academic performance of students research paper

ORIGINAL RESEARCH article

Academic performance in adolescent students: the role of parenting styles and socio-demographic factors – a cross sectional study from peshawar, pakistan.

• 1 Institute of Public Health & Social Sciences, Khyber Medical University, Peshawar, Pakistan
• 2 Department of Medicine, Aga Khan University Hospital, Karachi, Pakistan

Academic performance is among the several components of academic success. Many factors, including socioeconomic status, student temperament and motivation, peer, and parental support influence academic performance. Our study aims to investigate the determinants of academic performance with emphasis on the role of parental styles in adolescent students in Peshawar, Pakistan. A total of 456 students from 4 public and 4 private schools were interviewed. Academic performance was assessed based on self-reported grades in the latest internal examinations. Parenting styles were assessed through the administration of the Parental Bonding Instrument (PBI). Regression analysis was conducted to assess the influence of socio-demographic factors and parenting styles on academic performance. Factors associated with and differences between “care” and “overprotection” scores of fathers and mothers were analyzed. Higher socio-economic status, father’s education level, and higher care scores were independently associated with better academic performance in adolescent students. Affectionless control was the most common parenting style for fathers and mothers. When adapted by the father, it was also the only parenting style independently improving academic performance. Overall, mean “care” scores were higher for mothers and mean “overprotection” scores were higher for fathers. Parenting workshops and school activities emphasizing the involvement of mothers and fathers in the parenting of adolescent students might have a positive influence on their academic performance. Affectionless control may be associated with improved academics but the emotional and psychosocial effects of this style of parenting need to be investigated before recommendations are made.

Introduction

Despite residual ambiguity in the term, definitions over time have identified several elements of “academic success” ( Kuh et al., 2006 ; York et al., 2015 ). Used interchangeably with “student success,” it encompasses academic achievement, attainment of learning objectives, acquisition of desired skills and competencies, satisfaction, persistence, and post-college performance ( Kuh et al., 2006 ; York et al., 2015 ). Linked to happiness in undergraduate students ( Flynn and MacLeod, 2015 ) and low health risk behavior in adolescents ( Hawkins, 1997 ), a vast amount of literature is available on the determinants of academic success. Studies have shown socioeconomic characteristics ( Vacha and McLaughlin, 1992 ; Ginsburg and Bronstein, 1993 ; Chow, 2000 ; McClelland et al., 2000 ; Tomul and Savasci, 2012 ), student characteristics including temperament, motivation and resilience ( Ginsburg and Bronstein, 1993 ; Linnenbrink and Pintrich, 2002 ; Farsides and Woodfield, 2003 ; Valiente et al., 2007 ; Beauvais et al., 2014 ) and peer ( Dennis et al., 2005 ), and parental support ( Cutrona et al., 1994 ; Sanders, 1998 ; Dennis et al., 2005 ; Bean et al., 2006 ) to have a bearing on academic performance in students.

The influence of parenting styles and parental involvement is particularly in focus when assessing determinants of academic success in adolescent children ( Shute et al., 2011 ; Rahimpour et al., 2015 ; Weis et al., 2016 ; Checa and Abundis-Gutierrez, 2017 ; Zhang et al., 2019 ). The influence may be of significance from infancy through adulthood ( Steinberg et al., 1989 ; Weiss and Schwarz, 1996 ; Zahedani et al., 2016 ) and can be appreciated across a range of ethnicities ( Desimone, 1999 ; Battle, 2002 ; Jeynes, 2007 ). Previously, the authoritative parenting style has been most frequently associated with better academic performance among adolescent students ( Steinberg et al., 1989 , 1992 ; Deslandes et al., 1997 , 1998 ; Aunola et al., 2000 ; Adeyemo, 2005 ; Checa et al., 2019 ), while purely restrictive and negligent styles have shown to have a negative influence on academic performance ( Hillstrom, 2009 ; Parsasirat et al., 2013 ; Osorio and González-Cámara, 2016 ). Parenting styles have also been linked to academic performance indirectly through regulation of emotion, self-expression ( Deslandes et al., 1997 ; Weis et al., 2016 ), and self-esteem ( Zakeri and Karimpour, 2011 ).

Significant efforts have been made to explore and integrate factors which influence parenting stress and behaviors ( Belsky, 1984 ; Abidin, 1992 ; Östberg and Hagekull, 2000 ). A number of factors, including parent personality and psychopathology (in terms of extraversion, neuroticism, agreeableness, depression and emotional stability), parenting beliefs, parent-child relationship, marital satisfaction, parenting style of spouse, work stress, child characteristics, education level, and socioeconomic status have been highlighted for their role in determining parenting styles ( Belsky, 1984 ; Simons et al., 1990 , 1993 ; Bluestone and Tamis-LeMonda, 1999 ; Huver et al., 2010 ; Smith, 2010 ; McCabe, 2014 ). Studies have also highlighted differences between fathers and mothers in how these factors influence them ( Simons et al., 1990 ; Ponnet et al., 2013 ).

Insight into determinants of academic success and the role of parenting styles can have significant impact on policy recommendations. However, most existing data comes from western cultures where individualistic themes predominate. While some studies highlight differences between the two ( Wang and Leichtman, 2000 ), evidence from eastern collectivist cultures, including Pakistan, is scarce ( Masud et al., 2015 ; Khalid et al., 2018 ).

The aim of this study is to identify the determinants of academic performance, including the influence of parenting styles, in adolescent students in Peshawar, Pakistan. We also aim to investigate the factors affecting parenting styles and the differences between parenting behaviors of father and mothers.

Materials and Methods

The manuscript has been reported in concordance with the STROBE checklist ( Vandenbroucke et al., 2014 ).

Study Design

A cross sectional study was conducted by interviewing school-going students (grades 8, 9, and 10) to assess determinants of academic grades including the influence of parenting styles.

The study took place in the city of Peshawar in Pakistan at eight schools, four from the public sector and four from the private sector. The data collection process began in January 2017 concluded in December 2017.

The prevalence of high grades (A and A plus) among adolescent students was between 42.6 and 57.4% in a previous study ( Cohen and Rice, 1997 #248). Based on this, a sample size of 376 students was calculated to study the determinants of high grades in adolescent students with a confidence level of 95%. Assuming a non-response rate of approximately 20%, we decided to target 500 students from four public and four private schools. A total of 456 students participated in our study.

Participants

Inclusion criteria.

From the eight schools which provided admin consent to conduct the study, students enrolled in grade 8, 9, or 10 were invited to take part in the study. Following consent from the parents and assent from the student, he or she was included in the study.

Exclusion Criteria

Any student unable to understand or fill out the interview pro forma or questionnaire independently.

Data Sources and Measurement

Data was collected through a one on one interaction between each student and the data collector individually. The following tools were used.

Demographic pro forma ( Supplementary Datasheet 1 )

A brief and simple pro forma was structured to address all demographic related variables needed for the study.

Parental Bonding Instrument (PBI) ( Supplementary Datasheet 2 )

The original version of the Parental Bonding Instrument ( Parker et al., 1979 ), previously validated for internal consistency, convergent validity, satisfactory construct, and independence from mood effects in several different populations, including Turkish and Chinese ( Parker et al., 1979 ; Parker, 1983 , 1990 ; Cavedo and Parker, 1994 ; Dudley and Wisbey, 2000 ; Wilhelm et al., 2005 ; Murphy et al., 2010 ; Liu et al., 2011 ; Behzadi and Parker, 2015 ), was employed in our study. This tool, composed of 25 questions, assesses parenting styles as two independent measures of “care” and “control” as perceived by the child. It is filled out separately for the father and the mother. It is available online for use without copyright. The use of PBI has been validated for British Pakistanis ( Mujtaba and Furnham, 2001 ) and Pakistani women ( Qadir et al., 2005 ). A paper by Qadir et al. on the validity of PBI for Pakistani women, reports the Cronbach alpha scores to be 0.91 and 0.80 for the “care” and “overprotection” scales, respectively ( Qadir et al., 2005 ).

The demographic pro forma and the parental bonding index were translated into Urdu by an individual fluent in both languages and validated with the help of an epidemiologist and two experts in the field ( Supplementary Datasheet 3 ). Pilot testing of translated versions was done with 20 students to ensure clarity and assess understanding and comprehension by the students. Both versions for the two tools were provided in hard copy to each student to fill out whichever one he/she preferred. The data collector first verbally explained the items on the demographic pro forma and the PBI to the student following which the student was allowed to fill it out independently.

Using the data sources mentioned above, data was collected for the following variables.

Student Related

Gender, type of school (public or private), class grade (8th, 9th, and 10th) and academic performance.

In Pakistan, public and private schools may differ in several aspects including fee structures, class strength and difficulty levels of internal examinations, with private schools being more expensive, with fewer students per classroom, and subjectively tougher internal examinations.

The academic performance was judged as the overall grade (a combination of all subjects including English, Mathematics and Science) in the latest internal examinations sat by the student as A+, A, B, C, or D.

Family Related

Family structure and type of accommodation (rented or owned).

Parent Related

Information on living status, education level, employment status, employment type and parenting styles was obtained from the student separately for the father and mother.

Quantitative Variables

Academic performance.

The grades A+, A were categorized as “high” grades and grades B, C, and D were categorized as “low” grades.

Socio-Economic Status

We used variables which adolescent students are expected to have knowledge of to calculate a score which categorized students as belonging to either a high or low socioeconomic status. The points assigned to each variable are show in Table 1 .

Table 1. Calculation of an estimated socioeconomic status.

Parenting Styles

The PBI is a 25 item questionnaire, with 12 items measuring “care” and 13 items measuring “overprotection.” All responses have a 4 point Likert scale ranging from 0 (very unlikely) to 3 (very likely). The responses are summed up to categorize each parent to exhibit low or high “care” and low or high “overprotection.” Based on these findings, each parent can then be put into one of the 4 quadrants representing parenting styles including “affectionate constraint,” “affectionless control,” “optimal parenting,” and “neglectful parenting.” This computation is explained in Figure 1 obtained from the information provided with the PBI ( Parker et al., 1979 ).

Figure 1. Assigining parenting styles using the PBI ( Parker, 1979 #192).

Students were allowed to fill in the pro forma and questionnaire independently to avoid bias during the data collection process. However, self-reporting of grades in latest examination may be subject to recall bias.

Statistical Methods

Statistical analysis was performed using SPSS v.23 (IBM Corp., Armonk, NY, United States). Descriptive analyses were conducted on all study variables including socio-demographic factors and parenting styles. Categorical variables were reported as proportions and continuous variables as measures of central tendency. All continuous variables were subjected to a normality test. Mean and median values were reported for variables with normally distributed and skewed data, respectively.

The summary t -test was used to study the differences between mean “care” and “overprotection” scores of fathers and mothers. The independent sample t -test was used to study the factors associated with “care” and “overprotection” scores of fathers and mothers. Threshold for significance was p = 0.05.

The determinants of high grades including the influence of parenting styles were assessed using regression analysis. The outcome variable, student grades, was treated as binary (high grades and low grades). The threshold for statistical significance was p = 0.05. Crude Odds Ratios were adjusted for gender, school type, socioeconomic status, family structure, class grade, parents’ employments and education status.

Ethics Statement

The study was approved by the Ethical Committee of the Khyber Medical University, Advance Studies and Research Board (KMU-AS&RB) in August 2016. Identifying information of students was not obtained. Permissions were obtained from the relevant authorities in the school administration before approaching the students and their parents. Written consent was obtained from the parents through the home-work diary of the students and verbal assent of each student was obtained.

Participants and Descriptive Data

A total of 456 students were interviewed, with 249 (54.6%) males and 207 (45.4%) females. The majority (52.5%) were students of grade 8. Despite including an equal number of public and private schools, 63.6% of the students belonged to a public sector school. The reason may be due to the larger class strength in public schools in comparison to private schools. The nuclear family structure was dominant (64.3%), with most students living in rented accommodation (70.4%) with 42.8% reporting to have obtained high grades (A plus or A) in their latest internal examinations ( Table 2 ).

Table 2. Participant and descriptive data.

Majority of the students had both parents alive at the time of the interview. While all students’ mothers were alive, 14 students reported their father to have passed away. Surprisingly, only 46% of the students were able to report their father’s level of education compared to 99.5% for their mother. 9.2% of students reported their father to have an education level of grade 12 or above compared to 26% regarding their mother’s qualification. This was in contrast to 90% of the fathers being employed compared to only 11% of the mothers ( Table 2 ).

A Total of 257 (56%) students reported their mother to exhibit a high level of “care” vs. only 9 (2%) students reporting the same for their father. In terms of “overprotection,” 343 (75%) and 296 (65%) students reported a high level for their father and mother, respectively. Based on combinations of these measures, the most common parenting style for both fathers (73%) and mothers (35%) was affectionless control and the least common for fathers was optimal parenting (0%) and neglectful parenting for mothers (9%). 121 (26%) students had both parents with the same parenting style, with 23% students having both parents show affectionless control and not a single student with both parents showing optimal parenting ( Figure 2 ).

Figure 2. “Care,” “overprotection” and parenting styles for fathers and mothers as reported by students ( n = 456). Green circles represent students with both parents showing the same parenting style – none of the students received “Optimal parenting” from both parents while 106 students received affectionless control from both parents.

Determinants of High Grades

Our results show that high socioeconomic status [adjusted OR 2.78 (1.03, 7.52)], father’s education level till undergrad or above [adjusted OR 4.58 (1.49, 14.09)], father’s high “care” [adjusted OR 1.09 (1.01, 1.18)] and father’s affectionless control style of parenting [adjusted OR 3.23 (1.30, 8.03)] are significant factors contributing to high grades ( Table 3 ).

Table 3. Academic performance: Determinants of “high” grades in the latest internal examinations.

Differences in “Care” and “Overprotection” Between Fathers and Mothers

The mean “care” score for mothers were significantly higher than fathers overall. The difference remained significant for male and female students, public and private schools, joint and nuclear family structures and low and high socioeconomic statuses ( Table 4 ).

Table 4. Differences between mean “care” and “overprotection” scores between fathers and mothers.

Overprotection

The mean “overprotection” score was significantly higher for fathers overall. The difference remained significant for female students, private schools, nuclear family structure, and low socioeconomic status. However, there was no significant difference in mean “overprotection” scores between fathers and mothers for male students, public schools, joint family structures and high socioeconomic status ( Table 4 ).

Factors Associated With “Care” and “Overprotection” in Fathers and Mothers

The mean “care” score was significantly higher for fathers as reported by children in public schools and with higher grades. There was no significant difference in mean care scores based on student gender, socioeconomic status or family structure ( Table 5 ).

Table 5. Factors associated with “care” and “overprotection” for mothers and fathers.

For “overprotection” the only factor associated with a significantly higher mean score was “high” grades ( Table 5 ).

A significantly higher mean “care” score for mothers was reported by female students and students in public schools. No significant differences were observed for the other factors ( Table 5 ).

A significantly higher mean “overprotection” score was reported by male students, students in public schools and those with “high” grades for mothers ( Table 5 ).

Summary of Findings

Results of regression analysis show that socioeconomic status, father’s education level and fathers’ care scores have a significantly positive influence on the academic performance of adolescent students in Peshawar, Pakistan. The most common parenting style for both fathers and mothers was affectionless control. However, affectionless control exhibited by the father was the only parenting style significantly contributing to improved academic performance.

Overall, the mean “care” score was higher for mothers and the mean “overprotection” score was higher for fathers. However, differences in “overprotection” were eliminated for male students, public schooling, joint family structures and high socioeconomic status.

Public schooling was associated with a significantly higher mean “care” score for both fathers and mothers and a significantly higher mean “overprotection” score for mothers. High grades were associated with a significantly higher mean “overprotection” score for both fathers and mothers and a significantly higher mean “care” score for fathers. For mothers, female students reported a significantly higher mean care score and male students reported a significantly higher mean “overprotection” score.

An additional interesting finding from the results of the study was that only about half the students were able to report their father’s level of education compared to almost a 100% for their mother. From amongst those who did report, less than 10% of the father’s had an education level equal or above grade 12 compared to a quarter of the mothers. However, only 11% of the mothers were employed in contrast to 90% of the fathers.

Previous Literature and Comparison of Main Findings

The results of our study have identified socioeconomic status, father’s education level and high care scores for fathers to be significant predictors of academic success in adolescent students. Previous literature has shown socioeconomic status to be a predictor of academic success ( Gamoran, 1996 ; Sander, 1999 ; Lubienski and Lubienski, 2006 ).

Parental education has been frequently associated with improved academic performance ( Dumka et al., 2008 ; Dubow et al., 2009 ; Masud et al., 2015 ). In 2011, a study by Farooq et al. described the factors affecting academic performance in 600 students at the secondary school level in a public school in Lahore, Pakistan. Results of their study also associate parental education level with academic success in students. However, their results are significant for the education level of the mother as well as the father. Additionally, they also reported significantly higher academic performance in females and in students belonging to a higher socioeconomic status, factors not significant in our study ( Farooq et al., 2011 ). Differences may be explained by cultural variations in Lahore and Peshawar within Pakistan, which should be explored further.

The description of parenting styles and behaviors has evolved over the years. With some variation in terminologies, the essence lies in a few common principles. Diana Baumrind initially described three main parenting styles based on variations in normal parenting behaviors: authoritative, authoritarian and permissive ( Baumrind, 1966 , 1967 ). Building on the concepts put forth by Baumrind, Maccoby and Martin identified two dimensions, “responsiveness” and “demandingness,” which could classify parenting styles into 4 types, three of those described by Baumrind with the addition of neglectful parenting ( Maccoby et al., 1983 ). The two dimensions, “responsiveness” and “demandingness,” often referred to as “warmth” and “control” in literature ( Lamborn et al., 1991 ; Tagliabue et al., 2014 ), are similar to the two measures, “care” and “overprotection” assessed by the parental bonding instrument ( Parker et al., 1979 ; Parker, 1989 ; Dudley and Wisbey, 2000 ). Based on this, the authoritative, authoritarian, permissive and neglectful parenting styles described by Baumrind and Maccoby are similar to the affectionate constraint, affectionless control, optimal, and neglectful styles as classified by the parental bonding instrument, respectively ( Baumrind, 1991 ; Cavedo and Parker, 1994 ).

Results of our study show that affectionless control, similar to the authoritarian style of parenting, adapted by the father is significantly associated with improved academic performance. This differs from the popularity of the authoritative parenting style, similar to affectionate constraint, in determining academic success in literature from western cultures ( Steinberg et al., 1989 , 1992 ; Deslandes et al., 1998 ; Aunola et al., 2000 ; Adeyemo, 2005 ; Masud et al., 2015 ; Pinquart, 2016 ; Checa et al., 2019 ). Evidence from societies with cultural similarities with Pakistan presents varied findings. A study from Iran shows support for the authoritarian parenting style similar to our study ( Rahimpour et al., 2015 ). A review of 39 studies published by Masud et al. (2015) in 2015 assesses the effect of parenting styles on academic performance ( Masud et al., 2015 #205). The review very aptly described how the authoritative parenting style is the dominant and most effective style in terms of determining academic performance in the West and European countries while Asian cultures show more promising results for academic success for the authoritarian style ( Dornbusch et al., 1987 ; Lin and Fu, 1990 ; Masud et al., 2015 ). The results of our study are in synchrony with these findings. However, our results also show that high father’s “care” scores are significant contributors to higher academic grades. Since no father showed optimal parenting and only 9 fathers had affectionate constraint, both parenting styles with high care scores, these results may be a reflection of the importance of father’s role in determining academic performance in Asian cultures. Findings supporting the authoritarian/affectionless control style may be due to the abundance of this parenting style. Perhaps a fairer comparison may be possible with a larger sample population with fathers showing all types of parenting styles equally.

Interpretation and Explanation of Other Findings

Observations of factors associated with and differences in “care” and “overprotection” between fathers and mothers may be attributed to reverse causality and should be used as hypothesis generating.

Our results show that mothers have higher mean “care” score and fathers have a higher mean “overprotection” score. Since these scores are based on perceptions of the child, part of these observations may be explained by the cultural norms of expression of love and concern by fathers and mothers. With the difference in “overprotection” being eliminated for male and female children, it is possible that mothers are more overprotective of their sons. Male gender preference in Pakistan may be an explanation for this ( Qadir et al., 2011 ).

Our results show lower employment rates for women despite higher education levels. The finding of higher education levels for females compared to males does not agree with national data, which reports findings from rural areas as well where education opportunities are limited for females ( Hussain, 2005 ; Chaudhry and Rahman, 2009 ). Our results provide a zoomed in look at an urban population, which may have progressed enough to improve women’s education but cultural norms, gender discrimination and lack of opportunity still prevent women from stepping into the workface ( Chaudhry, 2007 ; Begum and Sheikh, 2011 ).

Implications and Future Direction

The findings of our study may have implications for future research and policy making.

Affectionless control is associated with improved academic performance but further research investigating the effects of this style on other aspects of child development, particularly emotional and psychological health, is needed. Factors affecting care and overprotection need to be studied in more detail so that parenting workshops and interventions are tailored to our population. Results also suggest that fathers should play a stronger role in parenting of adolescent students. School policies should make it mandatory for both parents to attend parent-teacher meetings and assigned home activities should include both parents.

Limitations

Since the study is based on the urban population of Peshawar, results may not be generalizable to the adolescent students of the country which includes large rural populations. Academic performance was judged on latest internal examinations, the marking criteria for which may vary across schools. The use of external examinations would have standardized grades across schools but limited the sample to students of grade 9 and 10.

Our study concludes that socioeconomic status, father’s level of education and high care scores for fathers are associated with improved academic outcomes in adolescent students in Peshawar, Pakistan. Affectionless control is the most common parenting style as perceived by the students and when adapted by the father, contributes to better grades. Further research investigating the effects of demonstrating affectionless control on the emotional and psychological health of students needs to be conducted. Parenting workshops and school policies should include recommendations to increase involvement of fathers in the parenting of adolescent children.

Data Availability Statement

Data collected and stored as part of this study is available upon reasonable request.

The studies involving human participants were reviewed and approved by the Khyber Medical University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

SM contributed in conceiving, designing, data acquisition, grant submission, and manuscript review. SHM involved in data analysis and manuscript writing. NQ involved in manuscript writing. MK was the principal investigator and supervisor for the project. FK and SK contributed in literature review and data management. All authors proofread and agreed on the final draft and accept responsibility for the work.

This project was graciously funded by the Research Promotion and Development World Health Organization Regional Office for the Eastern Mediterranean (RPPH Grant 2016-2017, TSA reference: 2017/719467-0).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank Dr. Nazish Masud (King Saud bin Abdulaziz University), and Dr. Khabir Ahmad and Dr. Bilal Ahmad (The Aga Khan University) for their contributions to the project.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02497/full#supplementary-material

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Keywords : parenting styles, academic performance, adolescent students, Pakistan, care, overprotection, parental bonding instrument

Citation: Masud S, Mufarrih SH, Qureshi NQ, Khan F, Khan S and Khan MN (2019) Academic Performance in Adolescent Students: The Role of Parenting Styles and Socio-Demographic Factors – A Cross Sectional Study From Peshawar, Pakistan. Front. Psychol. 10:2497. doi: 10.3389/fpsyg.2019.02497

Received: 16 May 2019; Accepted: 22 October 2019; Published: 08 November 2019.

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Copyright © 2019 Masud, Mufarrih, Qureshi, Khan, Khan and Khan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sarwat Masud, [email protected] ; Muhammad Naseem Khan, [email protected] ; [email protected]

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• Open access
• Published: 23 May 2022

Determinants of good academic performance among university students in Ethiopia: a cross-sectional study

• Mesfin Tadese 1 ,
• Alex Yeshaneh 2 &
• Getaneh Baye Mulu 3  

BMC Medical Education volume  22 , Article number:  395 ( 2022 ) Cite this article

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Education plays a pivotal role in producing qualified human power that accelerates economic development and solves the real problems of a community. Students are also expected to spend much of their time on their education and need to graduate with good academic results. However, the trend of graduating students is not proportional to the trend of enrolled students and an increasing number of students commit readmission, suggesting that they did not perform well in their academics. Thus, the study aimed to identify the determinants of academic performance among university students in Southern Ethiopia.

Institution-based cross-sectional study was conducted from December 1 to 28, 2020. A total of 659 students were enrolled and data was collected using a self-administered questionnaire. A multistage sampling technique was applied to select study participants. Data were cleaned and entered into Epi-Data version 4.6 and exported to SPSS version 25 software for analysis. Bivariable and multivariable data analysis were computed and a p -value of ≤0.05 was considered statistically significant. Smoking, age, and field of study were significantly associated with academic performance.

Four hundred six (66%) of students had a good academic performance. Students aged between 20 and 24 years (AOR = 0.43, 95% CI = 0.22-0.91), and medical/ health faculty (AOR = 2.46, 95% CI = 1.45-4.20) were significant associates of good academic performance. Students who didn’t smoke cigarettes were three times more likely to score good academic grades compared to those who smoke (AOR = 3.15, 95% CI = 1.21-7.30).

In this study, increased odds of good academic performance were observed among students reported to be non-smokers, adults, and medical/health science students. Reduction or discontinuation of smoking is of high importance for good academic achievement among these target groups. The academic environment in the class may be improved if older students are invited to share their views and particularly their ways of reasoning.

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Higher education institutions play a pivotal role in producing qualified human power that enables solving the real problems of a community [ 1 ]. Education is a powerful agent of change that improves health and livelihoods and contributes to social stability. At the micro-level, it is associated with better living standards for individuals through improved productivity; given that those who have received a higher education tend to have more economic and social opportunities. At the macro level, education builds well-informed and skilled human capital, which has been considered an engine of economic growth, that positively contributes to economic development [ 2 ]. However, gaining knowledge, attitudes, values, and skills through education is not a simple task; rather it is a long and challenging trip in life. Students are expected to spend much of their time studying and need to graduate with good academic results.

Academic performance/ achievement is the extent to which a student, teacher, or institution has attained their short or long-term educational goals and is measured either by continuous assessment or cumulative grade point average (CGPA) [ 3 ]. A correlational study among vocational high school students in Indonesia found that students who had good academic achievements have higher income, better employment benefits, and more advancement opportunities [ 4 ]. Besides, academically successful students have higher self-esteem and self-confidence, low levels of anxiety and depression, are socially inclined, and are less likely to engage in substance abuse, i.e., alcohol and khat [ 5 ]. However, a cross-sectional study in Malaysia in higher learning institutions reported that an increasing number of students still do not graduate on time, suggesting that they did not perform well in their studies [ 6 ].

Despite excessive government investment in education, most students fail to achieve good academic performance at all levels of education. A correlational study in Arba Minch University, South Ethiopia, reported that the trend of graduating students is not proportional to the trend of enrolled students and more students commit readmission due to poor academic performance [ 7 ]. This resulted in unemployment, poverty, drugs elicit, promiscuity, homelessness, illegal activities, social isolation, insufficient health insurance, and dependence. Additionally, a systematic review in India concluded that poor academic achievement causes significant stress to the parents and low self-esteem to the students [ 8 ]. It is also significantly associated with high anxiety scores among university students in Pakistan [ 3 ]. Further, in public schools in Pakistan, academic failure affects self-concept and leads to a feeling of disturbance and shock. In this way, students finally drop out of the education system at all [ 9 ].

Beyond the quality of schools, various personal and family factors, including socioeconomic factors, English ability, class attendance, employment, high school grades, and academic self-efficacy have been proposed to influence academic performance. Besides, other factors, i.e., teaching skills, study hours, family size, and parental involvement have an association with academic performance as well [ 2 , 10 ]. A cohort study among university students in Australia concluded that aging does not impede academic achievement [ 11 ]. A secondary data analysis among fifth-grade students in Colorado showed that eating breakfast, normal body mass index, adequate sleep, and ≥ 5 days’ physical activity per week was significantly associated with higher cumulative grades [ 12 ]. A significant association was also found between joining the medical profession and good academic performance in Pakistan [ 13 ]. At Arba Minch University, students with a good academic record before campus entry were more likely to have academic success in higher education programs [ 7 ]. A descriptive study on Bahir Dar university students showed that the education status of parents and attending night club affect academic performance [ 14 ]. Also, a survey in Nigerian high schools indicated students whose parents were government employees achieved better performance [ 15 ]. However, the impact of these factors varies from region to region and differs in cities and rural areas. This might be due to diverse data measurement methods and quality or the context of each study.

One of the critical barriers to academic success is substance use. A cross-sectional study in the US among high school seniors showed that substance users had greater odds of skipping school and having low grades [ 16 ]. Similarly, a descriptive survey among primary school students in Jordan indicated that smoking affects children’s physical and mental development and reduces academic achievement. Smoking was considered a barrier to optimal learning [ 17 ]. A cross-sectional study among university students in Wolaita Sodo found that substance use (smoking, khat chewing, drinking alcohol, and having an intimate friend who uses substances) was significantly and negatively associated with students’ academic performance [ 18 ]. In Jordan Primary school students, smoking was more likely to impair cognitive development, and decrease attentiveness and memory. This in turn leads to difficulty in remembering information and verbal learning impairment [ 17 ].

Most of the previous studies focus on primary and secondary education levels and the problem is not well addressed at the university level. The poor performance of university students requests attention. Moreover, in Ethiopia, limited studies were done on this topic and it was complicated by confounding factors. Thus, this study intended to identify the predictors of academic performance among university students in Southern Ethiopia.

Methods and materials

Study design, setting, and period.

This is an institution-based cross-sectional study conducted among Hawassa University students from December 1 – 28, 2020. The University is one of the oldest public and residential national universities found in Hawassa city, Sidama Region. It is located 278 km south of Addis Ababa on the Trans-African Highway for Cairo-Cape Town. By the year 2020/21, the university has enrolled 21,579 students: 7955 Females and 13,624 Males. In general, there is one Institute of Technology and 10 colleges that offer 81 undergraduate, 108 Master’s, and 16 Ph.D. programs.

Sample size and eligibility criteria

The sample size was calculated using Open Epi version 3.03 statistical software using percent of controls exposed (58%), odds ratio 0.63 [ 19 ], 80% power, and 95% confidence interval. By considering a 5 % non-response rate the final sample size was 659. All students who undergo their education in the selected departments and are available at the time of data collection were included in the study. Non-regular students, mentally and physically incompetent, and those who were not willing to fill out the questionnaire were excluded.

Sampling procedure

The study was conducted among Regular Hawassa University students. A multi-stage sampling technique was applied to select study participants. The simple random sampling (SRS) technique was used to select representative colleges and departments. Students were stratified based on their batch/academic year. The sample size was distributed using probability proportional to size (PPS). Thereafter, SRS was applied to pick the required sample size from the predetermined sampling frame.

Academic performance was the dependent variable. Independent variables include sociodemographic characteristics (age, gender, residence, parents’ education, family size, and faculty), individual factors (study hours, working after school, English language proficiency, sleeping hour, missing class, and entrance exam score), lifestyle and behavioral factors (substance use, breakfast, attending night club, and physical activity), and family and psychosocial variables (parents’ occupation, weight loss, and parent’s involvement).

Data collection tool and quality control

The data was collected using a structured, self-administered questionnaire. Four data collectors and two supervisors participated in data collection. The questionnaire was prepared by reviewing similar published articles [ 2 , 7 , 20 ]. It was translated from English to the local language, Amharic, and then back to English by an independent translator to keep the consistency of the tool. Pre-testing was done on 5 % of the samples (33 students) at Dilla University and necessary adjustments were considered following the result (i.e., ethnicity, income). The principal investigators trained data collectors and supervisors about the objective and procedure of the study. The data were daily checked for completeness, consistency, and clarity.

Measurement

• Academic performance

Students who scored a cumulative GPA of 2.75 and above were categorized as “Good”, whereas those with a cumulative GPA of below 2.75 were categorized as “Poor” [ 7 ].

Participants who smoke at least one cigarette per day will be evaluated as smokers, and those who use more than one drink per day (any type of alcohol) will be considered alcohol consumers. Similarly, those who consume at least four glasses of tea and three cups of coffee per day will be accepted as those consuming tea and coffee, respectively [ 21 ].

Sugar intake

Excessive if individuals took 12 or more teaspoons of table sugar daily, moderate if 6 to 12 teaspoons; and restricted use if less than 6 teaspoons [ 22 ].

Extracurricular activities

Participation in school-based activities, i.e., sports, arts, and academic clubs [ 23 ].

Data management and analysis

Data were cleaned and entered into Epi-Data version 4.6 and SPSS statistical package version 25 was applied to perform all the statistical analysis. Cross-tabulation of variables was computed and the Chi-Square (X 2 ) test was used to analyze the variables. Pearson Chi-Square test was reported for variables that fulfill the assumption of the X 2 test. Whereas Fisher’s Exact Test was reported for variables having an expected count of less than five. Bivariable and multivariable logistic regression analysis were performed to identify independent predictors of academic performance. Variables with a p -value of ≤0.25 in the bivariable logistic regression were included in the final model. Descriptive statistics were used to describe the characteristics of participants. Adjusted odds ratios (AORs) with 95% confidence intervals were used to interpret the strength of association, and the Hosmer-Lemeshow goodness-of-fit was used to check for model fitness. A two-tailed p -value of ≤0.05 was considered to declare statistically significant.

Baseline characteristics of participants

Six hundred fifteen (615) students were involved in the study, making a 93.3% response rate. The age of students ranged from 18 to 29 years with the mean age of 21.62 ± 1.89 and 21.73 ± 2.08 for academically poor and good students, respectively. About 39% of rural residents had poor academic performance (PAP), whereas 69.3% of urban residents had good academic performance (GAP) ( p  = 0.035). Further, more than one-third (38.9%) of non-medical/non-health students and 82.9% of medical/health students scored PAP and GAP, respectively ( p  < 0.00) (Table  1 ).

Family and psychosocial characteristics

As shown in Table two below, 34% of students who experience weight loss scored poor academic results, while 66% of students who didn’t experience weight loss scored good academic results. Additionally, 38.7% of students who belong to agriculturalist families registered poor academic points, whereas 69.7% of students who belong to government employees scored academically good results (Table  2 ).

Behavioral characteristics

One-third (67%) of students involved in regular physical activity scored GAP. About 58.8% of students who smoke cigarettes had PAP, whereas 66.7% of students who didn’t smoke scored GAP (chi 2 p  = 0.028). Additionally, 35% of students who attend night club scored PAP, while 66.2% of students who didn’t attend night club scored GAP (Table  3 ).

Personal characteristics

A higher proportion of participants who studied more than 4 hours per day (69.3%) scored GAP. One-third (35.4%) of students who sleep more than 7 hours per night registered PAP, while 68.4% of students who sleep less than 7 hours scored GAP. About 46.2% of students who had a pre-intermediate level of English proficiency were poor in academics, whereas 80.6% of students with an advanced level of proficiency were good in academics (chi 2 p  = 0.002) (Table  4 ).

Overall, 406 (66%) of students had a good academic performance. The mean CGPA of students was 2.92 (SD ± 0.48), with a minimum of 1.80 and a maximum of 4.00 points. The mean CGPA of academically poor students was 2.39 points, which is lower by 0.81 compared to academically good students (3.20 points).

Determinants of academic performance

In the multivariable logistic regression analysis, age, faculty, and smoking have shown a statistically significant association with academic performance (Table  5 ).

Students aged between 20 and 24 years were 56% less likely to score good academic performance compared to those who were aged between 25 and 29 years (AOR = 0.43, 95% CI = 0.22-0.91). Medical/ health science students were two times more likely to attain good academic points compared to their counterparts (AOR = 2.46, 95% CI = 1.45-4.20). Students who didn’t smoke cigarettes were three times more likely to register good academic grades compared to those who smoke (AOR = 3.15, 95% CI = 1.21-7.30).

This study investigated the determinants of academic performance. The finding showed that only two-thirds (66%) of university students score good academic grades. Age, faculty, and cigarette smoking were found to have a statistically significant association with academic performance.

Students who didn’t smoke cigarettes were more likely to register good academic grades compared to those who smoke. This is consistent with the findings observed among university students in western societies. Smoking cigarettes were associated with decreased odds of high academic achievement in Norwegian students [ 19 ]. A cohort study in England showed that tobacco use was strongly linked with subsequent adverse educational outcomes [ 24 ]. Similarly, in Jordan, lower academic performance was positively associated with smoking [ 17 ]. In both Pakistan [ 25 ] and Korea [ 26 ], students who achieve good academic performance were less likely to smoke. Besides, a study from Finland suggested that smoking both predicts and is predicted by lower academic achievement [ 27 ]. The use of substances including smoking is known for its significant association with mental distress and depression. It also increases the risk of respiratory infections, asthma, tuberculosis, certain eye diseases, and problems of the immune system as well as increases the risk of bacterial meningitis, especially among freshman living in dorms. Additionally, smoking had a great influence on the attitude, emotion, and behavior of students, and can motivate them to perform their bests. For instance, in Australia, 69% of smokers attended bars, nightclubs, or gaming venues at least monthly [ 28 ] . Further, smokers are substantially engaged in khat chewing and alcohol drinking [ 29 ]. Having a serious health complication, wasting study hours, and concomitant substance use in college might prevent students from being able to perform their best in school. This finding call attention on prevention efforts aimed at students to reduce the detrimental consequences on academic performance.

In this study, students aged between 20 and 24 years were less likely to score good academic performance compared to those who were aged between 25 and 29 years. This effect favors the older students. A comparable result was obtained in Australia. The study showed that aging does not impede academic achievement and discrete cognitive skills as well as lifetime engagement in cognitively stimulating activities promote academic success in adults [ 11 ]. Similarly, age was positively related to the CGPA of the students in Nigeria [ 30 ]. According to a cross-sectional study in Norway, higher age was associated with better average academic performance of students [ 31 ]. Older students, that is 25 years and above are wiser and more mature. Students of a higher age may have a stronger motivation for studying and follow a more productive approach to studying; that means, they may employ more deep and strategic approaches than surface approaches. Additionally, older have more life experience than younger ones. Older students may personally or by their relationships to others, have experience with failure and success, illness and recovery, and loneliness and companionship in a range of settings and domains. Experience with the bright and the dark sides of life, and reflecting on and learning from that experience may encourage students’ ability to apply a variety of theoretical perspectives for academic assignments. As a result, older students may benefit and achieve good academic results.

The current study found that medical/ health science students were more likely to attain good academic points compared to their counterparts. Similarly, in Pakistan, joining the medical profession was significantly linked with good academic scores [ 13 ]. Admission to medical school was also a significant predictor of good academic performance in Nigeria [ 32 ]. Additionally, in Southern Ethiopia, poor academic performance was significantly higher among agriculture students than health science students [ 33 ]. The possible explanation might be that medicals students have higher levels of stress than non-medical and this was mostly attributed to their studies (75.6%) [ 34 ]. The stress showed beneficial effects on medical students. Exam, test, and assignment-related stress was associated with high attendance, better day-to-day activities, and good academic results [ 35 ]. In addition, medical students had significantly higher intrinsic motivation for academics [ 36 ].

The study has some limitations. First, there might be social desirability bias as a result of self-administered data collection techniques. However, anonymity and confidentiality were assured. Second, some potential confounders, i.e., institutional influences were not controlled. Third, self-reporting may have resulted in under or over-reporting of some factors. Fourth, the cross-sectional nature does not allow the making of direct causal inferences.

Implication

Education is a powerful agent of change that produces qualified human power, improves health and livelihoods, accelerates economic development, and solves the real problems of a community. Students are expected to spend much of their time on their education and need to graduate with good academic results. Academically good students have better employment benefits, higher income, higher self-esteem and self-confidence, low levels of anxiety and depression, and are less likely to engage in substance abuse. However, in this study, only two-thirds of university students achieved good academic grades. Smoking, age, and field of study were significantly associate with academic performance. The finding of the study had the academic implication that cessation of smoking had a paramount benefit for academic success, and hence more employment opportunities and good quality of life.

Increased odds of good academic performance were observed among students reported to be non-smokers, adults, and medical/health science students. Reduction or discontinuation of smoking is of high importance for good academic achievement among these target groups. The finding suggests that higher university officials need to raise awareness regarding the adverse educational outcomes of smoking through public service announcements and curriculum-based education. Additionally, policies concerning smoking restrictions in community spaces and university facilities may help reduce the onset of smoking. The current action taken to promote a smoke-free student population can impact the future health of Ethiopians, future leaders, scholars, and professionals. Further, the academic environment in the class may be improved if older students are invited to share their views and particularly their way of reasoning. Although this study had provided some primary evidence, more similar studies documenting the association between tobacco use and academic performance among Ethiopian University students are warranted.

Availability of data and materials

The datasets used in the current study are available from the corresponding author and can be presented upon a reasonable request.

Abbreviations

Cumulative Grade Point Average

Good Academic Performance

Poor Academic Performance

Statistical Package for Social Science

Simple Random Sampling

World Health Organization

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Department of Midwifery, College of Medicine and Health Sciences, Debre Berhan University, Debre Berhan, Ethiopia

Mesfin Tadese

Department of Midwifery, College of Medicine and Health Sciences, Wolkite University, Wolkite, Ethiopia

Alex Yeshaneh

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MT conceptualized the study, developed a questionnaire, followed the data collection process, performed analysis, and prepared the final draft. AY and GBM critically revised and made basic adjustments to the final paper. All authors read and approved the final manuscript for submission.

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Tadese, M., Yeshaneh, A. & Mulu, G.B. Determinants of good academic performance among university students in Ethiopia: a cross-sectional study. BMC Med Educ 22 , 395 (2022). https://doi.org/10.1186/s12909-022-03461-0

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Effect of sleep and mood on academic performance—at interface of physiology, psychology, and education

• Kosha J. Mehta   ORCID: orcid.org/0000-0002-0716-5081 1  

Humanities and Social Sciences Communications volume  9 , Article number:  16 ( 2022 ) Cite this article

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Academic achievement and cognitive functions are influenced by sleep and mood/emotion. In addition, several other factors affect learning. A coherent overview of the resultant interrelationships is essential but has not been presented till date. This unique and interdisciplinary review sits at the interface of physiology, psychology, and education. It compiles and critically examines the effects of sleep and mood on cognition and academic performance while including relevant conflicting observations. Moreover, it discusses the impact of several regulatory factors on learning, namely, age, gender, diet, hydration level, obesity, sex hormones, daytime nap, circadian rhythm, and genetics. Core physiological mechanisms that mediate the effects of these factors are described briefly and simplistically. The bidirectional relationship between sleep and mood is addressed. Contextual pictorial models that hypothesise learning on an emotion scale and emotion on a learning scale have been proposed. Essentially, convoluted associations between physiological and psychological factors, including sleep and mood that determine academic performance are recognised and affirmed. The emerged picture reveals far more complexity than perceived. It questions the currently adopted ‘one-size fits all’ approach in education and urges to envisage formulating bespoke strategies to optimise teaching-learning approaches while retaining uniformity in education. The information presented here can help improvise education strategies and provide better academic and pastoral support to students during their academic journey.

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Introduction.

Academic performance and cognitive activities like learning are influenced by sleep and mood or emotion. This review discusses the roles of sleep and mood/emotion in learning and academic performance.

Sleep, mood, and emotion: definitions and descriptions

Sleep duration refers to “total amount of sleep obtained, either during the nocturnal sleep episode or across the 24-hour period” (Kline, 2013a ). Sleep quality is defined as “one’s satisfaction of the sleep experience, integrating aspects of sleep initiation, sleep maintenance, sleep quantity, and refreshment upon awakening” (Kline, 2013b ). Along similar lines, it is thought to be “one’s perception that they fall asleep easily, get sufficient duration so as to wake up feeling rested, and can make it through their day without experiencing excessive daytime sleepiness” (Štefan et al., 2018 ). Sleep disturbance includes disorders of initiating and maintaining sleep (insomnias) and sleep–wake schedule, as well as dysfunctions associated with either sleep or stages of sleep or partial arousals (Cormier, 1990 ). Sleep deprivation is a term used loosely to describe a lack of appropriate/sufficient amount of sleep (Levesque, 2018 ). It is “abnormal sleep that can be described in measures of deficient sleep quantity, structure and/or sleep quality” (Banfi et al., 2019 ). In a study, sleep deprivation was defined as a sleep duration of 6 h or less (Roberts and Duong, 2014 ). Sleep disorder overarches disorders related to sleep. It has many classifications (B. Zhu et al., 2018 ). Sleep disorders or sleep-related problems include insomnia, hypersomnia, obstructive sleep apnoea, restless legs and periodic limb movement disorders, and circadian rhythm sleep disorders (Hershner and Chervin, 2014 ).

Mood is a pervasive and sustained feeling that is felt internally and affects all aspects of an individual’s behaviour (Sekhon and Gupta, 2021 ). However, by another definition, it is believed to be transient. It is low-intensity, nonspecific, and an affective state. Affective state is an overarching term that includes both emotions and moods. In addition to transient affective states of daily life, mood includes low-energy/activation states like fatigue or serenity (Kleinstäuber, 2013 ). Yet another definition of mood refers to mood as feelings that vary in intensity and duration, and that usually involves more than one emotion (Quartiroli et al., 2017 ). According to the American Psychological Association, mood is “any short-lived emotional state, usually of low intensity” and which lacks stimuli, whereas emotion is a “complex reaction pattern, involving experiential, behavioural and physiological elements”. Emotion is a certain level of pleasure or displeasure (X. Liu et al., 2018 ). It is “a response to external stimuli and internal mental representations” (L. Zhang et al., 2021 ). It is “a conscious mental reaction (such as anger or fear) which is subjectively experienced as a strong feeling usually deriving from one’s circumstances, mood, or relationships with others”. “This feeling is typically accompanied by physiological and behavioural changes in the body”. “This mental state is an instinctive or intuitive feeling which arises spontaneously as distinguished from reasoning or knowledge” (Thibaut, 2015 ).

Since there is some overlap between the descriptions of mood and emotion, in the context of the core content of this review, here, mood and emotion have not been differentiated based on their theoretical/psychological definitions. This is because the aim of the review is not to distinguish between the effects of mood and emotion on learning. Thus, these have been referred to as general affective states; essentially specific states of mind that affect learning. Also, these have been addressed in the context of the study being discussed and cited in that specific place in the review.

Rationale for the topic

Sleep is essential for normal physiological functionality. The panel of National Sleep Foundation suggests sleep durations for various age groups and agrees that the appropriate sleep duration for young adults and adults would be 7–9 hours, and for older adults would be 7–8 hours (Hirshkowitz et al., 2015 ). Today, people sleep for 1–2 hours less than that around 50–100 years ago (Roenneberg, 2013 ). Millions of adults frequently get insufficient sleep (Vecsey et al., 2009 ), including college and university students who often report poor and/or insufficient sleep (Bahammam et al., 2012 ; Curcio et al., 2006 ; Hershner and Chervin, 2014 ). During the COVID-19 pandemic, sleep problems have been highly prevalent in the general population (Gualano et al., 2020 ; Jahrami et al., 2021 ; Janati Idrissi et al., 2020 ) and the student community (Marelli et al., 2020 ). Poor and insufficient sleep is a public health issue because it increases the risk of developing chronic pathologies, and imparts negative social and economic outcomes (Hafner et al., 2017 ).

Like sleep, mood and emotions determine our physical and mental health. Depressive disorders have prevailed as one of the leading causes of health loss for nearly 30 years (James et al., 2018 ). Increased incidence of mood disorders amongst the general population has been observed (Walker et al., 2020 ), and there is an increase in such disorders amongst students (Auerbach et al., 2018 ). These have further risen during the COVID-19 pandemic (Son et al., 2020 ; Wang et al., 2020 ).

The relationship between sleep, mood and cognition/learning is far more complex than perceived. Therefore, this review aims to recognise the interrelationships between the aforementioned trio. It critically examines the effects of sleep and mood on cognition, learning and academic performance (Fig. 1 ). Furthermore, it discusses how various regulatory factors can directly or indirectly influence cognition and learning. Factors discussed here are age, gender, diet, hydration level, obesity, sex hormones, daytime nap, circadian rhythm, and genetics (Fig. 1 ). The effect of sleep and mood on each other is also addressed. Pictorial models that hypothesise learning on an emotion scale and vice-versa have been proposed.

Sleep and mood/emotion affect cognition and academic achievement. Their effects can be additionally influenced by other factors like diet, metabolic disorders (e.g., obesity), circadian rhythm, daytime nap, hydration level, age, gender, and genetics. The figure presents the interrelationships and highlights the complexity emerging from the interdependence between factors, action of multiple factors on a single factor or vice-versa and the bidirectional nature of some associations. These associations collectively determine learning and thereby, academic achievement. Direction of the arrow represents effect of a factor on another.

Effect of sleep on cognition and academic performance

Adequate sleep positively affects memory, learning, acquisition of skills and knowledge extraction (Fenn et al., 2003 ; Friedrich et al., 2020 ; Huber et al., 2004 ; Schönauer et al., 2017 ; Wagner et al., 2004 ). It allows the recall of previously gained knowledge despite the acquisition of new information and memories (Norman, 2006 ). Sleeping after learning acquisition regardless of the time of the day is thought to be beneficial for memory consolidation and performance (Hagewoud et al., 2010 ). Therefore, unperturbed sleep is essential for maintaining learning efficiency (Fattinger et al., 2017 ).

Sleep quality and quantity are strongly associated with academic achievement in college students (Curcio et al., 2006 ; Okano et al., 2019 ). Sufficient sleep positively affects grade point average, which is an indicator of academic performance (Abdulghani et al., 2012 ; Hershner and Chervin, 2014 ) and supports cognitive functionality in school-aged children (Gruber et al., 2010 ). As expected, insufficient sleep is associated with poor performance in school, college and university students (Bahammam et al., 2012 ; Hayley et al., 2017 ; Hedin et al., 2020 ; Kayaba et al., 2020 ; Perez-Chada et al., 2007 ; Shochat et al., 2014 ; Suardiaz-Muro et al., 2020 ; Taras and Potts-Datema, 2005 ). In adolescents aged 14–18 years, not only did sleep quality affect academic performance (Adelantado-Renau, Jiménez-Pavón, et al., 2019 ) but one night of total sleep deprivation negatively affected neurobehavioral performance-attention, reaction time and speed of cognitive processing, thereby putting them at risk of poor academic performance (Louca and Short, 2014 ). In university students aged 18–25 years, poor sleep quality has been strongly associated with daytime dysfunctionality (Assaad et al., 2014 ). Medical students tend to show poor sleep quality and quantity. In these students, not sleep duration but sleep quality has been shown to correlate with academic scores (Seoane et al., 2020 ; Toscano-Hermoso et al., 2020 ). Students may go through repeated cycles wherein the poor quality of sleep could lead to poor performance, which in turn may again lead to poor quality of sleep (Ahrberg et al., 2012 ). Sleep deprivation in surgical residents tends to decrease procedural skills, while in non-surgical residents it diminishes interpretational ability and performance (Veasey et al., 2002 ).

Such effects of sleep deprivation are obvious because it can impair procedural and declarative learning (Curcio et al., 2006 ; Kurniawan et al., 2016 ), decrease alertness (Alexandre et al., 2017 ), and impair memory consolidation (Hagewoud et al., 2010 ), attention and decision making (Alhola and Polo-Kantola, 2007 ). It can increase low-grade systemic inflammation and hinder cognitive functionality (Choshen-Hillel et al., 2020 ). Hippocampus is the region in the brain that plays the main role in learning, memory, social cognition, and emotion regulation (Y. Zhu et al., 2019 ). cAMP signalling plays an important role in several neural processes such as learning and memory, cellular excitability, motor function and pain (Lee, 2015 ). A brief 5-hour period of sleep deprivation interferes with cAMP signalling in the hippocampus and impairs its function (Vecsey et al., 2009 ). Thus, optimal academic performance is hindered, if there is a sleep disorder (Hershner and Chervin, 2014 ).

Caveats to affirming the impact of sleep on cognition and academic performance

Despite the clear significance of appropriate sleep quality and quantity in cognitive processes, there are some caveats to drawing definitive conclusions in certain areas. First, there are uncertainties around how much sleep is optimal and how to measure sleep quality. This is further confounded by the dependence of sleep quality and quantity on various genetic and environmental factors (Roenneberg, 2013 ). Moreover, although sleep enhances emotional memory, during laboratory investigations, this effect has been observed only under specific experimental conditions. Also, the experiments conducted have differed in the methods used and in considering parameters like timing and duration of sleep, age, gender and outcome measure (Lipinska et al., 2019 ). This orientates conclusions to be specific to those experimental conditions and prevents the formation of generic opinions that would be applicable to all circumstances.

Furthermore, some studies on the effects of sleep on learning and cognitive functions have shown either inconclusive or apparently unexpected results. For example, in a study, although college students at risk for sleeping disorders were thought to be at risk for academic failure, this association remained unclear (Gaultney, 2010 ). Other studies showed that the effect of sleep quality and duration on academic performance was trivial (Dewald et al., 2010 ) and did not significantly correlate with academic performance (Johnston et al., 2010 ; Sweileh et al., 2011 ). In yet another example, despite the reduction in sleep hours during stressful periods, pharmacy students did not show adversely affected academic performance (Mnatzaganian et al., 2020 ). Also, the premise underlining the significance of sleep hours in enhancing the performance of clinical duties was challenged when the average daily sleep did not affect burnout in clinical residents, where the optimal sleep hours that would maximise learning and improve performance remained unknown (Mendelsohn et al., 2019 ). In some other examples, poor sleep quality was associated with stress but not with academic performance that was measured as grade point average (Alotaibi et al., 2020 ), showed no significant impact on academic scores (Javaid et al., 2020 ) and there was no significant difference between high-grade and low-grade achievers based on sleep quality (Jalali et al., 2020 ). Insomnia reflects regularly experienced sleeping problems. Strangely, in adults aged 40–69 years, those with frequent insomnia showed slightly better cognitive performance than others (Kyle et al., 2017 ).

The reason for such inconclusive and unanticipated results could be that sleep is not the sole determinant of learning. Learning is affected by various other factors that may alter, exacerbate, or surpass the influence of sleep on learning (Fig. 1 ). These factors have been discussed in the subsequent sections.

Effect of mood/emotion on cognition and learning

Emotions reflect a certain level of pleasure or displeasure (X. Liu et al., 2018 ). Panksepp described seven basic types of emotions, whereby lust, seeking, play and care are positive emotions whereas anger, fear and sadness are negative emotions (Davis and Montag, 2019 ). Emotions influence all cognitive functions including memory, focus, problem-solving and reasoning (Tyng et al., 2017 ). Positive emotions such as hope, joy and pride positively correlate with students’ academic interest, effort and achievement (Valiente et al., 2012 ) and portend a flexible brain network that facilitates cognitive flexibility and learning (Betzel et al., 2017 ).

Mood deficit often precedes learning impairment (LeGates et al., 2012 ). In a study by Miller et al. ( 2018 ), the negative mood is referred to as negative emotional induction, as was achieved by watching six horror films by the subjects in that study. Other examples of negative emotions given by the authors were anxiety and shame. Negative mood can unfavourably affect the learning of an unfamiliar language by suppressing the processing of native language that would otherwise help make connections, thereby reiterating the link between emotions and cognitive processing (Miller et al., 2018 ). Likewise, worry and anxiety affect decision-making. High level of worry is associated with poor task performance and decreased foresight during decision-making (Worthy et al., 2014 ). State anxiety reflects a current mood state and trait anxiety reflects a stable personality trait. Both are associated with an increased tendency of “more negative or more threatening interpretation of ambiguous information”, as can be the case in clinically depressed individuals (Bisson and Sears, 2007 ). This could explain why some people who show the symptoms of depression and anxiety may complain of confusion and show an inability to focus and use cognition skills to appraise contextual clues. Patients with major depressive disorder have scored lower on working and verbal memory, motor speed and attention than healthy participants (Hidese et al., 2018 ). Similarly, apathy, anxiety, depression, and mood disorders in stroke patients can adversely affect the functional recovery of patients’ cognitive functions (Hama et al., 2020 ). These examples collectively present a positive correlation between good mood and cognitive processes.

Caveats to affirming the impact of mood/emotion on cognition and academic performance

Based on the examples and discussion so far, a direct relationship between emotions and learning could be hypothesised, whereby positive emotions would promote creative learning strategies and academic success, whereas negative emotions would lead to cognitive impairment (Fig. 2a ). However, this relationship is far more complex and different than perceived.

Emotions have been shown on a hypothetical learning scale. a Usually, positive and negative emotions are perceived to match with optimal and poor learning, respectively. b Emotions that lead to sub-optimal/poor and optimal/better learning have been shown on the hypothetical learning scale. Here, distinct from ( a ), both negative emotions and high arousal positive emotions have been implicated in poorer learning compared with low-intensity positive emotion like pleasantness; the latter is believed to lead to optimal learning. The question mark reflects that some negative emotions like shame might stimulate learning, but the exact intensity of such emotions and whether these would facilitate better or worse learning than high arousal positive emotions or pleasantness need to be investigated.

Although positive mood favours the recall of learnt words, it correlates with increased distraction and poor planning (Martin and Kerns, 2011 ). High levels of positive emotions like excitedness and elatedness may decrease achievement (Fig. 2b ) (Valiente et al., 2012 ). It may be surprising to know that negative emotions such as shame and anxiety can arouse cognitive activity (Miller et al., 2018 ). Along similar lines, it has been observed that participants exposed to sad and neutral moods performed similarly in visual statistical (learning) tasks but those who experienced sad stimuli showed high conscious access to the acquired statistical knowledge (Bertels et al., 2013 ). Dysphoria is a state of dissatisfaction that may be accompanied by anxiety and depression. Participants with dysphoria have shown more sensitivity to temporal shifts in outcome contingencies than those without dysphoria (Msetfi et al., 2012 ), and these participants reiterated the depressive realism effect and were quicker in endorsing the connection between negative words and ambiguous statements, demonstrating a negative bias (Hindash and Amir, 2012 ). Likewise, not the positive emotion but negative emotion has been shown to influence the learning outcomes, and it increased the efficiency and precision of learning morphosyntactic instructions involving morphology and syntax of a foreign language (X. Liu et al., 2018 ). Thus, negative emotions can allow, and at times, stimulate or facilitate learning (Figs. 2 and 3 ). Further investigation is needed on the intensity of these emotions, whether these would facilitate better or worse learning than high-intensity positive emotions and whether the results would be task specific.

The figure depicts that low-to-medium intensity positive emotion like pleasantness leads to optimal learning, whereas high-intensity emotions, either positive or negative, may lead to suboptimal or comparatively poorer learning. The model considers the apparently unexpected data that negative emotions may stimulate learning. However, which negative emotions these would be, their intensities and their corresponding level of learning are not known, and so these are not shown in the figure. Also, the figure shows bias towards positive emotions in mediating optimal learning. This information is based on the literature so far. Note that the figure represents concepts only and is not prescriptive. It shows inequality and differences between the impacts of high arousal positive and high arousal negative emotions. This concept needs to be investigated. Therefore, the figure may/may not be an accurate mathematical representation of learning with regards to the intensities of positive and negative emotions. In actuality, the scaling and intensities of emotions on the negative and positive sides of the scale may not be equal, particularly in reference to the position of optimal learning on the scale. Furthermore, upon plotting the 3rd dimension, which could be one or more of the regulatory factors discussed here might alter the position and shape of the optimal learning peak.

Moreover, the intensity of positive emotions does not show direct mathematical proportionality to learning/achievement. In other words, the concept of ‘higher the intensity of positive emotions, higher the achievement’ is not applicable. Low-intensity positive emotions such as satisfaction and relaxedness may be potentially dysregulating and high-intensity positive emotions may hamper achievement (Figs. 2b and 3 ). Optimal achievement is likely to be associated with low to medium level intensity of positive emotions like pleasantness (Valiente et al., 2012 ) (Fig. 3 ). Therefore, it has been proposed that both positive and negative high arousal emotions impair cognitive ability (Figs. 2b and 3 ) whereas low-arousal emotions could enhance behavioural performance (Miller et al., 2018 ).

Interestingly, some studies have indicated that emotions do not play a significant role in context. For example, a study showed that there was no evidence that negative emotions in depressed participants showed negative interpretations of ambiguous information (Bisson and Sears, 2007 ). In another study, improvements in visuomotor skills happened regardless of perturbation or mood states (Kaida et al., 2017 ). Thus, mood can either impair, enhance or have no effect on cognition. The effect of mood on cognition and learning can be variable and depend on the complexity of the task (Martin and Kerns, 2011 ) and/or other factors. Some of these factors have been discussed in the following section. The discrepancies in the data on the effects of mood on cognition and learning may be partly attributed to the influence of these factors on cognitive functions.

Factors affecting cognition and its relationships with sleep and mood/emotion

The relationship of cognition with sleep and mood is confounded by the influence of various factors (Tyng et al., 2017 ) such as diet, hydration level, metabolic disorders (e.g., obesity), sex hormones and gender, sleep, circadian rhythm, age and genetics (Fig. 1 ). These factors and their relationships with learning are discussed in this section.

A healthy diet is defined as eating many servings per day of fruits and vegetables, while maintaining a critical view of the consumption of saturated fat, sugar and salt (Healthy Diet—an Overview|ScienceDirect Topics, n.d.). It is also about adhering to two or more of the three healthy attributes with regards to food intake, namely, sufficiently low meat, high fish and high fruits and vegetables (Sarris et al., 2020 ). Another definition of a healthy diet is the total score of the healthy eating index >51 (Zhao et al., 2021 ).

The association between an unhealthy diet and the development of metabolic disorders has been long established. In addition, food affects both cognition and emotion (Fig. 1 ) (Spencer et al., 2017 ). Food and mood show a bidirectional relation whereby food affects mood and mood affects the choice of food made by the individual. Alongside, poor diet can lead to depression while a healthy diet reduces the risk of depression (Francis et al., 2019 ). A high-fat diet stimulates the hippocampus to initiate neuroinflammatory responses to minor immune challenges and this causes memory loss. Likewise, low intake of omega-3 polyunsaturated fatty acids can affect endocannabinoid and inflammatory pathways in the brain causing microglial phagocytosis, i.e., engulfment of synapses by the brain microglia in the hippocampus, eventually leading to memory deficits and depression. On the other hand, vegetables and fruits rich in polyphenolics can lower oxidative stress and inflammation, and thereby avert and/or reverse age-related cognitive dysfunctionality (Spencer et al., 2017 ). Fruits and vegetables, fish, eggs, nuts, and dairy products found in the Mediterranean diet can reduce the risk of developing depression and promote better mental health than sugar-sweetened beverages and high-fat food found in Western diets. Consumption of dietary antioxidants such as the polyphenols in green tea has shown a negative correlation with depression-like symptoms (Firth et al., 2020 ; Huang et al., 2019 ; Knüppel et al., 2017 ). Likewise, chocolate or its components have been found to reduce negative mood or enhance mood, and also enhance or alter cognitive functions temporarily (Scholey and Owen, 2013 ). Alcohol consumption is prevalent amongst university students including those who report feelings of sadness and hopelessness (Htet et al., 2020 ). It can lead to poor academic performance, hamper tasks that require a high degree of cognitive control, dampen emotional responsiveness, impair emotional processing, and generally cause emotional dysregulation (Euser and Franken, 2012 ). Further details on the effects of diet on mood have been discussed elsewhere (Singh, 2014 ). Diet also affects sleep (Binks et al., 2020 ), which in turn affects learning and academic performance. Thus, diet is linked with sleep, mood, and brain functionality (Fig. 1 ).

Water is a critical nutrient accounting for about 3/4th of the brain mass (N. Zhang et al., 2019 ). Unlike the previously thought deficit of 2% or more in body water levels, loss of about 1–2% can be detrimental and hinder normal cognitive functionality (Riebl and Davy, 2013 ). Thus, mild dehydration can disrupt cognitive functions and mood; particularly applicable to the very old, the very young and those living in hot climatic conditions or those exercising rigorously. Dehydration diminishes alertness, concentration, short-term memory, arithmetic ability, psychomotor skills and visuomotor tracking. This is possibly due to the dehydration-induced physiological stress which competes with cognitive processes. In children, voluntary water intake has been shown to improve visual attention, enhance memory performance (Popkin et al., 2010 ) and generally improve memory and attention (Benton, 2011 ). In adults, dehydration can elevate anger, fatigue and depression and impair short-term memory and attention, while rehydration can alleviate or significantly improve these parameters (Popkin et al., 2010 ; N. Zhang et al., 2019 ). Thus, dehydration causes alterations in cognition and emotions, thereby showcasing the impact of hydration levels on both learning and emotional status (Fig. 1 ).

Interestingly, when older persons are deprived of water, they are less thirsty and less likely to drink water than water-deprived younger persons. This can be due to the defective functionality of baroreceptors, osmoreceptors and opioid receptors that alter thirst regulation with aging (Popkin et al., 2010 ). Since water is essential for the maintenance of memory and cognitive performance, the decline of cognitive functionality in the elderly could be partly attributed to their lack of sufficient fluid/water intake when dehydrated.

Obesity and underweightness

Normal weight is defined as a body mass index between 18.5 and 25 kg/m 2 (McGee and Diverse Populations Collaboration, 2005 ) or between 22 and 26.99 kg/m 2 (Nösslinger et al., 2021 ). Being underweight reflects rapid weight loss or an inability to increase body mass and is defined through grades (1–3) of thinness. In children, these are associated with poor academic performance in reading and writing skills, and mathematics (Haywood and Pienaar, 2021 ). Basically, underweight children may have health issues and this could affect their academic abilities (Zavodny, 2013 ). Also, malnourished children tend to show low school attendance and may show poor concentration and impaired motor functioning and problem-solving skills that could collectively lead to poor academic performance at school (Haywood and Pienaar, 2021 ). Malnourished children can show poor performance on cognitive tasks that require executive function. Executive functions could be impaired in overweight children too and this may lead to poor academic performance (Ishihara et al., 2020 ). The negative relation between overweightness and academic performance also implies that the reverse may be true. Poor academic outcome may cause children to overeat and reduce exercise or play and this could lead to them being overweight (Zavodny, 2013 ).

The influence of weight on academic performance is reiterated in observations that in children independent of socioeconomic and other factors, weight loss in overweight/obese children and weight gain in underweight children positively influenced their academic performance (Ishihara et al., 2020 ). Interestingly, independent of the BMI classification, perceptions of underweight and overweight can predict poorer academic performance. In youth, not only larger body sizes but perceptions about deviating from the “correct weight” can impede academic success. This clearly indicates an impact of overweight and underweight perceptions on the emotional and physical health of adolescents (Fig. 1 ) (Livermore et al., 2020 ).

Cognitive and mood disorders are common co-morbidities associated with obesity. Compared to people with normal weight, obese individuals frequently show some dysfunction in learning, memory, and other executive functions. This has been partly attributed to an unhealthy diet, which causes a drift in the gut microbiota. In turn, the obesity-associated microbiota contributes to obesity-related complications including neurochemical, endocrine and inflammatory changes underlying obesity and its comorbidities (Agustí et al., 2018 ). The exacerbated inflammation in obesity may impair the functionality of the region in the brain that is associated with learning, memory, and mood regulation (Castanon et al., 2015 ).

Obesity and mood appear to have a reciprocal relationship whereby obesity is highly prevalent amongst individuals with major depressive disorder and obese individuals are at a high risk of developing anxiety, depression and cognitive malfunction (Restivo et al., 2017 ). In patients with major depressive disorder, obesity has been associated with reduced cognitive functions, likely due to the reduction in grey matter and impaired integrity of white matter in the brain, particularly in areas related to cognition (Hidese et al., 2018 ). Obesity has been shown to be a predictor of depression and the two are linked via psychobiological mechanisms (LaGrotte et al., 2016 ). Notably, sleep deprivation increases the risk of obesity (Beccuti and Pannain, 2011 ) and sleep helps evade obesity (Pearson, 2006 ). Collectively, this links cognition and academic achievement with sleep, obesity, and mood.

Sex hormones and gender

According to the Office of National Statistics, the UK government defines sex as that assigned at birth and which is generally male or female, whereas gender is where an individual may see themselves as having no gender or non-binary gender or on a spectrum between man and woman. The following section discusses both sex and gender in context, as addressed within the cited studies.

Studies show that females outperform males in most academic subjects (Okano et al., 2019 ) and show more sustained performance in tests than male peers (Balart and Oosterveen, 2019 ). This indicates that biological sex may play a role in academic performance. The hormone oestrogen helps develop and maintain female characteristics and the reproductive system. Oestrogen also affects hippocampal neurogenesis, which involves neural stem cells proliferation and survival, and this contributes to memory retention and cognitive processing. Generally, on average, females show higher levels of oestrogen than males. This may partly explain the observed sex-based differences in academic achievement. Administration of oestrogen in females has been proposed to positively affect cognitive behaviour as well as depressive-like and anxiety-like behaviours (Hiroi et al., 2016 ). Clinical trials can establish whether there are any sex-based differences in cognition following oestrogen administration in males and females.

Progesterone, the hormone released by ovaries in females is also produced by males to synthesise testosterone. It affects some non-reproduction functions in the central nervous system in both males and females such as neural circuits formation, and regulates memory, learning and mood (González-Orozco and Camacho-Arroyo, 2019 ). The menstrual cycle in females shows alterations in oestrogen and progesterone levels and is broadly divided into early follicular, mid ovulation and late luteal phase. It is believed that the low-oestrogen-low-progesterone early follicular phase relates to better spatial abilities and “male favouring” cognitive abilities, whereas the high-oestrogen-high-progesterone late follicular or mid-luteal phases relate to verbal fluency, memory and other “female favouring” cognitive abilities (Sundström Poromaa and Gingnell, 2014 ). Thus, sex-hormone derivatives (salivary oestrogen and salivary progesterone) can be used as predictors of cognitive behaviour (McNamara et al., 2014 ). These ovarian hormones decline with menopause, which may affect cognitive and somatosensory functions. However, ovariectomy of rats, which depleted ovarian hormones, caused depression-like behaviour in rats but did not affect spatial performance (Li et al., 2014 ). While this suggests a positive effect of these hormones on mood, it questions their function in cognition and proposes activity-specific functions, which need to be investigated.

Serotonin is a neurotransmitter. Reduced serotonin is correlated with cognitive dysfunctions. Tryptophan hydroxylase-2 is the rate-limiting enzyme in serotonin synthesis. Polymorphisms of this enzyme have been implicated in cognitive disorders. Women have a lower rate of serotonin synthesis and are more susceptible to such dysfunctions than men (Hiroi et al., 2016 ; Nishizawa et al., 1997 ), implying a greater impact of serotonin reduction on cognitive functions in women than in men. Central serotonin also helps to maintain the feeling of happiness and wellbeing, regulates behaviour, and suppresses appetite, thereby modulating nutrient intake. Additionally, it has the ability to promote the wake state and inhibit rapid eye movement sleep (Arnaldi et al., 2015 ; Yabut et al., 2019 ). Thus, any sex-based differences in serotonin levels may affect cognitive functions directly or indirectly via the aforementioned parameters.

Interestingly, data on the relationship between sex and sleep have been ambiguous. While in one study, female students at a university showed less sleep difficulties than male peers (Assaad et al., 2014 ), other studies showed that female students were at a higher risk of presenting sleep disorders related to nightmares (Toscano-Hermoso et al., 2020 ) and insomnia was significantly associated with the risk of poor academic performance only in females (Marta et al., 2020 ). Collectively, sex and gender may influence learning directly, or indirectly by affecting sleep and mood; the other two factors that affect cognitive functions (Fig. 1 ).

Circadian rhythm

Circadian rhythm is a biological phenomenon that lasts for ~24 hours and regulates various physiological processes in the body including the sleep–wake cycles. Circadian rhythm is linked with memory formation, learning (Gerstner and Yin, 2010 ), light, mood and brain circuits (Bedrosian and Nelson, 2017 ). We use light to distinguish between day and night. Interestingly, light stimulates the expression of microRNA-132, which is the sole known microRNA involved in photic regulation of circadian clock in mammals (Teodori and Albertini, 2019 ). The photosensitive retinal ganglions that express melanopsin in eyes not only orchestrate the circadian rhythm with the external light-dark cycle but also influence the impact of light on mood, learning and overall health (Patterson et al., 2020 ). For example, we frequently experience depression-like feelings during the dark winter months and pleasantness during bright summer months. This can be attributed to the circadian regulation of neural systems such as the limbic system, hypothalamic–pituitary–adrenal axis, and monoamine neurotransmitters. Mistimed light in the night disturbs our biological judgement leading to a negative impact on health and mood. Thus, increased incidence of mood disorders correlates with disruption of the circadian rhythm (Walker et al., 2020 ). Interestingly, a study involving university students showed the significance of short-wavelength light, specifically, blue-enriched LED light in reducing melatonin levels [best circadian marker rhythm (Arendt, 2019 )], and improved the perception of mood and alertness (Choi et al., 2019 ). While these examples depict the effect of circadian rhythm on mood, the reverse is also true. Individuals who demonstrate depression show altered circadian rhythm and disturbances in sleep (Fig. 1 ) (Germain and Kupfer, 2008 ). Also, since circadian rhythm regulates physiological and metabolic processes, disruption in circadian rhythm can cause metabolic dysfunctions like diabetes and obesity (Shimizu et al., 2016 ), eventually affecting cognition and learning (Fig. 1 ).

Delayed circadian preference including a tendency to sleep later in the night is common amongst young adults and university students (Hershner and Chervin, 2014 ). This delayed sleep phase disorder, often seen in adolescents, negatively impacts academic achievement and is frequently accompanied by depression (Bartlett et al., 2013 ; Sivertsen et al., 2015 ). Alongside, there is a positive correlation between sleep regularity and academic grades, implying that irregularity in sleep–wake cycles is associated with poor academic performance, delayed circadian rhythm and sleep and wake timings (Phillips et al., 2017 ). Even weekday-to-weekend discrepancy in sleeping patterns has been associated with impaired academic performance in adolescents (Sun et al., 2019 ). Further connection between sleep pattern, circadian rhythm, alertness, and the mood was observed in adolescents aged 13–18 where evening chronotypes showed poor sleep quality and low alertness. In turn, sleep quality was associated with poor outcomes including low daytime alertness and depressed mood. Evening chronotypes and those with poor sleep quality were more likely to report poor academic performance via association with depression. Strangely, sleep duration did not directly affect their functionality (Short et al., 2013 ). Contrastingly, in adults aged 40–69 years, the evening and morning chronotypes were associated with superior and poor cognitive performance, respectively, relative to intermediate chronotype (Kyle et al., 2017 ). In addition to this age-specific effect, the effect of chronotype can be subject-specific. For example, in subjects involving fluid cognition for example science, there was a significant correlation between grades and chronotype, implying that late chronotypes would be disadvantaged in exams of scientific subjects if examined early in the day. This was distinct from humanistic/linguistic subjects in which no correlation with chronotype was observed (Zerbini et al., 2017 ). These observations question the “one size fits all” approach of assessment strategies.

Daytime nap

The benefits of daytime napping in healthy adults have been discussed in detail elsewhere (Milner and Cote, 2009 ). In children, daytime nap facilitates generalisation of word meanings (Horváth et al., 2016 ) and explicit memory consolidation but not implicit perceptual learning (Giganti et al., 2014 ). A 90-min nap increases hippocampal activation, restores its function and improves declarative memory encoding (Ong et al., 2020 ). Generally, daytime napping has been found to be beneficial for memory, alertness, and abstraction of general concepts, i.e. creating relational memory networks (Lau et al., 2011 ). Delayed nap following a learning activity helps in the retention of declarative memory (Alger et al., 2010 ) and exercising before the daytime nap is thought to benefit memory more than napping or exercising alone (Mograss et al., 2020 ). Also, napping for 0.1–1 hour has been associated with a reduced prevalence of overweightness (Chen et al., 2019 ).

Contrastingly, in some studies, napping has been found to impart no substantial benefits to cognition. For example, despite the daytime nap of 1 hour, procedural performance remained impaired after total deprivation of night sleep (Kurniawan et al., 2016 ), indicating that daytime nap may not always be reparative. In other studies, 4 weeks of 90-minute nap intervention (napping or restriction) did not alter behavioural performance or brain activity during sleep in healthy adults aged 18–35 (McDevitt et al., 2018 ) and enhancements in visuomotor skills occurred regardless of daytime nap (Kaida et al., 2017 ). Age is a factor in relishing the benefits of napping. A 90-min nap can benefit episodic memory retention in young adults but these benefits decrease with age (Scullin et al., 2017 ) and may be harmful in the older population, particularly in those getting more than 9 hours of sleep (Mantua and Spencer, 2017 ; Mehra and Patel, 2012 ).

Napping can increase the risk for depression (Foley et al., 2007 ) and show a positive association with depression, i.e., napping is associated with greater likelihood of depression (Y. Liu et al., 2018 ). Cardiovascular diseases, cirrhosis and kidney disease have been linked with both daytime napping and depression (Abdel-Kader et al., 2009 ; Hare et al., 2014 ; Ko et al., 2013 ). While a previous study indicated that the time of nap, morning or afternoon, made no difference to its effect on mood (Gillin et al., 1989 ), a subsequent study suggested that the timing of nap influenced relapses into depression. Specifically, in depressed individuals, morning naps caused a higher propensity of relapse into depression than afternoon naps, thereby proposing the involvement of circadian rhythm in this process. Apart from depression, studies have struggled to identify the direct effect of nap on mood (Gillin et al., 1989 ; Wiegand et al., 1993 ). As daytime napping has been associated with poor sleep quality (Alotaibi et al., 2020 ), it may lead to irregular sleep–wake patterns and thereby alter circadian rhythm (Phillips et al., 2017 ). Also, nap duration was found to be important. In patients with affirmed depression, shorter naps were found to be more detrimental than longer naps (Wiegand et al., 1993 ), whereas, in the elderly, more and longer naps were associated with increased risk of mortality amongst the cognitively impaired individuals (Hays et al., 1996 ). Thus, daytime napping can affect cognitive processes directly or indirectly via its association with circadian rhythm, metabolic dysfunctions, mood, or sleep (Fig. 1 ).

Aging is associated with decreased neurogenesis and structural changes in the hippocampus amongst other neurophysiological effects. This in turn is associated with age-related mood and memory impairments (Kodali et al., 2015 ). Study on the effect of age on mood and emotion regulation in adults aged 20–70 years showed that older participants had a higher tendency to use cognitive reappraisal while reducing negative mood and enhancing positive mood. Interestingly, while women did not show correlations between age and reappraisal, men showed an increment in cognitive reappraisal with age. This indicates gender-based differences in the effect of aging on emotion regulation (Masumoto et al., 2016 ). The influence of age on sleep is well known. Older people that have sleep patterns like the young demonstrate stronger cognitive functions and lesser health issues than those whose sleep patterns match their age (Djonlagic et al., 2021 ). Collectively, this interlinks age, cognition, mood, and sleep.

Apparently, there is a genetic influence on learning and emotions. Approximately 148 independent genetic loci have been identified that influence and support the notion of heritability of general cognitive functions (Davies et al., 2018 ). This indicates the role of genetics in cognition (Fig. 1 ). The α-7 nicotinic acetylcholine receptor (encoded by the gene CHRNA7 ) is expressed in the central and peripheral nervous systems and other peripheral tissues. It has been implicated in various behavioural and psychiatric disorders (Yin et al., 2017 ) and recognised as an important receptor of the cholinergic anti-inflammatory pathway that exhibits a neuroprotective role. Its activation has been shown to improve learning, working memory and cognition (Ren et al., 2017 ). However, there have been some contrasting results related to this receptor. While its deletion has been linked with cognitive impairments, aggressive behaviours, decreased attention span and epilepsy, Chrna7 deficient mice have shown normal learning and memory, and the gene was not deemed essential for the control of emotions and behaviour in mice. Thus, the role of α-7 nicotinic acetylcholine receptor in maintaining mood and cognitive functions, although indicative, is yet to be fully deciphered in humans (Yin et al., 2017 ). Similarly, the gene Slitrk6 , which plays a role in the development of neural circuits in the inner ear may also play a role in some cognitive functions, but it does not appear to play a clear role in mood or memory (Matsumoto et al., 2011 ). Notably, inborn errors of metabolism, i.e., rare inherited disorders may show psychiatric manifestations even in the absence of obvious neurological symptoms. These manifestations could involve impairments in cognitive functions, and/or in the regulation of learning, mood and behaviour (Bonnot et al., 2015 ).

Other factors and associations

Indeed, optimal learning is additionally influenced by factors beyond those discussed here. These factors could be adequate meal frequency, physical activity and low screen time (Adelantado-Renau, Jiménez-Pavón, et al., 2019 ; Burns et al., 2018 ). In adolescents, the time of internet usage was identified as a factor that mediated the association between sleep quality (but not duration) and academic performance (Adelantado-Renau, Diez-Fernandez, et al., 2019 ; Evers et al., 2020 ). Self-perception is another determinant of performance. The American Psychological Association defines self-perception as “person’s view of his or herself or of any of the mental or physical attributes that constitute the self. Such a view may involve genuine self-knowledge or varying degrees of distortion”. Compared to other residents, surgery residents indicated the less perceived impact of sleep-loss on their performance (Woodrow et al., 2008 ). This may be related to specific work culture or profession where there is the reluctance of acceptance of natural human limitations posed by sleep deprivation. Whether there is real resistance to sleep deprivation amongst such professional groups or a misconception requires investigation. Exercise affects both sleep and mood; the latter probably affects in a sex-dependent manner. Thus, moderate exercise has been proposed as a therapy for treating mood disorders (Lalanza et al., 2015 ).

Sleep and mood: a bidirectional but unequal relationship

While the cause of the relationship between sleep and mood is not fully understood, adequate quality and quantity of sleep has shown physiological benefits and may enhance mood (Scully, 2013 ). Sleep encourages insightful behaviour (Wagner et al., 2004 ) and regulates mood (Vandekerckhove and Wang, 2017 ). Sleeping and dreaming activate emotional and reward systems that help process information, and consolidate memory “with high emotional or motivational value”. Inevitably, sleep disturbances can dysregulate these motivational and emotional processes and cause predisposition to mood disorders (Perogamvros et al., 2013 ). Sleep loss can reinforce negative emotions, reduce positive emotions, and increase the risk for psychiatric disorders. In children and adolescents, it can increase anger, depression, confusion and aggression (Vandekerckhove and Wang, 2017 ). Thus, sleep disorder has been associated with depression, where the former can predict the latter (LaGrotte et al., 2016 ). Sleep deprivation and daytime sleepiness amongst adolescents and college students cause mood deficits, negatively affect their mood and learning, and lead to poor academic performance (Hershner and Chervin, 2014 ; Short and Louca, 2015 ). Thus, disrupted sleep acts as a diagnostic factor for mood disorders, including post-traumatic stress disorder, major depression and anxiety (Walker et al., 2020 ).

In turn, mood affects sleep quality. Emotional events and stress during the daytime can affect sleep physiology. Negative states such as sadness, loneliness, and grief are related to sleep impairments, whereas positive states like love can be associated with lessened sleep duration but better sleep quality; the latter needs further evidence. Although dysregulation of emotion relates to poor sleep quality (Vandekerckhove and Wang, 2017 ), the effect of mood on sleep can be modulated by our approach of coping with our emotions (Vandekerckhove and Wang, 2017 ). Therefore, this effect is significantly smaller than the reverse (Triantafillou et al., 2019 ).

Summary and future direction

Sleep and mood influence cognitive functions and thereby affect academic performance. In turn, these are influenced by a network of regulatory factors that directly or indirectly affect learning. The compilation of observations clearly demonstrates the complexity and multifactorial dependence of academic achievement on students’ lifestyle and physiology, as discussed in the form of effectors like age, gender, diet, hydration level, obesity, sex hormones, circadian rhythm, and genetics (Fig. 1 ).

The emerged picture brings forth two points. First, it partly explains the ambiguous and conflicting data on the effects of sleep and mood on academic performance. Second, these revelations collectively question the ‘one-size fits all’ approach in implementing education strategies. It urges to explore formulating bespoke group-specific or subject-specific strategies to optimise teaching–learning approaches. Knowledge of these factors and their associations with each other can aid in forming these groups and improving educational strategies to better support students. However, it is essential to retain parity in education, and this would be the biggest challenge while formulating bespoke approaches.

In the context of sleep, studies could be conducted that first establish standardised means of measuring sleep quality and then measure sleep quality and quantity simultaneously in individuals of different ages groups, sex, and professions. This could then be related to their performance in their respective fields/professions; academic or otherwise. Such studies will help to better understand these interrelationships and address some discrepancies in the data.

Limitations

One limitation of this review is that it addresses only academic performance. Performance should be viewed broadly and be inclusive of all types, for example, athletic performance, dance performance or performance at work on a desk job that may include creative work or financial/mathematical calculations. It would be interesting to investigate the effect of alterations in sleep and mood on various types of performances and those results will be able to provide us with a much broader picture than the one depicted here. Notably, while learning can be assessed, it is difficult to quantify emotions (Ayaz‐Alkaya, 2018 ; Nieh et al., 2013 ). As such, it is believed that qualitative research is a better approach for studying emotional responses than quantitative research (Ayaz‐Alkaya, 2018 ).

Another point of limitation is related to the proposed models in Figs. 2 and 3 . These show hypothetical mathematical scales of learning and emotion where emotions are placed on a scale of learning, and learning is placed on the scale of emotions, respectively. While these models certainly help to better visualise and understand the interrelationships, these scales show only 2-dimensions. There could be a 3rd dimension, and this could be either one of the factors or a combination of the several factors discussed here (and beyond) that determine the effect of mood/emotion on learning/cognition. Additionally, the depicted scales and their interpretations may vary between individuals because the intensity of the same emotion felt by different individuals may differ. Figure 3 depicts emotions and learning. Based on the studies so far, here, negative emotions have been shown to stimulate learning, but which negative emotions these would be (for e.g., shame or anxiety), at what intensities these would stimulate optimal learning if at all, and how this compares with optimal learning induced by positive emotions remains to be investigated. Therefore, the extent to which these scales can be applied in real-life needs to be verified.

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Exploring the determinants of students’ academic performance at university level: The mediating role of internet usage continuance intention

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This study investigates the impact of integrating essential factors on academic performance in university students’ context. The proposed model examines the influence of continuance intention, satisfaction, information value, and Internet addiction on academic performance. Additionally, it investigates the mediating role of continuance intention on the relationship of satisfaction and information value on academic performance among university students. A survey questionnaire method was adopted to collect data from university students in Jordan. Data was collected from 476 voluntary participants, and the analysis was conducted using SPSS and AMOS. The analysis results show that continuance intention, satisfaction, information value have a significant positive influence on academic performance. Besides, the results show that satisfaction and information value positively and significantly influence continuance intention. While continuance intention full mediation the relationship between satisfaction and academic performance, it partial mediation the relationship between information value and academic performance. This study is the first to examine the integrating of continuance intention, satisfaction, information value, and Internet addiction on students’ academic performance. Furthermore, this study is also distinguished from other studies by investigating the mediating role of continuance intention gap.

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1 Introduction

There is a significantly increasing influence of using Internet and communication technology in the education industry. University students use the Internet daily to access information, gather data, and conduct research (Bagavadi Ellore et al. 2014 ). Moreover, they use the internet for entertainment and enjoyment fulfilment. In addition to the importance of the internet as an educational tool, students use the internet for entertainment and enjoyment fulfilment (Al-Fraihat et al. 2020 ). Students worldwide are reported to spend on average around two hours and 24 min per day on social media alone in 2019 (Statista 2019 ). The amount of time spent on the internet or social network sites (SNS) provoked researchers in the past to examine the antecedents or determinants of continuous intention. Various media tools have been examined to understand what drives users to spent more time on the Internet (Joorabchi et al. 2011 ) (Błachnio et al. 2019 ), Facebook (Karnik et al. 2013 )(Houghton et al. 2020 ), social networking sites (SNS) (Y. Kim et al. 2011 )(Marengo et al. 2020 ).

Previous research showed that using technology enhances academic performance (Basak and Calisir 2015 )(Choi 2016 )(Naqshbandi et al. 2017 ) (Bae 2018 )(Hou et al. 2020 ) (Çebi and Güyer 2020 ). For example, (Naqshbandi et al. 2017 ) found that Facebook mediates the relationship between different personality dimensions (extraversion, agreeableness and loneliness) and academic performance. Therefore, it is paramount to identify the antecedents of continuous intention. Enjoinment (Choi 2016 ), satisfaction (Bae 2018 ), entertainment and status-seeking (Basak and Calisir 2015 ) are among the many antecedents evidenced in the literature. (Hou et al. 2020 ) examined the impact of WeChat as a social network site on learning. Besides, it investigates how social network sites would influence university students’ academic performance. They found that WeChat usage played a significant positive role in students’ academic performance by engaging and enhancing sharing information and resources. Another study examined the impact of student interaction with different online learning activity on learning performance (Çebi and Güyer 2020 ). They found that spending longer time on learning activities enhance their academic performance.

Nowadays, Internet resources have become a very important component in educational systems (Salam and Farooq 2020 ). Students continuously and extensively use the Internet to interact online, search information, and perform specific tasks and activities. The use of the Internet implicates positive and negative effects on university students’ academic performance (Chang et al. 2019 ). Nowadays, students are using the Internet excessively to do various tasks and access social networking sites. The Internet’s intensive use is mainly for online communications, socializing, chatting, and gaming purposes (Byun et al. 2009 ). The overload of information can negatively influence students’ academic performance (Sinha et al. 2001 ). Students use the Internet to perform tasks related and non-task-related to their study, influencing students’ academic performance (Chang et al. 2019 ). For instance, (Kolek and Saunders 2011 ) had not found any association between Facebook use and students’ academic performance. On the contrary, (Kirschner and Karpinski 2010 ) found students without using Facebook had higher GPAs compare with students had extensive use of Facebook. Thus, the impact of using the Internet and social media network on students’ academic performance is varied. It depends on the type of websites they are visiting and the tools they are using (Michikyan et al. 2015 ). A research study revealed that Internet use for academic purposes was influence positively academic performance, whereas the Internet use for other purposes was influencing negatively academic performance (Kim et al. 2017 ). Recently, another research study conclude the use of Internet affecting negatively physical and mental health of people, while it provides people with information and improves timely work-related data transmission (Saini et al. 2020 ). Currently, adopting online learning in higher education during COVID-19 Pandemic had a significant impact on learners, educators and learning performance (Ustun 2020 ). Many research studies examined the impact of using online learning systems on university student’s satisfaction and academic performance (Kapasia et al. 2020 ) (Maqableh et al. 2015 ). However, there is a need to understand the factors that positively or negatively influence students’ academic performance from the use of the Internet. Therefore, it emerges a potential research direction to investigate the factors that influence students’ academic performance.

The purpose of this study is to investigate the positive and negative impact of integrating essential factors (continuance intention, satisfaction, information value, and Internet addiction) that influence students’ academic performance. Additionally, it investigates the mediating role of continuance intention on the relationship between satisfaction and academic performance and information value and academic performance gap. This study is the first to examine the relationship between integrating four essential factors and students’ academic performance. Additionally, it is distinguished from other studies by investigating the mediating role of continuance intention and Internet usage on students’ academic performance gap.

2 Literature review and hypotheses development

2.1 academic performance.

Academic performance is defined as students’ ability to carry out academic tasks, and it measures their achievement across different academic subjects using objective measures such as final course grades and grading point average (Busalim et al. 2019 ) (Anthonysamy et al. 2020 ). Researchers agree that the Internet is becoming more important for students. For example (Bagavadi Ellore et al. 2014 ) note that the Internet is an important part of college/university students’ lives. Similarly, (Naqshbandi et al. 2017 ) note that most students use Facebook daily, making it a significant component of their daily lives.

Many studies confirm the benefits that Internet users provide for students. For example: (Mccamey et al. 2015 ) argue that as a result of the expansion of the Internet, the college students are increasingly having more resources available to help them widen their knowledge. Similarly, (Emeka and Nyeche 2016 ) argue that the Internet is beneficial for students, which enhances their capabilities and skills which are helpful in their studies, which students use for research purposes, assignments, and presentations in their respective fields of study.

Several studies have examined the relationship between using the Internet’s resources/services and different foci’ academic performance. For example: (Sampath Kumar and Manjunath 2013 ) found that university teachers and researchers’ use of Internet sources and services positively impacted their academic performance. (Emeka and Nyeche 2016 ) found that the use of the Internet has a positive influence on undergraduate students’ academic performance in a university in Nigeria.

2.2 Continuance intention

Continuance intention refers to the user’s initial decision to reuse Internet sites (Al-Debei et al. 2013 ). According to (Amoroso and Lim 2017 ) continuance intention refers to the strength of an individual intends to perform a specific activity. Subsequently, in this study, continuance intention refers to Internet usage continuance intention. Many studies examined the initial intention to use technology in the information system (IS) literature based on the technology acceptance model (TAM) (Schierz et al. 2010 ). Some studies integrated serval constructs based on several theoretical perspectives with the TAM to better understand users continuance intention (Nysveen et al. 2005 ). Consequently, Innovation Diffusion Theory (IDF) (Shin et al. 2010 ) and Task Technology Fit (TTF) (Junglas et al. 2008 ) are introduced. Research results were crucial to the development of a better theoretical understanding of technology initial intention to use and the enhancement of different practical practices to encourage users to use technology.

However, the initial intention to use technology is not enough. It is essential also to explore and understand the continuance intention to use technology; aspects that would encourage users to stay loyal and keep using the technology (C. Kim et al. 2010 )(Alzougool 2019 )(Bölen 2020 ). Companies have invested their resources to develop technologies based on users’ needs and requirements. They need to protect their investment by applying measure for continuance intention to use the technology. Literature directed towards understanding the continuance use of technology is growing (Authors et al. 2016 ) (Pai et al. 2018 ) (Bölen 2020 ). However, the Internet is rich cases for studying as they have high levels of interactions between users and would help researchers explore the different factors that affect continuance intention to use technology (Gao et al. 2014 ) (Fang and Liu 2019 ). Consequently, it is necessary to do exploratory research to identify and measure factors affecting continuance intention to use Internet sites. Overall, continuance intention previous has mainly examined in the literature as dependent variable literature (Yang and Lin 2014 ; Yang et al. 2018 ; Yang; Zhang et al. 2017 ; Zong et al. 2019 ). However, we will examine its relationship with satisfaction, Internet addiction and students’ academic performance. Based on these arguments, it is expected that students’ continuance intention to use the Internet and its resources will help them improve their academic performance. Thus, the following hypothesis is proposed:

Continuance intention significantly influences students’ academic performance.

2.3 Satisfaction

User satisfaction refers to the general feeling of fulfilment resulting from using the internet (Patwardhan et al. 2011 ). Satisfaction is an old but contemporary construct that has been used by many researchers in different disciplines (Ki Hun Kim et al. 2019 ). It has been used in the work context to measure job satisfaction (Locke 1976 ) (Saari and Judge 2004 ) and in the organizational context to customer satisfaction (Oliver and Gerald 1981 ) (Barrett 2004 ). Satisfaction is measured in the IS literature as well as many theories have been deployed accordingly. An Expectation-Confirmation Model of continued IT usage (ECM-IT) developed by Odel and Bhattacherjee ( 2001 ) compares user continued IT decisions to consumer repeat purchase decision. The research found that continuous usage of an IT has three antecedents, one of which is satisfaction with the IT used (Odel and Bhattacherjee 2001 ). Chen et al. ( 2009 ) found that consumers’ satisfaction positively and significantly influences continuance intention to use self-service technologies (S. C. Chen et al. 2009 ). In relation to the reuse health information, Kim et al. ( 2010 ) found that customer satisfaction had a significant positive influence on the decision to reuse health information provided by the internet (Kyoung Hwan Kim 2010 ). Bae ( 2018 ) found satisfaction with social network sites to have a significant impact on continuance intention to use social network sites (Bae 2018 ).

Based on the Expectation Confirmation Model (EDM), satisfaction is analyzed to understand the relationship between satisfaction and experiences while using technology (Melone 1990 ) (Bhattacherjee 2014 ); customers usually expect the performance of a product or a service before the actual usage. If their expectations relatively match their experience, then they would be satisfied. Therefore, the positive customer experience at first glance is a crucial determinant of user satisfaction. (Kuo et al. 2009 ) suggested that satisfaction can also be the aggregated positive emotional states developed through several experiences with the product or the service. Users’ IT continuance use behaviour is positively influenced by their satisfaction with prior IT usage (Bhattacherjee and Lin 2015 ). The uses and gratification theory is also performed as a theoretical basis to ground a better understanding of satisfaction and its relationship with continuance intention to use social networking systems. (Chiu and Huang 2015 ) revealed that user satisfaction with contents and features of social networking systems had a positive relationship with continuance use. Another research study examined the relationship between students satisfaction from Internet usage and students performance (Goyal et al. 2011 ). They found that Internet usage satisfaction had a significant positive impact on students academic performance. (Samaha and Hawi 2016 ) found that a low level of life satisfaction were less likely to achieve satisfactory cumulative GPAs. Based on the significant influence of satisfaction on continuance usage intention and academic performance, the following hypotheses are proposed:

Satisfaction significantly influences continuous intention to use the Internet.

Satisfaction significantly influences students’ academic performance.

2.4 Information value

Some research studies proposed another antecedent to continuous usage of an IT product/service is perceived usefulness which is closely related to information value (Zhang et al. 2017 ) (S. Yang et al. 2018 ) (Wang et al. 2020 ). The benefit of acquiring useful information through using the internet determines information value, especially if the information helps the user solve problems of developing his skills and abilities (Zhang et al. 2017 ). The uses and gratifications theory (U&G theory) explains why users select and adopts certain medium to fulfil their social and psychological needs (Ku et al. 2013 ) (Ma and Lee 2012 ). This theory has been linked with continuous intention, factors that satisfy users’ gratification needs, such as information needs and social needs. As found by (Wei et al. 2015 ), those two needs are critical factors to motivate users to interact with each other and enhance their stickiness towards using social networking sites. Moreover, (Chiang 2013 ) found that informativeness, social interactivity and playfulness needs affect users’ continuance intention towards social networking sites.

Information value refers to the useful information acquired from friends or information providers (Zhang et al. 2017 ). (Chiang 2013 ) argues that website informativeness is a potential influence on a user’s intentions and behaviours. (Liao and Shi 2017 ) found that web content (i.e. the accuracy, usefulness and completeness, and website information) directly influences the continuance intention to use online tourism services. (Zheng et al. 2013 ) found that information quality directly affects user satisfaction which in-turn influences a user’s continuance intention to use information-exchange virtual communities. Similarly, (Valaei and Baroto 2017 ) found that information quality had a positive impact on continuance intention to follow a government’s Facebook page. (Jin et al. 2007 ) found that information usefulness positively and significantly affects the continuance intention of virtual communities for information adoption. Based on these results and arguments, the following hypotheses are proposed:

Information value significantly influences continuous intention to use the Internet.

Information value significantly influences students’ academic performance.

2.5 Internet addiction

Facebook addiction refers to the excessive use of Facebook due to being psychologically reliant on its use that somewhat hinders other essential actions that the user could perform and, in the process, yield negative results (Moqbel and Kock 2018 ). About 350 million Facebook users are between 16 and 25 years old showing Facebook addiction syndrome (Leong et al. 2019 ). Overall, previous literature has mainly examined the concept of continuance intention as a dependent variable (Yang and Lin 2014 ; Yang et al. 2018 ; Yang 2019 ; Zhang et al. 2017 ; Zong et al. 2019 ). However, we will examine its relationship with Facebook addiction. Numerous theories and findings have established the relationship between behavioural intention and actual behaviour (Obeidat et al. 2017 ; Pelling and White 2009 ; Turel et al. 2010 ). Consequently, if the continuance intention of Facebook use is present, the user will continue to do so, thereby increasing the chances of addiction to the website. Furthermore, previous studies found that when a certain behaviour is exhibited, and the person is willing to do it again, future behaviour becomes an automatic, aligned response (Ronis et al. 1989 ). Therefore, the more a person uses social media to communicate with others, the more likely it will become a habit and lead to addiction (Turel et al. 2010 ). (J. V. Chen et al. 2008 ) conducted a research study that confirms higher Internet addiction can lead to a high degree of Internet abuse. Also, (Samaha and Hawi 2016 ) conducted a research study that showed smartphone addiction had a negative impact on students’ academic performance.

Following the same logic, we propose that the Facebook continuance intention resulting from the perceived values will increase Facebook addiction. Thus, this study is the first study that investigates the relationship between continuance intention and addiction gap. Generally, this factor strongly influences the association between online purchase intention and actual behaviour (Miyazaki and Fernandez 2001 ; Nepomuceno et al. 2014 ). Thus:

Internet addiction significantly negative influences on students’ academic performance.

2.6 The mediating role of continuance intention

In research, mediating factors are used to understand the mechanism that establishes the underlying relationship between the independent and dependent variables. The mediating role of employees’ satisfaction on the relationship between Internet actual usage and performance impact was examined (Isaac et al. 2017 ). The analysis results confirmed the mediating role of satisfaction. Moreover, some researchers examined the mediating role of social interaction on the relationship between network externalities on perceived values (Zhang et al. 2017 ). Also, satisfaction has been considered a mediating variable for the relationship between perceived security and continuance intention (Ki Hun Kim et al. 2019 ). Finally, Another research study examined the mediating effect of perceived value between the relationship of security and continuance intention in mobile government service (Wang et al. 2020 ). In this research, it proposed to have continuance intention as a mediating variable to measure the following relationships:

Continuance intention mediates the relationship between satisfaction and academic performance.

Continuance intention mediates the relationship between information value and academic performance.

3 Research methodology

This section provides the methodology applied in the current study. It consists of the research model of the study’s independent and dependent variables, research hypotheses, besides data collection tool and research population and sample.

3.1 Research model

In this research, the proposed model examines the impact of continuance intention, satisfaction, information value, and Internet addiction on students’ academic performance gap. Moreover, it investigates the mediating role of continuance intention on the relationship between satisfaction and academic performance and information value and academic performance gap. Figure 1 shows the proposed research model.

Research model

3.2 Data collection and sample

Data were collected from targeted participants with Internet experience using an online survey. Participants were selected opportunely from 4000 bachelor students from the School of Business at the University of Jordan in the Hashemite Kingdom of Jordan. However, what constitutes an adequate sample size for regression analysis is uncertain among researchers. Some researchers (O’Rourke and Hatcher 2013 ) recommend that the sample size of a study that applies multiple linear regression should be 100 participants or more than five times the number of items measured. The questionnaire was made up of 22 items, so the sample size should be over 110 students. Also, (Joseph Hair et al. 2014 ) recommended between 100 and 200 while (Krejcie and Morgan 1970 ) required 351 from a population of 4000. Therefore, the number of returned surveys is 476 that meets the sample size requirement for a structural equation model and shows adequate representation with the highest probability assessment. In Table 1 , the respondents’ characteristics of this study are summarized.

The 476 valid responses compromised of 70.6% female student. The sample’s dominant age range was 20 to 23 years, with a percentage of 73.3%. The respondents were mainly in their second and third years at the university, with 65.6% of the sample. 44.7% of students spend 1 to 3 h daily on internet activities. Moreover, almost 33% uses the internet from 10 to 29 h weekly. The full respondent’s profiles are shown in Table 1 .

3.3 Measurement development

The 5-points Likert scale is used to explore the associations among the research variables. It varies between strongly disagree =1 and strongly agree =5. Reliability and validity analyses were conducted, descriptive analysis was used to describe the characteristics of the sample and the respondent to the questionnaires besides the independent and dependent variables. Besides, SEM analysis was employed to test the research hypotheses. Table 2 shows the measured constructs and the items measuring each construct.

4 Data analysis and results

4.1 validity and reliability.

To check for the research model validity, and since all the measures were previously established, confirmatory factor analysis (CFA) was conducted using SPSS 20.0 and AMOS 22.0. The standardized factor loading of the item was examined since 0.55 represent a good fit (Harrington 2008 ) any item with standardized factor loading less than 0.55 was eliminated. Accordingly, item (Academic Performance 1), (Addiction 1, 2, and 3), (Information Value 4) were excluded from any further calculations. The full-standardized factor loading values from the CFA are presented in Table 3 . The model fit was assessed relaying on the model fit summary results, the cut points used in this research were χ 2 /df < 5, Root Mean Square of Error Approximation (RMSEA) <0.08, while all the other indices (i.e. GFI, CFI, TLI, IFI and NFI) should be close to 1 where higher than 0.9 is acceptable (Harrington 2008 ). Results are shown in Table 3 .

To check the reliability of the scale, Cronbach’s-Alpha test was used to assess the internal consistency the cut point usually used in researches is 0.7, but it can be lowered to 0.6 (Joe Hair et al. 2011 ). Cronbach’s-Alpha results in this research were between 0.754 and 0.864. Results are shown in Table 3 .

4.2 Descriptive statistics and correlations

Pearson’s correlation coefficient results are presented in Table 4 . Pearson’s correlation coefficient indicates the existence of a linear association between the variables according to person correlations values. No significant linear effect was found between the demographic variables and the dependent variable except for the demographic variable using the Internet (Hours per week) was found to have a significant negative correlation with academic performance (r = −.114*, p  < 0.01).

The highest mean score for information value (3.73) indicates a high positive respondents’ attitude toward continuance intention regarding the descriptive statistics. In contrast, the lowest mean score was for satisfaction (2.69). The skewness and kurtosis values were within the range of −2 to +2 (Garson 2012 ), which indicates normally distributed data. The results are provided in Table 5 .

4.3 Hypotheses testing

Multiple linear regression was used to test Hypotheses 1, 3, 5 and 6, where continuous intention, satisfaction, information value, and Internet addiction were the independent variables, and academic performance was the dependent variable. The normality plot p-p indicates that most of the points are near the best fit line, and the scatter plot produces no pattern and no multicollinearity issue was not detected. The tolerance ranged between 0.755 and 0.987, which are >1, and the variance inflation factor (VIF) statistics ranged between 1.013 and 1.325, which are less than 4, respectively (Garson 2012 ). The results are shown in Table 6 , the overall model was significant (F = 32.323, p  = 0.000 < 0.05), the R-value indicates that the whole model is correlated with the dependent, R = 0.464, R 2 indicate the amount of variance in the dependent variable that is caused by the independent variables R 2  = 21.5%. The adjusted R 2  = 20.9% is an indicator of the variance caused by the independent variables if the whole population were tested, the differences between R2 and Adj-R2 are 0.006. The regression coefficients values revealed that continuous intention, information value, and satisfaction have a significant positive effect on academic performance with effect values of B = 0.153, p  = 0.003 < 0.05, B = 0.085, p  = 0.026 < 0.05, and B = 0.424 and p  = 0.000 < 0.05 respectively. Nevertheless, in this model, Internet addiction negatively affects academic performance B = −0.057, p  = 0.169 > 0.05. Accordingly, hypotheses 1, 3 and 5 were supported, while hypothesis 6 was not supported. Results are shown in Table 6 .

To test Hypotheses 2 and 4, multiple linear regression was used where satisfaction and information value were the independent variables, and the continuous intention was the dependent variable. The normality plot p-p indicates that most of the points are near the best fit line, and the scatter plot produces no pattern. No multicollinearity issue was not detected. The results are shown in Table 7 indicate that the overall model was significant (F = 76.564, p  = 0.000 < 0.05), the R-value indicates that the whole model is correlated with the dependent, R = 0.495 and R 2 indicate the amount of variance in the dependent variable that is caused by the independent variables R 2  = 24.5%. The adjusted R 2  = 24.1% is an indicator of the variance caused by the independent variables if the whole population were tested, the differences between R2 and Adj-R2 are 0.004. The regression coefficients values revealed that both information value and satisfaction have a significant positive effect on continuous intention. The effect values were B = 0.431, p  = 0.000 < 0.05 and B = 0.157, p = 0.000 < 0.05 respectively. Accordingly, both hypotheses 2 and 4 were supported. Results are shown in Table 7 .

To test Hypotheses 7, a multiple linear regression was used to test the mediation effect using PROCESS Macro by Hayes V 3.3. Using PROCESS, the mediation effect will be tested based on 5000 Bootstrapped sample. The results of the mediation paths are shown in Table 8 . Where C represents the effect of satisfaction on performance (i.e. Total effect), (a) represents the effect of satisfaction on continuous intention, b is the effect of continuous intention on performance in the presence of satisfaction and C′ is the effect of satisfaction on performance in the presence of continuance intention (i.e. Direct effect). The mediation path can be calculated either by multiplying path a coefficient with path b coefficient or by subtracting path C coefficient form path C′ coefficient (Hayes 2015 ).

Findings showed that 95% bias-corrected bootstrap confidence intervals based on 5000 bootstrap samples ((BootLLCI) and (BootULLCI)) for specific indirect effects through continuance intention do not include zero accordingly the mediation path was found to be significant. Additionally, since the direct effect is insignificant, continuance intention fully mediates the relationship between satisfaction and continuance intention, which indicates that satisfaction affects academic performance because of continuance intention.

To test Hypotheses 8, multiple linear regression was used to test the mediation effect using PROCESS Macro by Hayes V 3.3; using PROCESS, the mediation effect will be tested based on the 5000 bootstrapped sample. The results of the mediation paths are shown in Table 9 . Where C is the effect of information value on performance (i.e. Total effect), a is the effect of information value on Continuous intention, b is the effect of continuous intention on Performance in the presence of information value and C′ is the effect of information value on Performance in the presence of continuance intention (i.e. Direct effect). The mediation path can be calculated either by multiplying path a coefficient with path b coefficient or by subtracting path C coefficient form path C′ coefficient (Hayes 2015 ).

Findings showed that 95% bias-corrected bootstrap confidence intervals based on 5000 bootstrap samples ((BootLLCI) and (BootULLCI)) for specific indirect effects through continuance intention do not include zero accordingly the mediation path was found to be significant. Additionally, since the direct effect is significant, information value partially mediates the relationship between satisfaction and continuance intention, which indicate that information value affects academic performance directly and because of continuance intention. Table 10 show the results of tested hypotheses in this research.

5 Discussion and conclusion

Former research studies have not investigated the impact of integrating essential factors that influence students’ academic performance. Thus, this study investigates the impact of continuance intention satisfaction, information value, and Internet addiction on students’ academic performance gap. Moreover, it investigates the mediating role of continuance intention on the relationship between information value and academic performance and the relationship between satisfaction and the academic performance gap. Therefore, we also tested the relationships between satisfaction and continuance intention and information value and continuance intention. The analysis results in Tables 6 and 7 show that the overall model was significant, and the whole model is correlated with the dependents. The analysis results show that most of the proposed hypotheses are supported. It shows that continuance intention, satisfaction, and information value explain 21.5% of academic performance variance. It also shows that the independent variables of continuance intention cause 19% of variances.

The research results show that continuance intention to use the Internet has a significantly positive effect on students’ academic performance. This finding supports previous research such as (Emeka and Nyeche 2016 ) (Sampath Kumar and Manjunath 2013 ) that confirmed the advantages of using the Internet as students. Using the Internet can help students search for information related to their modules and assignment. In addition, using the Internet can help students working together as groups to connect and collaborate online. Many universities nowadays integrate online learning with traditional teaching methods to create more interactive student-centred learning. Another research study showed that Facebook usage increase students’ academic performance (Naqshbandi et al. 2017 ).

The analysis results confirmed the positive influence of satisfaction on students’ academic performance, which is aligned with previous research results (Goyal et al. 2011 ) (Samaha and Hawi 2016 ). Moreover, it also confirmed that information value has a positive and significant impact on students’ academic performance. Regarding the impact of Internet addiction, the results show that Internet addiction is insignificant influence academic performance. The analysis results show that Internet addiction has a negative but insignificant effect on academic performance B = -0.057, p  = 0.169 > 0.05, which is consistent with the finding of (Kolek and Saunders 2011 ). This can be explained as the type of the tools students are using and the type of the website would had a major role on the impact of the students’ academic performance. For instance, the students who use Internet tools that support their study might be improve their academic performance, whereas the extensive use of Internet on unrelated website to their study might be reduce academic performance. Instead, a balance use of Internet between related and unrelated websites might be not effect students’ academic performance. Therefore, the impact of extensive use of Internet on academic performance might be varied from one group to another based on the type of visited websites and time spent on each type of websites. Moreover, based on the Pearson correlation coefficient, there was no significant linear effect between the demographic variables and the dependent variable except for using the Internet (hours per week). It found that Internet usage has a negative significant correlation with academic performance (r = −.114*, p  < 0.01). This can be justified as the students spend a long time using the Internet; they will waste their time on irrelative contents to their academic study that negatively affects their academic performance. This finding supports the results of previous research (J. V. Chen et al. 2008 )(Samaha and Hawi 2016 ).

This study investigated the relationship between satisfaction and continence intention. The results confirmed that satisfaction has a significant positive impact on students’ Internet continuance intention. This finding supports previous research that found satisfaction with social network sites to have a significant impact on continuance intention to use social network sites (Bae 2018 ). Furthermore, this study examined the influence of information value on continuance intention. The research findings confirmed that information value exhibits a significant influence on continuous intention, which is consistent with (S. Yang et al. 2018 ). The descriptive statistics show the information value has the highest mean score (3.73), which indicate a high positive respondents attitude toward continuance intention.

The mediating role of continuance intention on the relationship between satisfaction and academic performance is examined. The analysis results show that while satisfaction has a significant effect on academic performance, the direct effect of satisfaction on student academic performance in the presence of continuance intention is insignificant. These results indicate that continuance intention is fully mediate the relation between satisfaction and continuance intention. Finally, this research examined the mediating role of continuance intention on the relationship between information value and academic performance. The results confirmed the significant direct effect of information value and the significant effect of information value on academic performance in the presence of continues intention. These findings confirmed the partially mediating role of continuance intention on the relationship between satisfaction and continuance intention.

To conclude, this study investigated the impact of integrating four main factors of Internet usage in students’ context that influence students’ academic performance. It investigated the effect of continuance intention, satisfaction, information value, and Internet addiction on academic performance. The analysis results showed that continuance intention, satisfaction, and information value are positively influencing the students’ academic performance. Moreover, the analysis results showed that satisfaction and information value significantly influence continuance intention to use the Internet. In addition, this study investigates the mediating role of continuance intention on the relationship of satisfaction and students’ academic performance and information value and academic performance gap. The results showed that while continuance intention partially mediates the relationship between information value and academic performance, it fully mediates the relationship between satisfaction and academic performance in university students. Finally, the analysis results showed that Internet addiction does not influence students’ academic performance. Still, it has a negative impact and the number of hours to use the Internet has a negative impact on academic performance. This research study contributes to the emerging body of knowledge by extending the associations between four main factors that influence academic performance. It also contributes to the evolving body of knowledge about the mediating role of continuance intention to use the Internet on the relationship of satisfaction and information value on students’ academic performance. The finding of this research can help educators to advice their students to use Internet appropriately for academic purpose especially for students with low academic performance and grades to improve their academic performance.

6 Limitations and future research

This study was conducted on undergraduate students at one university in Jordan, which would limit the generalizability to other contexts. Therefore, future research can investigate other demographic groups, for example, employees or students from different year levels (or postgraduates). Besides, future research can address cultural differences to investigate if culture can influence continuance intention and academic performance. Furthermore, future research can be applied across different countries to compare and contrast the findings considering contextual factors peculiar for each country or region. This research only focused on four integrating factors that would influence students’ academic performance. Thus, future research can investigate another variable, such as perceived enjoyment and perceived usefulness to enrich the current research. A noteworthy result is that against our expectation, Internet addiction is not a factor that determines academic performance. It can be suggested based on the literature that perceived enjoyment and emotional experience could affect Internet addiction. Therefore, further studies can examine the impact of Internet addiction with another group of variables to identify its effect on academic performance.

Data availability

( Not applicable )

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Maqableh, M., Jaradat, M. & Azzam, A. Exploring the determinants of students’ academic performance at university level: The mediating role of internet usage continuance intention. Educ Inf Technol 26 , 4003–4025 (2021). https://doi.org/10.1007/s10639-021-10453-y

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Management Factor of Private Secondary Schools and Students’ Academic Performance in External Examinations in Rivers State, Nigeria

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This study investigates the impact of school management factors, specifically principal leadership styles and teachers’ characteristics, on students’ academic performance in private secondary schools in Rivers State, Nigeria. Drawing on a descriptive survey design, data were collected from 40 principals and 60 teachers using questionnaires. The research objectives include examining the relationship between principal leadership styles (such as autocratic, democratic, and laissez-faire) and students’ academic performance, as well as assessing the influence of teacher characteristics (including teaching experience, qualification, and in-service training) on academic outcomes. Findings are expected to shed light on effective management strategies to improve student achievement in secondary education.

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JONESBORO – Arkansas State University staff members gathered recently for the 31st Distinguished Performance and Service Recognition Awards ceremony. This program for non-faculty university employees was first conducted in 1994 and has become a popular campus tradition.

Distinguished Performance Awards The staff Distinguished Performance and Service Recognition program has recognized A-State’s most outstanding staff members each year since 1994. Selected from the nominees, the award winners this year are: Fowler Center, Team Achievement; Elizabeth Jennings, Team Player; Elianet Roldan Cubero, Enhancing the Student Experience; Dr. Melissa Jackson, STAR (service, teamwork, attitude, reliability); Kelli Listenbee, Unsung Hero; Kelsey Deford, Rookie of the Year; and Kenya Jones, Outstanding Part-Time.

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Bell’s academic journey wasn’t smooth sailing. Forced relocations disrupted her studies, affecting her grades and stability. However, when she finally found steady housing, her academic performance improved, and she secured a solid job as an electrical technician. Her hard work didn't go unnoticed, as she began to receive recognition and accolades, including the prestigious Chancellor's Civic Leadership Medallion.

When life threw more challenges her way, and she faced yet another forced relocation, this time, Bell had the support and solace among her fellow Pumas and mentors at PVCC's Intercultural Center and Women Rising. These safe spaces provided her with the opportunity to confront her own feelings and share experiences with peers facing similar struggles.

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