Your LMS Timestamps Betray You: Log Data Catches the Student Procrastinators That Questionnaires Miss

Every time a student uploads a homework file, the learning management system quietly records the moment. A new study from Türkiye suggests those timestamps may reveal more about academic failure than students are willing to admit about themselves. Researchers Oktay Akkurt of Middle East Technical University and Sibel Somyürek of Gazi University tracked twelve weeks of assignment submissions in an undergraduate flipped programming course, then compared the digital traces against self-reported procrastination scores, final exam results, and in-depth interviews. Their findings, published in Discover Education, show that the students who consistently waited until the deadline scored dramatically lower on the final exam, and that questionnaires alone miss a meaningful share of the students whose behavior is quietly eroding their grades.

The flipped classroom is an appealing testing ground for this kind of question because it loads nearly all of the time-management burden onto the student. In the course the researchers studied, a compulsory Programming Languages I class for second-year computer education students in Ankara, materials went online a week before each Tuesday session. Students were expected to work through recorded lectures, readings, and curated videos on their own, then arrive in class for a short diagnostic quiz, instructor-led clarification of difficult constructs like if-else statements and loops, and hands-on coding exercises. After class came a second quiz and a lab assignment with a four-day submission window. With no lecturer standing over them in the preparation phase, the course effectively measures how well each student regulates their own time, which is precisely where procrastination does its damage.

The methodological design is what sets the study apart from the long line of papers linking procrastination to poor grades. Akkurt and Somyürek used an explanatory sequential mixed-methods approach with 45 students. First came the behavioral data: every submission timestamp across twelve weeks was converted into a sequence of states, first-day submission, mid-week submission, deadline-day submission, or no submission at all. Those sequences were then fed into sequence dissimilarity analysis using the TraMineR package in R, followed by hierarchical clustering with Ward’s algorithm. The Silhouette method confirmed a two-cluster solution with a dendrogram coefficient of 0.90, indicating unusually clean separation between the groups.

The clusters were stark. Thirty-seven students, 82 percent of the class, fell into the Procrastinator profile, characterized by chronic last-day submissions and a noticeable rate of non-submissions. Only eight students, 18 percent, formed the Non-Procrastinator group, whose submissions clustered early and mid-week and whose failures to submit were nearly non-existent. The temporal analysis added an important wrinkle: during the first two weeks, both groups submitted early at high rates, a novelty effect that faded fast. From the third week onward the two profiles diverged sharply, and first-day submissions across the whole term eventually declined to just 22 percent, down from 96 percent in the opening fortnight.

The performance gap between the clusters was substantial. A Mann-Whitney U test found a statistically significant difference in final exam scores, with an effect size of r = .42. Non-Procrastinators averaged 58.44 points while Procrastinators averaged 33.08, a difference of more than 25 points on the exam. In a cumulative subject like programming, where each week’s logic builds on the last, the researchers argue that this is exactly what spaced practice theory would predict. Students who distributed their work across the four-day window were effectively practicing coding in multiple sessions, while deadline-day submitters compressed everything into a single massed effort that the literature consistently associates with weaker retention.

Then came the psychometric layer. All 45 students completed the 19-item Academic Procrastination Scale, which showed strong internal consistency in this sample with a Cronbach’s alpha of 0.922. A TwoStep cluster analysis in SPSS sorted the students into three groups by self-reported tendency: low, with a mean score of 33.89; moderate, at 51.52; and high, at 69.87. A Kruskal-Wallis test confirmed that final exam performance differed significantly across these groups, and post-hoc comparisons showed the low-procrastination group significantly outperformed the high-procrastination group with an effect size of r = .52. Procrastination, in other words, behaves as a continuum rather than a binary trait, and both ends of it show up in the gradebook.

The most revealing result, however, is what happened when the two classification systems were laid side by side. They overlapped only partially. Every single student in the high-procrastination group did indeed delay their online submissions, which suggests the questionnaire is a reliable flag for the most entrenched cases. But behavioral procrastination also appeared among 33 percent of the moderate group and 16 percent of the low group, students whose self-images did not match their timestamps. The authors conclude that self-reports and submission logs capture related but distinct aspects of the same phenomenon, and that relying on questionnaires alone would overlook students whose procrastination is situational, task-dependent, or simply not recognized by the students themselves.

The interviews with 13 students explain why the same outward behavior can have different roots. One theme, labeled Time Planners, gathered students who finish early to avoid anxiety and produce careful work. One participant explained that leaving things to the last minute creates difficulty, especially with factors beyond their control, which could cost points. The second theme, Last-minute Crammers, was dominated by a single code: habit. One student described how the transition from high school to university broke an early-finishing routine and installed a procrastination habit in its place. Another offered the study’s most sobering line about what deadline-driven work actually does to learning, saying they do not feel like they really learn, they just complete the task to meet the deadline and the information goes in one ear and out the other.

From these patterns the researchers propose a five-stage support framework for flipped courses: early identification through LMS indicators, profile differentiation using both logs and check-ins, targeted support matched to the underlying cause, continuous monitoring across assignment cycles, and adjustment of intensity based on how students respond. The timing detail matters. Because the behavioral divergence emerged from week three, the authors suggest monitoring becomes most informative after the initial weeks, when early enthusiasm gives way to stable patterns. They are careful, though, to frame the whole framework as a hypothesis. The study was observational, the strategies were never experimentally tested, and the authors explicitly state that dashboards, reminders, and intermediate deadlines cannot yet be claimed to reduce procrastination or raise achievement.

The limitations are honestly cataloged and worth keeping in mind. Forty-five students in a single course taught by one instructor is a context-specific sample, not a population. There was no traditional-classroom comparison group, so the submission patterns cannot be attributed to the flipped format itself. Causality runs in neither proven direction, since prior achievement, programming skill, workload, and motivation could all influence both submission timing and exam performance. The two behavioral clusters were badly unequal in size, and timestamps record when work was submitted, not when it was started; a student who begins early and revises until the deadline looks identical to a crammer in the log data. What the study does establish is a methodological point with real consequences for learning analytics: the digital exhaust of a course, read longitudinally and triangulated with self-reports and interviews, exposes the temporal anatomy of procrastination in a way no end-of-term questionnaire can. For instructors running demanding, cumulative courses, the third week of the semester may be the moment to start watching the timestamps.

Subject of Research: Academic procrastination and assignment submission behavior in a flipped undergraduate programming course

Article Title: Academic procrastination and assignment submission patterns in a flipped programming course

Article References: Akkurt, O., & Somyürek, S. (2026). Academic procrastination and assignment submission patterns in a flipped programming course. Discover Education, 5(1), Article 838. https://doi.org/10.1007/s44217-026-02111-7

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02111-7

Keywords: academic procrastination, flipped classroom, learning analytics, programming education, assignment submission, LMS log data, mixed methods, self-regulated learning, hierarchical clustering, academic achievement, spaced practice, higher education