the last minute to start studying for an exam (Elvers, et. al, 2003). With the corona virus outbreak, it is necessary to investigate the effects of distance learning and procrastination on university students, which is the focus of this study.
2.3 Relationship between Academic Procrastination and Academic Performance
The meta-analysis by Kim and Seo (2015), which gathers 33 research papers, revealed two things. First, there is not a specific scale to measure academic performance. Prior studies measured it in many ways including self-reported GPA, examination score, midterm exam grade, assignment grades or final term grade. Second, there is a negative relationship between academic procrastination and academic performance. It means that students with high levels of procrastination tend to fail on achieving a passing grade, or at least show lower grades than their non-procrastinating classmates. This could be caused by the delay on doing homework or studying for exams that is reflected in poor academic outcomes. Kim and Seo (2015) also contradict previous research papers from 1989 to 2000 that found a weak relationship or even no significant relationship between procrastination and academic performance (Rotenstein, et. al 2009). Given the inconsistency between both studies the goal of this research is to find if this negative relationship persists in an online environment during Covid-19.
H3. There is a negative relationship between academic procrastination and academic performance in university students in online environments during Covid-19.
7 2.4. The moderating role of self-regulation in online environment
Self-regulation is defined as “self-regenerated thoughts, feelings, and actions that are planned and cyclically adapted to the attainment of personal goals” (Zimmerman, 2000 p. 14).
Self-regulation has been identified as one of the means to reduce procrastination (Rakes & Dunn, 2010) and predict positive academic achievement and sucess in online learning environments (Lynch & Dembo, 2004; Yukseltruk & Bulut, 2007; Cho & Kim, 2013; Wandler & Imbriale, 2017). In other words, students with high self-regulation skills are able to manage their responsibilities in a timely manner, which allows them to achieve good grades. In fact, during Covid-19 lockdown studies have recommended academic institutions to adopt self-regulation learning strategies in order to support remote learning (Carter et. al, 2020; Hong et al., 2021).
There are different models to evaluate self-regulation, but for this research the model by Zimmerman and Molan (2009) was used. This model explains self-regulation in three different phases; the forethought phase, the performance phase, and the self-regulation phase. Due to time and practical constraints, this research will only evaluate the performance phase since it focuses on strategies the student adopts to improve their performance through self-control and self- observation. This study will focus on self-control through the variables task strategies and time management since previous studies have found a strong correlation between procrastination and lack of self-control (Zhao et al., 2019; Van Eerde & Venus, 2018). Following this reasoning, it is proposed that these two variables moderate the negative effect of procrastination on academic performance. Task strategies are tactics used by students to have a clear understanding of tasks (Panadero, & Tapia, 2014), while time management refers to control of time, setting goals and priorities throught the use of mechanics such as lists (Lay & Schouwenburg, 1993).
8 H4: The negative relationship between academic procrastination and academic performance will be moderated by task strategies, such that this negative relationship will be weaker when time management is high.
H5. The negative relationship between academic procrastination and academic performance will be moderated by task strategies, such that this negative relationship will be weaker when task strategies are high.
2.5 The moderating role of Online Participation
In traditional and online environments participation has a vital function which is enable students to communicate with the teacher and peers to externalize ideas and ask questions (Fassinger, 1995;
Michinov, 2011). Indeed, both keep the same types of interaction : learner – instructor, learner - content and learner-learner (Moore, 1989; Hiltz & Goldman, 2004). However, there is a big difference between both types of learning; it is very difficult to encourage students to actively engage online participation (Moore & Kearsley, 1996). Given the flexibility of interaction through messages and forums, some students prefer to pass these activities or do them later. This leads to procrastination behavior (Michinov, 2011). In fact, Vonderwell & Zachariah, (2005) defines participation as being more than counting number of messages and positions, but more taking part in discussion and engaging in active learning. This is because past researchers have evaluated online participation through numbers instead of active participation (Hrastinski, 2008). However, the students who do take part in participating in class are also the ones who pass courses and
9 achieve high academic performance (Bento & Schuster, 2003). According to Petress (2006) this is because students retain course content better when they engage in class. In the same way, Michinov’s (2011) study found that online participation can also work as a moderator of procrastination in online education. However, since this was developed a decade ago and new technologies have been implemented to encourage online participation, this research will confirm his findings are the same during Covid-19 lockdown.
H6: The negative relationship between academic procrastination and academic performance will be weaker when university students participate in online classes
3.1 Design, Sample and Procedure
In order to test the hypotheses developed above, a cross-sectional design was employed. In an effort to gather data from different countries, the instrument adopted for data collection was a Qualtrics survey, which was delivered in two languages, English and Spanish. With this strategy, it was aimed to approach two groups of students. On the one hand, students from the faculty of Business Economics from the University of Amsterdam. On the other hand, students from the faculty of Philosophy and Education from 2 universities in Ecuador; Universidad Central del Ecuador online modality and Instituto Tecnológico Superior Japón. These students were reached by using convenience sampling. Each student provided their demographics and gave their personal
10 opinion regarding several topics. The survey took 15 minutes and was distributed by 3 thesis students from the University of Amsterdam.
A total of 344 university students voluntarily participated in the data collection. In an attempt to protect the validity of this study only 215 answers were included in the research based on two rules, complete and reliable answers. First, it was required to fulfil a participation rate of 100%, this is why 101 students that did not fully complete the survey were removed. Second, the survey included one question to check if the respondent was paying attention or answering every question randomly. This question said “please select never for this item”, 28 people that did not choose the correct option were excluded from the research.
The demographics of the data revealed that 39 nationalities participated in the study (See appendix), more than half represented by Ecuadorians and the other half by European and Asian students, among the great majority were women, accounting for 74% of the data. The average age of the surveyed people was 25 years old (SD=7.63) and on average, students were pursuing a bachelor degree and not working at the time the survey was taken.
Regions is a categorical variable that was created out of the surveys. People were asked to answer the question “where are you from ?”, 39 nationalities were identified and grouped into 4 regions; Western Europe, Eastern Europe, South America and Asia (See Appendix).
11 3.2.2 Procrastination
For the procrastination variable, Sirois et al. 's (2019) 9-items scale was measured by 5- points Likert scale where1=False and 5=True of me. In this scale Q5 and Q9 were reverse-coded.
Some examples of the questions included in the scale were “I am continually saying I’ll do it tomorrow.” and “I often find myself performing tasks that I had intended to do days before”. The scale showed sufficient reliability, Cronbach’s alpha 0.869
3.2.3 Academic performance
The method used was similar to Heneberry (1976) in which expected and final grade were compared. The first question stated “Think about one specific subject you struggled with in the last academic period/semester. In a range from 0 to 100 (1 to 10). What was your aimed grade for this subject when the period/semester started?”. The second question said “Think about one specific subject you struggled with in the last academic period/semester. In a range from 0 to 100 (1 to 10). What grade did you get in this subject at the end of the period/ semester?”. In this way, these questions tried to find the difference between expected grades and real outcomes and how they were affected by procrastination.
3.2.4 Time management
For the moderating role of time management, students responded to Won & Shirley (2018).
nine-item scale (α=0.917). It was measured by a 5-point Likert scale where 1=strongly disagree and 5= strongly agree. Example items were “I often set goals or make lists regarding what I need
12 to get done each day” and “I frequently use a planner, schedule or calendar to organize all my time commitments”.
3.2.5 Task Strategies
To measure the second moderator, task strategies, 4-items Barnard et. al (2009) scale was used, (α=0.811) through a 5-point Likert scale in which 1= False and 5= True of me. Example questions of some items were “I try to take more thorough notes for my online courses because notes are even more important for learning online than in a regular classroom” and “I read aloud instructional materials posted online to fight against distractions.”
3.2.6 Online participation
For the third and last moderator I decided to adapt the revised 5-point scale proposed by Fissinger (1995), and include features of the modern era. Some example of the questions modified were “ How often do you volunteer when you know the answer” transformed into “How of then do you answer questions when you know the answer either by turning on the microphone or writing in the chat” and “How often do you contribute without hesitation” into “How often do you ask questions or provide feedback to your classmates after they give a (individual or group) online presentation”. Modifying these questions was a practical decision given the lack of any other scale that evaluated online participation using current technology. This is because previous literature has evaluated online participation mostly in longitudinal and experimental studies. Besides, two new questions were included such as “How often do you keep your camera on during the online
13 session?” and “How often do you actively listen to online sessions from beginning to end?”. These types of questions intended to evaluate online participation in a way no previous studies have done.
This 7-item scale counts with a Cronbach alpha value = 0.837, an acceptable reliability score.
3.2.7 Control variables.
Based on theory and prior researchers, the variables that potentially could affect the dependent variable in this study were gender and level of education. Balkis and Erdinç (2017) pointed out that female students are more active and engaged in courses than male students. On another study, are more likely to procrastinate more than older people as shown in the study of (Özer, 2011). procrastinate less than undergraduate students.
3.3 Analytical Plan
The hypotheses were evaluated with three different methods. Since the first hypothesis included categorical variables, Krustkal Wallis One Way ANOVA and a post-hoc test was utilized to analyze if there was a significant difference in procrastination among the four regions. Second, to evaluate if the region of South America was the one that procrastinated the most was linear regression. Given that the variable region was categorical, it had to be transformed into dummy variables first, taking South America as reference group, to then proceed with linear regression. In the same way, the third hypothesis, which tested the negative relationship between procrastination and academic performance, was analyzed with linear regression. The third method used to test hypotheses 3, 4 and 5 was PROCESS macro of Hayes (2018) (model 1). These hypotheses wanted
14 to investigate how the relationship between procrastination and academic performance was affected by the three moderating variables task strategies, time management and online participation.
4.1. Hypothesis 1 – Krustkal Wallis One Way ANOVA
The descriptive statistics for the categorical variable region showed that out of the 215 surveyed people, 64 were Western Europeans, 20 were from Eastern Europe, 107 came from South America and 24 were Asians (See appendix). Even though the groups were independent and normally distributed, the parametric ANOVA could not be used given the inequality of sizes among groups. Therefore, the difference among the four regions was analyzed through the non- parametric test Kruskal Wallis One Way ANOVA. In table 1 the rejection of the null hypothesis is depicted, meaning that at least one group differs in the level of procrastination with the rest.
Table 2 illustrates the results from the post-hoc test. In this test a significant difference (p<0.01) in the level of procrastination between Asia and South America was found. Consequently, h1 was rejected since there was no significant difference in the level of procrastination among the four regions but only between two. The results are also illustrated in the appendix.
15 Table 1.
Kruskal Wallis One ANOVA test
Multiple comparisons post-hoc test
4.2. Hypothesis 2 - Linear regression (Categorical IV and Quantitative DP)
The four region categories became dummy variables, with South America as the reference group. The coefficients of the regression analysis are shown in table 3. The results did not show significant differences in the level of procrastination between Eastern Europe and South America (p> 0.05). Overall, the procrastination rates for both, Western Europe (0.348) and Asia (0.582), were higher than in South America. Also, the standardized beta for Asia (0.222) was higher than
Null Hypothesis Test Sig. Decision
1 The distribution of Procrastination Independent- 0.004 Reject the is the same across categories of "Region" Samples null
Asymptotic significance is displayed. The significance level is .05
Sample 1 - Sample 2 Test
Error Std. Test Statistic Sig Adj. Sig South America - Eastern
Europe 21.010 15.139 1.388 0.165 0.991
South America - Western
Europe 24.621 9.820 2.507 0.012 0.073
South America - Asia -44.806 14.036 -3.192 0.001 0.008
Eastern Europe- Western
Europe 3.611 15.920 0.227 0.821 1.000
Eastern Europe - Asia -23.796 18.815 -1.265 0.206 1.000
Western Europe - Asia -20.185 14.875 -1.357 0.175 1.000
NOTES. Each row tests the null hypothesis that the Sample 1 and Sample 2 distributions are the same.
Asymptotic significances (2-sided tests) are displayed. The significance level is 0.05. Significance values have been adjusted by Bonferroni correction for multiple corrections for multiple tests.
16 the beta for Western Europe (0.189), meaning that Asia had the strongest effect on procrastination.
In other words, South America was not the region that procrastinate the most, but the one that procrastinated the least. Asia was the region in which procrastination was the strongest, followed by Western Europe. Therefore, hypothesis two was rejected.
Linear Regression between Regions and Procrastination
1 (Constant) 2.916 .079 36.716 .000
West_Eu .348 .130 .189 2.681 .008
East_Eu .219 .205 .074 1.069 .286
Asia .582 .182 .222 3.189 .002
NOTES. Dependent Variable: Academic Procrastination.
The significance level is p-value <0.05
4.3 Hypothesis 3 - Linear regression (Quantitative IV and Quantitative DP)
Before performing the linear regression analysis between the independent variable procrastination and dependent variable academic performance, a brief description of the statistics and correlations was developed (Table 4). Contrary to past research (Özer, 2011; Balkis and Erdinç, 2017) the variables gender (r= -.02, p>0.05), and level of education (r=.03, p>0.05) showed weak and no significant correlations with procrastination. Therefore, these variables were not further included in the hypothesis testing. Procrastination appeared to be significant and negative correlated with all the moderators (p<.10). However, it was clear there was a higher
17 correlation between procrastination and time management (r=-.58), task strategies (r=-.45) and online participation (r= -.47) while keeping a weak and significant correlation with academic performance (r=-.15).
VARIABLES Mean SD 1 2 3 4 5 6 7
1. Gender 1.74 .44
2. Level of education 2.87 .43 -.01
3. Procrastination 3.11 .84 -.02 .03 (.869)
4. Acad. Performance 70.65 19.65 -.07 .03 -.15*
5. Time Management 3.48 .98 .162* -.03 -.58** .19** (.917)
6. Task Strategies 2.89 1.11 .196** -.08 -.45** .17* .57** (.811)
7. Online Participation 4.31 1.23 .13 -.05 -.47** .18** .51** .61** .837) NOTES. N= 215 students. Cronbach’s Alphas are in parentheses on the diagonal. 0=female, 1=male. Age was measured in years. Gender male =1, female =2.
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
Preceding the third hypothesis testing through linear regression, five preliminary analyses were conducted. These analyses tested for linearity; for independence, normality and homoscedasticity of residuals; and for outliers and multicollinearity. All assumptions were tested in SPSS (See appendix). The tests for assumptions showed a linear positive relation between academic procrastination and academic performance. Independence of residuals was irrelevant for this study given that a cross-sectional survey was developed. However, residuals did show to be approximately normally distributed and equally variable. Thus, being a positive sign for normality and homoscedasticity. Besides, 14 outliers were present in the data, however none of them were removed as they did not significantly change the relationship among the two variables (See appendix). Finally, the variance inflation factor for the independent and moderator variable was
18 close to 1. Hence, multicollinearity was discarded. After all requirements for linear regression were met, the first hypothesis testing was performed in order to find if procrastination negatively influenced students’ academic performance. The results found the relationship to be significant (p<0.05), as F (80778.91, 82642.24) = 4.91. Interestingly, only 23% of the variability in academic performance was explained by the independent variable alone. Therefore, hypothesis 1 was supported since the model showed to be significant (p-value=0.28<0.05; se=1.58; t=-2.22; 95% CI -6.621, -.388) (See appendix).
4.4.1 Hypotheses 4,5,6 – PROCESS macro Hayes (2018)
In order to test hypotheses 4, 5, and 6, PROCESS macro (model 1) of Hayes (2018) was utilized. This test aimed to find if time management, task strategies and online participation can positively affect the negative relationship between academic procrastination and academic performance. As displayed in Table 5, a significant and negative interaction between procrastination and time management was present (b= -4.06, se=1.51, t=-2.68; p=0.008<0.01;
95% CI -7.056, -1.0710) (See appendix), thus weakening the relationship between academic procrastination and academic performance. In other words, if students start managing their time better, it could mitigate the negative effect of procrastination on their academic performance. In the same way, Table 6 show how task strategy also significantly affected the relationship between procrastination and academic performance by making it weaker when task strategies were present (b= -4.01, se=1.33, t=-2.99; p=0.003<0.01; 95% CI -6.646, -1.375) (See appendix), meaning that
19 students who apply these strategies can improve their grades. In addition, online participation was also found to have a significant and negative interaction with procrastination (b= -3.36, se=1.02, t=-3.28; p=0.001<0.01; 95%; CI -5.379, -1.348) (See appendix) as Table 7 shows, meaning that students who actively participate in online classes can get better grades than the ones who do not.
Results for the interaction effect between Academic Procrastination and Time Management on Academic Performance
β se t p
Constant 11.37 22.38 0.51 >0.05
Academic Procrastination (X) 13.01 5.73 2.27 <0.05
Time Management (W) 17.50 5.63 3.10 <0.01
Interaction (X*W) -4.06 1.51 -2.68 <0.001
Notes: R square= 0.0697; p<0.001, p<0.05, p<0.01= significant
Results for the interaction effect between Academic Procrastination and Task Strategies on Academic Performance
β se t p
Constant 33.38 14.66 2.28 <0.05
Academic Procrastination (X) 9.05 4.10 2.21 <0.05
Task Strategies (W) 15.05 4.42 3.40 <0.001
Interaction (X*W) -4.01 1.34 -3.00 <0.001
Notes: R square= 0.0762; p<0.001, p<0.05, p<0.01= significant
20 Table 7.
Results for the interaction effect between Academic Procrastination and Online Participation on Academic Performance
β se t p
Constant 20.03 17.07 1.17 >0.05
Academic Procrastination (X) 12.17 4.65 2.62 <0.01
Online Participation (W) 13.04 3.51 3.72 <0.001
Interaction (X*W) -3.36 1.02 -3.28 <0.01
Notes: R square= 0.0841; p<0.001, p<0.05, p<0.01= significant
As mentioned in the literature review, procrastination is not representative of only one place. Past literature in the field convey findings from different countries and cultures (Hong et.
al, 2020; El-Sayed et. al, 2011; Schouwenburg et. al, 1995; Kim et. al, 2017). Besides, few cross- cultural studies have been developed in order to find the difference in procrastination among countries or regions. Therefore, the first hypothesis wanted to find out if there was a difference in the level of procrastination among the four regions; Western Europe, Eastern Europe, South America and Asia. The most obvious finding that emerged from the analysis was that only Asia and South America showed a significant difference while no difference was found among the other regions. In this way, rejecting the first hypothesis. The second hypothesis wanted to scrutinized if South America was the region that procrastinated the most. Through a linear regression between regions (independent variable) and procrastination (dependent variable), it appeared that Asia procrastinated more than Western Europe and South America. On the other hand, Eastern Europe did not show to have a significant effect on procrastination. Therefore, rejecting hypothesis two.
21 One of the possible explanations for these results is that English is not the first language in Asian countries, which raises difficulties to cope with academic challenges. (e.g., ability to manage academic workload, completing assignments on time, making oral presentations) (Lee, Farruggia, and Brown, 2013). According to Lowinger et al., (2014) English language ability is a predictor for procrastination behavior among Chinese international students. In their analysis, they also found other factors that can contribute Asian students to procrastinate in international environments such as cultural shock, homesickeness and percieved discrimination. In fact, the levels of acculturation in Asians students tend to be lower than South Americans or Europeans international students due to big differences in language and culture between their home and host countries (Fritz, Chin, &
DeMarinis, 2008), therefore, being more prompt to feel stressed, anxious and procrastinate more.
The reason why Western European countries procrastinate more than South American countries could be explained by the fact that Western and Eastern European students belong to the same geographical place, also known as the “eurozone”. Being part of the European Union means that students enjoy freedom of movement (Treaty of Maastricht, 1992) and can obtain university education for lower fees than international students (University of Amsterdam tuition fees, 2021), are allowed to legally work while studying (European Commission, n.d.), and with no visa restrictions they can extend their education for more years if needed. Therefore, reducing the pressure to finish their studies on time.
On the contrary, the South American region is composed of two groups. On the one hand, there are international students who pay a tuition fee that is four times higher than european students (University of Amsterdam tuition fees, 2021), are unable to freely work (European
22 Commission, n.d) and need a high budget to live in Amsterdam. This could raise intrinsic motivation to study acknowledging the effort parents make to provide this type of education for them (Zalaquett, 2006). On the other hand, this region is mostly composed of Ecuadorian students, who study in public universities. In Ecuador, university education is free but restricted through a national test (SENESCYT, 2021). In addition, Ecuador is a developing country with a high index of poverty and unemployment (INEC, 2021), in which a bachelor degree is needed to improve the opportunities to find a job (El Universo, 2018). Therefore, the difficulty to access university education and the economic factor encourage people to study hard and procrastinate less to enter the labor market and support their families.
The results also revealed that the third hypothesis, which defended that participants who reported procrastination were also the ones who received lower grades than what they expected, was correct. This result was consistent with past literature (Heneberry, 1976), which reviewed the effects of procrastination on academic performance through student’s self-report. However, one unanticipated finding was that either gender or level of education showed to be correlated with procrastination. This differs from the findings presented by (Özer, 2011; Balkis and Erdinç, 2017) who found male procrastinate more than females, and that students in lower university levels procrastinate more than higher university levels. There are several possible explanations for these results. One of the main reasons could be attributed to the fact that almost three quarters of the sample were women, therefore affecting the correlation between genders and procrastination. In the same way, the majority of the surveyed people were doing a bachelor degree. Hence, there was not enough information to see the difference between educational levels.
23 A second interesting outcome was the correlation between procrastination and the moderators; task strategies, time management and online participation. This was an interesting but not surprising finding. Previous studies have discovered significant and negative correlations among procrastination, self-control, time management and self-regulation strategies (Zhao et. al, 2019; Taura et. al, 2015). However, the only distinction with this study was the differentiation between two types of procrastination; active and passive. Since active procrastination enhances academic performance while passive procrastination produces worse academic outcomes (Kim, Fernandez, & Terrier, 2017), it could imply that the respondents were active procrastinators and they use it as another way of self-regulation (Chun et. al., 2015).
The fourth and fifth hypotheses were related to self-regulation. But, given the broadness of its meaning only one the second phase explained by (Zimmerman & Molan, 2009) was examined.
This phase was task performance that referred to self-control of the student and included nine variables from which only two were taken; time management and task strategies. The second hypothesis wanted to investigate if time management could affect the negative relationship between academic procrastination and academic performance. This hypothesis was supported, meaning that students who are able to administer their time through planers, calendars and lists can improve their academic achievement. In the same way, the third hypothesis, which tried to find if task strategies such as reading instructions out loud to avoid distraction and take notes during online classes could counteract the negative effect of procrastination on academic performance, showed to be significant. Different from previous research which only evaluated self-control as a moderator of procrastination (Zhao et al., 2019; Van Eerde & Venus, 2018) but never the effect of individual variables, these findings add to the literature in the education field to
24 encourage the creation of task strategies and the use of time management tools in university education.
The sixth and last assumption of this research sustained that online participation could moderate the negative relationship between academic procrastination and academic performance.
This hypothesis showed to also be significant. In this way, simple actions like turning on the camera and answering questions in the chat can significantly affect procrastination. The findings of this study further develop the importance highlighted by past research about class participation in traditional Fassinger (1995) and online environments Michinov (2011). However, even though the results show what was expected, it is important to recall that the method used for this variable was not validated. This is because none past research approached online participation with a scale but was rather mostly evaluated by counting messages and posts (Hrastinski, 2008) in longitudinal studies. Therefore, given the difference in type of research and the need to reflect the current reality of students during Covid-19 pandemic, it became the most practical option.
In order to reduce procrastination in online education, this study suggests universities understand the needs of different students and encourage strategies that will help them to overcome procrastination. For example, during the first year at the University of Amsterdam students can not choose their own schedule. Therefore, some students are randomly located in different groups.
However, given the difference in procrastination rates among regions these schedules should be made depending on the needs of students. Asian students should have priority for morning classes while Western Europeans and South American students could take later schedules. Another
25 application of the results from this study is to actively encourage the adoption of self-regulation strategies such as task strategies and time management that include prepare material before doing a task, prepare questions before a class, take notes during lectures and seminar sessions, manage their time with tools such as calendars, to-do lists, planers, etc. These actions can not be controlled by the teacher since it is the responsibility of the student. However, highlighting the benefits of these actions and providing templates to organize their time better could encourage them to start adopting these techniques. In the same way, teachers should encourage students to actively participate in online classes by giving comments, asking and answering questions. Since this is the only way in which students around the world connect with each other, it is very important to encourage interaction. In this way, reducing isolation and academic procrastination.
7. Limitations and future study
It is important to mention that this research suffered from the three following limitations.
First, since convenience sampling was used for data collection, respondents’ integrity, loyalty and willingness to answer truthfully could have exerted an effect in their answer (Azman, et al., 2011).
Random sampling is advised in order to rule out personal biases. Second, there was a big difference between students from Ecuador and students from the University of Amsterdam. This affected the analysis of data given the unevenness of groups. In the same way, there were more women who participated than men, which reduced the possibility to analyze the difference between genders. It is recommended to ask more men to participate in surveys since they were the ones with the lowest response rate. Third, procrastination evaluation did not differentiate between passive and active procrastinators, being the latter positively related to academic performance (Kim, Fernandez, &
26 Terrier, 2017). Future research should take this difference into account to explore if the type of procrastination affects the moderation effect of task strategies, time management and online participation on academic performance. Nevertheless, the results from this study are still useful for university authorities to identify priority groups and focus strategies to help them deal with procrastination. These findings also raise awareness about the type of procrastination as active procrastinators are conscious about their decision to delay tasks while passive tend to be unaware and suffer the negative consequences.
Covid-19 forced the world to adapt to a new reality in which education became online.
This brought many benefits such as flexibility in time and space, but also challenges like academic procrastination. It is true procrastination has always been accompanied university students affecting their academic performance, however, online education seems to be the perfect environment to enhance this negative behavior. Previous researches have focused on analyzing academic procrastination in one specific place, while students that come from different regions might show stronger tendencies to procrastinate and therefore need more attention or support from the educational institutions. Thus, this study wanted to address this issue by comparing procrastination rates among four regions; Western Europe; Eastern Europe, South America and Asia. The results showed that the biggest difference in procrastination rates was between South America and Asia, being the latter the region that procrastinates the most followed by Western Europe and South America. There are several reasons why Asian students show higher procrastination than others. For instance, they face more challenges such as a new language,
27 culture, and discrimination. On the other hand, Western European students procrastinate more than South Americans as they pay lower tuition fees, can work without restriction and can extend their education if they need. These benefits are not available to international South American students, which force them to study harder and finish their bachelor on time. Last but not least, the economic background of students in Ecuador motivates them to study to be able to work and help their families. The second part of the study related to the moderation effect of task strategies, time management and online participation in the negative relationship between procrastination and academic performance. The results indicated that the three moderators could counteract the negative effect of procrastination. However, given the moderators were correlated with the independent variable, it is believed that students were active procrastinators. Being an active procrastinator is actually considered a positive type of procrastination which helps them to organize their time and tasks in their own time. Since this study did no distinguish between the two types of procrastination, future research should investigate this phenomenon in order to better understand students’ needs.
28 9. Sources
Balkis, M. (2013). The relationship between academic procrastination and students' burnout. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 28(28-1).
Balkis, M., & Duru, E. (2016). Procrastination, self-regulation failure, academic life satisfaction, and affective well-being: underregulation or misregulation form. European Journal of Psychology of Education, 31(3), 439-459.
Balkis, M., & Erdinç, D. U. R. U. (2017). Gender differences in the relationship between academic procrastination, satifaction with academic life and academic performance. Electronic Journal of Research in Educational Psychology, 15(1), 105-125.
Bäulke, L., Eckerlein, N., & Dresel, M. (2018). Interrelations between motivational regulation, procrastination and college dropout intentions. Unterrichtswissenschaft, 46(4), 461-479 Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S. L. (2009). Measuring self-regulation
in online and blended learning environments. The internet and higher education, 12(1).
Bento, R., & Schuster, C. (2003). Participation: The online challenge. In Web-based education:
Learning from experience (pp. 156-164). IGI Global.
Bozkurt, A., & Sharma, R. C. (2020). Education in normal, new normal, and next normal:
Observations from the past, insights from the present and projections for the future. Asian Journal of Distance Education, 15(2), i-x.
Carter Jr, R. A., Rice, M., Yang, S., & Jackson, H. A. (2020). Self-regulated learning in online learning environments: strategies for remote learning. Information and Learning Sciences.
Cheng, G., & Chau, J. (2016). Exploring the relationships between learning styles, online
participation, learning achievement and course satisfaction: An empirical study of a blended learning course. British Journal of Educational Technology, 47(2), 257-278.
29 Cho, M. H., & Kim, B. J. (2013). Students' self-regulation for interaction with others in online
learning environments. The Internet and Higher Education, 17, 69-75.
Chun Chu, A. H., & Choi, J. N. (2005). Rethinking procrastination: Positive effects of" active"
behavior on attitudes and performance. The Journal of social psychology, 145(3), 245-264.
Didenko, I., Filatova, O., & Anisimova, L. (2021). Covid-19 Lockdown Challenges or New Era for Higher Education. Propósitos y Representaciones, 9(SPE1), 914.
Drohan, D., deLeastar, E., & Seeling, P. (2020, October). Online Education and the" New
Normal". In Proceedings of the 21st Annual Conference on Information Technology Education (pp. 301-301).
Durante, F., & Fiske, S. T. (2017). How social-class stereotypes maintain inequality. Current opinion in psychology, 18, 43-48.
Elfaki, N. K., Abdulraheem, I., & Abdulrahim, R. (2019). Impact of E-learning vs Traditional Ellis, A., & Knaus, W. J. (1979). Overcoming procrastination: or, how to think and act
rationally in spite of life's inevitable hassles. Signet Book.
El-Sayed, A., Mustafa, F., & Al-Mutawa, A. (2011, April). Procrastination in online learner-led courses. In Proceedings of the Second Kuwait Conference on e-Services and e-Systems.
El Universo (2018). 28% de los desempleados tienen estudios superiores en Ecuador
Elvers, G. C., Polzella, D. J., & Graetz, K. (2003). Procrastination in online courses: Performance and attitudinal differences. Teaching of Psychology, 30(2), 159-162.
Enesco, I., Navarro, A., Paradela, I., & Guerrero, S. (2005). Stereotypes and beliefs about
30 different ethnic groups in Spain. A study with Spanish and Latin American children living in Madrid. Journal of Applied Developmental Psychology, 26(6), 638-659.
European commission (n.d) “University fees and financial help”
European Union, Treaty on European Union (Consolidated Version), Treaty of Maastricht , 7 February 1992, Official Journal of the European Communities C 325/5; 24 December 2002, https://www.refworld.org/docid/3ae6b39218.html
Fassinger, P. A. (1995). Understanding classroom interaction: Students' and professors' contributions to students' silence. The Journal of Higher Education, 66(1), 82-96.
Fritz, M. V., Chin, D., & DeMarinis, V. (2008). Stressors, anxiety, acculturation and adjustment among international and North American students. International Journal of Intercultural Relations, 32(3), 244-259.
Hayes, A. F. (2018). The PROCESS macro for SPSS and SAS (version 3.0).
Henneberry, J. K. (1976). Initial progress rates as related to performance in a personalized system of instruction. Teaching of Psychology, 3(4), 178-181.
Hiltz, S. R., & Goldman, R. (Eds.). (2004). Learning together online: Research on asynchronous learning networks. Routledge.
Hong, J. C., Lee, Y. F., & Ye, J. H. (2021). Procrastination predicts online self-regulated
learning and online learning ineffectiveness during the coronavirus lockdown. Personality and individual differences, 174, 110673.
Hu, Q., Schaufeli, W., Taris, T., Hessen, D., Hakanen, J. J., Salanova, M., & Shimazu, A. (2014).
31 East is East and West is West and never the twain shall meet: Work engagement and workaholism across Eastern and Western cultures. Journal of Behavioral and Social Sciences, 1(1), 6-24.
Instituto Nacional de Estadística y Censos (Mayo, 2021). Encuesta Nacional de Empleo, Desempleo y Subempleo. https://www.ecuadorencifras.gob.ec/empleo-mayo-2021/
Klassen, R. M., Ang, R. P., Chong, W. H., Krawchuk, L. L., Huan, V. S., Wong, I. Y., & Yeo, L.
S. (2010). Academic procrastination in two settings: Motivation correlates, behavioral patterns, and negative impact of procrastination in Canada and Singapore. Applied Psychology, 59(3), 361-379.
Kazakova, J. K., & Shastina, E. M. (2019). The impact of socio-cultural differences on formation of intrinsic motivation: The case of local and foreign students. Learning and Motivation, 65, 1-9.
Keramidas, C. (2012). Are undergraduate students ready for online learning? A comparison of online and face-to-face sections of a course. Rural Special Education Quarterly, 31(4).
Knaus, W. J. (2000). Procrastination, blame, and change. Journal of Social Behavior and Personality, 15(5), 153.
Kim, K., & Seo, E. (2015). The relationship between procrastination and academic performance: A meta-analysis. Personality and Individual Differences, 82, 26-33.
Kim, S., Fernandez, S., & Terrier, L. (2017). Procrastination, personality traits, and academic performance: When active and passive procrastination tell a different story. Personality and Individual differences, 108, 154-157.
Lay, C. H., & Schouwenburg, H. C. (1993). Trait Procrastination, Time Management. Journal of social Behavior and personality, 8(4), 647-662.
32 Lee, B., Farruggia, S. P., & Brown, G. T. (2013). Academic difficulties encountered by East Asian
international university students in New Zealand. Higher Education Research &
Development, 32(6), 915-931.
Lowinger, R., He, Z., Lin, M., & Chang, M. (2014). The impact of academic self-efficacy, acculturation difficulties, and language abilities on procrastination behavior in Chinese international students. College Student Journal, 48(1), 141-152.
Lynch, R., & Dembo, M. (2004). The relationship between self-regulation and online learning in a blended learning context. The International Review of Research in Open and Distributed Learning, 5(2).
Michinov, N., Brunot, S., Le Bohec, O., Juhel, J., & Delaval, M. (2011). Procrastination,
participation, and performance in online learning environments. Computers &
Education, 56(1), 243-252.
Moore, M. (1989) Three types of interaction. American Journal of Distance Education.
Moore, M.G., & Kearsley, G. (1996). Distance education: A systems view. Belmont, CA:
Wadsworth Publishing. National Center for Education Statistics (NCES).
Özer, B. U. (2011). A cross sectional study on procrastination: who procrastinate more.
In International Conference on Education Research and Innovation (Vol. 18, pp. 34-37).
Panadero, E., & Tapia, J. (2014). How do students self-regulate?: review of Zimmerman’s cyclical model of self-regulated learning. Anales de psicologia.
Petress, K. (2006). An operational definition of class participation. College Student Journal, 40(4), 821-824.
Poppe, E., & Linssen, H. (1999). In‐group favouritism and the reflection of realistic dimensions
33 of difference between national states in Central and Eastern European nationality stereotypes. British Journal of Social Psychology, 38(1), 85-102.
Rakes, G. C., & Dunn, K. E. (2010). The Impact of Online Graduate Students' Motivation and Self-Regulation on Academic Procrastination. Journal of interactive online learning, 9(1).
Rotenstein, A., Davis, H. Z., & Tatum, L. (2009). Early birds versus just-in-timers: The effect of procrastination on academic performance of accounting students. Journal of Accounting Education, 27(4), 223-232.
Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (2021). Retorno a la educación superior. http://admision.senescyt.gob.ec/#
Schouwenburg, H. C., & Lay, C. H. (1995). Trait procrastination and the big-five factors of personality. Personality and Individual differences, 18(4), 481-490.
Shraw, G., Wadkins, T., & Olafson, L. (2007). Doing the things we do: A grounded theory of academic procrastination. Journal of Educational Psychology, 99(1), 12-25.
Sirois, F. M., Yang, S., & van Eerde, W. (2019). Development and validation of the General Procrastination Scale (GPS-9): A short and reliable measure of trait procrastination. Personality and Individual Differences, 146, 26-33.
Svartdal, F., Pfuhl, G., Nordby, K., Foschi, G., Klingsieck, K. B., Rozental, A., & Rębkowska, K. (2016). On the measurement of procrastination: comparing two scales in six European countries. Frontiers in Psychology, 7, 1307.
Taura, A. A., Abdullah, M. C., Roslan, S., & Omar, Z. (2015). Relationship between self-efficacy, task value, self-regulation strategies and active procrastination among pre-service teachers in colleges of education. International journal of psychology and counselling, 7(2), 11-17.
University of Amsterdam (2021) Tuition fees.
Van Eerde, W., & Venus, M. (2018). A daily diary study on sleep quality and procrastination at work: the moderating role of trait self-control. Frontiers in psychology, 9, 2029.
Vonderwell, S., & Zachariah, S. (2005). Factors that influence participation in online learning. Journal of Research on Technology in education, 38(2), 213-230.
Wandler, J. B., & Imbriale, W. J. (2017). Promoting undergraduate student self-regulation in online learning environments. Online Learning, 21(2), n2.
Won, S., & Shirley, L. Y. (2018). Relations of perceived parental autonomy support and control with adolescents' academic time management and procrastination. Learning and Individual Differences, 61, 205-215.
Yukselturk, E., & Top, E. (2013). Exploring the link among entry characteristics, participation behaviors and course outcomes of online learners: An examination of learner profile using cluster analysis. British Journal of Educational Technology, 44(5), 716-728.
Zalaquett, C. P. (2006). Study of successful Latina/o students. Journal of Hispanic Higher Education, 5(1), 35-47.
Zhao, J., Meng, G., Sun, Y., Xu, Y., Geng, J., & Han, L. (2019). The relationship between self- control and procrastination based on the self-regulation theory perspective: The moderated mediation model. Current Psychology, 1-11.
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M.
Boekaerts & P. R. Pintrich (Eds.), Handbook of self-regulation (pp. 13–39). New York:
Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect.
35 10. Appendix
1. Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
What is your gender? 215 1 2 1.74 .440
What is your age? 215 17 56 24.62 7.528
Where are you from? 215 1 8 3.25 2.599
Which region are you from? 215 1 4 2.43 1.038
What is your highest level of education you are currently enrolled in?
215 1 4 2.87 .434
What is your employment status?
215 1 7 4.32 1.448
Valid N (listwise) 215
1.1 Where are you from?
Frequency Percent Valid Percent Cumulative %
Valid The Netherlands 49 22.8 22.8 22.8
Ecuador 102 47.4 47.4 70.2
India 3 1.4 1.4 71.6
South Korea 3 1.4 1.4 73.0
China 10 4.7 4.7 77.7
Germany 4 1.9 1.9 79.5
Belgium 2 .9 .9 80.5
Other (please list below) 42 19.5 19.5 100.0
Total 215 100.0 100.0