Evidence map›Paper›PMID 41009272›Full record

ArticleBehavioral sciences (Basel, Switzerland)2025

Predicting Academic Performance from Future-Oriented Daily Time Management Behavior: A LASSO-Based Study of First-Year College Students.

Mingzhang Zuo, Kunyu Wang, Pengxuan Tang, Meng Xiao, Xiaotang Zhou, Heng Luo

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mingzhang ZuoFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Kunyu WangFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Pengxuan TangFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Meng XiaoFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Xiaotang ZhouFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Heng LuoFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.ORCID 0000-0002-6551-8885

Funding

the Institute of Curriculum and Textbook Research under the Ministry of Education, People's Republic of China No. JCSZD2024KCZX006
6 · The paper itself

Abstract

This study examined how the time management behavior of first-year college students predicted their academic performance. Data on 44 objective indicators of daily time management behaviors were collected from 110 first-year students via a WeChat Mini Program, through one month of consecutive daily tracking. To identify stable predictors, Least Absolute Shrinkage and Selection Operator (LASSO) regression with 5000 bootstrap resamples was conducted, and variables with high selection frequency were subsequently entered Elastic Net regression to examine explanatory relationships. Six key behavioral indicators were found to predict overall academic performance. Subject-specific models revealed varying associations: time management behaviors appeared more influential in subjects such as Physical Education and English, while their role was less evident in Mathematics. The number and nature of retained predictors also differed across disciplines.

Indexed as

academic performancefirst-year college studentspredictive factorstime-management

Identifiers

PMID41009272
PMCPMC12466482

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.