From Research to Practice: Facilitating Time Management Instruction in Higher Education
Bibliographic record
Abstract
Time management is crucial for college students’ academic success, well-being, and productivity, yet its integration into curricula remains underexplored. This systematic review examines the effectiveness of time management instruction in higher education, identifying key strategies that improve students’ time management skills and academic performance. To explore how time management instruction affects students and which strategies are most effective in teaching these skills, we analyzed 18 studies involving 11,724 students. These studies were identified through a thorough search of academic databases (PsycINFO, ERIC, and ProQuest Dissertations and Theses) using terms related to time management training and were screened based on predefined criteria. Our analysis reveals critical components such as goal-setting, planning, prioritizing, and evidence-based prompts to scaffold time management instruction in the classroom. The review highlights that structured time management instruction significantly enhances students’ academic achievement, reduces procrastination, and improves well-being. These findings provide educators with actionable insights for integrating time management strategies into their curriculum, equipping students with lifelong skills for academic success and personal growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".