{"title":"Predicting Academic Performance from Future-Oriented Daily Time Management Behavior: A LASSO-Based Study of First-Year College Students.","authors":"Mingzhang Zuo, Kunyu Wang, Pengxuan Tang, Meng Xiao, Xiaotang Zhou, Heng Luo","doi":"10.3390/bs15091242","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":8742,"journal":{"name":"Behavioral Sciences","volume":"15 9","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466482/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavioral Sciences","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3390/bs15091242","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
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.
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