Introduction

Academic procrastination is a form of self-regulation failure in which students voluntarily postpone academic tasks they intend to complete (in order to reduce/change their negative moods; Sirois & Pychyl, 2013) despite knowing that the negative consequences of this delay will leave them worse off (Sirois & Pychyl, 2013; Steel, 2007). “Self-regulation is a process in which people organise and manage their capacities—that is, their thoughts…, emotions…, behaviours…, social—contextual surroundings…—in the service of attaining some future state” (Reeve et al., 2008, p. 223). Procrastination is highly prevalent among the student population, as 80–95% of college students occasionally procrastinate (Ellis & Knaus, 1979; Fentaw et al., 2022; O'Brien, 2000; Steel, 2007) and 25–50% of college students chronically procrastinate (Day et al., 2000; Haycock, 1993; Onwuegbuzie, 2000; Özer et al., 2009; Rahimi & Hall, 2021; Solomon & Rothblum, 1984). Chronic procrastinators are students who habitually delay initiating and completing most, if not all, of their academic work.

Research findings have consistently indicated that academic procrastination impedes students’ academic success, with chronic procrastinators having lower GPAs, assignment grades, and exam scores than non-procrastinators (Akpur, 2020; Day et al., 2000; Goroshit & Hen, 2019; Kim & Seo, 2015; Kim et al., 2017; Klassen et al., 2008; Steel, 2007; Steel & Klingsieck, 2016; Tao et al., 2021; Tian et al., 2021). In addition, the pervasive effects of academic procrastination extend to increased stress levels, sleep-related problems that can impair future learning and self-control, emotional difficulties (e.g., feelings of anxiety, shame, guilt, anger, sadness, time pressure, and dissatisfaction; Grunschel et al., 2013; Steel & Klingsieck, 2016), poor health and well-being (Sirois et al., 2003; Sirois & Pychyl, 2013; Tice & Baumeister, 1997), lower life satisfaction (Rozental et al., 2022; Sirois, 2016), and higher rates of school dropout (Grunschel et al., 2013). Moreover, procrastination can become habitual (Svartdal & Løkke, 2022) and generalise across other life domains (Hen & Goroshit, 2018; Rozental et al., 2022; Sirois et al., 2003; Sirois & Pychyl, 2013; van Eerde & Klingsieck, 2018). These aspects indicate that it is critical to intervene to reduce students’ procrastination.

Literature Review

Conceptual Frameworks Informing the Study of Procrastination

Two major conceptual frameworks explain why students procrastinate their academic work. These are the Temporal Motivation Theory (Steel, 2007) and the Short-Term Mood Repair Theory (Sirois & Pychyl, 2013). The former proposes that students’ motivation to complete an academic task is determined by expectancy of success in that task, value associated with doing or completing the task, impulsiveness, and delay. That is, the higher students’ expectations of success (i.e., high self-efficacy beliefs) and the more they value the task (e.g., high intrinsic motivation, low task aversion) and/or the outcome of task engagement, the more likely they are to complete this academic task without unnecessary delay. However, even when students have high expectancies of success for, and value an academic task, they may still delay working on it if they are impulsive (i.e., have high distractibility and low self-control) and the deadline to complete the task is in the distant future (sensitivity to delay; Steel, 2007). With regard to intervention, this theory suggests that effective interventions that reduce academic procrastination increase students' expectancy and value whilst also reducing their impulsivity and sensitivity to delay.

The Short-Term Mood Repair Theory proposes that aversive tasks induce negative affect (e.g., frustration, uncertainty, boredom, anxiety) that leads to a self-control dilemma between students’ desire of “giving into the feel good” now and their long-term goals (Tice & Bratslavsky, 2000, p. 149). Thus, in this theory, procrastination is considered an emotion (mis)regulation strategy because students are motivated to regulate their immediate mood by postponing working on their academic task and engaging in a more pleasurable activity (e.g., scrolling social media, hanging out with friends). However, the benefits of procrastination on mood are only short-term, and students often feel worse than if they had worked on their assignment without the unnecessary delay(s) (Sirois & Pychyl, 2013; Tice & Bratslavsky, 2000). Interventions informed by this theory aim to ensure decreases in procrastination by reducing task aversiveness (e.g., by strengthening emotion regulation skills).

Procrastination is not solely influenced by individual differences, such as student motivation or ability to regulate task-related negative emotions. In fact, “the core problem of procrastination, poor self-regulation, … is amplified by common aspects of the student environment” (Svartdal et al., 2020, p. 8). A recent review identified nine context-related factors (operating at the institution, program, and/or course levels) that make it more likely that students procrastinate in their learning (Svartdal et al., 2020). Specifically, unstructured conditions for learning – such as when students face complex choices regarding their learning or program of study – make procrastination more likely. In addition, long deadlines and learning environments where temptations/distractions (including distractions from peers) abound also create “procrastination-friendly” learning contexts (Svartdal et al., 2020, p. 1). Moreover, learning environments that seldom facilitate students’ access to information needed to monitor their performance in a course (or evaluate their progress toward attaining their learning goals) are also linked to elevated procrastination. Furthermore, learning contexts that offer students limited opportunities to develop their self-efficacy regarding learning, include little/no group work, or involve poorly designed group work are also conducive to procrastination (Svartdal et al., 2020).

Svartdal et al.’s (2020) review and the empirical research supporting its conclusions indicate that efforts to reduce procrastination should not solely rely on interventions aiming to strengthen individuals’ self-regulation. These efforts need to be “supplemented with specific contextual and organizational measures that can support productive self-regulation” (Svartdal et al., 2020, p. 8). Consistent with this call, existing research has highlighted some promising initiatives that targeted the learning environment or context. For example, Motz and colleagues have recently conducted personalized interventions that reminded students when they were about to miss an assignment deadline (Motz et al., 2021b) or praised them for submitting their work on time (Motz et al., 2021a). Both interventions, which aimed to strengthen students’ learning/achievement by increasing adherence to assignments in a non-paternalistic way, were successful. Specifically, the first was associated with significantly lower levels of missed assignments (compared to active controls), stronger assignment adherence, and higher course grades (Motz et al., 2021b). The second led to elevated assignment submission rates and course performance (Motz et al., 2021a).

Key Findings from Previous Reviews of Procrastination Interventions

In undertaking this research, we identified four published reviews on the efficacy of interventions aiming to reduce academic procrastination (i.e., Malouff & Schutte, 2019; Rozental et al., 2018; van Eerde & Klingsieck, 2018; Zacks & Hen, 2018). In the following, we highlight their key findings and conclusions. Malouff and Schutte’s (2019) meta-analysis found that psychological interventions produced large reductions in procrastination (i.e., Hedge’s g = 1.18). This review comprised of a variety of interventions (e.g., cognitive-behaviour therapy (CBT), paradoxical, rational emotive behavioral therapy, strengths training, visual feedback, etc.); no clear differences in the effectiveness of different types of interventions were found. This review also revealed that interventions were more effective when they targeted student samples, were delivered in person, and involved a no-treatment control group.

Rozental et al.’s (2018) meta-analysis also examined the efficacy of a variety of psychological interventions, including CBT, paradoxical, therapeutic metaphor, emotion regulation, dyadic coaching, SMART goals, acceptance commitment and behaviour therapy, and self-monitoring. Overall, interventions collectively produced modest benefits in reducing academic procrastination (i.e., Hedge’s g = 0.34). At the same time, there was significant heterogeneity in the effect sizes across studies. These authors reported that the type of intervention that produced more robust effects was CBT (i.e., Hedge’s g = 0.55).

Zacks and Hen’s (2018) review highlighted the limited number of intervention studies, randomised control trials, or holistic interventions that target individual and situational causes of procrastination. These authors discussed three main types of interventions: therapeutic prevention targeting the general student population, therapeutic treatment targeting procrastinators, and instructor-based interventions. Therapeutic procrastination interventions consist of counselling services administered to students by a trained psychologist in order to reduce academic procrastination. Zacks and Hen (2018) argued that although therapeutic interventions produce large effect sizes, instructor-based interventions are more cost-effective, easier to implement, and provide an opportunity to address concerns about the prevalence of procrastination among students by teaching students how to effectively regulate their own learning.

Finally, van Eerde and Klingsieck’s (2018) meta-analysis discussed four main types of interventions: self-regulation training, CBT, other therapeutic approaches, and strengths-based interventions. These authors reported that interventions caused medium to large reductions in academic procrastination. In line with Rozental et al. (2018), van Eerde and Klingsieck (2018) found (a) significant heterogeneity in effect sizes and (b) that CBT produced the largest effect sizes of all types of interventions.

Unresolved Issues and Unanswered Questions in Procrastination Intervention Research

What Types of Procrastination Interventions Have Been Recently Used? To what Extent are they Informed by the Main Conceptual Frameworks Supporting Procrastination Research?

Rozental et al.’s (2018) review and meta-analysis included only randomized control trials (RCTs) and assessed the intervention effects only by means of self-reports. Similarly, Malouff and Schutte’s (2019) review and meta-analysis included only RCTs (i.e., 12 articles). In contrast, van Eerde and Klingsieck’s (2018) review and meta-analysis included several different types of studies. Overall, these previous reviews reported that it was difficult to classify the interventions they examined in clear-cut types. This is because different interventions had overlapping content and used many different intervention methods (Malouff & Schutte, 2019). Case in point, van Eerde and Klingsieck (2018) noted that they found it “difficult to assign labels to intervention types because these combined different approaches” (p. 82). These prior reviews suggest that the field of procrastination interventions is rapidly developing from a heterogeneous base. In this review, we continue previous efforts and try to map the types of procrastination interventions that were conducted after the publication of these major reviews; see our first research question below.

Existing reviews could not provide a clear picture on the extent to which specific interventions were informed by a given theory or model. Regarding this aspect, Malouff and Schutte (2019) stated that the interventions they reviewed “were not designed to test a specific model” (p. 123). In a related vein, van Eerde and Klingsieck (2018) noted that while some of the interventions they examined were based on theories, “others vaguely relied on empirical findings concerning procrastination” (p. 83). The only exception to this pattern is that Rozental et al. (2018) proposed that procrastination interventions involving CBT fit well with Steel’s (2007) Temporal Motivation Theory. The lack of (or uncertain) theoretical grounding of many procrastination interventions is clearly not optimal and van Eerde and Klingsieck (2018) suggested that “in future studies, theory-based and evidence-based interventions would be preferable” (p. 83). In our review, we evaluate the extent to which recent interventions have a strong conceptual grounding; see our second research question.

Are Recent Interventions Effective in Reducing Academic Procrastination?

Previous reviews found that most (yet not all) interventions they analyzed were effective. For example, of the 21 comparisons that comprised their meta-analysis, Rozental et al. (2018) found five (about 23%) that favored control over treatment. Similarly, Malouff and Schutte (2019) reported that for three of the 12 studies they analyzed (i.e., 25%), the 95% confidence intervals for the intervention’s effect size included zero (see their Figure 2). This suggests that the effects of the given interventions were not reliably different from zero. In the van Eerde and Klingsieck (2018) review, of the 35 studies that included comparisons between change in treatment and control groups, in 12 studies (about 34%) the 95% confidence intervals for the effect size included zero; in one study, the intervention appeared to favor the control group (see their Figure 2). Considering that the effectiveness of an intervention is a pivotal desideratum, in this review, we map the extent to which recent interventions were effective (see our third research question). In addition, we investigate whether any important information could be uncovered by examining the overarching characteristics of successful and unsuccessful interventions.

Do Recent Interventions Assess their Long-Term Effects on Reducing Procrastination?

The recent reviews and meta-analyses we discussed paint a slightly different picture regarding the extent to which the interventions they examined included follow-up measures to assess the interventions’ long-term effects. On the one hand, Rozental et al. (2018) noted that their review explored only post-treatment outcomes because “there were too few studies reporting data at follow-up” (p. 3). On the other hand, the Malouff and Schutte’s (2019) review – which, similarly to Rozental et al. (2018) included only RCTs – found that 50% of the interventions they analyzed included follow-up measures; the length of follow-up ranged between one week and 24 weeks. A similar pattern was found by van Eerde and Klingsieck (2018). These authors reported that many of the interventions they analyzed included follow-up tests (i.e., 23 follow-up effect sizes compared to 44 pre-post effect sizes); the interval for the follow-up measures in the van Eerde and Klingsieck (2018) review ranged between one week and one year. Our review continues this work and investigates whether recent procrastination interventions assessed their long-term effects (see our fourth research question).

Do Recent Interventions Evaluate Boundary Conditions for the Intervention (i.e., Moderation)? Do these Interventions Investigate (Mediation) Mechanisms/Processes via which Procrastination Interventions Influence Outcomes?

Twenty years ago, van Eerde (2003) argued that the absence of moderators in studies examining procrastination “is a serious shortcoming” (p. 1401). In intervention research, information on potential moderators of intervention effects enables mapping possible boundary conditions of the intervention (e.g., whether an intervention has differential effects in different subgroups or under different conditions; MacKinnon et al., 2007). The need for consideration of moderators in the procrastination intervention literature has also been highlighted in a recent review and meta-analysis. Specifically, Malouff and Schutte (2019) found that although the interventions they examined were effective overall, they benefitted more some participants than others. In addition to mapping boundary conditions of the effects of current interventions, knowledge of moderating processes could productively inform future work in procrastination (intervention) research (Huang et al., 2021). Considering these aspects, and consistent with recent calls for studies focusing on procrastination to “investigate moderators” (Koppenborg & Klingsieck, 2022a, p. 7), we examined the extent to which interventions reviewed in the present research assessed moderators (see our fifth research question).

In addition to moderating factors, it is important that the procrastination intervention literature considers mediation processes. In intervention programs, examination of mediation mechanisms “generates evidence for how a program achieved its effects” (MacKinnon et al., 2007, p. 597). In turn, identifying the critical elements that channel intervention effects “can streamline and improve these programs by focusing on effective components” (MacKinnon et al., 2007, p. 597) and discarding the ones that are not effective or relevant (Windgassen et al., 2016). Moreover, theory-informed assessment of mediation processes in intervention research could contribute to both theory-refinement and improved outcomes for participants (Windgassen et al., 2016). Despite the importance of examining mediation mechanisms, a recent review of procrastination interventions (Rozental et al., 2018) identified only one study that involved mediation. Considering these aspects, we thought it was important to examine in this review the extent to which recent procrastination intervention investigated mediation mechanisms/processes (see our fifth research question).

The Elephant in the Room: The Missing Type of Procrastination Interventions

A large and consistent body of substantive and meta-analytic research has reported strong negative associations between procrastination and a personality trait, namely conscientiousness (e.g., Cheng et al., 2023; Sanchez-Ruiz & El Khoury, 2019; Steel, 2007; Steel & Klingsieck, 2016; van Eerde, 2003). “Conscientiousness encompasses several overlapping constructs that describe individual differences in the propensity to be self-controlled, responsible to others, hard-working, orderly, and rule abiding” (Spielmann et al., 2022, p. 2746; see also Roberts et al., 2014). Although between four and ten facets of conscientiousness have been proposed in the personality literature (Spielmann et al., 2022), three facets have received consistent support across different measures of conscientiousness and samples. These facets are (i) industriousness, which subsumes the tendencies to work diligently to get things done, persist when encountering difficulties, be ambitious, and aspire to excellence; (ii) orderliness, which encompasses the tendencies to plan one’s future/actions and be systematic/meticulous in what one does; and (iii) impulse control (also called self-control), which captures the tendencies to delay gratification and control/inhibit impulses that go counter to long-term goals (Costantini & Perugini, 2016; Spielmann et al., 2022).

Recent research and theorizing suggest that conscientiousness is generally malleable (Magidson et al., 2014; Roberts et al., 2017; see also Allemand & Flückiger, 2022) and most changeable during adolescence (Spielmann et al., 2022). Especially relevant to its association with academic procrastination, conscientiousness “is influenced by various life experiences, such as school activities” (Spielmann et al., 2022, p. 2746). Therefore, interventions could help strengthen conscientiousness (Spielmann et al., 2022). Considering these key aspects (i.e., the strong and consistent negative association between procrastination and conscientiousness and the malleable nature of conscientiousness), it follows that conscientiousness could be productively targeted in interventions aimed to reduce academic procrastination. Nevertheless, no such interventions were identified in the pre-2018 literature. This study examines whether procrastination interventions focusing on strengthening students’ conscientiousness were recently conducted; see our sixth reearch question.

To what Extent Can Instructors/Teachers Implement More Recent Procrastination Interventions in their Classrooms?

The recent major reviews of procrastination interventions indicated that many effective procrastination interventions are therapeutic in nature and resource intensive (Malouff & Schutte, 2019; Rozental et al., 2018; van Eerde & Klingsieck, 2018; Zacks & Hen, 2018). Although therapeutic interventions tend to produce sizeable reductions in academic procrastination, they need to be administered by a trained therapist, which, in turn, might lead to budgetary strains for some educational institutions. In addition, these types of interventions could involve specific staff training and are generally more resource-intensive than non-therapeutic interventions (e.g., they generally require individual or small group settings). Another potentially important limitation of therapeutic interventions is that they might be less accessible to students who do not have time outside of class to participate in this intervention (Zacks & Hen, 2018).

Some researchers commented that insufficient attention has been paid to what interventions can be readily implemented in classrooms by instructors (Miyake & Kane, 2022; Zacks & Hen, 2018). Administering non-therapeutic interventions, such as instructor led interventions, has several advantages over therapeutic approaches. For example, non-therapeutic approaches can be administered to all students to ensure equity. In addition, small changes in how the assignments are implemented/scheduled or how the learning materials are made accessible do not require extensive resources (e.g., in terms of time, training, and materials), could be cost-effective (Zacks & Hen, 2018), and might be more likely to be adopted by educational institutions (Miyake & Kane, 2022). At the same time, classroom-based procrastination interventions also have drawbacks. For instance, these types of interventions require buy-in from teachers; considering the notoriously high workloads of the teachers, this buy-in is far from being guaranteed. In addition, teacher-led interventions may be implemented with varying levels of fidelity. In this review, we evaluate the extent to which recent interventions could be relatively easily implemented by instructors/teachers in their classrooms; see our seventh (and final) research question.

Research Questions/Aims

Our study aims to address the questions we highlighted above; the specific research questions we examined are included below. To answer these questions, we conducted a systematic search of the literature to identify interventions designed to reduce academic procrastination that were developed after these major reviews were published (i.e., Malouff & Schutte, 2019; Rozental et al., 2018; van Eerde & Klingsieck, 2018; Zacks & Hen, 2018). In this article, we use the term “intervention” in a broad sense to describe any intentional effort to reduce academic procrastination by means of (i) altering students’ internal states (e.g., reducing negative moods), (ii) changing relevant individual difference factors (e.g., motivation; impulsivity; ability to manage emotions), and/or (iii) modifying one or more aspects of students’ learning-related contexts (e.g., assigning group work vs. individual work for an assessment). Examining the research questions we propose is important because new information gleaned from engaging with them could be productively taken into account when designing or selecting future procrastination interventions. Our review employs this specific timeframe (2018-March 2023) to examine recent developments in academic procrastination intervention research.

This review examines seven research questions (RQs):

  • RQ1: What types of interventions were reported post 2018?

  • RQ2: What theory of academic procrastination (if any) informed these interventions?

  • RQ3: Were the reported interventions effective?

  • RQ4: Were follow-up measures used to assess the long-term effects of the interventions on academic procrastination?

  • RQ5: Were moderation and/or mediation factors/mechanisms assessed?

  • RQ6: Were interventions that targeted conscientiousness to reduce academic procrastination reported?

  • RQ7: What types of interventions are realatively easy to implement and, thus, could be realistically applied by instructors/teachers in the classroom?

Method

We conducted a systematic search of seven online databases (i.e., Google Scholar, ERIC, Web of Science, Scopus, ProQuest, PsycINFO, and A + Education) to identify empirical studies testing the efficacy of an intervention in reducing academic procrastination. The publication date was restricted to 2018-March 2023 to capture the articles published after the most recent major reviews of procrastination interventions (i.e., Malouff & Schutte, 2019; Rozental et al., 2018; van Eerde & Klingsieck, 2018; Zacks & Hen, 2018). We conducted the initial search in November 2022 using “academic procrastination” OR “procrastinat*” with any of the following search terms: intervention, treatment*, school, university, college, reduce, decrease, experiment*, control group, or random*. The search was rerun in March 2023 to ensure that no relevant articles were missed from our review. Following the recommendations of Alexander (2020) we searched the reference lists of relevant articles identified from our search for additional articles. However, no additional articles were identified.

To be included in the review, articles had to: (i) use a student sample ranging from primary to tertiary education, (ii) employ a pre-test post-test quantitative design with a control group, (ii) be peer-reviewed, (iii) be written in English, (iv) include a self-reported or behavioral measure of academic procrastination, and (v) provide sufficient information to understand what the intervention consisted of and how it was evaluated.

Our search excluded grey literature (e.g., conference papers, thesis, dissertations, government reports, or unpublished articles that have not been peer reviewed) because we did not have a large enough research team to undertake a timely examination of these studies, they are at times difficult to access, and there is high variability in the quality of reports from grey literature (Alexander, 2020). Although removing from consideration grey literature may increase the risk of publication bias by potentially excluding non-significant results from our review (Alexander, 2020), it is important to note that Rozental et al. (2018) reported "small sample sizes and high risk of bias in many of these doctoral theses” (p. 12), which would be a key part of the grey literature. Moreover, we also excluded articles that did not describe their intervention in enough detail (n = 2; e.g., Armani Kian et al., 2020; Kang & Zhang, 2020), did not include a control group (n = 3; e.g., Gagnon et al., 2018; Gonda et al., 2021), involved a non-experimental study (n = 1; Hensley & Munn, 2020), had a small sample size (i.e., less than 20 participants per condition; n = 3; e.g., Dinç & Ekşi, 2019; Wilson Van Voorhis & Morgan, 2007), did not include a post-test measure of academic procrastination (n = 1; Gading, 2020), involved an intervention that was not specifically designed to reduce academic procrastination (n = 3; e.g., Amoke et al., 2021; Baker et al., 2018), or involved a combination of some of the aspects highlighted here (n = 9; e.g., López-López et al., 2020; Motie et al., 2019).

The search identified a total of 1,320 articles, out of which 1,173 were excluded for not being relevant to the research question, leaving 147 articles. We then removed duplicates (n = 54) leaving 93 articles. We then screened the titles and abstracts to exclude irrelevant articles based on our exclusion criteria (n = 46) leaving 47 articles. Finally, the first author read the full text of each article, excluding those that did not meet our eligibility criteria (n = 25), leaving 22 articles. During the review process, we became aware that one of these 22 articles was retracted. This is why, our final set included 21 articles.

Results

The search process identified 21 empirical studies that met all the eligibility criteria. These studies, together with their key characteristics and outcomes investigated, are listed in the online supplemental material (OSM). As indicated in its Table of Contents, the OSM comprises seven parts. Part 1 of the OSM includes summary information on 10 articles in which the interventions focused on a general student sample and examined self-reports of procrastination. Part 2 of the OSM includes information on five articles in which the interventions focused on a student sample that had one or more specific characteristics (e.g., students having high levels of procrastination) and examined self-reported academic procrastination. Parts 3 (six articles) and 4 of the OSM (one article) group studies focusing on general and specific samples, respectively, and used behavioral measures of procrastination. One article included both types of measures (i.e., self-report and behavioral measures of procrastination) and was, thus, included twice in the OSM (i.e., Koppenborg & Klingsieck, 2022a). Within each of the first four parts of the OSM, interventions that manipulated students’ physical or social environment are listed first (i.e., situational strategies), followed by interventions that both manipulated students’ environment and altered their mental representations (i.e., situational and cognitive strategies; Duckworth et al., 2018). Interventions that only focused on altering students’ mental representations (i.e., cognitive strategies; Duckworth et al., 2018) are listed last. Part 5 of the OSM describes how each article was coded. Part 6 includes information on the aspects that were considered when evaluating the ease of implementation of each intervention. Part 7 explains our rationale for not retaining for this review articles with a sample size of less than 20 participants per condition.

To assess the threat of publication bias, we conducted a p-curve analysis (Simonsohn et al., 2014). Unfortunately, the p-curve analysis could not capture all the interventions included in our review because for five studies we were unable to find or calculate the information needed to be entered in the p-curve analysis app; for example, one article estimated a complex model, and we could not map the test statistics reported in the article to the type of data that could be entered in the p-curve app. For this p-curve analysis, the p-value for the binomial test regarding evidential value was p = 0.0112. For evidential value pertaining to the continuous test, for full p-curve, z = 13.6, p < 0.0001; for half p-curve, z = 14.08, p < 0.0001. The power of tests included in the p-curve was 99%. This analysis did not indicate that evidential value is inadequate or absent: p = 0.9266 for the binomial test; the p-values for continuous test (both for the full-curve and the half-curve) were bigger than 0.9999. Thus, the findings of this incomplete p-curve analysis suggest that there is some evidence of publication bias.

Of the 21 studies included in our review, three (14.3%) used a randomised-control trial design, nine (42.9%) used an experimental design, four (19.0%) employed a field experimental design, three (14.3%) used a quasi-experimental design, one study (4.8%) implemented a longitudinal quasi-randomised control trial design, and one study used a quasi-experimental interrupted time series design; Table 1, column 2 provides information on the research design for each of the 21 studies. The studies reviewed included three types of control groups: business as usual (BAU; that is, no treatment), waitlist, or active waitlist. We found that 14 studies (66.7%) used a BAU control group, six (28.6%) used a waitlist control group, and one study used an active waitlist control group; Table 1, column 3, provides information on the type of control group employed in each intervention.

Table 1 Characteristics of the studies included in our systematic review
Full size table

RQ1: Types of Interventions

Our review of this recent literature identified that many different types of interventions were conducted (e.g., mindfulness, corrective feedback, social norms, deadline reminders, etc.; for a full account, see Table 1 column 6). Ten interventions (47.6%) were offered in person, eight (38.1%) were offered online, and one was administered both in person and online. Two studies in which the interventions involved policy changes in examination practices interventions were labelled as N/A (see Table 1, column 8). The intervention lengths ranged from one day (Koppenborg & Klingsieck, 2022b) to one year (Gershoni & Stryjan, 2023). The total sample sizes of the interventions considered ranged from 50 to 29,468 students. Twenty articles (95.2%) collected data from university students; only one intervention was conducted with secondary school students (i.e., Kaur et al., 2021). In terms of age, the narrowest reported age range in these studies was 16 – 19 years (Kaur et al., 2021), whereas the broadest age range was 19 – 62 years (Eckert et al., 2018). In regard to gender, seven studies (33.3%) collected data from a predominantly female sample and one from a predominantly male student sample; gender distribution was relatively balanced for the remaining articles.

RQ2: Conceptual Frameworks Informing the Interventions

The theoretical foundations of the interventions reviewed in this article included both the Temporal Motivation Theory (Steel, 2007) and the Short-Term Mood Repair Theory (Sirois & Pychyl, 2013). Specifically, 10 studies (47.6%) were informed by the tenets of the former theory and two studies (9.5%) by those of the latter theory. Eight studies (38.1%) drew from both theories, whereas one intervention was not clearly linked to either of the two major theories of academic procrastination (see Table 1 column 9).

RQ3: Effectiveness of Interventions

Investigating RQ3, we found that 17 interventions (80.9%) were effective; that is, these interventions were associated with statistically significant reductions in academic procrastination. Four interventions (19.0%) failed to cause significant reductions in academic procrastination (i.e., Abuhmaid & Abood, 2020; Gershoni & Stryjan, 2023; Nicholls, 2023; Oram et al., 2022).

RQ4: Follow-Up Measures Assesing Long-Term Effects of Interventions

With regard to RQ4, our review found that six studies (28.6%) in our sample included a follow-up measure of academic procrastination (see Table 1 column 11). The follow-up time frame ranged from five weeks (Grunschel et al., 2018) to three months post-intervention (Krispenz et al., 2019; Otermin-Cristeta & Hautzinger, 2018).

RQ5: Moderation/Mediation Factors/Processes

Regarding this research question, our investigation found that four articles (19.0%) examined moderating factors. Three of these studies examined whether the effectiveness of the intervention was moderated by trait procrastination (Himmler et al., 2019; Koppenborg & Klingsieck, 2022b; Wessel et al., 2020); one study examined the moderating effect of workload (Huang et al., 2021). Only two articles (9.5%) undertook mediation analyses; one of these studies tested whether the intervention reduced academic procrastination via strengthening students’ emotion regulation skills (Schuenemann et al., 2022), whereas the other investigated whether the effects of the intervention were mediated by improvements in self-efficacy (Krispenz et al., 2019).

RQ6: Conscientiousness and Procrastination Interventions

When we examined RQ6 we found that, contrary to our expectations, no interventions aimed to reduce academic procrastination by increasing students’ conscientiousness.

RQ7: Ease of Implementation and Suitability to Classroom Use

Investigating RQ7, we found that among the interventions we identified there was a balanced mixture of studies that tested the effects of situational strategies, which aimed to change some aspects of students’ contexts/environments, and cognitive strategies, which tried to strengthen some parts of students’ self-regulation (Duckworth et al., 2018). In engaging with RQ7, we evaluated whether each intervention could realistically be applied in classrooms by rating its relative ease of implementation as high, moderate, or low. In so doing, we considered four factors: (a) the amount of effort required for the teachers and students to participate in the intervention; (b) characteristics of the learning environment that may make it difficult to implement the intervention in classrooms; (c) the financial costs associated with intervention materials and training staff; and (d) the time costs associated with students participating in the intervention and teachers administering the intervention in the classroom; for more detail about the ratings for each article refer to Part 6 in the OSM. We found that 10 interventions (47.6%) could be (relatively) easily implemented into classrooms (see in the last column of Table 1 the entries where the ease of implementation was rated as ‘high’) and four interventions (19.0%) would be moderately difficult to implement in classrooms. Seven interventions (33.3%) are likely to involve significant effort, time, and financial costs that severely constrain their implementation in classrooms; hence, for these interventions, the ease of implementation was rated as ‘low’ (see Table 1).

Discussion

We conducted this systematic review to undertake an in-depth examination of recent interventions designed to reduce academic procrastination. In addition, we set out to use the results of this examination to uncover commonalities and differences among the interventions, identify the boundaries of current knowledge on procrastination interventions, and map a set of recommendations for effective interventions that can be feasibly administered by teachers/instructors. Below, we discuss the implications of our findings pertaining to the first six research questions. The significance of the seventh research question is addressed in the subsection titled “Recommendations for Instructional Practice”.

Types of Interventions and their Conceptual Grounding

About half of the interventions examined in this review were offered in person. This percentage is comparable to the corresponding figure reported in Malouff and Schutte’s (2019) review (i.e., 58%). Likewise, the percentage of interventions offered online was comparable in our and Malouff and Schutte’s (2019) review (i.e., 38.1% and 33%, respectively). Both our review and that of Malouff and Schutte (2019) found that one intervention was delivered in a format that included both in person and online components. In terms of control groups used in the interventions, our study found a larger percentage of no-treatment groups (i.e., 66.7%) and a smaller percentage of waitlist groups (i.e., 28.6%) than Rozental et al. (2018) did (i.e., 50% in each category).

Similar to results reported by van Eerde and Klingsieck (2018), our study found a wide range of types of interventions, which included calls to action, policy changes in the examination process, and CBT, to mention just a few; see Table 1 for more details. Interestingly, the percentage of CBT in our review (4.8%) was markedly smaller than the corresponding percentage in Rozental et al. (2018) (i.e., 33%). As it was the case in the van Eerde and Klingsieck (2018) review, several of the interventions we examined focused on enhancing participants’ strengths and resources (e.g., mindfulness, psychological well-being, self-reflection, self-regulation).

Some of the interventions examined in our review used apps to send daily reminders prompting students to reflect on their study habits and complete online self-regulation training modules (e.g., Eckert et al., 2018; Loeffler et al., 2019; Schuenemann et al., 2022; Wessel et al., 2020)1. Findings from the studies reviewed suggest that there are four important benefits linked to using app-based interventions. First, they reduce the demand on instructors, thus enabling them to focus their resources on implementing intervention strategies that students would struggle to initiate themselves (i.e., other-deployed cognitive and situational strategies; Duckworth et al., 2018). This may involve social norm interventions where teachers send out reminders to students about the due date for an assignment and highlight the percentage of the class that have already submitted this assignment (i.e., Huang et al., 2021).

Second, when the app-based interventions include sending regular SMS reminders to students, this is likely to increase student engagement with the intervention and may even strengthen its effectiveness (Eckert et al., 2018). Third, they encourage students to engage with the intervention in stable environments (e.g., studying at the same time of day, in the same place, and with the same people); in turn, this facilitates the development of productive study habits (Galla & Duckworth, 2015; Verplanken & Orbell, 2022; Fiorella, 2020), which further reduce the likelihood of procrastination. Finally, app-based interventions can be customised to target each students’ unique reasons for procrastinating. This is a key aspect, as some researchers have theorised that customised interventions are more effective than undifferentiated ones (Klingsieck, 2013; Rozental et al., 2015; Steel & Klingsieck, 2016; Svartdal & Løkke, 2022; van Eerde, 2015; Wessel et al., 2020).

Several studies included in this review personalised their interventions to target each student's reasons for procrastinating. An analysis of these studies uncovered five main ways researchers customised their interventions. First, they asked students to sign a personalised behavioral contract where they agree to hand in their assignment before a self-imposed deadline (Himmler et al., 2019; Otermin-Cristeta & Hautzinger, 2018). Second, they provided students with multiple techniques to reduce procrastination and encouraged them to select the ones that work best for them (Luo et al., 2022; Schuenemann et al., 2022). Third, they used app-based interventions that offered students personalised recommendations about self-regulation training modules to complete based on students’ self-reported reasons for procrastinating (Loeffler et al., 2019). Fourth, they provided personalised feedback on students’ work (Nourinezhad et al., 2021). Finally, they created one-on-one training sessions (Kaur et al., 2021). Notably, the first four types of strategies are relatively low-cost ways to customise interventions that may heighten students’ engagement and subsequently increase the likelihood that students will benefit from the intervention (Eckert et al., 2016, 2018). In contrast, the final strategy highlighted above would be resource intensive as many instructors (as well as institutions) might struggle to provide one-on-one training sessions to all their students.

Notwithstanding the aspects highlighted in the above paragraph, most interventions reviewed in this study were administered to all students without any customisation. It is unlikely that undifferentiated interventions are effective for all students when learners may procrastinate for multiple reasons (Miyake & Kane, 2022). Notably, research suggests that reasons for procrastinating vary depending on the person, academic task, and their environment/context (Klingsieck, 2013; Miyake & Kane, 2022; Nordby et al., 2017; Steel, 2007; Svartdal et al., 2020; Yan & Zhang, 2022). For example, students have different personal resources; therefore, the strategies they find valuable and easier to implement might differ across students.

When examining the conceptual grounding of the interventions we reviewed, we found that only one of the 21 interventions analyzed could not be clearly linked to any of the major theoretical frameworks informing procrastination research. This finding is encouraging, and it suggests that recent calls to undertake theory- or empirically-based procrastination interventions (van Eerde & Klingsieck, 2018) did not go unheard.

Effectiveness of Interventions

Our review identified 17 interventions that successfully reduced academic procrastination. Consistent with previous reviews (Malouff & Schutte, 2019; Rozental et al., 2018; van Eerde & Klingsieck, 2018), we found significant variability in effect sizes. Specifically, of the 10 studies that reported effect sizes, four reported medium effect sizes (Koppenborg & Klingsieck, 2022a; Krispenz et al., 2019; Luo et al., 2022; Wessel et al., 2020) and six reported large effect sizes (Eckert et al., 2018; Grunschel et al., 2018; Kaur et al., 2021; Koppenborg & Klingsieck, 2022b; Schuenemann et al., 2022; Ugwuanyi et al., 2020). Following, we discuss some overarching aspects characterizing successful and unsuccessful interventions that we uncovered in the present study.

The successful interventions identified in this review focused on building students’ self-regulation abilities by developing time management and emotion regulation skills, increasing their motivation, self-efficacy, and meta-cognitive awareness (e.g., Eckert et al., 2018; Grunschel et al., 2018; Wessel et al., 2020). Notably, eight of the reviewed interventions involved aspects that are conceptually consistent with both major theories informing procrastination research (i.e., Temporal Motivation Theory; Steel, 2007; Short-Term Mood Repair Theory; Sirois & Pychyl, 2013). This is important because interventions that target factors that address (to some extent) both theoretically posited sets of pathways leading to procrastination (i.e., emotion regulation and expectancy-value-impulsiveness-delay) and encourage students to try multiple strategies to reduce their procrastination might go a long way toward ensuring that all students benefit from the same intervention. In addition, interventions that target multiple antecedents of procrastination are likely to be more effective than interventions that focus on a single antecedent (Miyake & Kane, 2022).

Overviewing some of the characteristics of the four unsuccessful interventions pinpoints three important aspects. First, the intervention reported by Abuhmaid and Abood (2020), which neither focused on developing students' emotion regulation skills nor attempted to enhance expectancy/value, was ineffective. Second, the study authored by Gershoni and Stryjan (2023) found that precommitment devices in the form of students setting self-imposed deadlines for their final project were ineffective at reducing academic procrastination (e.g., reducing delayed graduation rates). The authors’ findings are consistent with previous literature showing mixed results on commitment devices’ effects on academic procrastination (Anderberg et al., 2017; Ariely & Wertenbroch, 2002; Baker et al., 2016; Bisin & Hyndman, 2014; Burger et al., 2011; Patterson, 2018; Robinson et al., 2018).

Third, the intervention reported by Nicholls (2023), which enhanced students' intentions to change their behaviour but did not teach them strategies to reduce academic procrastination, was also ineffective. This finding is consistent with both theory and research on effective self-regulated learning (McDaniel & Einstein, 2020; McDaniel et al., 2021). Specifically, McDaniel and Einstein (2020) theorised that effective interventions that teach self-regulated learning skills require targeting four dimensions (knowledge, belief, commitment, and planning). Applying this conceptual framework to procrastination interventions suggests that successful interventions need to help students learn about what procrastination is, its antecedents, and consequences. In addition, as part of these interventions, teachers (or other individuals involved with the intervention) have to demonstrate effective interventions for reducing procrastination and provide students opportunities to practice them and receive feedback. Consistent with McDaniel and Einstein’s (2020) framework, other key features of effective procrastination interventions involve developing/strengthening students’ beliefs that they can effectively implement strategies targeting procrastination in their daily lives and that doing this will improve their well-being and academic performance (i.e., self-efficacy). Finally, effective interventions must prompt (and assist) students to plan in detail how they will use these strategies, when, and where, and how they will respond to obstacles/temptations that could fuel the tendency to procrastinate.

Long-Term Effects of Interventions

Our review found that only six articles included a follow-up measure of academic procrastination. This finding is consistent with that of Malouff and Schutte (2019) who reported that most studies in their review only measured academic procrastination immediately after the intervention ended. The length of the follow-up measures in our review ranged between 0–13 weeks, with the longest follow-up measure being three months post-intervention (Otermin-Cristeta & Hautzinger, 2018). Considering this aspect, the corpus of studies reviewed here cannot provide insights on how long the benefits of successful interventions are sustained over time and on whether refresher sessions are needed to prolong their effects and lead to improvements in key student outcomes (e.g., learning, academic achievement, well-being).

Moderation and Mediation Factors/Processes

As we previously highlighted, moderation and mediation mechanisms were rarely examined in the studies we reviewed, despite their pivotal conceptual and practical significance (Koppenborg & Klingsieck, 2022a; MacKinnon et al., 2007; van Eerde, 2003; Windgassen et al., 2016). Case in point, only three articles in our review compared their intervention’s efficacy for high and low trait procrastinators (i.e., Himmler et al., 2019; Koppenborg & Klingsieck, 2022b; Wessel et al., 2020). This is unexpected because such comparisons could help shed light on the mechanisms underpinning the interventions effects and assist with the identification of their boundary conditions (e.g., for whom the intervention is, or is not, effective).

Results from a subset of studies we reviewed provide some indications of possible differential effects between high and low procrastinators. Specifically, Himmler et al. (2019) found that their intervention was more effective for high (trait) procrastinators than for low procrastinators (see their p. 136). Results from the two studies reported by Koppenborg and Klingsieck (2022b) lead to the same conclusion, namely that the effects of the intervention were stronger for high trait procrastinators than for low trait procrastinators (for Study 1, see their Table 2, p. 260; for Study 2, see their pp. 264 – 266). In a similar vein, findings reported by Li and colleagues (2021) indicate that for male (but not for female) students, the intervention effect was stronger for participants who had procrastinated on the previous assignment than for those who did not (see their p. 176); for this analysis, past procrastination was conceptualized as starting work on the previous assignment “later than at least half of the other students” (p. 177). In contrast with findings from Himmler et al. (2019), Koppenborg and Klingsieck (2022b), and Li et al. (2021), Wessel et al. (2020) reported that the intervention was effective in significantly reducing students’ behavioral delay only for low procrastinators but not for high procrastinators (see their pages 1681 and 1684). Overall, regarding moderation factors, our review found that only a few of the interventions reviewed examined potential moderating effects on intervention effectiveness. In addition, their findings were not always consistent. Consequently, it is currently not well understood for whom effective interventions work well and for whom they do not work at all. This is an important concern because, for example, Huang et al. (2021) found that deadline reminders backfired and increased academic procrastination when students’ course load was low.

As far as mediation processes are concerned – and as it was the case for moderation – a dearth of evidence characterized the corpus of interventions we reviewed. That is, our review identified only two studies that conducted mediation analyses. This finding of the present study is consistent with previous meta-analyses that reported no or very few results from mediation analyses (Malouff & Schutte, 2019; Rozental et al., 2018; van Eerde & Klingsieck, 2018).

Conscientiousness and Procrastination Interventions

This review did not identify any intervention that attempted to strengthen students’ conscientiousness to reduce their academic procrastination. This is surprising because conscientiousness has been found to be a key (and consistent) predictor of pivotal student outcomes, including but not limited to procrastination (Spielmann et al., 2022; Steel, 2007). Thus, interventions that target procrastination by means of strengthening students’ conscientiousness are likely to have multiple benefits that go beyond procrastination itself.

Limitations of the Corpus of Studies Investigated

Our review identified five limitations of the corpus of studies reviewed here, which should be considered when evaluating their findings. First, gender was unequally distributed in many of the interventions we identified; this, in turn, raises questions about whether the findings generalise beyond the respective samples. Nonetheless, this may not be a major concern considering that a recent meta-analysis found that gender composition was unrelated to the effect sizes associated with procrastination interventions (van Eerde & Klingsieck, 2018).

Second, five articles collected data from specific student samples (i.e., non-general student samples). This includes studies with English as a foreign language learners (e.g., Nourinezhad et al., 2021), students with high trait procrastination (e.g., Grunschel et al., 2018; Kaur et al., 2021; Koppenborg & Klingsieck, 2022a; Krispenz et al., 2019), and learners having high test anxiety (e.g., Krispenz et al., 2019). This aspect may constrain the generalisability of the findings of these interventions.

Third, behavioral measures of academic procrastination, which were employed in seven studies, may underestimate the effects of the intervention because they do not differentiate between irrational, strategic, and unexpected delays (Miyake & Kane, 2022). In addition, behavioral measures also make it difficult to capture longitudinal trends and are not likely to be a reliable measure of academic procrastination for some students (e.g., for learners who strategically delay handing in their assignment to ensure they can get the highest mark possible or avoid feeling that they rushed their submission; Higgins, 2012; Kruglanski et al., 2000). In our review, 15 studies used self-report measures of academic procrastination. This finding reflects a trend in procrastination literature, which predominantly relies on self-report measures of academic procrastination. There are several limitations with self-report measures, which need to be considered in conjunction with intervention studies relying on these types of measures. Specifically, (a) effect sizes are larger with behavioral measures of academic procrastination compared to self-reports of procrastination (Kim & Seo, 2015); (b) there has been significant variability in the predictive utility of different self-report measures of academic procrastination (Krause & Freund, 2014); (c) behavioral measures of academic procrastination can have stronger predictive validity than self-reports (Imhof et al., 2021); and (d) more generally, people do not always provide accurate retrospective self-reports on their mental processes (Kim & Seo, 2015; Nisbett & Wilson, 1977; Steel et al., 2001).

Fourth, findings from (an admittedly incomplete) p-curve analysis conducted in conjunction with this research suggest that there is some evidence of publication bias. This result is not congruent with previous reviews, which, by and large, concluded that “publication bias was not a severe threat” (Van Eerde & Klingsieck, 2018, p. 79; Malouff & Schutte, 2019; Rozental et al., 2018). Our decision to exclude grey literature might have contributed to this discrepancy about publication bias between the current review and previous meta-analyses (Alexander, 2020). Finally, our review found that the overwhelming majority of the interventions were not pre-registered and did not publicly share data and data-analytic codes/scripts.

Future Directions of Research and Intervention Work

Our review identified important gaps in current knowledge that could be productively addressed in future research. Although most interventions reviewed here were effective at reducing academic procrastination, our review highlighted that very few studies investigated whether the interventions were equally effective for all participants (i.e., moderation effects). This is critical because extensive information on moderating factors will help determine if low-cost interventions can be implemented on a large-scale to reduce high rates of procrastination in the student population and prevent it from becoming habitual (Steel, 2007). For example, earlier in the Discussion section, we highlighted that this review found some inconsistent results regarding the effectiveness of interventiuon for high and low procrastinators (e.g., Himmler et al., 2019; Koppenborg & Klingsieck, 2022b; Li et al., 2021; Wessel et al., 2020). Hence, additional research is needed to clarify whether (trait) procrastination moderates the effects of procrastination interventions (and, if true, in which ways). If this hypothesis receives strong support from future studies, then subsequent procrastination interventions might benefit from taking into consideration that compared to low procrastinators high trait procrastinators are more sensitive to task-induced negative moods (Koppenborg et al., 2023; Sirois & Pychyl, 2013), procrastination-friendly environments (Nordby et al., 2017; Svartdal et al., 2020), and struggle with translating their intentions into behaviour (Koppenborg et al., 2023; Pierro et al., 2011; Steel, 2007).

In addition, future intervention work must amass information on mediators and mediation processes to shed fresh light upon the specific way(s) in which effective interventions influence academic procrastination (i.e., what makes them effective). Importantly, filling this gap in the extant knowledge will allow researchers to devise ways to modify existing interventions to enhance their benefits as well as help stakeholders identify cost-effective interventions to be implemented in their own contexts/settings2. Thus, future research examining theoretically relevant mediators and mediation mechanisms is urgently needed.

This review did not identify any intervention that tried to reduce academic procrastination by mean of intervening on one its strongest and most consistent predictors, namely conscientiousness (Spielmann et al., 2022; Steel, 2007). Thus, an important direction for future research would involve examining the effectiveness of interventions that strengthen conscientiousness on academic procrastination. To this end, researchers could focus, for example, on a motivation orientation that is both malleable and occupies a key role in the nomological network of conscientiousness (i.e., promotion; Costantini & Perugini, 2016).

Briefly, promotion guides self-regulation by focusing people’s attention on future outcomes, gains, and own aspirations (Higgins, 1997, 2012). Costantini and Perugini (2016) found that promotion provided strong and direct connections between facets of conscientiousness (industriousness, orderliness, and impulse control) and constructs that contribute to changes in procrastination (e.g., responsiveness to reward, having a positive orientation). Consistent with these findings, Costantini and Perugini (2016) proposed that “an interesting task for future research could be inspecting the short-term consequences of an experimental manipulation of promotion focus … on the network of conscientiousness” (p. 85). Hence, with regard to academic procrastination, future research could empirically test whether priming promotion enhances students’ (state level of) conscientiousness and, subsequently, reduces their tendency to procrastinate with regard to a given (aversive) learning task. If successful, these types of interventions would involve negligible costs and could be easily implemented by teachers. For example, instructors could often prompt students to engage in short written reflections of times when they were successful in acting in a promotion-oriented manner (Higgins, 1997, 2012). Similarly, when assignments are described as an opportunity for growing and advancing knowledge, this could prime promotion (Molden & Rosenzweig, 2016). In addition, providing feedback that highlights the strengths of a student’s assignment brings about a promotion focus (Molden & Rosenzweig, 2016). Notably, a strong promotion-focus is likely to be associated with higher levels of expectancies of success and utility value (Hodis, 2018, 2020; Hodis & Hodis, 2015), which, in turn, could themselves help reduce students’ tendency to procrastinate on academic tasks in addition to any beneficial effects associated with strengthening conscientiousness (Steel, 2007).

Findings from our review indicate that within a study, most of the interventions we assessed targeted either individual difference factors (e.g., self-regulation) or contextual factors (e.g., examination schedule) but not both; for notable exceptions, see Grunschel et al. (2018) and Loeffler et al. (2019). Of note, theorists have proposed that suboptimal self-regulation, which is at the center of procrastination, “is amplified by common aspects of the student environment” (Svartdal et al., 2020, p. 8). Considering these aspects, future intervention research could target both individual difference factors and contextual factors (e.g., in a fully-crossed factorial design), examine their interactions, and assess whether these interactions significantly influence the effects of the intervention. For instance, future research could assess the extent to which the benefits of an intervention to reduce procrastination via increasing students’ conscientiousness are magnified when this intervention is combined with an initiative that changes key aspects of students’ learning environments (e.g., replaces all large assignments with many smaller assignments).

Future research could examine whether incorporating the tenets of McDaniel and Einstein’s (2020) framework when designing procrastination interventions might strengthen their effectiveness. For example, future studies could assess whether a concerted focus to provide key knowledge about how to reduce procrastination and strengthen students’ beliefs that they can apply this knowledge to their learning routines could strengthen the effectiveness of procrastination interventions. Similarly, future research could examine the extent to which developing (and then supporting) students’ commitment to reduce their procrastination and providing them with scaffolds to plan and implement actions targeting procrastination have significant implications for the effectiveness of procrastination interventions. Finally, our research identified that an important limitation of the corpus of studies reviewed here is the lack of pre-registrations of interventions and the absence of publicly available data/data analytic codes pertaining to these interventions. To strengthen the extent to which future work is informative and contributes to a unified, reliable, and valid corpus of knowledge, it is important for future procrastination interventions to engage in open-science practices, pre-register interventions, and publicly share detailed protocols, data, and data analytic codes.

Recommendations for Instructional Practice

In this section, we use the findings from this review to highlight key aspects teachers/instructors could use in practice to reduce academic procrastination. Consistent with Miyake and Kane’s (2022) warning that “the field currently lacks effective intervention methods that can be easily adopted and implemented by instructors” (p. 296), we focus our recommendations on strategies we believe are less resource intensive and instructors could implement in their classrooms; for more extensive information on the ease of implementation of each intervention reviewed in this article see Part 6 of the OSM.

Our review included a variety of interventions that used situational (i.e., contextual- or environmental-level) and/or cognitive (i.e., individual-level) strategies (e.g., active choice and emotion regulation training; Eckert et al., 2018). When designing interventions to reduce academic procrastination, it is important to note that cognitive strategies require metacognitive awareness to use and are more taxing on students (Duckworth et al., 2018). In contrast, situational strategies are easier to implement (e.g., removing tempting objects from one’s study space) and can be used to reduce the number of self-control dilemmas students face (Duckworth et al., 2016). Nevertheless, situational strategies may not strengthen students’ confidence in their ability to reduce impulsiveness (e.g., by means of exerting self-control) and are unlikely to be effective if low academic self-efficacy is among students’ primary reasons for procrastinating (Duckworth et al., 2018). Therefore, instructors may find it beneficial to use a mixture of situational and cognitive strategies. When students acquire a variety of strategies to reduce their academic procrastination, this may increase the likelihood that they implement some of them into their own studying. In turn, self-managed successful use of strategies that reduce procrastination may make learning more effective and pleasant and, hence, build a strong foundation for the development of productive learning habits; for a recent innovative account pertaining to the roles of habit in learning, interested readers could see Fiorella (2020).

Several potentially productive strategies to reduce procrastination (requiring minimal training for teachers and low resources to implement) can be gleaned from the successful interventions reviewed here. First, teachers could use (parts of) app-based interventions to (i) prompt students to reflect on their study habits and (ii) recommend students to complete online self-regulated training modules (Loeffler et al., 2019; Schuenemann et al., 2022; Wessel et al., 2020). Second, teachers could provide feedback on students’ work in written or audio-visual formats (Nourinezhad et al., 2021) and use this tailored feedback to guide students toward resources pertaining to mindfulness interventions (Gray, 2021), emotion regulation training (Eckert et al., 2018), and self-regulation training (Grunschel et al., 2018); these resources help both reduce procrastination and strengthen other productive study habits. Third, teachers could help their students manipulate their physical or social environments to their advantage; for example, they could encourage students to remove from view objects that tempt them to delay a planned study session (e.g., smart phones; Duckworth et al., 2018). Finally, findings from this review indicate that teachers should only use deadline reminders when students' course load is high (Huang et al., 2021).

Conclusion

Academic procrastination is theorised to be a self-regulation failure (Steel, 2007). Notably, in addition to the well-documented drawbacks of procrastination on exam scores and GPA, frequent procrastination is associated with experiencing higher levels of stress and engaging in fewer positive health behaviours, which, together contribute to poorer physical health and mental well-being (Sirois, 2016; Sirois & Pychyl, 2013; Sirois et al., 2003; Steel, 2007). Thus, it is important to intervene to prevent academic procrastination or mitigate its consequences. This review found that many different types of interventions effectively reduced academic procrastination. Yet, attention on what interventions instructors can reliably administer in their classroom to reduce student procrastination has not been a key concern of extant research. To overcome this important drawback, in this article, we provided a set of recommendations rooted in evidence-based techniques currently available that teachers/instructors could adopt in their teaching to reduce students' tendency to irrationally delay their academic work, improve their academic performance, well-being, and life satisfaction. Another important contribution of this review is that it outlined new critical aspects that were not highlighted in previous reviews of academic procrastination. These are the lack of procrastination interventions focused on conscientiousness, the upward trend in the development of app-based interventions, the key benefits of designing customizable interventions, and the largely missing analyses of mediation and moderation effects that could help pinpoint how interventions work to reduce academic procrastination and for whom they are (most) effective. Finally, we outlined several avenues for future research that we believe could further inform the development and implementation of effective procrastination interventions.