Why the Average Student Underestimates How Long Studying Takes
A common pattern in struggling students is poor time estimation. New research suggests the gap between estimated and actual study time has measurable consequences.
Most students who struggle academically do not study less than their peers, at least not by the metric they would report. They simply spend more time on tasks than they planned to, and the cumulative slippage produces the impression of being overwhelmed. A 2025 study at the University of Toronto used time-tracking software to compare estimated and actual study time for 480 undergraduates over a semester. The findings explain a great deal about why study plans fail.
Students consistently underestimated the time required for reading assignments by 38 percent on average. The underestimate was largest for unfamiliar material, where students predicted reading times closer to what they could achieve with familiar topics. A textbook chapter that students estimated would take 90 minutes typically took two and a half hours. The hour difference, repeated across multiple courses, was enough to disrupt weekly schedules systematically.
Where the Underestimation Comes From
The Toronto study identified several sources of the misestimate. Students based their estimates on their best reading sessions, which were often short bursts of high focus on familiar material. Real reading sessions, particularly for harder material, included pauses to look up references, re-read confusing passages, and recover from distractions. Each of these added time that students rarely budgeted for.
Students also underestimated transition time between tasks. Switching from a math problem set to a history reading typically took 12 to 15 minutes of mental reset, time that students treated as either invisible or as part of break time. Across a full study session with multiple subjects, the cumulative transition time exceeded an hour.
The Compounding Effect
The chronic underestimation produced systematic effects. Students who planned weekly schedules based on optimistic time estimates fell behind by Tuesday or Wednesday, then either skipped work, rushed it, or stayed up late to catch up. The catch-up sessions produced lower quality work and disrupted sleep schedules, which further reduced the quality of subsequent work.
“It is not that students were lazy,” said Dr. Felicity Mwangi, who led the study. “Most were working as many hours as they had budgeted. The hours simply did not produce what they had assumed the hours would produce.”
What High Performers Did Differently
The study identified a subset of students whose time estimates were more accurate. They were not always the most talented students academically, but they were consistently the most stable. Their pattern involved building in significant buffer time, typically 30 to 40 percent above their initial estimates. They treated the buffer as expected rather than as slack.
The high performers also tended to estimate based on their worst recent performance rather than their best. A student who had read one chapter in 90 minutes when fully focused but had averaged 150 minutes over the past month would budget based on the 150-minute average, not the 90-minute best case.
The Calibration Practice
A practical exercise that helped students improve their estimation was tracking actual time spent on a specific assignment alongside the original estimate. Over four weeks of tracking, most students reduced their estimation error from the initial 38 percent to under 15 percent. The practice was simple but rarely undertaken without prompting.
For students who want to improve their academic time management, the most practical step is to track one assignment per week against the original estimate for a month. The data will reveal both the pattern of underestimation and the kinds of tasks that produce the largest gaps. The information is more useful than general advice about scheduling because it is specific to the individual student’s patterns.
Implications for Study Schedules
The research has implications for how students should design weekly schedules. The standard advice to “block out time for each course” assumes accurate estimates. In practice, schedules built on inaccurate estimates produce systematic stress. A more useful approach is to build schedules with significant slack and to treat the slack as core rather than optional.
Students who built schedules with 30 percent buffer time reported significantly lower stress and produced higher quality work than peers who packed schedules tightly. The trade-off is that the schedules look less ambitious on paper. The compensating benefit is that the schedules are more often executed as planned.
What Universities Could Do
Few universities provide explicit instruction in time estimation, though many include general time management advice in orientation. The study suggests that the missing skill is specific and learnable. A brief workshop on time estimation, with structured tracking exercises, could plausibly improve outcomes more than the generic time management content most programs offer. The University of Toronto is piloting such a workshop in its first-year orientation, with results expected in 2027.
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