A Study on the Relationship Between College Students' Academic

Performance and Sleep Time Based on Regression Analysis

Fujia Zhang

Stony Brook Institute at Anhui University, Hefei, Anhui Province, 230039, China

Keywords: Sleep Duration, Sleep Quality, Academic Performance, Regression Analysis.

Abstract: As the academic pressure on college students increases, the problem of insufficient sleep is becoming more

and more serious. Research indicates that both the duration and quality of sleep have a direct impact on

students' academic outcomes. In this study, regression analysis is employed to examine how variations in

sleep duration and quality among college students correlate with their academic performance. Based on survey

responses from 100 college students and subsequent statistical analysis, the study reveals a robust positive

association among sleep duration, sleep quality, and academic achievement. In particular, the impact of sleep

quality is more prominent, indicating that it has an independent and important role in academic performance.

Based on this, this study proposes that students should maintain adequate sleep time and pay attention to sleep

quality. At the same time, schools should also optimize their work and rest schedules and course arrangements

to provide students with a good learning and rest environment to promote academic performance. This can

not only help students improve their learning efficiency and memory but also help maintain their physical and

mental health so that they can still maintain a high level of learning engagement and a good emotional state

when facing heavy academic pressure.

1 INTRODUCTION

As competition intensifies and academic pressure

increases, college students generally lack sleep, with

the average sleep time being less than the

recommended 8 hours and poor sleep quality. Lack of

sleep not only affects daily life and learning efficiency

but also easily leads to staying up late (Chen et al.,

2020). Studies have shown that good sleep helps

consolidate short-term memory and improve brain

function and emotional regulation, while insufficient

sleep may lead to memory loss and lack of

concentration, which in turn affects academic

performance (Wu et al., 2017; Chen et al., 2022;

Monroe & Reid, 2009).

In recent years, more and more scholars have

begun to pay attention to the relationship between

sleep and academic performance. Survey results show

that a long-term lack of sleep is related to mental

health problems such as anxiety and depression which

in turn further affect students' academic performance

(Giosan et al., 2024). Due to the high pressure of life

faced by college students, many students have

https://orcid.org/0009-0001-0709-0649

invested a lot of time in extracurricular activities,

social activities, and part-time jobs, resulting in

insufficient rest. Insufficient sleep has become a

common problem for them.

Walker pointed out that sleep is essential for

learning and memory, and adequate sleep can

promote memory consolidation and cognitive

function improvement in the brain (Owens, 2017). On

the other hand, a long-term lack of sleep can affect

students' attention, emotions, and cognitive abilities,

thereby affecting learning efficiency and academic

performance. Through a meta-analytic review,

Dewald et al. uncovered a strong inverse association

between insufficient sleep and academic

achievement. In recent years, sleep problems among

college students have become more serious (Dewald

et al., 2010). Eliasson found that more than 60% of

college students sleep less than 6 hours a night, and

students generally have poor sleep quality due to

multiple factors such as academic pressure, social

activities and use of electronic products (Eliasson,

Lettieri, Eliasson, 2010). Doudell (2021) pointed out

that lack of sleep can lead to a decline in academic

124

Zhang, F.

A Study on the Relationship Between College Students’ Academic Performance and Sleep Time Based on Regression Analysis.

DOI: 10.5220/0013815200004708

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy (IAMPA 2025), pages 124-128

ISBN: 978-989-758-774-0

performance, especially during the final exam period,

when students who lack rest are more likely to

experience anxiety and memory decline. Thus, this

research not only enhances our understanding of how

sleep affects academic performance but also offers

practical insights for students, educational

institutions, and society, ultimately fostering the

balanced development of academic success alongside

physical and mental well-being.

The significance of this study is to reveal how

students' sleep time and sleep quality affect their

academic performance, and to provide practical

suggestions for improving students' learning

performance and physical and mental health. The

results of this study can also provide data support for

school health management policies and curriculum

arrangements.

2 RESEARCH METHODS

2.1 Study Design

This study employs regression analysis to investigate

how students’ sleep duration correlates with their

academic performance, while also examining whether

sleep quality exerts an independent influence on this

relationship. Our primary objective is to assess two

main hypotheses: first, that a positive association

exists between students’ sleep duration and their

academic outcomes; and second, that sleep quality

significantly shapes academic performance beyond

the effects of sleep duration. To evaluate these

propositions, we use regression analysis to explore

the connections among sleep duration, sleep quality,

and academic achievement.

2.2 Questionnaire Design and Data

Collection

The collection of data will be conducted by designing

a questionnaire that includes questions about sleep

time, sleep quality, and academic performance. The

questionnaire mainly includes three parts: the first is

the basic information part, which covers gender,

grade, major, etc., to describe the sample

characteristics; the second is the sleep situation part,

which mainly includes the students' average daily

sleep time and sleep quality score (1-10 points), using

a self-assessment scale to quantify the students' sleep

quality; the last is the academic performance part,

which investigates the students' GPA or the average

grade of the subject in the most recent semester.

The sample of this study was 100 students from a

university in Hefei, covering multiple majors and

grades. A stratified random sampling method was

used to randomly select students from different grades

and genders to ensure the representativeness of the

sample. Data collection was carried out through

online platforms such as Google Forms or

Questionnaire Star. All questionnaires were filled out

anonymously, and the data were strictly confidential

to ensure the privacy of the participants.

2.3 Data Analysis Methods

This study first conducted descriptive statistics to

show the distribution of sleep time, sleep quality, and

academic performance, and calculated basic statistics

such as the mean and standard deviation of each data

to understand the overall characteristics of the

sample.

Subsequently, the Pearson correlation coefficient

was applied to examine the associations among sleep

duration, sleep quality, and academic performance,

aiming to confirm whether a significant positive

correlation exists among them.

Ultimately, a multiple regression model was

developed to evaluate how sleep duration and quality

influence academic outcomes. The regression model

is presented as follows:

GPA = β0 + β1(sleep time) + β2(sleep quality) +

β3(gender) + β4(grade) + ϵ (1)

Using regression analysis, our study will assess

how each independent variable affects academic

performance, with particular emphasis on the link

between sleep duration and academic outcomes, and

will also evaluate the additional influence of sleep

quality.

3 DATA ANALYSIS

3.1 Descriptive Statistics

First , a descriptive statistical analysis was conducted

on the sleep time, sleep quality, and academic

performance of college students in the sample. By

calculating the mean, standard deviation, minimum,

and maximum values of each data, the overall

distribution of the data can be understood. In addition,

by drawing histograms and box plots, the distribution

characteristics of sleep time and academic

performance can be intuitively displayed. These

descriptive statistical results can further provide basic

A Study on the Relationship Between College Students’ Academic Performance and Sleep Time Based on Regression Analysis

125

data support for subsequent correlation analysis and

regression analysis.

Table 1: Descriptive statistics of sleep time, sleep quality and academic performance

variable Mean Standard Deviation (SD) Minimum value (Min) Maximum value (Max)

Sleep time (hours) 6.5 1.2 4.5 9.0

Slee

ualit

(

1-10

)

6.8 1.5 3.0 10.0

Academic

erformance

(

GPA

)

3.2 0.5 2.0 4.0

The results in Table 1 show that most students’

sleep time is concentrated between 6 and 7 hours,

which is significantly lower than the recommended

range of 7 to 9 hours. At the same time, the average

self-assessment of students’ sleep quality is 6.8,

reflecting that some students believe that their sleep

quality is poor; while academic performance shows a

relatively balanced distribution, with an average of

3.2, the highest score is 4.0, and the lowest score is

2.0.

3.2 Correlation Analysis

Using Pearson correlation analysis, the study

investigates the links among sleep duration, sleep

quality, and academic performance (Schober, Boer,

Schwarte, 2018). The results of correlation analysis

will provide a basis for regression analysis and help

us understand the interaction between various

variables.

Table 2: Pearson correlation coefficient analysis between sleep time, sleep quality and academic performance

Variable Academic

erformance

(

GPA

)

Slee

time Slee

ualit

Academic

erformance

(

GPA

)

1.00 0.42 0.51

Slee

time 0.42 1.00 0.37

Sleep qualit

0.51 0.37 1.00

Correlation analysis indicated that there was a

significant positive correlation between sleep time

and academic performance (r=0.42, p<0.01), while

the correlation between sleep quality and academic

performance was even higher (r=0.51, p<0.01),

indicating that sleep quality may be more critical in

affecting academic performance. Meanwhile, even

though sleep duration and sleep quality exhibited a

modest correlation (r=0.37, p<0.01), this effect was

relatively weak, suggesting that each factor

independently contributes to academic performance.

3.3 Regression Analysis

Table 3 summarizes the outcomes of the multiple

regression analysis.

Table 3: Results of multiple regression analysis:

Relationship between sleep duration, sleep quality and

academic performance

variable Regression

coefficient

(β)

Standard

error

(

)

value

Constant term 2.11 0.22 0.000

Sleep time 0.12 0.03 0.002

Sleep quality 0.19 0.04 0.000

Gender

(male=1)

0.08 0.0 5 0.102

grade -0.03 0.0 2 0.215

The results in Table 3 show that sleep time has a

significant positive impact on academic performance

(β=0.12, p<0.01), while sleep quality has an even

more significant impact (β=0.19, p<0.001). Even after

controlling other variables, this factor still has a

significant impact on academic performance. At the

same time, the impact of gender and grade is not

significant (p>0.05), indicating that the effect of sleep

on academic performance is not affected by these

factors.

3.4 Model Diagnosis and Evaluation

To verify the robustness and reliability of the

regression model, this study conducted further

diagnostic tests on the model after multiple regression

analysis, including multicollinearity detection and

residual normality test. First, the variance inflation

factor (VIF) of the independent variables (i.e., sleep

time, sleep quality, gender, and grade) was calculated

to assess the multicollinearity problem. The findings

indicated that the VIFs for all variables were well

below the critical value of 5—sleep time at 1.45, sleep

quality at 1.62, gender at 1.10, and grade at 1.08—

demonstrating that collinearity is not a significant

IAMPA 2025 - The International Conference on Innovations in Applied Mathematics, Physics, and Astronomy

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concern and does not adversely affect the model's

parameter estimates.

Secondly, in order to verify a basic assumption of

the regression model - the residual should

approximately follow the normal distribution, this

study used the Shapiro–Wilk test. The test results

show that the Shapiro–Wilk test statistic of the model

residual is 0.987, and the corresponding p value is

0.464. Since the p value is greater than the commonly

used significance level of 0.05, it can be considered

that the residual distribution conforms to the

normality assumption. This provides strong support

for subsequent hypothesis testing and model

inference.

In conclusion, the diagnostic tests for

multicollinearity and normality confirm that the

regression model developed in this study, with an R²

value of approximately 0.9133, not only fits the data

well but also adheres to the essential assumptions of

regression analysis, thereby establishing a robust

statistical framework for examining the relationships

among sleep duration, sleep quality, and academic

performance.

4 CONCLUSION

The findings reveal that longer sleep duration is

significantly linked to improved academic

performance, as indicated by a regression coefficient

of β=0.12 (p<0.01). This suggests that, holding other

factors constant, each additional unit of sleep time is

associated with a notable enhancement in students'

academic outcomes. In other words, the longer the

sleep time, the better the students perform in learning

and exams.

Additionally, the analysis indicates that sleep

quality plays an even more crucial role in shaping

academic outcomes, as reflected by a regression

coefficient of β=0.19 (p<0.001). This shows that even

with a short sleep time, as long as the quality of sleep

is guaranteed, students can achieve better academic

results. Good sleep quality helps integrate brain

information, consolidate memory, and stabilize

emotions, thereby providing students with a more

efficient learning state.

Furthermore, when controlling for gender and

grade, the analysis found that neither variable

significantly affected academic performance

(p>0.05). This suggests that, in this study's sample,

academic outcomes are primarily driven by sleep-

related factors rather than differences in gender or

grade.This finding further verifies that sleep factors

have a universal and independent impact on academic

performance.

In summary, this study not only emphasizes the

important role of prolonged sleep time in improving

students' academic performance, but also highlights

the core position of sleep quality in learning

performance. Therefore, in practical applications,

schools and parents should pay more attention to

students' sleep habits, and help students form healthy

sleep patterns by reasonably adjusting their work and

rest time, optimizing the sleep environment, and

providing sleep guidance, thereby improving their

academic level and overall quality of life.

Future research can further explore other factors

that may affect academic performance, such as

psychological pressure and lifestyle, in order to build

a more comprehensive and scientific evaluation

system and provide more precise guidance for the

healthy development of college students.

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