1 Introduction

Procrastination is an intentional delay of activity. Procrastination is derived from the Latin word ‘Procrastinatus’, meaning ‘to put off until tomorrow’. It is a behavior that is considered unfavourable and is known to have a negative influence on the procrastinator. Several studies reveal it is common among students to delay study and learning-related activities (Kim & Seo, 2015; Steel & Klingsieck, 2016) and that at least half of them (Solomon & Rothblum, 1984; Ebadi & Shakoorzadeh, 2015) procrastinate. Subsequently, it has been widely studied in the academic context by researchers in psychology and education. Hence, the term academic procrastination or student procrastination (Solomon & Rothblum, 1984; Beswick et al., 1988) was coined and is defined as “to voluntarily delay an intended course of study-related action despite expecting to be worse off for the delay” (Steel & Klingsieck, 2016).

Academic procrastination is a learner behavior that is of major concern for instructors both in the classroom and online settings. It negatively influences academic outcomes (Klingsieck et al., 2012), impacts performance (Steel & Klingsieck, 2016; Tuckman, 2002) lowers goal attainment (Morford, 2008), diminishes self-esteem (Harrington, 2005) reduces long-term learning (Schouwenburg & Lay, 1995), leads to stress (Tice & Baumeister, 1997), discomfort (Flett et al., 2012), lowers life satisfaction (Deniz, 2006), causes depression and anxiety (Rozental et al., 2015), as well as affects subjective well-being (Klingsieck et al., 2012). Moreover, procrastination is shown to be correlated with reduced use of cognitive and meta-cognitive learning strategies (Howell & Watson, 2007). Several studies have found procrastination as a major threat (Yang et al., 2020) and one of the major reasons for failure and dropout (Richardson et al., 2012), and dealing with it is one of the high-priority concerns in online learning settings (Goda et al., 2015; Cerezo et al., 2017). Therefore, studies on procrastination have been the focus of researchers for over a century. Studies have been conducted by psychologists and other researchers to determine procrastinating behavior, understand why people tend to procrastinate, what are the outcomes of such behavior and how can it be overcome.

Procrastination might be an individual trait (which would predict that students with a tendency to procrastinate would show similar behavior across all their higher education studies) or situational (where the students might procrastinate more on some tasks than others). While procrastination has predominantly been considered dysfunctional or irrational, several researchers have explored it as a functional delay (Klingsieck, 2013; Yamada et al., 2017). Chu and Choi (2005) categorized it into passive procrastination and active procrastination. While passive procrastinators hold an indecisive behavior to perform a task, active procrastinators make deliberate decisions to procrastinate because they prefer to work under pressure. Active procrastination offers positive outcomes as opposed to negative ones in the case of passive procrastination.

To determine procrastination, different measures have been used such as self-reported procrastination scales or behavioral measures. Behavioral measures can be recorded manually through observation (Steel et al., 2001) or collected via digital traces of interaction between student and learning environment (Cormack et al., 2020). Educational Data Mining (EDM) and Learning Analytics (LA) techniques are used to analyze digital traces/logs collected from Learning Management Systems (LMS) or Technology Enabled Learning Environments (TELE) and other learning environments (Cormack et al., 2020; Hooshyar et al., 2020).

1.1 Motivation

Extant studies conducted to date identify procrastinating behavior either using self-reported questionnaires or behavioral data logs recorded in LMS and find its relationship with performance. The existing studies determine procrastination majorly on individual tasks. It might be possible that procrastinating behavior might be different when individuals work collaboratively rather than individually. Moreover, research studies have shown that the learning gain in the group is significantly better than individual students. Therefore, it would be vital to investigate the behavior of procrastinators when they work in a group-based collaborative activity and identify the behavioral patterns of different types of procrastinators when they work in collaboration. Hence, in this study, we explore the behavioral patterns of different types of procrastinators in a group-based collaborative Wiki activity. This study uses the self-reported Tuckman Procrastination scale to determine different types of procrastinators (low, moderate and high). It then extracts behavioral data of the Wiki activity that is recorded in Moodle LMS to investigate the actual behavioral patterns of different types of procrastinators. This study also examines procrastination behavior within student groups.

1.2 Structure of the paper

The rest of this paper is structured as follows: Sect. 2 discusses related works, and the background work on wiki is described in Sect. 3. The research objectives are outlined in Sect. 4. The methodology and study design are detailed in Sect. 5. This is followed by Sect. 6 which covers descriptive statistics and results. The results are discussed in Sect. 7, and Sect. 8 discusses the limitations of our work and concludes this paper with future work.

2 Related work

Procrastination behavior has been measured in a variety of ways. Several researchers have developed procrastination scales such as General Procrastination Scale (Lay, 1986), Tuckman Procrastination Scale (TPS; (Tuckman, 1991), Adult Inventory of Procrastination (McCown & Johnson, 1989), and Academic Procrastination Scale (Milgram & Toubiana, 1999). While those determine procrastination from a negative perspective, Active Procrastination scale (Choi & Moran, 2009) measures it from a positive viewpoint. Several researchers utilised these scales in their studies to measure procrastination. For instance, Goroshit (Goroshit, 2018) used the Studying Procrastination Scale developed by Wohl, Pychyl, and Bennett (Wohl et al., 2010), Tuckman (Tuckman, 2005) and Michinov et al. (Michinov et al., 2011) used Tuckman Procrastination Scale, Klingsieck et al. (Klingsieck et al., 2012) used General Lay procrastination scale in their research to study procrastination.

With the emergence of LMS and proliferation of online learning, researchers are using behavioral data as a proxy measure of procrastination. These generally include assignment submission time (Cormack et al., 2020; Tani, 2017), time of first activity (Cerezo et al., 2017), pace of accesses (Jo et al., 2016), task completion time (Levy & Ramim, 2012), number of clicks, time spent until first activity (Paule Ruiz et al., 2015), variables related to effort and time spent working (Cerezo et al., 2017). Earlier, such behavior was recorded manually through observation (Steel et al., 2001), but now, researchers majorly extract digital traces from learning sites and then use EDM and LA techniques to find useful patterns and relationships among variables. Researchers have used diverse measures to determine procrastination behavior of students. For instance, several researchers utilised LMS logs to fetch data on the time of assignment submissions to measure procrastinaton. Hooshyar et al.(Hooshyar et al., 2020) used inactive and spare time as a measure of procrastination. While others (Cormack et al., 2020) (Tian et al., 2019) have taken assignment submission time or have calculated it as the average ratio of the interval between the submission time and the release time of each assignment and the interval between the deadline and the release time. Akram et al. (2019) used time to start and end the assignment as a measure. Goda et al. (Goda et al., 2015) considered time spent in learning materials as well as completion rates to determine procrastination. Yamada et al. (Yamada et al., 2017) and (Yamada et al., 2016) used a 2 × 2 time-related academic behavior self-reported scale as well as logs to find the average submission time and late submission time in reports and a 1-minute paper. Hensley (Hensley, 2014) used both self-reported scales (Tuckman Procrastination Scale, Active Procrastination Scale, Procrastination Assessment Scale-Students) and homework quizzes submission time to determine procrastination. Lee (Lee & Nichol, 2017) used both self-reported scale (Tuckman Procrastination Scale) and logs from a discussion forum for measuring procrastination. The factors included how long the student waited to complete the questionnaire, how long students took to make their first discussion post each week, number of days to complete the questionnaire, and how long students waited to make their discussion posts. Yang et al. (Yang et al., 2020) used the homework submission patterns, Paule-Ruiz et al. (Paule Ruiz et al., 2015) considered tasks (assignments, resources, quizzes) delay to determine procrastination. Other groups of researchers (Cerezo et al., 2016, 2017; Elvers et al., 2003; Lim, 2016) have used days (activity start date, activity end date and days in hand) as a factor to measure procrastination. Their analysis was based on the actions performed by the student during a practical activity, in a forum or general behavior in LMS. Park et al. (Park et al., 2018) took regularity (individual week daily activity count, aggregated Daily task count) as a factor. They used clickstream data based on activities (such as watching a lecture video or submitting a quiz) performed by the student. AlJarrah et al. (AlJarrah et al., 2018) used the number of clicks and days when the material was accessed as a way to determine procrastination. Elvers et al. (Elvers et al., 2003) used the difference between the date when the materials were first accessed and the date of the exam as the basis of measuring procrastination. You (You, 2015) has taken frequency of late assignment submissions to identify procrastinating behavior of a student.

The existing studies depict that the researchers have studied procrastination for independent tasks such as the time when the individual submits an assignment or completes an activity, time to start an activity by an individual, etc.; thus focusing on tasks that are performed in solidarity. Recently a research study analysed on how student’s procrastination will differ between individual and group for an assignment task. In this study (Koppenborg & Klingsieck, 2022), the authors compared the duration of task and number of errors in the assignment between individual and group. The results show that students while working in a group, exhibit less procrastination compared to while working individually. However, in this study (Koppenborg & Klingsieck, 2022), the authors selected only students with high procrastination traits. In this study we propose to explore how students change their procrastination behavior when they work in group and collaborative activity such as creating wiki page in an LMS. This study analyzes the behavior of the students in wiki and identify if grouping different types of procrastinators in a group changed their behavior. This study depicts this behavior using descriptive analytics.

3 Background: wiki

Wiki is one of the common tools for computer-supported collaborative learning (Elgort et al., 2008). Wikis are sets of interlinked web pages, created through the incremental development collaboratively by a group of users situated at different locations (Leuf & Cunningham, 2001). The users can create new pages, add content, reorganize information on the existing pages, as well as create links between existing and new pages. One of the distinguishing features of wiki is that it is possible to keep record of different versions of wiki documents created by each user. Every single contribution made by each user to a wiki page generates a new version of a page and is recorded. Thus, it is possible to track edits, determine the difference between two consecutive wiki versions and restore previous versions (TREnTIn, 2009; Balderas et al., 2018). Consequently, it is possible to analyze these recorded wiki logs to examine the behavior of each student on a wiki assignment. Subsequently, this study aims to use these wiki logs to analyze the behavior of different types of procrastinators.

There are a few studies that attempt to find effectiveness of wiki for different types of activities (Elgort et al., 2008; TREnTIn, 2009), use of wiki as a tool for measuring engagement (Stanca & Felea, 2016) and enhancing learning (Tsai et al., 2010; Wang, 2016). Few authors find an association between wiki activities and grades. For instance, Conijn et al. (2016) found no such relationship but L´opez-Zambrano et al. (L´opez-Zambrano et al., 2020) found a relationship between wiki and grade. While other studies are focused on finding the assessment technique for review of wiki assignment (De Wever et al., 2011); (Lai & Ng, 2011)). This study uses Wiki to explore the procrastination behavior of the students in a collaborative group activity.

4 Research objectives and questions

The objective of this research was to.

  1. 1.

    examine patterns of students’ procrastination behavior in a wiki activity using logs of a Moodle LMS.

  2. 2.

    determine if there are significant differences among different types of procrastinators.

  3. 3.

    explore the behavioral patterns of different types of procrastinators within the student groups.

This study attempts to use the collaborative work performed in a wiki environment to examine student procrastination behavior. This study will explore the behavior between and within different groups of procrastinators.

5 Materials and methods

For this research study, a group-based Wiki activity “Evolution of JAVA,” was designed on Moodle LMS. The participants were 4th-semester undergraduate Computer Science & Engineering students at an Indian University. The class consisted of fifty students from the Java Programming course. The students were divided into nine groups (sequenced A to I) with 5 to 6 members in each. At the beginning of the semester, the students were requested to fill out a questionnaire consisting of statements from the 16-item Tuckman Procrastination Scale (Tuckman, 1991). Tuckman questionnaire is a 4-point Likert scale (”That’s me for sure; That’s me; That’s not me; That’s not me for sure”) and the scores can range from 16 to 64, with higher scores indicating a greater tendency to procrastinate. All the students consented to participate in the research. Their names have been anonymized for research purposes.

Before the start of Wiki assignment, a 30-minutes hands-on training on creating Wiki pages on Moodle LMS was given to the students on MS Teams. The students were then briefed about the requirements of the wiki activity. A discussion forum was also created on Moodle LMS for facilitating discussions among the students. Due to Covid-19, the session was completely online/remote, and the classes were conducted synchronously. Therefore initially, breakout rooms were created on MS Teams to facilitate discussions among the group members. The faculty participated in each breakout session to enable discussions and clear any doubts among the group members. This session lasted for 50 min. The students were then required to complete the wiki assignment from 24th December 2020 at 11:59 AM to 10th January 2021 at 11:59 PM.

There were two students who did not fill out the survey and two did not perform any Wiki activity on LMS hence three students were excluded from the study resulting in a total of forty-seven students. One of the students who was excluded from the study was a high procrastinator which is evident as he did not perform even a single activity on the wiki page.

5.1 Data pre-processing

The logs for the wiki activities were gathered from Moodle LMS. The data was then transformed as per the requirements for analysis. The names were anonymized and the entries for the student who did not fill out the questionnaire were removed. Timestamp was converted to date-time format and split into data and time. The dates were changed to day number. For instance, the date 24th December 2020 was changed to Day 1 and 10th January 2021 to Day 18. Additional columns were added to the logs to determine the relevant procrastination category (low, moderate, high) and wiki group (A-I). Grouping was done based on the procrastination type, event name, date, and time. Python pandas were used for this data cleaning and transformation. The detailed analysis was done using Tableau and SPSS.

6 Descriptive statistics and results

6.1 Descriptive analysis

We computed Cronbach’s alpha value to confirm the reliability the Tuckman scale and the alpha = 0.851 value found to be reliable. Moreover, the total scores were normally distributed as depicted by Shapiro-Wilk’s test (p > 0.05) (Shapiro & Wilk, 1965; Razali et al., 2011). The visual representation of their histograms, normal Q-Q plots and box plots showed that all the scores with a skewness of 0.207 (SE = 0.347) and kurtosis of -0.307 (SE = 0.681) (Cramer & Howitt, 2004; Doane & Seward, 2011). Actual score ranged from 16 to 50 with a mean of 31.87 (SD = 7.63). The students were categorized as high, moderate, and low procrastinators based on a tertile split (Tuckman, 2002). Quartiles were used for splitting the data into three groups and shown in Table 1.

Table 1 Categorization of students based on the Total scores
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6.2 Result analysis

The aim of this study is to examine the.

• Differences among different types of procrastinators.

• Behavioral patterns of different groups of procrastinators.

To answer our first research question that is how different type procrastinators interact with Wiki tasks in an LMS, we first analyse frequency of action performed by different type of procrastinators. Secondly, we explore the difference in when the activity was performed (regularly or close to the deadline) by different types of procrastinators.

6.3 Differences among different types of procrastinators

In order to find the difference among the types of procrastinators we examine the difference in frequency of actions performed by different types of procrastinators. We computed the average number of activities by each type of learner and shown in Table 2. From the table it is evident that the low procrastinators performed the maximum number of activities followed by moderate and then high procrastinators.

Table 2 Number of wiki activities performed by different groups of procrastinators
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To analyse further, from Moodle log data we extracted activities related to wiki, they are number of Wiki pages created, viewed, updated, wiki history, page version, and map viewed, wiki difference viewed, page locks deleted, version restored, number of course module and comments viewed. Then we compared the frequency of each wiki activities among three types of procrastinators (low, moderate and high) and shown in Table 3.

From the Table 3 it is evident that low procrastinators have performed the wiki activities more frequently compared other two types of procrastinators. In order the establish statistical significance among the difference in frequency of actions performed by types of procrastinators we computed chi-square test, and the results indicates that there is a relationship between number of wiki activities performed and the three types of procrastinators X2 (10) = 77.866, p < 0.05, also shown in Table 4. For analysis, the activities with frequency less than 5 were removed.

The difference between the average number of activities by different types of procrastinators are show in the Fig. 1.

Figure 1 depicts the activity behavioral patterns showing the frequency of distinct activities performed by different types of procrastinators. It is evident from the graphs that overall low procrastinators dominated in performing almost all wiki activities. Next the question arises, when did different type of procrastinators perform the activities. Whether the activities are performed regularly or performed only close to the deadline.

Table 3 Frequency and Average of each Wiki activity for Low, Moderate and High procrastinators
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Table 4 Chi-Square Tests between wiki actions by different types of Procrastinators
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6.3.1 Temporal nature of wiki activities performed by different types of procrastinators

In order to analyse the difference between different type of procrastinator’s activities in Wiki, we computed the number of activities per day by each learner. Table 5 shows the number of activities done by different types of procrastinators each day and also the average by Low, Medium and High procrastinators. From the table it is clear that both low and moderate procrastinators were regular with their assignment compared to high procrastinators. Day 1 indicates the starting day of the Wiki task and Day 18 is the deadline to complete the task. Day 2 is a holiday (25th Dec 2020) and Day 4 is Sunday. From the table, we can see that Low and Moderate type of learners performed few activities initially then they started more activities from day 8 or day 9. On the contrary, high procrastinators were not very active until the 15th day. There were just a fraction of activities carried out by them before that. However, there are more activities close to the deadline irrespective of the type of procrastinator. For low procrastinators, we can go by the theory of Chu and Choi (2005) that argues that there might be active procrastinators who focus on perfectionism and might continuously add and upgrade content for enhancing the quality of Wiki pages. Overall, the number of activities performed by low procrastinators was greater followed by moderate and then high procrastinators. Although some high procrastinators did a few activities on the first day but started with the assignment late. 

Table 5 Frequency and Average of Activities Performed by Each Type of Procrastinators Day-wise. Day 1 indicates first day of activity. Day 18 is the deadline to complete the activity.
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Fig. 1
figure 1

Average number of Wiki Action by different types of Procrastinators

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We also plotted the data in Fig. 2. The figure clearly shows that low procrastinators were regular and carried out a maximum number of activities followed by moderate and high procrastinators. The number of activities is highest towards the deadline. Also, Day 18 being the last day, maximum work was done on this day. There were very few students who extended their work beyond the deadline.

Fig. 2
figure 2

Line graph showing the day the activity was started by each group

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6.4 Within-Group analysis

In this subsection, we explore the difference in students’ behavior in each group.

Since the groups are randomly assigned before we administrated the Tuckman Scale, each group had a mix of different types of procrastinators as shown in Table 6. Since Group G, H and I do not represent all types of learners, we considered Group A to F for the analysis.

Table 6 Tuckman based types of students in each group
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To explore the behavioral differences among the diverse group of students, we considered the number of activities of each group. The average number of activities by each group is shown in Table 7.

Table 7 Average Number of activities by types of procrastinators (group-wise)
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The previous sections depicted the activity patterns based on the type of procrastinators (low, moderate, high) and revealed that low procrastinators performed comparatively more activities when compared to moderate and low procrastinators. However, exploration of data (Table 7) indicates that it is difficult to generalise the assumption regarding procrastination behavior within the group. For instance, in Group B, high procrastinator/s performed more tasks on average compared to low and moderate peers, for Group D, moderate procrastinators, on average, carried out much more activities than the low procrastinators. Hence, we investigate the within-group behavior based on the time when the activities were performed to get better insights into the behavior of different types of procrastinators within each student group.

Line graph (Fig. 3) shows that Groups A, B, and E were more regular with their work compared to the other groups. Group F start the work close to the deadline. Figure 3 also indicates that Group E had only one low procrastinator, but s/he carried out most of the activities. High procrastinators in Group D performed fewer activities and that too close to the deadline. The behavior of those groups supports our assumption that low procrastinators are regular and high procrastinators are not. However, for Group A and B this does not hold. The high procrastinators are as regular as the low and moderate peers as well as perform more activities. For Group C, moderate procrastinators are more frequent with activities throughout the assignment.

Fig. 3
figure 3

Group-wise pattern of different types of procrastinators

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7 Discussion

This paper was an attempt to investigate the procrastination behavior of the students, specifically when they work in a group-based collaborative wiki activity. It was hypothesized that the low procrastinators would outperform the moderate and high procrastinators in the total number of activities and regularity of work. When the three types of procrastinators (low, moderate and high) were compared cumulatively, the findings supported our hypothesis. These result findings are in-line with the previous studies performed on individual tasks and suggest that low procrastinators are more active than moderate and low procrastinators or that the non-procrastinators are more consistent and early starters compared to the non-procrastinators. It was further assumed that when diverse types of procrastinators are put in a group, their behavior might change. It was also assumed that peer pressure might impact their behavior (Zhang, 2023). However, no generalised statement can be formed while examining with-in group behavior. A further assumption that individual behavior might not change even when different types of procrastinators are put in a group is also not supported. Few groups (E) support the assumption that the low procrastinators are regular and perform more activities. None of the assumptions regarding the student behavior for the group-based collaborative wiki activity could be established during with-in the group analysis. The discrepancies could be due to several reasons. Firstly, this might be due to the self-reported procrastination scale where the students either did not report their actual behavior or were not sure about their behavior. Therefore, it might have been useful to categorise the students based on their actual behavior recorded in the LMS logs rather than relying on self-reported questionnaires. There might be a shortcoming in the way, the groups were formed as the students were given a chance to form their own groups based on their convenience. As the assignment was given during the Christmas and New Year time, the students might have been busy with celebrations. Furthermore, it might be the case that some student was suffering from COVID-19 or other illness and hence could not actively participate. Such cases were not reported to the instructor. The actual reasons for inactive participation are not known.

8 Conclusion

This study tried to identify the procrastination behavior when it comes to group-based Wiki activity. This was different from the previous studies that emphasized individual activities to study students’ procrastination behavior. Although it is difficult to form any hypothesis with the current dataset, this study opens the pathway to conduct further studies, with a larger, more planned and structured categorisation of the students into different groups. There were a few limitations of this work that could be addressed in future work. Firstly, the quality of wiki posts is not taken into consideration. It would be important to explore the quality of the wiki posts and wiki pages and compare it with the different types of procrastinators. Secondly, the duration for which the wiki activity was performed could not be gathered due to the limitations of Moodle. These could be addressed in future studies. It could be interesting to explore the difference between various types of procrastinators for individual versus group tasks. Additionally, it would be useful to utilise the LMS logs to study student behavior rather than relying on the self-reported Procrastination scales. Also, multiple experiments on different types of group activities could be taken as some students might not be comfortable using Wiki. Additionally, the active procrastination scale by Chu and Choi (2005) could be used to identify active procrastinators.