Abstract
Predicting academic procrastination among college students in the context of a public crisis could provide essential academic support and decision-making strategies for higher education institutions to promote student psychological health. Notably, research focusing on predicting academic procrastination behavior among college students in the context of a global crisis is still limited. The purpose of this study is to address this gap by constructing a predictive model based on the decision tree algorithm to predict academic procrastination behavior among college students. A total of 776 college students from the Guangxi Zhuang Autonomous Region of China participated in this study. The study gathered data from multiple aspects relevant to academic procrastination behavior, including demographic information, academic achievements, subjective well-being, smartphone addiction, negative emotions, self-esteem, life autonomy, pro-environmental behavior, academic achievement, and sense of school belonging. Descriptive statistical analysis was conducted utilizing SPSS version 26.0, and decision tree model analysis was performed with Modeler 18.0. The findings of this study identified eight predictive factors of college students’ academic procrastination in order of importance: subjective well-being, smartphone addiction, negative emotions, self-esteem, life autonomy, pro-environmental behavior, academic performance, and sense of school belonging. The model accuracy was 85.78%, and indicating a relatively high level of prediction. The findings of this study not only provided a new perspective for understanding academic procrastination but also offered practical guidance for educators on how to mitigate this behavior.
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Introduction
The recent occurrence of public crisis, such as COVID-19, has caused unprecedented impacts on all levels of education (Toquero, 2020), particularly affecting the psychological well-being and learning of college students. The challenge of COVID-19 not only disrupts students’ academic and daily lives (Gamage et al., 2020; Tarkar, 2020) but also exacerbates academic procrastination problems. Academics further highlight that factors such as psychological well-being, life satisfaction, and learning environment contribute to the changes in academic procrastination behavior during COVID-19 (Arifiana et al., 2020; Peixoto et al., 2021; Svartdal et al., 2020). Therefore, investigating the predictive factors and underlying mechanisms behind college students’ academic procrastination behavior is not only crucial for better understanding their psychological changes and behavioral adjustments, but also critical for developing effective strategies to improve academic performance and well-being.
Academic procrastination behavior can be attributed to a combination of biological, psychological, and social factors, highlighting the multiple aspects and complexity of this phenomenon. It is not that uncommon for students to experience negative emotions such as anxiety and depression during the pandemic. These negative emotions not only affect the psychological well-being of students but could also lead to an increase in academic procrastination behavior (Bolbolian et al., 2021; Deng et al., 2022). Conversely, self-esteem and life autonomy can enhance intrinsic motivation and resilience when facing challenges and difficulties, thereby reducing the likelihood of procrastination behavior during crises (Tian et al., 2023; Yang et al., 2021). Moreover, subjective well-being may serve as an alleviating factor that helps students to stay positive and reduce procrastination behavior (García-Ros et al., 2023). Addictive behavior related to mobile phones is not new in the age of information technology. However, it is noteworthy that depression and anxiety are significantly linked with smartphone addiction, particularly during public crises, and are further associated with academic procrastination behavior (Albursan et al., 2022; Ratan et al., 2021). Excessive reliance on smartphones for information and social interaction could distract students’ attention and diminish learning efficiency, thereby worsening academic procrastination behavior (Albursan et al., 2022).
Pro-environmental behavior reflects a sense of social responsibility and organizational ability, which are particularly important for maintaining academic discipline and reducing tendencies toward academic procrastination (Yuriev et al., 2020). Studies posit that a sense of school belonging could reduce academic procrastination tendencies. Dong and Izadpanah (2022) found in their study that a strong sense of school belonging can provide emotional support and enhance social connections. These social interactions and supports promote a positive perception of the learning environment and thus mitigate academic procrastination behavior. Therefore, it can be seen that the interaction between personal and environmental factors has a significant impact on academic procrastination behavior including the time of crises. Furthermore, academic achievement is a significant predictor of academic procrastination as it reflects learning efficiency and motivation (Wu et al., 2020). Students who achieve high scores typically possess greater self-efficacy and goal orientation, thereby reducing tendencies to procrastinate (Goroshit & Hen, 2021).
A thorough investigation of the impact of these predictors on academic procrastination will enrich the understanding of the complex interplay of procrastination behavior, and provide insights for designing effective interventions. Academic procrastination is a problem to tackle even before the pandemic took place. It is important to note that this study contributes to understanding academic procrastination behavior during pandemic situations. Despite the pandemic subsiding now, stakeholders in education could still leverage these insights to better prepare for future crises beyond the pandemic era. Notably, prior studies that utilize the decision tree method to predict academic procrastination are still scarce in the literature. The decision tree model is one of the algorithms of machine learning for data mining (Sharma & Kumar, 2016). It involves classification or prediction based on attribute tests and outcomes (Wu et al., 2008).
The decision tree model has undergone several accuracy simplifications and improvements by Ross Quinlan (1987, 1996). The merits of the decision tree model lie not only in its high predictive accuracy but also in its strength to decompose a complex decision-making process into a series of simpler decisions, thereby providing a solution that is generally easier to interpret (Quinlan, 1987, 1996). For instance, previous studies have utilized decision tree models to identify innovative behavior (Safavian & Landgrebe, 1991; Skrbinjek & Dermol, 2019), and have accurately predicted factors influencing innovation success and failure (Park, 2019). In addition, decision tree models have been applied across various disciplines of research including marketing and psychology, to predict consumer satisfaction, attitudes, and loyalty (Han et al., 2019). Hence, the decision tree method is appropriate for this study to predict college students’ academic procrastination behavior and to identify variables that can serve as predictors of academic procrastination.
Literature review
Academic procrastination
Academic procrastination was first proposed by Solomon and Rothblum in 1984, they defined it as the act of postponing planned tasks and persistently delaying or deferring task completion within the academic context (Solomon & Rothblum, 1984; Svartdal et al., 2020). However, the academic community has not yet formed a unified definition or conceptualization of academic procrastination. The majority of scholars agree that academic procrastination typically refers to the behavior of postponing and delaying mandatory academic tasks without sufficient justifications (Ferrari & Camilleri, 2021). Some scholars further refine this definition by suggesting that academic procrastination is the behavior of choosing to delay task completion despite being well aware of the negative consequences (Steel, 2007; Wang et al., 2021).
Apart from that, researchers also postulate that academic procrastination consists of different dimensions such as behavior, cognition, and emotion (Saplavska & Jerkunkova, 2018). This is mainly characterized by cognitive distortions and irrational delays in initiating or completing academic tasks (Schouwenburg, 1995). Cai (2017) also endorsed Schouwenburg’s viewpoint and considered academic procrastination as the discrepancy between task planning and implementation that involves irrational delays in planning or completing academic tasks as well as remediation and summary after delays. Based on the above discussions, this study defines academic procrastination as the delaying behaviour exhibited by students during academic tasks. This behavior primarily involves intentionally postponing or avoiding the completion of academic tasks, and may or may not result in failing to complete the given tasks within specified timeframes.
The predictor of academic procrastination
Extensive literature suggests that researchers are deeply interested in understanding the influencing factors of academic procrastination (Abdi Zarrin & Gracia, 2020; Nwosu et al., 2020; Wang et al., 2021). The selection of predictors in this study is grounded in the Biopsychosocial Model and a review of the literature, which includes subjective well-being, smartphone addiction, negative emotions, self-esteem, life autonomy, pro-environmental behavior, academic achievement, and sense of school belonging. George Engel (1981) proposed a Biopsychosocial Model to explain the impact of psychological and social factors on individual behavior. During public crises, this model can be particularly useful in understanding how students’ behaviors, psychological reactions, and social environments interact and influence their academic procrastination and well-being.
Subjective well-being typically encompasses indicators such as life satisfaction, positive affect, and negative affect (Yilmaz, 2023). The prolonged lockdowns and isolation during the pandemic have had a significant negative impact on students’ subjective well-being (Foa et al., 2020), and this could lead to negative emotions, thereby increasing academic procrastination behavior (Vlachopanou & Karagiannopoulou, 2022). Previous studies have shown that students with higher subjective well-being demonstrate stronger self-regulation and resilience, allowing them to manage their learning behavior and time more effectively, thereby reducing procrastination behavior (Bu, Wu, & Wang, 2021; Morosanova et al., 2021; Yadav et al., 2020). Recent research has also endorsed the negative correlation between university students’ subjective well-being and academic procrastination (García-Ros et al., 2023). The literature underscores the prediction of subjective well-being on academic procrastination by highlighting its role in improving self-regulation and resilience, which in turn significantly reduces academic procrastination. Therefore, this study proposes:
H1: Subjective well-being negatively predicts academic procrastination.
Literature suggests that smartphone addiction is one of the reasons for academic procrastination behavior (Albursan et al., 2022). As of the year 2021, China ranked first globally in terms of smartphone users with the number surpassing 910 million (Horani & Dong, 2023). Studies also indicate that during the COVID-19 lockdown, the usage time of smartphones significantly increased (Katsumata et al., 2022; Statista, 2021). A study conducted by Chen et al. (2017) reveal that the smartphone addiction rate among university student in China is 29.8%. It is expected that the percentage will continue to increase due to social isolation and increased screen time during the pandemic. Hence, this study posits smartphone addiction is one of the significant predictors of academic procrastination. Studies have also discovered that poor self-regulation is one of the contributing factors to procrastination behavior, with a significant correlation found between poor self-regulation ability and smartphone addiction (Ching & Tak, 2017; Zhang & Wu, 2020).
This is because smartphone addiction can disrupt cognitive processes and impair brain regions associated with self-regulation (Gao et al., 2020), leading to poor self-regulation in academic tasks and subsequent procrastination behavior (Liu et al., 2022). Additionally, students with smartphone addiction often experience sleep deprivation, which can contribute to a decline in cognitive function, making it more challenging to complete academic tasks on time and thus leading to academic procrastination (Cui et al., 2021; Hamvai et al., 2023). The findings from Akinci’s (2021) study also supported that smartphone addiction is an important positive predictor of academic procrastination, as students addicted to smartphones tend to neglect and procrastinate academic responsibilities. The literature highlights smartphone addiction as a key factor in academic procrastination during the pandemic. Smartphone addiction is related to sleep deprivation, poor self-regulation, impaired cognitive function, and ultimately predicting academic procrastination. Therefore, this study proposes:
H2: Smartphone addiction positively predicts academic procrastination.
It is common for students to experience negative emotions such as fear, anxiety, and stress during crises. Albujar Moreno, Castro Portillas (2020) found that individuals with higher levels of anxiety, tend to have lower self-regulation abilities and are more prone to procrastination behavior. Research indicates that negative emotions such as stress and anxiety can cause structural and functional changes in the brain by impairing memory and cognitive functions. This includes planning and goal-directed actions that could intensify procrastination tendencies (Limone et al., 2020; Podlesek et al., 2021). Being the country with the longest lockdown period, Chinese students encounter heavier academic tasks and learning pressures, making them more susceptible to negative emotions and predicting academic procrastination (Deng et al., 2022). Studies conducted on the detection of negative emotions among university students indicated a significant increase from 12.94% before the onset of the pandemic to 46.6% during the pandemic (Feng, 2018; MENG, 2020). The findings, revealing approximately a fourfold increase in negative emotions among students, indicate a potential connection between negative emotions and academic procrastination. Therefore, this study proposes:
H3: Negative emotions positively predict academic procrastination.
Self-esteem refers to a psychological state that evaluates one’s own worth and abilities, which can have a profound impact during public crises. Maxwell (1992) found that self-esteem is more vulnerable and significantly decreases during public crises. Academics propose that self-esteem is one of the important factors that predict academic procrastination. Shu et al. (2022) highlighted that during the COVID-19 pandemic, factors such as isolation, social restrictions, and future uncertainty affected university students’ self-esteem levels by inducing feelings of isolation, anxiety, and stress. Low self-esteem could intensify negative emotions and feelings of pressure by making students more inclined to adopting academic procrastination to escape from learning tasks and pressure (Batool, 2020). Similarly, a longitudinal study conducted by Yang et al. (2021) on university students reports that as self-esteem continues to decline, academic procrastination worsens progressively. The literature highlights a significant link between self-esteem and academic procrastination, emphasizing the importance of addressing self-esteem during crises to mitigate procrastination behavior. Therefore, this study proposes:
H4: Self-esteem negatively predicts academic procrastination.
Tian et al. (2023) define life autonomy is the ability of an individual to independently guide own life direction, make choices for oneself, and take responsibility for the consequences of those choices. Life autonomy encompasses several dimensions, including autonomy, self-management, and a sense of life responsibility. Greater life autonomy promotes intrinsic motivation and increases learning engagement, thus reducing academic procrastination (Guay, 2022; Sobia et al., 2021). In fact, many Chinese universities adjusted or delayed their schedules due to the pandemic, despite granting students with more flexible time. However, students with immature mindsets often struggle to engage in autonomous learning (Town et al., 2022). They tend to exhibit academic laziness, poor self-management skills, and procrastinate on academic tasks (Albursan et al., 2022; Shim et al., 2022). Additionally, previous studies on university students also suggest that the stronger their sense of responsibility, the less likely they are to engage in academic procrastination (Jiao & Guo, 2020). Evidence from the literature underscores the predictive effect of life autonomy on academic procrastination. Hence, drawing from the research on various dimensions of life autonomy and their correlation with academic procrastination, this study proposes:
H5: Life autonomy negatively predicts academic procrastination.
Procrastination can be conceptualized as an irrational tendency to delay required tasks or assignments (Klingsieck, 2013). Early research from Lillemo (2014) has revealed the negative correlation between pro-environmental behavior and procrastination tendencies. Studies highlight that pro-environment behavior is an important indicator for psychological regulation and adaptation from social changes and environmental crises (Colombo et al., 2023; Mi et al., 2021; Zebardast & Radaei, 2022). Literature implies that students who are more engaged in pro-environmental behavior also possess various qualities and traits that could negatively predict academic procrastination tendency. For example, individuals who engage in pro-environmental behavior often possess greater self-regulation, goal-setting, monitoring, and self-motivation qualities. These qualities can help individuals manage learning tasks and time more effectively, thereby negatively predicting procrastination (Colombo et al., 2023; Sawitri et al., 2015; Wang et al., 2021). Furthermore, implementing pro-environmental behavior requires individuals to have strong executive and organizational skills, which are opposed to academic procrastination behavior (Colombo et al., 2023; Gutiérrez-García et al., 2020). The literature review indicates that pro-environment behavior is important for psychological adaptation to environmental challenges, and suggests that individuals who exhibit pro-environmental behavior also often possess qualities and skills that mitigate academic procrastination. Therefore, this study proposes:
H6: Pro-environmental behavior negatively predicts academic procrastination.
Academic achievement is an important indicator of learning outcomes, and the academic environment has a crucial role to play. Academic achievement serves as a crucial predictor in understanding academic procrastination behavior, as it not only reflects students’ performance but also influences their psychological state and behavioral patterns (Samson, 2021). Recent studies suggest that the academic interruptions caused by COVID-19 have had a significant negative impact on academic achievement (Kuhfeld et al., 2020; Serrano Aguirre, 2020). Academics are interested in investigating the relationship between academic achievement and procrastination behavior, with findings supporting a negative correlation between achievement and academic procrastination (Karataş, 2015; Türel & Dokumacı, 2022). Kurtovic, Vrdoljak, and Idzanovic (2019) explain the relationship between academic achievement and procrastination by considering the level of self-efficacy. They argue that students with better academic achievement are likely to experience a greater sense of self-efficacy, negatively predicting academic procrastination. Conversely, low self-efficacy is often associated with poor academic achievement and is likely to increase procrastination behavior. The literature supports that academic achievement is a significant factor in procrastination behavior, as it reflects students’ performance and influences their psychological state and patterns of behavior. Therefore, this study proposes:
H7: Academic achievement negatively predicts academic procrastination.
During the COVID-19 pandemic, most countries in the world enforced lockdown and isolation measures, with China having the longest-lasting lockdown policies (Yu et al., 2022). Throughout this period, many Chinese universities conducted their courses and exams only through online platforms. The sudden transition from physical classes to online learning posed significant challenges in promoting and sustaining students’ sense of school belonging (Gopalan et al., 2022). Existing research has revealed that a sense of school belonging can have a significant impact on academic procrastination (Dong & Izadpanah, 2022; Tian et al., 2023). For example, Lim, Yoo, Rho, & Ryu (2022) found that students lacking a sense of school belonging are more likely to engage in dropout behaviors. Consequently, students may experience problems related to learning interruption and academic procrastination. This is especially true in pandemic situations, where prolonged isolation and lockdowns diminish the sense of school belonging, resulting in academic disengagement and procrastination (Morán-Soto et al., 2022). Overall, the literature suggests an existing relationship between a sense of school belonging and academic procrastination. Students lacking this sense of school belonging are more likely to exhibit higher levels of academic procrastination and engage in dropout behaviors, and the pandemic has further aggravated this phenomenon. Therefore, this study proposes:
H8: Sense of school belonging negatively predicts academic procrastination.
To examine the above hypothesis, this study employed a decision tree model to predict academic procrastination behavior among college students. The strength of this method lies in decision trees’ capability to manage large amounts of data and identify various predictive factors and their interactions (Charbuty & Abdulazeez, 2021), which is important for understanding complex academic procrastination behaviors (Yang et al., 2020). Moreover, the decision tree prediction process is easy to understand and can be visualized by showing every decision step from the root node to the leaf nodes (Streeb et al., 2022). This approach enables intuitive display of the relationship patterns between predictors and the outcome variable (Yang et al., 2020). This study intended to utilize a decision tree model to clearly demonstrate the factors and pathways influencing academic procrastination, thereby providing insights for developing strategies and interventions to reduce academic procrastination. In short, the decision tree is an appropriate approach to adopt in this study as it is an intuitive, easy-to-understand, and effective method for prediction that enables the identification of key influencing factors. Hypothetical predictors of academic procrastination in this study are shown in Fig. 1.
The left panel lists hypothesized predictors of academic procrastination. The arrow symbolizes the predictive relationship. The right panel represents the predicted variable, academic procrastination, which is categorized into high and low level.
Research Method
Participants and procedures
The location of this study was the Guangxi Zhuang Autonomous Region of China, chosen for its proactive educational policies and practices during the COVID-19 pandemic. For instance, the region implemented strict academic measures by issuing dropout notices to students with severe academic procrastination or overdue completion. This study adopted a three-stage random sampling method to ensure the representativeness of the data collected from September 7th to October 15th, 2022. At the initial stage, because of the stringent lockdown policies and school management regulations during the pandemic, a simple random sampling method was employed to select 3 universities out of 83 in the region by utilizing a random number generator (Calculator.net, 2022). However, only one university was authorized to conduct the survey. It is noteworthy that due to the pandemic, the freshmen had not started their courses, and most of the seniors were engaged in off-campus internships. Therefore, this study focused its sampling on sophomore students, covering 46 different majors, with a total of 15,000 students. Subsequently, this study utilized the Online Random Number Generator website (Calculator.net, 2022), randomly selected 800 sophomore students. After data collection was completed, 24 questionnaires were deemed invalid due to poor response quality (such as arbitrary ticking, consecutive repetitive answers, etc.). Ultimately, 776 valid questionnaires were obtained, including 219 males (28.2%) and 557 females (71.8%), with ages ranging from 19 to 25 years.
This study utilized an online questionnaire to collect data. Because of lockdown policies during the COVID-19 pandemic, researchers were not allowed to enter the campus. Therefore, a trained research assistant, who also serves as a university counselor of the selected university, assisted the researchers in data collection. The questionnaire was expected to take approximately 20 minutes to complete. Prior to the survey, the research assistant provided detailed information about the purpose of the study, confidentiality, and the right to withdraw to ensure voluntariness.
Additionally, participants were required to read and sign an informed consent form, affirming their understanding and agreement to participate under the terms outlined. Afterward, the trained research assistants displayed QR codes for the questionnaire to students. Those who agreed to participate in the study were required to scan the QR codes, access the survey page, answer the questions, and then submit upon completion. Additionally, the trained research assistant was prepared to assist and answer any questions from participants if there was any confusion related to the questionnaire during the process.
Materials
The online questionnaire employed in this study consists of two parts: demographic information and scales, a total of 131 items. The demographic information includes gender, age, and academic achievement. The scales section comprises seven different scales to assess the predictors and academic procrastination in this study: subjective well-being, smartphone addiction, negative emotions, self-esteem, life autonomy, pro-environmental behavior, and sense of school belonging.
Subjective well-being scale
Subjective Well-Being (SWB), as coined by Diener (1984), refers to a personal overall assessment of quality of life that encompasses two dimensions: affective and cognitive. Xing (2002) modified this scale into a Chinese version, including 20 items. It is one of the most recognized and commonly used scales to measure well-being in China. This study adopted the Chinese version of SWB, which comprises 20 items similar to the original scale. The scale utilizing a 5-point Likert scale to measure well-being ranging from “1” as strongly disagree; “2” as disagree; “3” as somewhat disagree; “4” as agree; and “5” as strongly agree. In this study, the Cronbach’s alpha coefficient for the Subjective Well-Being Scale was 0.860.
Smartphone addiction scale
The Short version of the Smartphone Addiction Scale (SAS-SV) was developed by Kwon, Kim, Cho, and Yang (2013). It consists of 10 items to assess the degree of smartphone addiction. The items describe daily-life disturbance, positive anticipation, withdrawal, cyberspace-oriented relationships, overuse, and tolerance. This scale is rated on a 5-point Likert scale, ranging from “1” for strongly disagree to “5” for strongly agree. Higher scores indicate higher levels of smartphone addiction. The Cronbach’s alpha coefficient for SAS-SV found in this study was 0.840.
Depression, anxiety, and stress scale
The Depression, Anxiety, and Stress Scale (DASS-42) was developed by Lovibond, Lovibond (1995) based on the three-factor model of depression, anxiety, and stress. Then, DASS-42 was later revised into a shorter version known as DASS-21 by Antony et al., (1998) to measure the levels of depression, anxiety, and stress. This scale is simple, easy to use, novel unique, and fast to operate. It has been translated into many languages for research and application in countries around the world. This scale consists of 21 items and rated on 5-point Likert scale ranging from “1” for strongly disagree to “5” for strongly agree. Higher scores indicate higher levels of depression, anxiety, and stress. In this study, the Cronbach’s alpha coefficient for the scale was 0.965.
Rosenberg self-esteem scale
This study adopted the Rosenberg Self-Esteem Scale (RSE), which was developed by Rosenberg (1965) to evaluate general feelings of self-worth and self-acceptance. The scale consists of 10 items, and respondents report whether the items accurately describe themselves. This scale has been widely used, it is concise and easy to score and can help participants directly assess their own positive or negative feelings. All items are rated on a 5-point Likert scale, ranging from “1” for strongly disagree to “5” for strongly agree. In this study, the Cronbach’s alpha coefficient for the self-esteem scale was 0.714.
Life autonomy scale
This study adapted Life Autonomy Scale from Pan and Xie (2010) to measure students’ life autonomy. The original scale consists of 70 items, including six sub-scales: ideal, life autonomy, existence, love and care, life experience, and attitude toward death. To assess the degree of life autonomy of participants, this study only selected the sub-scale ‘life autonomy,’ which includes 12 items. All items were rated on a 5-point Likert scale, ranging from “1” for strongly disagree to “5” for strongly agree. Items 7 to 12 employ reverse scoring, a higher score indicates a lower life autonomy, while a lower score may suggest a higher life autonomy. In this study, the Cronbach’s alpha coefficient of the scale was 0.946.
Pro-environmental behavior scale
The Pro-environmental Behavior Scale utilized in this study was developed by Liu and Wu (2013). This scale consists of 11 items across public and private dimensions. Six items pertain to behaviors in the public domain, while five items concern behaviors in the private domain. In the public domain, behaviors mainly involve participation in environmental conservation activities within public organizations, such as donating to environmental NGOs (Non-Governmental Organizations) or conservation societies. Conversely, private domain behaviors refer to environmentally friendly actions in individuals’ daily lives, such as purchasing eco-friendly products. This scale was rated on a 5-point Likert scale, ranging from “1” for strongly agree to “5” for strongly disagree. Higher scores indicate higher levels of pro-environmental behavior. The scale in this study yielded a Cronbach’s alpha coefficient of 0.953.
Psychological sense of school membership scale
This study employed the Psychological Sense of School Membership (PSSM) scale to assess the sense of school belonging of respondents. The original scale was developed by Goodenow (1993) and consists of 18 items. It has been widely translated into multiple languages, including a Chinese version. This study adopted the Chinese version of PSSM, revised by Pan et al. (2011), which also comprises 18 items and assesses a student’s commitment to the school in terms of attachment, behavioral attitudes, and identification with the school. This scale is rated on a 5-point Likert scale ranging from “1” for strongly agree to “5” for strongly disagree. The scale used in this study yielded a Cronbach’s alpha coefficient of 0.838.
Academic procrastination scale
This study adopted the Academic Procrastination Scale developed by Tuckman (1991) to measure academic procrastination behavior. According to Tuckman, one of the strengths of this scale is its flexibility for allowing respondents to report their own behavior, and its specific usefulness in identifying academic procrastination behavior. Its rating scale was also converted from the original four points to five points before administration to maintain consistency with the other scales. The scale consists of 16 items and is rated on a 5-point Likert scale, ranging from “1” for strongly disagree to “5” for strongly agree. Higher scores indicate higher levels of academics. In this study, the reported Cronbach’s alpha coefficient of the scale was 0.920.
Statistical analyses
This study employed SPSS 26.0 for descriptive statistical analysis and Modeler 18.0 for decision tree model analysis. Descriptive statistics were used to analyze frequency distributions and trend changes in the observed data. Subsequently, the decision tree model was constructed using the C5.0 algorithm, an extension of the ID3 and the C4.5 algorithms proposed by Quinlan (1987, 1996) and Witten, Frank, and Hall(2005). This algorithm is not only suitable for large datasets but also offers faster computational speed and stronger predictive capabilities (Xiong, 2011). It was utilized to investigate which variables can predict the occurrence of academic procrastination.
Data coding
This study categorizes samples into two levels of academic procrastination: high and low. The questionnaire utilizes a Likert 5-point scale for scoring, with the study selecting 60% as the cutoff point. Consequently, scores of 3 or below are coded as 0, while scores above 3 are coded as 1. Based on this principle, the present study encodes the key variables predicting academic procrastination. (see Table 1).
The construction of decision tree
The construction of the decision tree model requires classifying samples based on the information entropy of the input dataset. Information entropy reflects the complexity within the samples. The greater the impurity within the samples (degree of impurity of a dataset), the larger the value of the information entropy, defined based on Mitchell (1997) as:
D is the training dataset with sample size m, and Pk is the probability of each class of samples.
The Gain Ratio is used to measure the difference in information entropy of datasets under different classification methods. If this study chooses variable C to divide the dataset D into n subsets, then based on Quinlan (1996), the Gain Ratio is defined as:
The C5.0 algorithm selects the attribute with the maximum Gain Ratio as the splitting point, establishing several branches based on the values of this attribute, and obtaining some subsets. This selection process is repeated until the final subsets contain only data of the same category, to perform the induction classification of data (Che et al., 2011).
Pruning of the decision tree
Based on the decision tree model constructed from the training samples, the dataset is recursively traversed to each leaf node. Specifically, the C5.0 algorithm employs post-pruning to systematically prune the leaf nodes, layer by layer. The mean square error of the dataset nodes is calculated. If the mean square error decreases after pruning, the node is removed; otherwise, it is retained (Quinlan, 1998).
Evaluation of the decision tree
In this study, as recommended by Gholamy, Kreinovich, and Kosheleva (2018), 70% of the sample data (n = 544) is selected as the training data, while 30% of the sample data (n = 232) is used as the testing data. The testing data reflects the extent to which the model constructed from the training data is suitable to new data. Accuracy, precision, and recall are indicators used to evaluate the quality of the model (Han et al., 2019). Accuracy refers to the proportion of correctly classified samples out of all samples. Precision refers to the proportion of true positive samples among the predicted positive samples. Recall refers to the proportion of true positive samples correctly predicted out of all actual positive samples. Specifically, recall is calculated as TP (true positive) divided by TP (true positive) plus FN (false negative), and accuracy is calculated as TP (true positive) divided by TP (true positive) plus FP (false positive).
Results
Descriptive statistics
The descriptive statistics are summarized in Table 2. The forecast target, students’ academic procrastination behavior, shows a good status. The mean value of academic procrastination was 2.593 (with a standard deviation of 0.635), which is lower than 60% of the full score. This means that no more than half of the students were in a state of high academic procrastination. We then encoded each variable by assigning a value of 1 to cases with scores above 60% of the full score and a value of 0 to all other cases.
Prediction analysis of academic procrastination
As presented in Fig. 2 below, the predictive factors of academic procrastination include subjective well-being, smartphone addiction, negative emotions, self-esteem, life autonomy, pro-environment behavior, academic achievement, and sense of school belonging. Additionally, respondents with low academic procrastination accounted for 79.963%. The root node is the topmost node in a decision tree model, and the branches below this top node represent the outcomes of decisions (see Fig. 2). The closer a predictive variable is to the root node, the higher its importance, indicating the degree of importance of the predictive variable.
The gray rectangle represents a node. The value inside a node indicates the quantity and distribution of samples. Blue and red squares represent the volume and proportion of samples within the node. The value ‘n’ denotes the number of samples in the node. The ‘%‘ value indicates the percentage of samples in the node relative to the total number of samples. ‘Total’ represents the cumulative total number of samples in the node.
The importance of predictor variables can be inferred from Fig. 3. Subjective well-being is the most important predictor variable to academic procrastination, followed by smartphone addiction and negative emotions in second and third place. Self-esteem, autonomy, and prosocial behavior rank fourth, fifth, and sixth in importance, while academic achievement and school belonging have the least importance.
The horizontal axis represents the importance of the predictive impact, while the vertical axis lists the predictors. The blue bars indicate the importance of each predictor on academic procrastination, with longer bars representing more important prediction.
Model evaluation
Tables 3 and 4 respectively present the confusion matrix and classification accuracy of the research model. The accuracy of training samples in the model is 87.50%, and the accuracy of test samples is 85.78%. As reported in Table 5, for the test samples, the model’s precision rate for predicting low academic procrastination is 90.24%, with a recall rate of 94.12%.
Discussion
This study utilized a decision tree model and the C5.0 algorithm to construct an eight-factor model for predicting academic procrastination. Additionally, the study ranked the importance of predictors to academic procrastination based on their contribution. The discussions of the findings of this study are based on the order of importance of predictive factors for academic procrastination, which are subjective well-being, smartphone addiction, negative emotion, self-esteem, life autonomy, pro-environmental behavior, academic achievement, and sense of school belonging.
The findings of this study suggesting the three most important predictors of academic procrastination were subjective well-being, smartphone addiction, and negative emotion. The reason may lie in the fact that students with higher levels of subjective well-being are more likely to possess conscientious and open positive personality traits (Abdullahi et al., 2020). These students also possess a heightened sense of time value and time monitoring, thereby avoiding academic procrastination behaviors in comparison to their counterparts who have lower levels of subjective well-being (Berber Çelik & Odaci, 2022). Moreover, higher levels of subjective well-being can assist students in adapting throughout the learning process and tackling academic tasks (Ran et al., 2023). Conversely, lower levels of subjective well-being tend to exhibit negative personality traits such as Neuroticism, which increases the likelihood of academic procrastination (Abdullahi et al., 2020).
Students with smartphone addiction often exhibit a lack of attention and self-control (Geng et al., 2021). This addiction can easily lead to a tendency to use smartphones to escape study pressure, thus increasing the likelihood of academic procrastination behaviors (Troll et al., 2021). This phenomenon is particularly pronounced in China, as the country has the highest number of smartphone users in the world. Smartphone addiction intensified even further as students became increasingly dependent on smartphones for online learning during pandemic (Albursan et al., 2022). However, heavy screen time can also easily lead to the misuse and overuse of smartphones, such as spending excessive time on social media, entertainment, or for gaming purposes. Smartphone addiction can deteriorate learning efficiency and self-control, while increasing procrastination behaviors (Troll et al., 2021).
Research indicates that negative emotions can predict procrastination behavior. Neuroimaging studies show that negative emotions activate specific brain regions in the anterior insula and amygdala. These regions are associated with procrastination behaviors (Barrett & Satpute, 2013; Seeley et al., 2007). Evidence suggests that the stronger the negative emotions, the more likely procrastination behaviors are to manifest (Wang et al., 2022). A study conducted by Rahimi and Vallerand (2021) during COVID-19 also reveals that negative emotions such as fear, anxiety, and depression are associated with academic procrastination. These findings from past literature could explain how prolonged lockdowns and high academic pressure are likely to diminish students’ goal-setting, self-monitoring, and self-regulation, leading to a significant increase in negative emotions among college students. This, in turn, further contributes to the occurrence of academic procrastination behaviors.
Khurshid, Batool (2018) considered academic procrastination as the product of low self-esteem, and they argued that self-esteem can negatively predict academic procrastination. In times of crisis, college students with higher self-esteem often find it easier to immerse themselves in academic life and are more likely to overcome academic procrastination (Yang et al., 2021). Additionally, Brando-Garrido et al. (2020) pointed out that self-esteem not only affects academic confidence and motivation but also indirectly influences academic procrastination behavior through various aspects, including emotional regulation and proficiency in goal establishment.
Past literature suggests life autonomy can negatively predict academic procrastination, students with higher life autonomy are more self-directed and have stronger self-control, thus less likely to engage in academic procrastination behavior (Codina et al., 2018). This is because students with higher life autonomy are also likely to possess greater self-regulation and take proactive action to avoid the occurrence of academic procrastination behavior. This viewpoint has received support from neuroscience studies, where non-invasive technologies such as Transcranial Direct Current Stimulation (TDCS) and Transcranial Magnetic Stimulation (TMS) were used to stimulate the dorsolateral prefrontal cortex (DLPFC) to enhance participants’ self-control and thereby reduce procrastination behavior (Feng, Wang, & Su, 2021).
Ateş (2020) proposes that pro-environmental behavior is an important predictor for academic procrastination given pro-environmental behavior often requires strong planning skills. Empirical evidence suggests that those who actively engage in pro-environmental behavior also likely demonstrate stronger proficiency in goal-setting and execution of plans, thereby mitigating procrastination behavior (Sawitri et al., 2015; Yuriev et al., 2020). Additionally, pro-environmental behavior also reflects personal self-regulation and self-discipline, both of which are crucial for reducing procrastination behavior (Akinci, 2021; Colombo et al., 2023). The findings of this study suggest that even in times of crisis, understanding and promoting pro-environmental behavior is important not only for promoting environmental protection but also for improving academic performance and reducing procrastination behavior.
In comparison to the other predictors mentioned above, the findings of this study indicate that the impact of academic achievement and a sense of school belonging on academic procrastination are relatively small. Although previous studies supported the association between these two predictors and academic procrastination (Lim et al., 2022; Morán-Soto et al., 2022), during times of crisis, students may prioritize health, family, and economic situation (Tadesse & Muluye, 2020; Verma & Prakash, 2020). Under such circumstances, academic achievement may no longer be the top priority for students, but rather addressing the various issues brought about by the crisis (Brion & Kiral, 2021; Hartshorn & Benjamin, 2020).
Lastly, the weakest predictor for academic procrastination found in this study is the sense of school belonging. One possible explanation could be the impact of long-term online learning during the epidemic, which weakens the connection between students’ sense of school belonging, and their tendency to procrastinate academically. Literature suggests that a sense of school belonging often has a role to play in academic procrastination through other mediators. For example, Dong and Izadpanah (2022) argue that the sense of school belonging can indirectly affect academic procrastination by influencing self-efficacy and emotions. Furthermore, a sense of school belonging can also reduce the degree of academic procrastination by alleviating psychological pressures such as anxiety and tension (Abdollahi et al., 2020). Despite academic achievement and a sense of school belonging are not the most important predictors for academic procrastination in this study, they are still important for students’ overall academic performance and psychological well-being during public crises. Therefore, these predictors should not be disregarded.
Conclusion
This study employs a decision tree model to predict academic procrastination. The results show that the model has an accuracy of 85.78%, indicating its effectiveness in predicting academic procrastination. The findings of this study suggest that subjective well-being, smartphone addiction, and negative emotions are core predictors of academic procrastination. In particular, subjective well-being, the most significant predictor, underscores the crucial role of psychological well-being and life satisfaction in academic behavior. Moreover, smartphone addiction and negative emotions reveal the significant impact of modern technology dependence and emotional health on academic procrastination. It is worth noting that factors such as self-esteem, life autonomy, pro-environmental behavior, academic achievement, and sense of school belonging, although having a relatively small predictive effect on academic procrastination in this study, should not be disregarded. Instead, they remain important considerations for understanding the complexity of academic procrastination behavior. These factors provide insights for mitigating academic procrastination by promoting personal well-being, enhancing self-regulation, and strengthening school belonging. The findings of this study not only offer new perspectives on understanding and predicting academic procrastination but also provide empirical evidence for developing effective interventions to address it. In conclusion, this study makes a significant contribution to the understanding of academic procrastination behavior, both in times of crisis and beyond, and offers practical guidance for effectively tackling academic procrastination among college education students.
Implication
This study has enriched the applicability of the Biopsychosocial Model in the context of public crises by suggesting that the predictors associated with academic procrastination are indeed interrelated with students’ biological, psychological, and social aspects. Simultaneously, this study could provide a series of practical implications for educational stakeholders. The findings of the study suggest that students bear the primary responsibility for combating academic procrastination, but maintaining personal well-being is essential for improving self-regulation, which is necessary for effectively addressing procrastination. This would enable students to better utilize campus resources, such as engaging in group studies and utilizing counseling services to mitigate tendencies toward academic procrastination. Additionally, it is important for lecturers to provide academic and emotional support to enhance students’ self-esteem and autonomy as they are significant factors in addressing academic procrastination behavior. Lastly, the findings of the study could offer insights to university management on optimizing student support services such as wellness centers, counseling services, and academic advising centers. This optimization would aim to consistently ensure student well-being and enhance the sense of school belonging, especially during times of crisis to reduce academic procrastination behavior among students.
Limitation and future research
This study has certain limitations. Adopting a cross-sectional design may restrict the depth of understanding over time. Moreover, the respondents all come from a university in the Guangxi Zhuang Autonomous Region, which could potentially limit the generalizability of the research results. Future research could focus on enhancing understanding by observing the interactions between the predictors and changes in academic procrastination over time. Additionally, recruiting participants from different regions or institutions could improve the generalizability of the findings. Researcher is also encouraged to investigate other potential variables that are not included in this study to expand the understanding of academic procrastination behavior and its determinants. This could be beneficial in the endeavor to reduce academic procrastination behavior beyond the pandemic.
Data availability
The datasets generated during and analyzed during the current study are not publicly available due to institutional confidentiality regulations but are available from the corresponding author on reasonable request.
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P.S.: Conceptualization; Supervision, Writing - review & editing. X.L. (Corresponding): Data curation, Methodology, Writing - original draft. X.C.: Methodology, Software. M.Z.: Software, Validation. Q.W.: Project administration, Resources. X.Z.: Formal analysis.
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Song, P., Liu, X., Cai, X. et al. Predictive analysis of college students’ academic procrastination behavior based on a decision tree model. Humanit Soc Sci Commun 11, 869 (2024). https://doi.org/10.1057/s41599-024-03300-1
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DOI: https://doi.org/10.1057/s41599-024-03300-1
