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The relationship between learning styles and academic performance: consistency among multiple assessment methods in psychology and education students.

1. Introduction

1.1. learning styles: experiential learning theory, 1.2. assessment method, academic performance and learning dimensions, 1.3. present study, 2. materials and methods, 2.1. participants, 2.2. instruments.

• Multiple choice questions (MCQ). The contents were evaluated through multiple choice tests. Using this closed question method, students had to identify a single valid response among four alternatives. The following is an example of an MCQ used in the assessment: “The increase in explicit memory in children is associated with: (a) the autonomy of the child when learning to walk and handle objects; (b) an increase in the density of synapses at four months; (c) an increase in the density of synapses at eight months (correct answer); (d) the importance of holophrases in children to store memory”. A total of 30 MCQ were used with a final score of 0 to 10.
• Short questions (SQ). This method used short and closed questions. In this test, the student is given a statement in order to identify a concept. An example is given below: “Identify the developmental theory that states that the zone of proximal development refers to the distance between the actual level of development and the level of potential development”. In this case, the correct answer would be: Vygotsky’s Sociocultural Theory. A total of 10 SQ were used with a final score ranging from 0 to 10.
• Creation-elaboration questions (CEQ). In this open-ended question, students must create a practical activity based on the contents studied in the developmental psychology course. This method involves mainly a practical question. The CEQ used in the tests was: “Create an activity to work on the understanding other people’s emotions in a class of 5-year-olds”. With a score between 0 and 10 points, the structure of the activity, creativity, and suitability with the contents of the course were used as criteria to evaluate performance on the CEQ.
• Elaboration Questions on the Relationship between Theory and Practice (EQRTP). In this open-ended assessment question, students must link theoretical concepts to practical application. A video is used to illustrate a teaching-learning process between children and an expert, and various interactions between children. After watching the video, students must analyze its content using theoretical concepts. Below is the examination question and a link to the video used ( https://www.dropbox.com/s/xux6p9di5pj885m/Sustainability.mp4?dl=0 ) (accessed on 29 January 2021) [ 48 ] “Associate the following video with theoretical contents of the Developmental Psychology course”. The number of associations between practical aspects and theoretical contents, coherence between concepts, presentation of theory and clarity in the written composition were used as criteria to assess performance from 0 to 10.

2.3. Procedure

2.4. data analysis, 3.1. learning styles according to personal and educational dimensions, 3.2. assessment methods: academic performance and consistency and their relation to learning dimensions and styles, 3.3. influence of learning dimensions on performance consistency in the different assessment methods, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, acknowledgments, conflicts of interest.

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Maya, J.; Luesia, J.F.; Pérez-Padilla, J. The Relationship between Learning Styles and Academic Performance: Consistency among Multiple Assessment Methods in Psychology and Education Students. Sustainability 2021 , 13 , 3341. https://doi.org/10.3390/su13063341

Maya J, Luesia JF, Pérez-Padilla J. The Relationship between Learning Styles and Academic Performance: Consistency among Multiple Assessment Methods in Psychology and Education Students. Sustainability . 2021; 13(6):3341. https://doi.org/10.3390/su13063341

Maya, Jesús, Juan F. Luesia, and Javier Pérez-Padilla. 2021. "The Relationship between Learning Styles and Academic Performance: Consistency among Multiple Assessment Methods in Psychology and Education Students" Sustainability 13, no. 6: 3341. https://doi.org/10.3390/su13063341

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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]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Home > Books > E-Learning and Digital Education in the Twenty-First Century

The Impact of Online Learning Strategies on Students’ Academic Performance

Submitted: 01 September 2020 Reviewed: 11 October 2020 Published: 18 May 2022

DOI: 10.5772/intechopen.94425

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Higher education institutions have shifted from traditional face to face to online teaching due to Corona virus pandemic which has forced both teachers and students to be put in a compulsory lockdown. However the online teaching/learning constitutes a serious challenge that both university teachers and students have to face, as it necessarily requires the adoption of different new teaching/learning strategies to attain effective academic outcomes, imposing a virtual learning world which involves from the students’ part an online access to lectures and information, and on the teacher’s side the adoption of a new teaching approach to deliver the curriculum content, new means of evaluation of students’ personal skills and learning experience. This chapter explores and assesses the online teaching and learning impact on students’ academic achievement, encompassing the passing in review the adoption of students’ research strategies, the focus of the students’ main source of information viz. library online consultation and the collaboration with their peers. To reach this end, descriptive and parametric analyses are conducted in order to identify the impact of these new factors on students’ academic performance. The findings of the study shows that to what extent the students’ online learning has or has not led to any remarkable improvements in the students’ academic achievements and, whether or not, to any substantial changes in their e-learning competence. This study was carried out on a sample of University College (UAEU) students selected in Spring 2019 and Fall 2020.

• online learning environment
• content-based research
• process-based research
• success factors assessment

Author Information

Khaled hamdan *.

• UAEU-University College, UAE

Abid Amorri

*Address all correspondence to: [email protected]

1. Introduction

With the advent of COVID-19 pandemic and the shutdown of universities worldwide for fear of contamination due to the spread of the coronavirus, higher educational institutions have deemed necessary to adopt new teaching strategies, exclusively online, to deliver their curriculum content and keep from the Corona virus widespread at bay [ 1 ]. Technology was called upon to play this pivotal teaching/learning online role, as it has influenced people’s task accomplishment in various ways. It has become a part of our ever changing lives. It is an important part of e-learning to create relationship-involving technology, course content and pedagogy in learning/teaching environment. Therefore, e-learning is becoming unavoidable in a virtual teaching environment where students can take control of their learning and optimize it in a virtual classroom and elsewhere. So, learning today has shifted from the conventional face to face learning to online learning and to a direct access to information through technologies available as e-learning has proven to be more beneficial to students in terms of knowledge or information acquisition. Online teaching promotes learning by encouraging the students’ use of various learning strategies at hand and increases the level of their commitment to studying their majors. Virtual world represents an effective learning environment, providing users with an experience-based information acquisition. Instructors set up the course outcomes by creating tasks involving problem or challenge-based learning situations and offering the learner a full control of exploratory learning experiences. However, there are some challenges for instructors such as the selection of the most appropriate educational strategies and how best to design learning tasks and activities to meet learners’ needs and expectations. Various approaches can lead towards strong students’ behavioral changes especially when combined with ethical principles. However, with careful selection of the learning environment, pedagogical strategies lining up with the concrete specifics of the educational context, the building of learners’ self-confidence and their empowerment during the learning process becomes within reach. Another benefit of using online teaching/learning is that here is a need to explore new teaching strategies and principles that positively influence distance education, as traditional teaching/learning methods are becoming less effective at engaging students in the learning process. Finally, e-learning can solve many of the students’ learning issues in a conventional learning environment, as it helps them to attend classes for various reasons, as it has made the communication/interaction between them and their instructors much easier and the access to lectures much more at hand. Students can attend online university courses and at the same time meet other social obligations. Therefore, the circumstances in a learner’s life, and whatever problems or distraction he/she may have such as family problems or illnesses, may no longer be an impediment to his education. Learners can practice in virtual situations and face challenges in a safe environment, which leads to a more engaged learning experience that facilitates better knowledge acquisition.

The work presents the educational processes as a modern strategy for teaching/learning. e-learning tends to persuade the users to be virtually available to act naturally. There are a few factors affecting the outcomes such as learning aims and objectives, and different pedagogical choices. Instructors use various factors to measure the learning quality like Competence, Attitude, Content Delivery, Reliability, and Globalization [ 2 , 3 , 4 ]. In this work, we are going to pass in review positive and negative impacts of online learning followed by recommendations to increase awareness regarding online learning and the use of this new strategic technology. Modern teaching methods like brainstorming, problem solving, indirect-consultancy, and inquiry-based method have a significant effect in the educational progress [ 5 ].

The aim of this research is to examine the effect of using modern teaching methods, such as teacher-student interactive and student-centered methods, on students’ academic performance. Factors that may affect students’ performance and success- the technology used, students’ collaboration/teamwork, time management and communication skills are taken into consideration [ 6 ]. It also attempts to identify and to show to what extent online learning environment, when well integrated and adapted in course planning and objectives, can cater for students’ needs and wants. Does online teaching make a significant improvement in students’ academic performance and their personal skills such as organizations, communications, responsibilities, problem-solving tasks, engagement, learning interest, self-evolution, and abilities to reach their potential? Is students’ struggle is not purely academic, but rather related to the lack of personal skills?

2. Online learning experience

There are many motives behind the implementation of the online learning experience. The online learning is mandatory nowadays to all audience due to COVID −19 pandemic, which forced the higher educational authorities to start the online teaching [ 1 ]. We believe that we reached a tipping point where making changes to the current learning process is inevitable for many reasons. Today learners have instant access to information through technology and the web, can manage their own acquisition of knowledge through online learning. As a result, traditional teaching and learning methods are becoming less effective at engaging students, who no longer rely exclusively on the teacher as the only source of knowledge. Indeed, 90% of the respondents use internet as their major source of information. So the teacher is new role is to be a learning facilitator, a guide for his students. He should not only help his students locate information, but more importantly question it and reflect upon it and formulate an opinion about it. Another reason for the adoption of the online learning is that higher institution did not hesitate one moment to integrate it as a primary tool of education. So, it transformed the conventional course and current learning process into e-learning concept. The integration of the online teaching into the curriculum resulted in several issues to instructors, curriculum designer and administrators, starting from the infrastructure to online teaching and assessment. Does the current IT infrastructure support this integration? What course content should the instructor teach and how it should be delivered? What effective pedagogy needs to be adopted? How learning should be assessed? What is the direct effect of the online learning on students’ performance? [ 7 ].

With reference to the survey findings, the majority of students were among the staunch supporters of online learning taking into consideration the imposed COVID-19 lockdown circumstances, as they expressed their full support and confidence in computer skills to share digital content, using online learning and collaboration platforms with their peers, and expressed their satisfaction with the support of the online teaching and learning [ 8 ].

However, a small percentage of the survey respondents, expressed their below average satisfaction when higher educational institutions have invested in digital literacy and infrastructure, as they believe they should provide more flexible delivery methods, digital platforms and modernized user-friendly curricula to both students and teachers [ 9 ]. On the same lines, the higher education authorities regard the quick and unexpected development of the UAE’s higher education landscape, ICT infrastructure, and advanced online learning/teaching methods, imposed by COVID-19, have had a tremendous adverse impact on the students’ culture, thus leading to students’ social seclusion from their peers, imposing new social norms and behavior regarding plagiarism, affecting students’ cultural ethics and learning and collaboration with their peers, when adopting the digital culture [ 10 ].

A current study emphasized the need for adoption of technology in education as a way to lessen the effects of Coronavirus pandemic lockdown in education to palliate the loss of face- to- face teaching/learning which has more beneficial aspects of learning for students than online learning as it offers more interactive learning opportunities.

We recommend that all these questions should be taken into consideration when designing a new course i.e. the e-learning strategies, the learners’ and instructor’s new roles, course content and pedagogy and students’ performance/achievement assessment ( Figure 1 ). In this experience, we focus only on the implementation of new learning academic objectives- how they are infused into the curriculum and how they are assessed. The ultimate objective of implementing a new learning process is to design a curriculum conveyed by a creative pedagogy and oriented towards the cultivation of a creative person yearning for the exploration of new ideas [ 11 ]. The afore-mentioned objectives lead to design a comprehensive learning experience with new learning outcomes where instructors infuse new practical skills - Critical thinking and Problem-Solving Tasks, Creativity and Innovation, Communication and Collaboration. Other skills are implicitly infused into the curriculum such as, self-independent learning, interdependence, lifelong learning, flexibility, adaptability, and assuming academic learning responsibilities. Online learning is defined as virtual learning using mobile and wireless computing technologies in a way to promote learners’ learning abilities [ 12 ]. In ( Figure 2 ), each component of the e-learning process is defined clearly below [ 13 ].

E-learning approach.

E-learning process.

2.1 Active instructor

His role is to facilitate learning process in the virtual classroom, to engage students in the learning process, to allow them to participate in designing their own course content and to contribute to design learning assessment parameters.

2.2 Active learner

He can access course content anytime and from anywhere, engage with his peers in a collaborative environment, formulate his opinions continuously, interact with other learning communities, communicate effectively, share and publish their findings with others in online environment.

2.3 Creative pedagogy

Both instructors and learners decide on what to learn online and how it should be learned. This experience is designed to promote an inquiry and challenge-based learning models where teachers and students work together to learn about compelling issues, propose solutions to real problems and take actions [ 11 ]. The approach involves students to reflect on their learning, on the impact of their actions and to publish their solutions to a worldwide audience [ 14 ].

2.4 Flexible curriculum

A core curriculum is designed, but the facilitator has the freedom to innovate and customize course content accordingly up to the aspiration of the learners; this means that the learner’s knowledge of the material will mainly come from his own online research (formal and informal content), and from his own creativity and collaboration with his peers (teamwork).

2.5 Communities outreach

This allows a group of students to formulate real-world context research question, connect with local learning and global communities to find creative solutions to their problems, create opportunities to connect themselves with international communities. These opportunities will foster students’ social and leadership skills [ 15 ].

According to students’ observation, more than 70% of instructors found that the online learning using Blackboard ultra-collaboration boosts students’ learning interest, engagement and motivation. 84% of teachers use required to use interactive tools in order to engage students in presenting and sharing a five minutes presentation to their classmates, write a reflective essay on their experience, be involved in a collaborative project (interest- based learning project). 97% of students contributed to self and peer assessments, and 97% interacted using online management systems. Students were also encouraged to interact with their peers using blackboard group collaborate. Thanks to the online teaching strategy, 70% of students were able to deliver on time their work.

For the study purpose, several assessments components incorporate both individual and group work. For the individual work, each student was required to make an individual presentation on any subject of his own interest, write a reflective essay, self -assessment, class peer assessment, midterm and final exams. For the collaborative work, students were assigned teams and each student should contribute to the project delivered every two weeks in the form of a final presentation and a final project. Rubrics were designed and all students were well instructed to use them. Teachers were trained to monitor and facilitate the experience and the internal learning management systems such as Blackboard.

The subsequent ( Figure 3 ) shows the feedback loop of content mapping of factors and their relationships in relation to students’ performance and intake. The first feedback loop begins at the node called “Students”. The second one begins at the node entitled “Teacher”. There are two major positive feedback loops. For instance, a good team improves co-operation and creativity which increase the team’s learning experience. Setting clear goals and interactive strategies will enhance online learning and performance results. The E-learning process and the project outcomes are influenced by technology use [ 13 ].

Conceptual model of students’ E-learning environment parameters.

3. Research methodology

We studied the impact of online learning using technology in virtual classrooms and the effect of performance factors on students’ learning behavior and achievement. The study focused on a sample of 6045 students, collected from the enrolment of University College students in spring 2020, at United Arab Emirates University has used online teaching strategy in comparison to fall 2019 teaching/learning experience, which used conventional teaching strategy involving 7369 students (See Table 1 ). The study shows the learning outcomes are similar for both virtual and conventional learning, although the assessment methods are different. They include students’ learning outcomes assessment, testing (assessing prior and post knowledge acquisition) and quantitative versus conventional research. The findings of the survey are discussed below. Descriptive statistics were obtained to summarize the sample characteristics and performance variables. Pearson Correlation was used to evaluate the association between the learning outcomes dimensions. Independent Samples t-test was used to compare the mean overall performance of the online learning. Linear Regression was used to determine the impact of the learning characteristics (Critical thinking, Creativity, Communication and Collaboration) on the overall performance score. Factor Analysis was used to study the inter-relationships among the learning characteristics and compare the online methods.

Students’ population.

The objectives of the learning process consist of providing a diversified learning environment. The positive impact of this diversity is reflected in the students’ performance. Students in various represented colleges have similar passing grades as high (80–98%) for both Online Approach (OLA) and Conventional learning -Face-to-Face (FoF). The University College is the largest college in the University with more than 4000 students. Most of UAEU students start their study in UC; they take English, Arabic, IT and Math ( Figure 4 ).

University college percentage passing rate.

This study was limited to GEIL101 foundation students. Surveys were sent out to all information literacy sections at the end of the first semester 2019/2020, but there were only 87 respondents. The survey had 2 parts, one part is about students’ achievement/performance, and the second part use is about online learning in a virtual classroom. All sessions were conducted online by trained instructors in tandem with the University library delivered by professional librarians. In this report, fall 2019 students’ data are used as the sample for the study ( Table 2 ).

GEIL students.

Overall, the results indicate the online learning was beneficial for students as it shown in their academic achievements and in tables below. A significant number of students reported high comfort levels of attending online courses in virtual classroom instead of conventional learning. Results indicated students have a positive reception to online approach rather than traditional classrooms. Additionally, qualitative data identified a clearconsiderations for the integration of new technology into the new teaching and learning experience.

4. E-learning results and analyses

Table 3 shows the IL students’ pre and post tests performance. The analysis on the pre and post-tests, using the means comparison and one sample test, shows an increase of students’ performance by 84%, the mean of the pre-test is around 7.5 and the post test is 13.85, a significant difference of 6.35. 65% of students score above 60% (passing rate for the course) in the post-test, only 2.4% of students scored above 60% in the pre-test. This means that 97.6% of students did not have basic information literacy knowledge, but after going through intensive 12 week learning under e-learning conditions, 65% achieved the course outcomes with higher scores.

Students’ academic performance.

The following tables ( Tables 3 and 4 ) shows the students’ performance by each learning activity:

Students’ learning activity.

The scores in the post-test ranged between 11 and 20, whereas it ranged between 6 to 9 in the pre-test ( Figure 5 ).

Pre and post-tests comparison distribution.

The above results show that OLA students scored higher than the FoF in the majority of the learning activities. There is an important performance of online students in the midterm and final exams though both approaches where offered the similar assessments criteria under the same test conditions. In the next section, the online learning process validity, the learning activities, and the learning outcome achievements, will be discussed in greater details. Several statistical models, qualitative and quantitative analysis have been applied for this purpose.

5. Impact analysis of the learning activities

It is important for an educator to evaluate which type of learning activity that has an important impact on students’ performance. It will help the curriculum designers to adjust and improve the syllabus content accordingly. Two types of analyses are conducted quantitatively and qualitatively; the first analysis relies on the learning activities grades and course final scores. The second one relies on students’ feedback through reflective essays and teachers’ perception towards their students’ learning progress.

5.1 Quantitative analysis

5.1.1 impact of the learning activities on students’ performance.

To analyze the significance of each learning activity on students’ performance, a regression linear model was used to analyze the impact of each learning skill on students’ performance. According to the output report, the model is significant at 95% (p < 0.000), and there is a strong correlation between 95.8% of the learning skills and students’ performance (r2 = 0.919).

Overall, all learning skills strategies have a significant impact on students’ performance. Each student’s learning skills and their impact will be analyzed. The following graph shows that individual contribution has less impact on the student’s performance, but the course component is very important where students demonstrate their interaction with the course content. The quality of the students’ online participation, their assiduity and interaction with others and their contribution in the projects are different from class participation. Therefore, statistically speaking, it has a lower impact. So, it is highly recommended to review how this component is graded.

5.1.2 Impact of each learning skill on students’ achievement

The following table describes the impact of each individual learning skill on students’ performance. To do this analysis, we used Pearson Correlation Coefficient to measure the strength of the linear relationship between the learning skills. The following figure shows the relationship between the learning skills.

From the table below, the test 1 (Midterm Exam) and test 2 (Final Exam) have the strongest impact (754 and 758) respectively on the final grades, even though students scored lower in these activities compared to other assessed learning activities. They are still the most efficient assessment methods to evaluate students’ achievement. The projects, individual presentation and reflective essays have also a significant impact on students’ performance. The only learning activity with the lowest impact is the individual participation and engagement in the class, which is an important learning activity, and it needs a review on how to assess it in an effective way.

6. Teachers’ observations

Students’ e-learning performance data is processed and presented. The six characteristic attributes are identified. Each characteristic is divided into further sub-items that are rated from 1 to 5 by the respondents. Then, for each of the six main characteristics, the average of the sub-items rating is calculated. The box plot (see Figure 6 ) shows a detailed distribution of each response. This is made up of the results, comparing the responses given to the different factors affecting learning. The result shows that the teachers rating of the effect of online learning in the following table. Example: 50% of teachers think that 70% of students improved their creativity skills.

Using e-learning in the virtual classroom.

Descriptive statistics for the learning variables are shown below in Table 5 . In general, the mean and median of all the characteristics are quite high-around 3.5 ( Table 6 ). Regarding correlations between learning parameters, the results show that almost all characteristics are highly inter-correlated (p < 0.001) (See Table 7 ).

Regression model on learning skill of students’ performance.

Dependent Variable: FinalGrades.

Correlation between the learning skills on students’ academic performance.

. Correlation is significant at the 0.01 level (2-tailed).

E-learning characteristics.

Correlation is significant at the 0.05 level (2-tailed).

7. Students’ results and analysis

The survey was to collect feedback from students after they started using online learning courses. The effects of this methods on students’ learning and understanding A scale of 1–5 range from strongly agree (5) to strongly disagree (1). Different dimensions of online approach are analyzed and Eighty-seven UAE College Students coming from different Universities were asked to give their perception on different aspects of online learning methods.

For the question (1), “Do you like online learning technology?” 84 respondents representing 97.6% of the students said they do. As for the question (2), “Do you feel ready to use online environment?”, 61 students representing 71.2% said they do.

While 7 students or 8% said, they do not. Only 19 student or 21.8% were neutral (see Table 8 ).

Ready for online transformation.

As for question (3), “whether students have all the required technology tools for online learning”, 71 of the respondents representing 83.53% agreed but only 4 students disagreed (See Table 9 ).

Do students have the required tools for online learning?

Regarding the question (4), as to “whether students have reliable internet connection for online learning, 56 of the respondents representing 64% said that they agreed, while 7 students said that they disagree (See Table 10 ).

Do students have the reliable internet connection for online learning?

For question (5), “Did Online learning help your study when you have flexible schedule?” 53 students representing 63% of the respondents said it helped them because of time restriction. On the other hand, 31 students representing 37% said that time was not visible (See Table 11 ).

Did you have a flexible schedule when online learning was used?

For question (6), “Did online learning help you to be more productive?” 38 students representing 45% of the respondents said that online class helped them to be more organized and productive. On the other hand, 19 students representing 23% said that it was not productive for them (See Table 12 ).

Did online learning help you be more productive?

For question (7), “How do rate your experience with your team online” 58 students representing 60% of the respondents said that online learning class is like normal class. On the other hand, 9 students representing 10% said that they were not satisfied with online learning (See Table 13 ).

How do you rate your online experience with your team?

For question (7), “How do rate your internet connectivity and how often problems occurred?” 37 students representing 43% of the respondents said that online class runs into technical issues which lead to reduce their productivity and confidence. On the other hand, 42 students representing 48% said that there were no issues with their internet connections (See Table 14 ).

How often do you face technical problems?

For question (8), “Did you develop any health issues since the start of online learning? 41 students representing 48% of the respondents said that online class causes health issues which lead to reduce their productivity and confidence. On the other hand, 25 students representing 29% said that there were no health issues using online learning (See Table 15 ).

Did you develop any health issues since the start of online learning?

For question (9), “Rate the distractions you have had online”, 31 students representing 37% of the respondents said that online class did not face distractions. On the other hand, 23 students representing 27% said that there were not issues concerning online distraction (See Table 16 ).

Rate the distractions you have had at home.

8. Conclusion

The ultimate purpose of this investigation was to explore the impact of online learning on students’ academic achievement as the demand has increased in recent times for online courses among institutions and college students who solely rely on flexible and comfortable education. We tried to measure in quantifiable terms the students’ final academic performance after their exposure to online learning during this pandemic lockdown. The final results obtained in this study were quite self-eloquent, as they unequivocally show the tremendous impact of e- learning on students’ academic performance and achievements, as it can benefit students in many ways, including enhancing and maximizing their learning independence and classroom participation. It is a good experience for students’ transitional preparation to pursue college education and seek employment. Students were more engaged in the learning process than in conventional teaching, and online learning experience has revealed that didactic teaching style is no longer effective. They no longer regard teachers as the only source of information, but as learning facilitator and online learning from different internet sources as their main source of information. They have proved that they can assume their responsibilities, contribute to course design assessment and learning process personalization. Online learning also helped overcome time and space constraints imposed by the convention learning process and helped students to effectively communicate their findings and share their ideas with their peers locally and globally. The introduction of a new technology such as the online learning will undoubtedly have more impact on the learning outcomes only if we reconsider the delivery mode, content redesign, new assessment system. A suitable pedagogy and an appropriate content are the most important sources of students’ learning motivation. Finally, e-learning has a bright future, tremendous learning potentialities and excellent organizational culture. Universities will incontrovertibly use many of the lessons learned during this pandemic lockdown period of this forced online teaching to adjust curriculum contents, teaching methods/lesson delivery, and assessment tools.

E-learning is here to stay and can make a much stronger contribution to higher education in the years to come. However, there are some negative effects of online class as it does not offer real a face to face contact and interaction with instructors and imposes time commitment and less accountability on students. There are also many online struggles that students face such as the impossibility to stay motivated all the time, as they sometimes feel that they are completely isolated. In addition, instructors feel impotent to control students’ cheating, impose classroom discipline. In addition to that, poor students struggle to get the necessary electronic equipment to access this new mode of learning to interact in due time with their instructor, make necessary comments and raise questions to clear ambiguities and any equivocal statements and get appropriate feedback from their instructor.

There are other academic issues that need to be investigated deeply such as the perspectives of higher education quality focusing on the study of cultural, emotional, technological, ethical, health, financial or academic achievements. Furthermore, more academic research should be done about e-learning theories/distance learning to truly improvise a new and adequate teaching/learning approach.

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

• Published: 09 February 2021
• Volume 26 , pages 4003–4025, ( 2021 )

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• Mahmoud Maqableh   ORCID: orcid.org/0000-0003-2376-7143 1 ,
• Mais Jaradat 2 &
• Ala’a Azzam 1  

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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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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

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Acknowledgments

The authors are thankful to the study participants, data collectors, and supervisors.

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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

Department of Nursing, College of Health Sciences, Debre Berhan University, Debre Berhan, Ethiopia

Getaneh Baye Mulu

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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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The Institutional review board (IRB) of Hawassa University approved the research (No. IRB/210), and a formal support letter was written to each department. Written informed consent was obtained from all study participants and confidentiality was assured. Informed consent from parents were taken for those aged less than 18 years. All methods were performed following the Declaration of Helsinki’s ethical principles.

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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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Received : 08 October 2021

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Published : 23 May 2022

DOI : https://doi.org/10.1186/s12909-022-03461-0

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