In online education, time management skills can make or break success. Some embrace a “weekend warrior” mindset, cramming work into tight windows just in time to complete assignments. Others pace their efforts across each online week. But which approach truly works? In an internal research project at an online university, we uncovered a clear link between engagement timing and academic outcomes. Supported by broader studies, the findings reveal that just-in-time management carries steep risks—especially in complex courses—while consistent effort paves the way for mastery and resilience.

 Also see Tips for Timing Your Success in Online Learning

Timing of engagement and student success

While some students can successfully adopt a “weekend warrior” approach, the practice has significant caveats. Relying on just-in-time management as their default strategy, students might complete tasks. However, the practice often compromises learning, performance, quality, career viability, and well-being.

A few years ago, I was on a team that conducted internal research for an online university to examine how the timing of student engagement in online classrooms affects success. Since that was an internal project, I can’t share the specific data here. However, the findings aligned with ample student success research and my 20 years of developing, managing, and teaching in online learning environments.

In short: The later students engaged each week, the higher their risk of failure. Consistently late engagement each week of an online course leads to increased dropout rates and weaker performance in individual and group tasks (Michinov et al., 2011). 

Let’s consider some of the research with my observations.

Benefits of paced engagement

Our research found that students who planned and paced their work throughout the week achieved nearly 100% success rates. Educational psychology highlights the advantages of distributed effort as follows:

• A strong connection exists between engagement and success. Early and consistent engagement over time yields near-perfect retention and performance (Roediger & Karpicke, 2006).
• Online students who regularly interact with course materials have higher completion rates and grades. Planning and paced engagement lead to success (Joksimović et al., 2015). 

 This means that students who plan and pace work excel. Success rates correlate with early and consistent engagement. 

Risks of delayed engagement

Conversely, students who delayed engagement until the weekend faced a 40% chance of failure, dropping to a 20% success rate if the students engaged on Day 6 of an online week. Procrastination research supports this pattern.

• Performance declines as deadlines near (Steel, 2007).
• Grades drop 30-40% for last-minute efforts (Tice and Baumeister, 1997).
• In online contexts, rates drop to 25% for students engaging late in the week (Jo et al., 2016).

Simply, inadequate planning and last-minute work increase failure rates. The later a student starts in an online week, the more dramatically their chances of failing rise.

Impact of assignment complexity

The risks of just-in-time management are less severe in courses with simpler assignments, like reading-based quizzes. Cognitive load theory explains this:

• Basic tasks require less processing, making last-minute work viable (Sweller, 1988).
• Distributed practice is less critical for recall tasks but essential for complex assignments where risks escalate (Kang, 2016).

This means that just-in-time management risks are lower for simple tasks. Students can cram for quizzes and get acceptable grades. However, learners should consider whether this last-minute approach promotes meaningful learning and personal growth. The reality is that cramming to remember long enough to pass a quiz fosters an illusion of learning (Oakley, 2014) that can be popped as the learner faces more complex tasks that require more advanced thinking.

Challenges of complex courses

The early stages of a course and program typically focus on gaining fundamental knowledge through quizzes and tests. Yet, those who habitually rely on just-in-time strategies may find themselves unprepared when faced with the natural progression of a typical program designed around Bloom’s Taxonomy (1956), a key framework educators use to develop curricula. As courses advance, they require more than cramming to pass a quiz. Most programs quickly grow more demanding, pushing students to exhibit higher-level thinking skills, from remembering enough to pass a quiz to applying concepts to solve problems and synthesizing ideas into original insights for increasingly complex projects.

Bloom’s approach is also supported by neurology research based on Dynamic Skills Theory (Fischer, 2008). In simple terms, Dynamic Skill Theory explains that your brain grows through steps that get more complicated over time. It starts with basic reflexes, like clicking “Join Meeting” to hop on a Zoom call, and builds up to abstract thinking, like combining ideas mastered in class to tackle problems at work. Skills in different areas improve as you practice and connect them, shaped by your surroundings and help from others, creating a flexible web of what you can do.

Those stuck in just-in-time mode might complete simple tasks like answering questions on a quiz or logging into a classroom. However, they may face significant difficulties as the sophistication of tasks escalates beyond remembering and reflexes. In other words, just-in-time approaches are more risky for courses with intricate tasks, as follows:

• Projects demanding synthesis and problem-solving suffer under time pressure, resulting in shallow learning and higher failure rates (Brown et al., 2014).
• Students who delay engagement in online courses with complex assignments performed significantly worse due to insufficient processing time (Xu and Jaggars, 2013). 
• Learning new concepts and completing complex tasks requires repeated exposure and persistent effort over a period of time (Oakley, 2014).

This research highlights how just-in-time strategies falter in courses with complex tasks, heightening the risks of failure. Time pressure hampers synthesis and problem-solving, leading to superficial learning. Delayed engagement in online settings worsens performance as students lack adequate processing time for intricate assignments.

From just-in-time to always-behind

As students progress through an academic program, they face increasingly challenging courses, new concepts, and complex projects. Students entering a challenging course with complex projects using a just-in-time strategy are often unprepared for the rigors required for success. For example:

• Just-in-time managers lack the planning for demanding courses entering a catch-up cycle (Zimmerman, 2002). 
• Late starters in complex online courses felt overwhelmed, widening performance gaps. (Glick and Millar, 2017).

Put another way, confronted with complex concepts and tasks, just-in-time managers often find themselves in always-behind mode. The risk of failure rises proportionally to the delay in their engagement. Proper planning and pacing make each task a building block toward completing complex projects and lifelong learning. With just-in-time management, each delayed task acts like a domino, knocking down subsequent tasks and reducing learning.

Pressure and academic dishonesty

Mounting pressure and procrastination-driven stress often lead students to cut corners or cheat, especially in high-stakes courses. Research links task difficulty and time constraints to increased academic dishonesty. Additionally, unmet challenging goals heighten the likelihood of unethical behavior in individuals and groups, as follows:

• Time pressure and task difficulty increase cheating in high-stakes courses (Whitley, 1998).
• Procrastination-driven stress in online settings correlates with academic dishonesty, such as plagiarism, as students seek shortcuts (Birch, T. S., & Aladia, S. 2019; Harton et al., 2019).
• Individuals and groups that cannot meet challenging goals will likely engage in unethical behavior (Schweitzer, M. E., Ordóñez, L., & Douma, B., 2004).

In other words, procrastination and mounting pressure often lead students to unethical actions, like cheating. The propensity to cheat increases with challenging tasks and tight deadlines. This presents an essential heads-up for just-in-time managers: consistent student procrastination becomes a yellow flag that faculty may monitor for integrity violations. 

Conclusion: Proactive planning vs. just-in-time risks

Though some thrive as weekend warriors, just-in-time management carries significant risks. Late engagement heightens failure odds—up to 40% by the weekend and 20% by Day 6 (Steel, 2007; Tice & Baumeister, 1997). The paced effort yields near-100% success (Roediger & Karpicke, 2006). In complex courses, these risks intensify, pushing students into perpetual lag and, under stress, toward cheating (Whitley, 1998; Harton et al., 2019). Proactive planning and paced engagement consistently outperform just-in-time strategies for academic and professional success.

References

Brown, P. C., Roediger, H. L., & McDaniel, M. A. (2014). Make it stick: The science of successful learning. Harvard University Press.Glick, D. M., & Millar, S. B. (2017). Time management and academic success in online courses: A longitudinal study. Journal of Online Learning Research, 3(2), 123-145.Harton, H. C.,

Birch, T. S., & Aladia, S. (2019). Academic dishonesty in online courses: The role of stress and procrastination. Ethics & Behavior, 29(5), 399-413.

Bloom, B. S., Englehart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. David McKay Company, Inc.

Fischer, K. W. (2008). Dynamic cycles of cognitive and brain development: Measuring growth in mind, brain, and education. In A. M. Battro, K. W. Fischer, & P. J. Léna (Eds.), The educated brain: Essays in neuroeducation (pp. 127–150). Cambridge, UK: Cambridge University Press.

Joksimović, S., Gašević, D., Loughin, T. M., Kovanović, V., & Hatala, M. (2015). Learning at distance: Effects of interaction traces on academic achievement. Computers & Education, 87, 204-217. 

Kang, S. H. K. (2016). Spaced repetition promotes efficient and effective learning: Policy implications for instruction. Policy Insights from the Behavioral and Brain Sciences, 3(1), 12-19. 

Michinov, N., Brunot, S., Le Bohec, O., Juhel, J., & Delaval, M. (2011). Procrastination, participation, and performance in online learning environments. Computers & Education, 56(1), 243-252.

Moore, M. G., & Kearsley, G. (2012). Distance education: A systems view of online learning (3rd ed.). Wadsworth Cengage Learning.Murdock, T. B., & Anderman, E. M. (2006). Motivational perspectives on student cheating: Toward an integrated model of academic dishonesty. Educational Psychologist, 41(3), 129-145. 

Oakley, B. (2014). A mind for numbers: How to excel at math and science. Penguin. 

Roediger, H. L., & Karpicke, J. D. (2006). The power of testing memory: Basic research and implications for educational practice. Perspectives on Psychological Science, 1(3), 181-210. 

Schweitzer, M. E., Ordóñez, L., & Douma, B. (2004, June). Goal setting as a motivator of unethical behavior. Academy of Management Journal, 47(3), 422-432.

Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin, 133(1), 65-94. 

Sweller, J. (1988). Cognitive load during problem-solving: Effects on learning. Cognitive Science, 12(2), 257-285. 

Tice, D. M., & Baumeister, R. F. (1997). Longitudinal study of procrastination, performance, stress, and health: The costs and benefits of dawdling. Psychological Science, 8(6), 454-458. 

Whitley, B. E. (1998). Factors associated with cheating among college students: A review. Research in Higher Education, 39(3), 235-274. 

Xu, D., & Jaggars, S. S. (2013). The impact of online learning on students’ course outcomes: Evidence from a large community and technical college system. Economics of Education Review, 37, 46-57. 

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70. 

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