Sage Journals: Discover world-class research

Abstract

This paper aims to analyze the structure of academic performance by investigating the influence of ascribed and achieved factors on student outcomes. Specifically, it explores the impact of gender, socioeconomic status of parents, parental education, type of high school attended, and level of study on academic success. Utilizing a longitudinal survey dataset of 15,102 university students in China, this study employs various statistical techniques, including mean, standard deviation, Mann-Whitney U, Kruskal-Wallis H, and path analysis to examine the relationships between ascribed and achieved factors and academic performance. The results reveal gender differences, with female students excelling in memorization and comprehension tasks, while males demonstrate stronger spatial reasoning and mathematical problem-solving abilities. Additionally, students from prestigious high schools performed better, attributed to access to better resources and teachers, and parental education—particularly the father’s education—was a significant predictor of academic success. Although wealthier students generally performed better due to access to private tutoring, the influence of socioeconomic status was moderated when controlling for school quality and teacher effectiveness. Achieved factors, especially among master’s and PhD students, played a crucial role in academic performance, underscoring the importance of personal effort in overcoming socioeconomic disadvantages. This study enriches the understanding of the interplay between ascribed and achieved factors, suggesting that while certain inherited advantages impact academic success, individual motivation and effort are key contributors to performance, particularly in higher education. These findings offer important insights for policymakers aiming to reduce educational disparities.

Keywords

gender high school background study level parental economic status academic performance

Introduction

The academic performance of students is influenced by a diverse array of factors spanning personal, social, and economic dimensions. Understanding these factors through the lens of ascribed and achieved characteristics provides valuable insights into the dynamics of student success. Gender and socioeconomic status of parents are examples of ascribed factors, which are largely beyond the student’s control (Prato et al., 2019). Socioeconomic status (SES) is commonly measured by two indicators: parental income and parental education, including the regular income of parents (Sirin, 2010). Parental income and parental education are largely beyond the student’s control (Sommer, 2013). Achieved factors, such as high school background and level of study, are earned or chosen and reflect a person’s skills, abilities, and efforts (Lawrence & Zolna, 2016). Research has shown that ascribed factors, particularly gender and SES, significantly affect students’ learning environments and access to resources. For instance, studies indicate that female students tend to demonstrate higher motivation toward academic achievement than their male counterparts (Kuśnierz et al., 2020). Educational data and global media consistently highlight a noticeable gender gap in academic performance, with boys falling behind girls in areas such as subject grades, high school graduation rates, and enrollment and completion at the tertiary level (Clark et al., 2008; Parker et al., 2018). Despite the significance of this issue, researchers have largely overlooked it from the perspective of the students themselves (Jackman et al., 2019).

In addition, Dong et al. (2020) noted the impact of socioeconomic background and parental education on student outcomes. Students from higher SES backgrounds generally have better access to educational materials, quality schools, and supportive learning environments, which positively influence their academic outcomes (Chen et al., 2023). High SES facilitates competition for superior educational opportunities, leading to enhanced academic performance (Fang & Feng, 2008). Furthermore, SES influences parenting behaviors and educational support, which help cultivate beneficial learning habits. Notably, the impact of SES is often more pronounced among urban students compared to those in rural areas (Fang & Feng, 2008). Parental unemployment also adversely affects children’s educational enrollment and performance, especially during adolescence, although its effects are less pronounced in early childhood (Lehti et al., 2019). SES mediates the relationship between parental involvement and children’s academic success, with higher SES families more effectively leveraging their cultural capital (Sengonul, 2022). While the relationship between SES and educational outcomes is moderate—with SES exerting a relatively small effect when cognitive ability or prior achievement is considered—cognitive ability and prior achievement have a significantly stronger influence on student outcomes independent of SES (Marks, 2017). Notably, SES is positively associated with language scores in young children (Lurie et al., 2021). Additionally, a meta-analysis suggests a positive yet weak relationship between SES and academic performance in higher education (Rodríguez-Hernández et al., 2020).

Several studies emphasize the critical role of ascribed factors in shaping academic success. For example, Brew et al. (2021) found that various ascribed factors, such as parental education levels and family income, significantly impact academic performance. A father’s education level, in particular, has a strong correlation with a student’s GPA and affects related academic variables (Nelson, 2009). While a mother’s education may not directly influence all academic outcomes, students with educated mothers tend to excel, with 87% achieving high scores compared to only 13% of those whose mothers were uneducated (Shaheen & Awan, 2020). This underscores the importance of parental education, especially maternal education, for better academic performance. Analyses show that students from families with higher educational qualifications outperform those from lower educational backgrounds (Bakar et al., 2017).

In contrast, achieved factors encompass attributes shaped by personal effort, including study level, and the type of high school attended. These individual efforts can either amplify or mitigate the effects of ascribed factors on academic performance. Previous research consistently highlights the significance of achieved factors in influencing academic success. Additionally, personal academic endeavors, such as high school type and learning behaviors, play a critical role (Haegg, 2020). Regarding achieved factors, attending private schools has been linked to increased student success, with estimates suggesting an 87-point increase in scores (29.6%) compared to public school students (Cansız et al., 2019). Interestingly, having more prior knowledge does not always correlate with enhanced learning gains; sometimes, it may have minimal impact (Simonsmeier et al., 2021). However, students with substantial prior knowledge in a subject tend to learn more effectively within that domain, demonstrating a “rich-get-richer” effect, where greater prior knowledge enhances curiosity and confidence in learning, thereby facilitating better information absorption (Witherby & Carpenter, 2022).

Further, Husaini and Shukor (2023) identified both ascribed and achieved factors—such as gender, parental socioeconomic status, type of high school, and study level as significant determinants of academic outcomes. Kocak et al. (2021) emphasized the complex interplay among socio-economic, socio-demographic, and achieved factors including individual characteristics, along with teaching strategies and family support, underscoring the multifaceted nature of academic performance determinants. Within the Chinese context, several studies have explored these dynamics. C. Li et al. (2023) emphasized the role of internet access in shaping student satisfaction and academic success, while Gu and Lu (2023) identified school reputation, personal growth, and financial support as critical contributors to student outcomes. Sun et al. (2022) highlighted how personal characteristics, including gender, age, residency, and parents’ occupations together with achieved factors influence academic performance. Despite these valuable insights, gaps remain in comprehensively understanding the interaction between ascribed and achieved factors, particularly in the context of Chinese universities.

This study aims to address these gaps by applying the Ascribed and Achieved Factors Framework to analyze the determinants of academic performance among students in Chinese higher education. Specifically, the research seeks to elucidate how ascribed factors—such as gender, high school background, socioeconomic status, and educational level of parents—and achieved factors—such as study level, high school performance, and prior academic achievements—contribute to academic success. The study will address the following research questions:

What is the effect of students’ ascribed factors, such as gender, and achieved factors such as high school background and study level on academic performance?

How does parental education level (both father’s and mother’s education) influence academic performance?

What role do the economic factors of parents, including their economic situation and monthly income, play in academic performance?

By addressing these questions, this research contributes to a deeper understanding of how inherited social characteristics and personal achievements interact to shape academic outcomes in the Chinese university context. The findings will offer valuable insights for designing educational policies that support students from diverse backgrounds, thereby fostering academic success through both personal efforts and structural support.

Literature Review

Ascribed Versus Achieved Factors

Ascribed factors refer to inherent characteristics such as gender and the socioeconomic status (SES) of parents, which significantly shape students’ academic experiences. Gender, shaped by societal expectations and biases, often creates disparities in educational opportunities and outcomes (Alabdulkarem et al., 2021). Socioeconomic status, typically measured by parental education and income, also plays a critical role. Higher parental education is associated with greater academic support and access to resources, while higher family income enables access to better-quality education and learning materials (Alabdulkarem et al., 2021; Jeynes, 2007; Xie et al., 2023). Lin (2020) highlights that ascribed factors represent social positions beyond individuals’ control, which often become persistent barriers to educational and social mobility.

In contrast, achieved factors arise from personal efforts, such as academic progress, educational attainment, and career accomplishments. Unlike ascribed factors, achieved factors are earned through hard work and individual decisions (Foner, 1979; Lin, 2020). For example, high school background and study level are considered achieved factors as they represent students’ success within the education system. Personal motivation, a critical aspect of achieved factors, often leads to better academic outcomes, regardless of ascribed characteristics. However, ascribed factors like parental education and income can still influence one’s ability to attain high achieved factors, demonstrating the complex interaction between these two forms of social positioning (Luo, 2024).

This section reviews the literature on the interaction between ascribed factors (e.g., gender, parental education, and socioeconomic status) and achieved factors (e.g., high school background, study level) in influencing academic performance. The literature addresses three key themes: (a) the effect of students’ ascribed factors (gender) and achieved factors (high school background, study level) on academic performance, (b) the influence of parents’ education on academic outcomes, and (c) the role of parents’ socioeconomic status on academic achievement.

Effect of Students’ Ascribed Factor (Gender) and Achieved Factors (High School Background, Study Level) on Academic Performance

Prior research highlights the importance of achieved factors in shaping academic performance. High school background, a significant achieved factor, has been shown to influence university outcomes, with students from well-resourced schools often performing better (Kisigot et al., 2022). This advantage arises from stronger prior knowledge, which enhances students’ ability to process and integrate new information effectively (Brod et al., 2013; Tanaka et al., 2008; van Kesteren et al., 2014). Conversely, insufficient prior knowledge can lead to misconceptions, hindering academic progress (Brod, 2021). Motivation, another critical achieved factor, is closely linked to academic success. Students who exhibit higher levels of motivation, perseverance, and well-organized study habits generally achieve better academic performance (Alkhateeb, 2020; Ayala & Manzano, 2018; Bailey & Phillips, 2016; Rhodd et al., 2009).

Furthermore, academic level, such as progression from junior college to Ph.D., reflects achieved factors, with more advanced students benefiting from accumulated knowledge and experience, contributing to improved academic outcomes (Bailey & Phillips, 2016; McKenzie & Schweitzer, 2001). Prior academic achievements are often strong predictors of future performance, underscoring the significance of achieved factors in academic success (Ringland & Pearson, 2003). In contrast, the ascribed factor of gender has also been extensively studied. Gender differences in learning styles and academic performance are frequently shaped by societal expectations, with males often excelling in mathematics and females in language-related subjects, although these patterns may vary across educational contexts (Alabdulkarem et al., 2021; Anderson et al., 1994; Dong et al., 2020; Goldin et al., 2006; Sun et al., 2022). Voyer and Voyer (2014) presents a summary of gender disparities in academic grades, highlighting that girls consistently attain higher scores than boys in every subject.

However, the majority of the research presented above tends to examine these factors in isolation, often focusing either on gender disparities (Alabdulkarem et al., 2021; Anderson et al., 1994; Goldin et al., 2006) or on the influence of high school background and motivation (Brod et al., 2013; Kisigot et al., 2022; Rhodd et al., 2009). Few studies explore how these elements interact to shape academic performance within specific educational systems or across varying socioeconomic and cultural settings. While there is evidence that gender and high school background can influence academic outcomes, less is known about how these factors, as ascribed and achieved characteristics, contribute to or mitigate these effects over time. The potential for gender differences to manifest differently depending on students’ academic levels or the quality of their high school background has not been fully explored.

Influence of Parents’ Education Level (Both Father’s and Mother’s Education) on Academic Performance

Prior research has extensively examined the relationship between parental involvement and students’ academic performance, highlighting both direct and indirect influences. Parental education, a key ascribed factor, has been found to significantly affect student outcomes. Studies consistently show that higher parental education leads to improved student achievement, as parents with advanced educational backgrounds are more likely to hold high academic expectations for their children (Epping, 2018; Pinquart & Ebeling, 2020). For instance, research by Avnet et al. (2019) emphasizes the crucial role of parental involvement and education in shaping student success, particularly through the provision of educational support at home. Additionally, students from families with educated parents tend to perform better academically due to access to superior educational resources and higher academic expectations, as noted by Pfeffer et al. (2013) and Weis et al. (2023), who underscore the positive relationship between maternal education and children’s academic outcomes.

Parental education, as an element of ascribed factors, plays a pivotal role in shaping academic success. The education levels of parents not only influence their ability to provide academic support but also contribute to setting higher expectations for student performance (Campbell et al., 1999; Idris et al., 2020; A. Khan et al., 2015). Both maternal and paternal education are linked to enhanced academic outcomes, as more educated parents create supportive learning environments at home (Wang et al., 2020). According to the Saunders hypothesis, the significance of genetic predisposition toward education diminishes when parents are highly educated, as these parents can mitigate potential disadvantages by providing their children with greater academic opportunities and maintaining educational advantages across generations (Lin, 2020). This reinforces the idea that parental education not only fosters children’s academic aspirations but also equips them with the resources and guidance needed to navigate complex educational systems, further enhancing their academic performance (Martinez et al., 2022). Despite extensive research on the positive impact of parental education on student academic performance, there is a lack of specific focus on how the education levels of both fathers and mothers individually influence academic outcomes. Most studies tend to generalize parental education without examining the distinct contributions of each parent’s education, leaving a gap in understanding the nuanced effects of maternal versus paternal education on students’ academic success.

Role of Parents’ Economic Status (Economic Situation and Monthly Income) on Academic Performance

Prior research highlights the importance of parents’ economic situations or their regular income in influencing academic outcomes. Economic disparities in access to educational resources and performance have been well-documented, with numerous studies showing that higher-income families are better positioned to support their children’s learning through access to private tutoring, quality schools, and extracurricular opportunities (Birch & Miller, 2007; Munir et al., 2023). Studies consistently find that students from wealthier backgrounds tend to perform better academically due to their increased access to these advantages (Z. Li & Qiu, 2018; Maryanai et al., 2017). However, the relationship between economic status and academic success is not always straightforward. While higher income generally correlates with better academic outcomes, other factors, such as school quality and student motivation, can also influence this relationship (Richardson et al., 2012; Westrick et al., 2015).

For instance, students attending well-funded schools with high tuition fees often receive more rigorous academic preparation than those in underfunded schools, leading to higher academic success (Kisigot et al., 2022). However, individual effort and school-related factors, such as teaching quality, also play a crucial role in determining outcomes, suggesting that not all students from affluent families will necessarily excel (Schneider & Preckel, 2017). This indicates that while economic status is a significant factor, it interacts with other personal and institutional variables to shape academic performance.

The complexity of the relationship between economic status and academic outcomes highlights the need to consider a broader range of variables. While students from higher-income families generally have access to better opportunities, the manner in which these resources are utilized, along with factors like personal motivation and school quality, is critical in determining academic success (Richardson et al., 2012). This intricate interaction between economic status, school type, and academic outcomes emphasizes the importance of school resources—whether from private or public institutions—which often differ significantly in terms of funding and academic preparation (Birch & Miller, 2007; Kisigot et al., 2022).

Economic status, as an ascribed factor, significantly influences academic performance, but its impact is multifaceted. While a higher family income offers advantages through enhanced educational resources, the overall effect is mediated by factors such as school quality, personal effort, and various institutional elements. Research supports a positive correlation between parental economic status and student achievement; however, there is a gap in understanding the specific effects of monthly income and overall economic stability. Most studies tend to focus on general wealth and access to resources, highlighting the need for further exploration into how fluctuations in parental income and economic security directly affect students’ academic outcomes.

Building a Conceptual Framework: Ascribed and Achieved Factors Roles in Students Performance

Understanding academic performance requires a holistic view of both ascribed and achieved factors. Ascribed factors refer to traits that students inherit, such as gender, socioeconomic background, and parental education—elements largely outside their control. These factors can create disparities in access to education and resources, posing persistent barriers to academic success. Achieved factors, on the other hand, reflect students’ personal efforts, including motivation, prior knowledge, and educational attainment, which are critical for success and advancement within the educational system.

By applying the framework of ascribed and achieved factors, this study explores how these elements interact to shape academic outcomes. It provides a clear lens through which the impact of inherited and self-earned characteristics can be analyzed, offering insights into where interventions can mitigate disparities caused by ascribed factors while promoting the factors that drive personal achievement.

Gender, as an ascribed factor, can influence learning styles and opportunities shaped by societal expectations. Research suggests that gender differences in academic performance vary by subject area, with females excelling in language-related subjects and males often outperforming in mathematics (Alabdulkarem et al., 2021; Goldin et al., 2006). Despite these patterns, these differences do not solely account for academic disparities, as other factors, including school quality and motivation, also intersect.

SES, another ascribed factor typically measured by parental education and monthly income, is one of the most significant predictors of academic performance. Higher family income provides better access to quality education and learning materials, while higher parental education levels often correlate with stronger academic support at home (Jeynes, 2007; Xie et al., 2023). These factors deeply influence a student’s educational journey. The educational background of parents, both maternal and paternal, directly affects student achievement by providing enhanced academic support and fostering higher expectations (Pinquart & Ebeling, 2020). Children of educated parents typically perform better academically due to access to superior resources and support structures (Avnet et al., 2019).

Achieved factors are closely linked to personal effort and study habits. High motivation and well-organized study behaviors are associated with better academic outcomes (Bailey & Phillips, 2016; Rhodd et al., 2009). Motivated students are more likely to engage deeply with learning materials, enhance their knowledge retention, and outperform their peers. Additionally, students from well-resourced high schools often enter higher education with a stronger foundation of prior knowledge, which enhances their ability to process new information and achieve academic success (Brod et al., 2013). Specifically, high school background, as a key achieved factor, plays a critical role in determining university success (Kisigot et al., 2022).

While each factor individually influences academic outcomes, the complex interaction between ascribed and achieved factors amplifies their effects. For instance, socioeconomic status and parental education (ascribed factors) can either enhance or limit the impact of student motivation and prior knowledge (achieved factors). Disparities arising from ascribed factors may restrict opportunities for students to reach their full potential through achieved factors, underscoring the need for targeted interventions.

In this framework, key variables include ascribed factors—such as gender, high school background, socioeconomic status, parental education, and religious beliefs—and achieved factors, which encompass motivation, study level, prior academic achievements, and study habits. Economic factors are represented by two variables: perceived income level (the family’s economic situation), measured on a 5-point scale ranging from “very bad” to “very good,” and monthly income, measured on a 6-point scale (up to 5,000; 5,000–10,000; 10,000–15,000; 15,000–20,000; 20,000–25,000; and over 25,000).

The outcome variable is academic performance, assessed through grades, test scores, or academic rankings, categorized into six levels: overall ranking in the top 10% (AP1), 10% to 25% (AP2), 25% to 50% (AP3), 50% to 75% (AP4), 75% to 90% (AP5), and bottom 10% (AP6), relative to their peers. Figure 1 illustrates the conceptual framework of this study.

Figure 1.

Conceptual framework.

This framework (see Figure 1) helps identify how ascribed factors, often beyond students’ control, and achieved factors, driven by personal effort, shape academic performance. By differentiating these elements, it provides a foundation for targeted interventions aimed at reducing disparities caused by ascribed factors while enhancing students’ opportunities for success through personal achievements.

Research Methodology

Study Setting

This study constitutes a pivotal component of the comprehensive “China College Students Longitudinal Survey,” a significant sociological investigation spearheaded by the esteemed Chinese Academy of Social Sciences (CASS). The initiative was meticulously executed through a collaborative effort between the Institute of Sociology of the Chinese Academy of Social Sciences (ISCASS) and the China Institute for Educational Development (CIED). Spanning the extensive timeframe from 2013 to 2022, the project meticulously conducted 10 curated rounds of surveys, encompassing the entire spectrum of Chinese university students and graduates. Notably, the present study draws upon data collected during the specific survey period from May to June 2018, thereby contributing to the broader longitudinal narrative of this substantial undertaking.

Sample and Sampling Technique

In the year 2018, this study meticulously acquired a total of 15,102 valid samples as part of the survey, resulting in an impressive overall response rate of 70.9%. The sampling strategy employed herein was the stratified random sampling technique, chosen for its effectiveness in ensuring robust and representative sample selection. To execute this research, a systematic approach was adopted. Specifically, four prominent key universities, four conventional universities, and four vocational colleges were methodically chosen. Each of these institutions represented different tiers of academic establishments, including key universities, regular universities, and vocational colleges. This selection process also encompassed diverse university types, spanning fields such as comprehensive studies, science and engineering, humanities, and other disciplines. Geographical diversity was meticulously considered, with universities from Beijing, Shanghai, Guangzhou, as well as regions across Northeast, North China, Northwest, Southwest, Central China, East China, and South China all included in the study’s purview.

Following the selection of universities, the research further refined its sample pool by randomly singling out one class from each of the eight chosen schools, departments, or majors. Subsequently, a specific number of students from these selected classes were chosen in a wholly random and unbiased manner, ensuring the integrity of the sampling process. Data collection was conducted through an online platform, wherein respondents were tasked with completing an electronic questionnaire hosted on the Survey and Data Information Center of the Chinese Academy of Social Sciences. This meticulous sampling methodology was crucial in ensuring the reliability and comprehensiveness of the dataset underpinning this research endeavor.

Data Analysis Technique

In our research, we employed a combination of descriptive and inferential statistics to analyze the data. Descriptive statistics allow us to summarize and describe the main features of our data. We utilized frequency and percentage calculations to understand the distribution of categories within the independent variables. Inferential statistics go beyond mere description. They help us draw conclusions and make predictions based on our sample data. Specifically, we applied Mann-Whitney U test and Kruskal-Wallis H test: Mann-Whitney U test is non-parametric test assessed whether there were significant differences in academic performance levels based on independent variables. It is suitable for ordinal data. Another Kruskal-Wallis H test non-parametric test, it allowed us to compare academic performance levels across multiple independent variables. Mann-Whitney U test and Kruskal-Wallis H test were applied to identify significant differences among the categories of internal and external factors on academic performance.

The Mann-Whitney U test was used to determine significant differences in the mean ranks of academic performance of gender, whereas the Kruskal-Wallis H test was applied to assess significant differences in the mean ranks of academic performance with respect to the remaining internal and external factors, as they had more than two categories. Additionally, path analysis was to explore the causal relationships between independent variables and the academic performance level of learners. Path analysis helps us understand how each independent variable directly influences the dependent variable. By rigorously applying these techniques, we ensured the reliability and validity of our findings.

Results

Table 1 presents the significant findings related to gender, types of high school, study level, and religion. The results indicate a significant correlation between academic performance and gender, favoring females as evidenced by their higher mean rank. Additionally, the results show a significant preference for provincial key high schools due to their highest mean rank compared to other types of high schools. Groupwise comparisons further highlight significant differences between various school types. In terms of study level, there are significant results with students at the master’s and PhD levels demonstrating higher mean ranks compared to other categories, favoring these groups. Groupwise comparisons confirm significant differences among most groups except for master’s and PhD students.

Table 1.

Role of Gender and Study Level on Academic Performance.

Variables	Categories	N	Mean rank	Mean	p-Value
Gender					.00
	Male (Ma)	7,154	7,115.87	3.94
	Female (Fe)	7,947	7,942.71	4.20
High school					.00
	Provincial key high school (HS1)	2,971	7,830.12	4.16
	City key high school (HS2)	3,109	7,612.54	4.09
	County (district) key high school (HS3)	2,734	7,638.22	4.10
	Normal high school (HS4)	4,898	7,433.42	4.05
	Secondary vocational schools (HS5)	1,389	7,059.20	3.94
Study level
	Junior college (SL1)	5,975	7,511.57	4.08
	Undergraduate (SL2)	7,146	7,197.12	3.96
	Master’s degree (SL3)	1,933	8,931.04	4.48
	PhD candidate (SL4)	47	9,610.13	4.68

Table 2 displays the academic performance of students categorized by their parents’ educational qualifications. The results indicate that there is no significant difference in academic performance based on the mother’s education level. However, the father’s education level significantly impacts academic performance. Groupwise comparisons reveal significant differences, particularly between fathers with a junior high school education versus those with an unclear level, and between those with an unclear level and primary school education, as well as master’s/doctoral graduates. The highest mean rank is observed among students whose parents have a master’s or PhD degree.

Table 2.

Role of Parents’ Education on Learning Performance.

Education levels	Fathers’ education level				Mothers’ education level
Education levels	N	Mean rank	Mean	p-Value	N	Mean rank	Mean	p-Value
Unclear (PE1)	247	6,788.69	3.83	.02	238	6,819.45	3.82	.16
No formal education (PE2)	195	7,428.64	3.99		718	7,379.21	4.01
Primary school (PE3)	2,618	7,609.90	4.11		3,768	7,529.61	4.07
Junior high school (PE4)	5,236	7,562.74	4.08		4,530	7,617.96	4.10
High school (PE5)	2,520	7,488.55	4.06		2,056	7,596.25	4.10
Technical/vocational high school/secondary specialized (PE6)	916	7,462.70	4.05		1,027	7,540.43	4.07
Junior/undergraduate college (PE7)	1,364	7,593.92	4.08		1,318	7,494.42	4.05
Bachelor’s degree (PE8)	1,646	7,511.55	4.05		1,242	7,537.83	4.07
Master’s/Doctoral graduate (PE9)	359	8,222.55	4.27		204	7,960.15	4.19

The significant results related to academic performance based on income level and monthly income are presented in Table 3. The findings indicate a significant difference between academic performance with perceived income level and monthly income. Groupwise comparisons reveal significant differences between various income categories, such as very bad versus relatively good and very good, not so good versus relatively good and very good, and average versus relatively good and very good. Notably, students from families with a good economic status demonstrate higher academic performance. Additionally, significant differences were observed between students with monthly incomes up to 5,000 versus 5,000 to 10,000, and between 5,000 and 10,000 versus 10,000 and 15,000.

Table 3.

Role of Parents’ Economic Status on Learning Performance.

Variable	Categories	N	Mean rank	Mean	p-Value
Income level					.00
	Very bad (VB)	742	7,567.98	4.04
	Not so good (NG)	3,495	7,586.29	4.09
	Average (AV)	9,450	7,455.70	4.05
	Relatively good (RG)	1,311	8,029.24	4.22
	Very good (VG)	103	8,887.73	4.46
Monthly income					.00
	Up to 5,000 (MI1)	7,025	7,760.47	4.14
	5,000–10,000 (MI2)	5,643	7,238.57	3.98
	10,000–15,000 (MI3)	1,117	7,733.79	4.14
	15,000–20,000 (MI4)	570	7,643.90	4.11
	20,000–25,000 (MI5)	398	7,611.76	4.09
	>25,000 (MI6)	348	7,580.37	4.08

Figure 2 illustrates the impact of students’ personal background, parents’ education level, and financial status on academic performance. The results indicate that gender significantly influences academic performance, favoring female students. Similarly, the type of high school attended has a significant negative impact on academic performance. On the other hand, study level has a positive and significant effect, favoring higher levels of education. The relationship between parents’ education levels shows a positive and significant impact, while the economic status and monthly income of families also yield similar results. Notably, parents’ education level does not significantly affect academic performance. Moreover, students who perceive their financial condition as favorable compared to their peers’ family economic status tend to have better academic performance. Conversely, the impact of monthly income is significant and negative. These results indicate that students who view themselves as financially better off compared to other students achieve higher academic performance (Figure 3).

Figure 2.

Flexplot of achievement score based on: (a) gender, (b) high school, (c) study level, (d) father education, (e) mother education, (f) income level, and (g) gender.

Figure 3.

Effect of parents’ education level, and income level on academic performance.

The Figure 2a illustrates the academic performance of male and female participants across six assessment points (AP1–AP6). The male group shows a fluctuating trend, with strong positive performance at AP6 and AP4, while exhibiting negative performance at AP1, AP2, and AP3, with the lowest performance at AP2. On the other hand, the female group displays positive results at AP3 and AP5, but negative results at AP1 and AP6, with the most significant drop occurring at AP6.

The Figure 2b shows the academic performance based on the high school background of the students. Group HS1 shows positive performance in AP1, AP5, and AP6, with AP1 being the highest, but dips slightly below zero at AP2 and AP3. HS2 displays a mix of results, with strong positive performance at AP3 and AP4, and slight negative fluctuations at AP1, AP2, and AP6. HS3 maintains a relatively stable performance across all assessment points, with only minor dips at AP2, AP3, and AP6. HS4 exhibits a fluctuating trend, with a positive peak at AP5 and slight positive performance at AP6, while showing negative results at AP1, AP3, and AP4. HS5 experiences the most variation, with a significant positive peak at AP4 but strong negative results at AP1, AP2, and AP6.

The Figure 2c shows the academic performance based on the study level of the respondents. SL1 shows a relatively stable performance, with moderate positive results at AP6, AP3, and AP5, but dips below zero at AP1 and AP2. SL2 demonstrates more variability, with a strong positive peak at AP4, but significant negative performance at AP1 and AP3. SL3 exhibits more consistent performance with slight positive and negative fluctuations, where AP6 is slightly higher, while AP1, AP2, and AP4 dip below zero. SL4 shows the most extreme changes, with a sharp positive spike at AP1 and a major negative drop at AP5, followed by another peak at AP6.

Figure 2d presents a bar plot illustrating the relationship between academic performance with reference to fathers’ education level. The vertical axis (AP) ranges from −0.2 to 1.2, indicating some performance metric or score. The most prominent feature is the pink bar (AP6), which consistently shows the highest and most positive values for PE1 and PE8, suggesting a significant increase for those conditions. Other APs fluctuate, with smaller, positive, and negative contributions across PEs, indicating varying impact across different conditions. AP5 and AP3 also exhibit notable peaks and troughs, though they are less pronounced than AP6.

Figure 2e visualizes the relationship between academic performance with different categories of mothers’ education (PE1–PE9). AP6 stands out with prominent positive values, particularly for PE8 and PE9, suggesting that this condition has the highest impact in these scenarios. Smaller positive and negative fluctuations are observed for the other APs across the remaining mother’s education categories, with some instances of negative values, especially for AP1 and AP4, indicating possible negative contributions in certain mothers’ education. The figure suggests that AP6 consistently dominates, particularly in PE8 and PE9, while the effects of other APs are subtler and more distributed across the MEs.

Figure 2f represents the relationship between academic performance categories with income level of the respondents. AP6 shows the most significant positive impact in the VB category, reaching a value above 1.0. AP1 and AP3 also have notable positive contributions in VG and RG, respectively. Meanwhile, AP4 exhibits a significant negative value for VG, suggesting a strong negative impact in this case. AP5 has moderate positive values in some conditions but shows negative influence in VG. In general, AP6 consistently shows higher positive impacts, particularly in VB and VG, while other APs exhibit more nuanced and variable effects across the different IL categories. Similarly, Figure 2g represents the relationship between academic performance categories with monthly income of the respondents. The results showing that those having higher income level have comparatively lower academic performance.

Discussion

In examining the findings of this study through the lens of ascribed and achieved factors, we gain valuable insights into how both inherited and personal factors influence academic performance. We organized the discussion around each research question, followed by a synthesis of the relevant findings, highlighting the interaction between ascribed and achieved factors.

Effect of Gender, High School Background, and Study Level on Academic Performance

The study findings align with the established framework of ascribed and achieved factors, emphasizing how inherited characteristics, such as gender and parental education, shape educational experiences, while personal efforts, such as motivation and study habits, influence academic outcomes. The gender disparity, where female students outperform male counterparts, corroborates previous research, which found that female students tend to excel in tasks requiring memorization and comprehension (Dong et al., 2020; Mata et al., 2012). However, there are contrasting studies suggesting that male students tend to perform better in subjects that rely on spatial reasoning and mathematical problem-solving (Hyde et al., 2008). This indicates that gender differences in academic performance might be subject-specific and dependent on the learning environment, requiring a nuanced approach when interpreting the overall trends.

The performance gap based on high school background is also consistent with existing research on educational stratification. Students from provincial key high schools, which provide better-trained teachers and resources, consistently outperform those from lower-ranked schools. Similar findings have been noted in studies where students from elite schools’ benefit from a range of socio-economic advantages (Lareau, 2011; Reardon, 2013). However, critics argue that focusing solely on institutional quality can overshadow individual agency. Studies such as Bourdieu’s (1986) emphasize that students from disadvantaged backgrounds can still succeed through cultural capital and personal motivation. This presents a case for interventions that not only improve institutional quality but also foster resilience and self-efficacy in students from less prestigious schools.

Further supporting the ascribed and achieved factors framework, the influence of parental education, particularly the father’s qualifications, emerged as a significant predictor of academic success. Students with highly educated fathers benefit from access to superior educational resources, aligning with Wang et al. (2020) and Martinez et al. (2022), who similarly found that parental education significantly impacts children’s academic achievements. However, some studies question whether parental education alone explains academic success. The work of Fan and Chen (2001) suggests that the quality of parental involvement, rather than educational credentials, plays a more significant role in shaping students’ academic attitudes and behaviors. Thus, future research should consider emotional and psychological support as critical elements in understanding the parental impact on student performance.

In terms of socioeconomic status (SES), the results corroborate existing evidence that students from wealthier families tend to perform better academically due to access to resources such as private tutoring and extracurricular activities (Munir et al., 2023). However, a counter-argument arises from studies by Sirin (2005) and Rothstein (2017), which suggest that while SES significantly influences academic outcomes, its impact diminishes when controlling for school quality and teacher effectiveness. This challenges the notion that SES is determinative and highlights the potential for educational interventions to mitigate some of the disparities associated with socioeconomic background.

Lastly, the finding that master’s and PhD students achieve the highest mean ranks in academic performance reaffirms the link between educational attainment and academic success. This trend, supported by research on self-selection bias, where individuals who pursue advanced degrees are often highly motivated and academically capable (Bowen & Bok, 1998), suggests that personal effort plays a crucial role. However, studies like those of Pascarella and Terenzini (2005) caution against equating higher educational levels with superior performance, noting that the structure and expectations of graduate education might inherently favor academic achievement, making it challenging to compare across different study levels.

Socioeconomic Roots of Academic Performance: The Interplay Between Parental Education, Economic Status, Monthly Income, and Student Outcomes

The study’s findings align with prior research highlighting the significant influence of parental education on academic outcomes, specifically the impact of fathers’ educational levels. The pronounced effect of fathers’ education corroborates existing literature, such as Pinquart and Ebeling (2020) and Epping (2018), which emphasize that higher parental education often translates to higher academic expectations and better academic performance. This suggests that fathers, traditionally seen as primary providers, may also assume a more dominant role in shaping their children’s academic trajectories through direct educational support and the setting of higher academic expectations. Studies by Pfeffer et al. (2013) and Weis et al. (2023) further underscore the relationship between parental education, particularly fathers’ education, and access to superior educational resources, which facilitate improved student outcomes.

However, the lack of a significant effect of mothers’ education raises interesting questions about the differentiated roles of parents in educational support. Research by Avnet et al. (2019) and Wang et al. (2020) suggests that while both maternal and paternal education contribute to academic success, their influences might differ. Mothers may provide more emotional and psychological support, which, though crucial, may not directly translate into measurable academic performance outcomes. This observation highlights the need for a more nuanced understanding of parental involvement beyond formal educational credentials. The Saunders hypothesis (Lin, 2020) offers additional insight, positing that highly educated parents—regardless of gender—help mitigate disadvantages through their ability to provide academic opportunities and sustained educational advantages. While fathers’ education might result in more direct academic benefits, mothers’ roles might be equally significant but through less quantifiable channels.

The findings on the importance of achieved factors—motivation, prior knowledge, and academic level—align with the broader discourse on student success. As noted by Liu et al. (2019) and Gamage et al. (2021), intrinsic motivation and accumulated prior knowledge play a pivotal role in enhancing academic performance, especially at higher education levels. Master’s and PhD students, who displayed the highest academic performance, exemplify how personal efforts and learning strategies complement ascribed advantages, underscoring the interplay between these factors. The role of motivation, in particular, reflects a key aspect of the achieved factors framework, highlighting how students’ internal drive can compensate for or amplify the effects of their socioeconomic and familial backgrounds.

While this study emphasizes the influence of ascribed factors, such as parental education, on academic performance, it also reinforces the importance of recognizing the role of achieved factors. As A. Khan et al. (2015) and Idris et al. (2020) suggest, students with high levels of motivation and effective study habits can overcome certain disadvantages associated with lower ascribed factors, further illustrating the complex relationship between these factors. In line with the Martinez et al. (2022) findings, this study demonstrates how personal effort, supported by a conducive family environment, can create pathways to academic success.

The Relationship Between Ascribed and Achieved Factors Influences Academic Success

The results align with previous research that highlights the role of economic resources in fostering academic success. Students from economically advantaged backgrounds perform better due to their access to quality educational resources, such as private tutoring and extracurricular activities, which cultivate an enriching learning environment (Birch & Miller, 2007; Munir et al., 2023). These findings reinforce the established link between economic status, as an ascribed factor, and academic performance. However, the data also revealed a negative correlation between monthly income and academic performance, particularly in students’ self-perceptions and motivations, suggesting that mere financial stability is not always a predictor of success. This resonates with the work of Brod et al. (2013), who highlighted the psychological impact of perceived economic disparity on students’ motivation and self-esteem.

While economic stability generally provides better access to resources, the complexity of the relationship between economic status and academic performance becomes evident when considering other mediating factors like school quality and student motivation (Richardson et al., 2012; Westrick et al., 2015). For example, students from affluent families attending under-resourced schools may not necessarily benefit from the same educational gains as those attending well-funded institutions, regardless of their financial background. This indicates that the quality of school resources and teaching methods, which vary significantly between public and private schools, remains a critical determinant of academic success, as observed by Kisigot et al. (2022).

Furthermore, while economic resources facilitate access to better opportunities, the way these resources are utilized, in conjunction with personal motivation, is vital. As Schneider and Preckel (2017) argue, students from wealthier backgrounds do not always excel academically if they lack the personal drive or are placed in schools where teaching quality is substandard. This points to the need for a broader perspective on how economic status interacts with achieved factors, such as personal effort and prior knowledge, to fully comprehend the pathways to academic achievement.

Implications

Theoretical Implications

The findings of this study contribute to the existing theoretical knowledge by illuminating the complex interplay between ascribed and achieved factors in academic performance. The significant role of parental education as an ascribed factor reinforces the need to incorporate socioeconomic considerations into educational frameworks. The study challenges the notion that economic status alone determines educational success, highlighting the importance of student motivation and prior knowledge as achieved factors. This insight advances current theoretical perspectives by emphasizing the necessity of a multidimensional approach to understanding educational equity, where both ascribed and achieved factors interact to shape academic outcomes.

Practical Implications

From a practical standpoint, this study provides actionable recommendations for educators and policymakers. The evidence underscores the need for targeted interventions that not only enhance parental involvement but also foster student motivation and engagement. Specific findings, such as the impact of economic status on access to educational resources, suggest that schools should develop programs that bridge resource gaps, particularly for low-income students. By addressing both the material and psychological challenges associated with economic instability, educational institutions can create supportive environments that enable all students to leverage their potential, thereby promoting equitable academic outcomes.

Conclusion and Limitations

In summary, this study highlights the intricate relationship between ascribed and achieved factors in shaping academic performance. While parental education is a significant ascribed factor, the influence of personal motivation and prior knowledge cannot be underestimated. These findings advocate for a nuanced understanding of educational dynamics, emphasizing the necessity of comprehensive strategies that address both inherited characteristics and individual efforts. By implementing targeted interventions that support all dimensions of student success, educators and policymakers can work toward creating a more equitable academic landscape.

This study acknowledges several limitations, including potential biases stemming from self-reported data on students’ perceptions of their financial status and its impact on academic performance. Such perceptions can significantly influence outcomes, as noted by Richardson et al. (2012), complicating the relationship between actual economic status and academic success. Additionally, the research is geographically constrained, which may limit the generalizability of findings across different educational systems. The disparities in educational quality between well-funded private schools and less-resourced public schools illustrate that economic status alone cannot determine academic outcomes (Birch & Miller, 2007; Kisigot et al., 2022). Future research should seek to replicate these findings in diverse contexts to better understand how ascribed and achieved factors interact in various settings.

Footnotes

Acknowledgements

The authors would like to acknowledge the China College Students Longitudinal Survey project team members for their exceptional efforts and contributions to the data collection process.

ORCID iD

Jeevan Khanal

Author Contributions

Yongzhong Jiang was responsible for conceptualization, data curation, and manuscript revision. Dirgha Raj Joshi conducted data analysis and wrote the methodology and results sections. Jeevan Khanal contributed to conceptualization, drafted the original manuscript, and participated in its revision.

Funding

This work was supported by the “Double First-Class” Initiative Construction of Philosophy and Social Sciences Discipline Group Major Indicator Tackling Projects at Chengdu University of Technology and the Sichuan Social Science Fund Key Project. The views expressed in this article are those of the authors and do not represent the positions of the funding agencies.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting the findings of this study are available upon request. Interested parties may contact the corresponding author for access to the dataset.

References

Alabdulkarem

Alhojailan

Alabdulkarim

(2021). Comprehensive investigation of factors influencing university students’ academic performance in Saudi Arabia. Education Sciences, 11(8), Article 375. https://doi.org/10.3390/educsci11080375

Alkhateeb

M. A.

(2020). The impact of social media on students’ academic performance: A case study at the University of Jordan. The Scientific Journal of King Faisal University, 21(2), 54–61. https://scholar.ptuk.edu.ps/handle/123456789/802

Anderson

Benjamin

Fuss

(1994). The determinants of success in university introductory economics courses. Journal of Economic Education, 25(2), 99–119. https://dx.doi.org/10.2307/1183277

Avnet

Makara

Larwin

K. H.

Erickson

(2019). The impact of parental involvement and education on academic achievement in elementary school. International Journal of Evaluation and Research in Education, 8(3), 476–483. https://dx.doi.org/10.11591/ijere.v8i3.20249

Ayala

J. C.

Manzano

(2018). Academic performance of first-year university students: The influence of resilience and engagement. Higher Education Research & Development, 37(7), 1321–1335. https://doi.org/10.1080/07294360.2018.1502258

Bailey

T. H.

Phillips

L. J.

(2016). The influence of motivation and adaptation on students’ subjective well-being, meaning in life and academic performance. Higher Education Research & Development, 35(2), 201–216. https://doi.org/10.1080/07294360.2015.1087474

Bakar

N. A.

Mamat

Ibrahim

(2017). Influence of parental education on academic performance of secondary school students in Kuala Terengganu. International Journal of Academic Research in Business and Social Sciences, 7(8), 296–304. https://dx.doi.org/10.6007/IJARBSS/v7-i8/3230

Birch

E. R.

Miller

P. W.

(2007). The influence of type of high school attended on university performance. Australian Economic Papers, 46(1), 1–17. https://doi.org/10.1111/j.1467-8454.2007.00302.x

Bourdieu

(1986). The forms of capital. In Richardson

J. G.

(Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). Greenwood.

10.

Bowen

W. G.

Bok

D. C.

(1998). The shape of the river: long-term consequences of considering race in college and university admissions. Princeton, N.J: Princeton University Press.

11.

Brew

E. A.

Nketiah

Koranteng

(2021). A literature review of academic performance, an insight into factors and their influences on academic outcomes of students at senior high schools. Open Access Library Journal, 8, Article e7423. https://doi.org/10.4236/oalib

12.

Brod

(2021). Toward an understanding of when prior knowledge helps or hinders learning. npj Science of Learning, 6(24), 1–3. https://doi.org/10.1038/s41539-021-00103-w

13.

Brod

Werkle-Bergner

Shing

Y. L.

(2013). The influence of prior knowledge on memory: A developmental cognitive neuroscience perspective. Frontiers in Behavioral Neuroscience, 7, Article 139. https://doi.org/10.3389/fnbeh.2013.00139

14.

Campbell

Hombo

Mazzeo

(1999). NAEP 1999 trends in academic progress: Three decades of student performance (pp. 1–137). National Center for Education Statistics. https://nces.ed.gov/nationsreportcard/pdf/main1999/2000469.pdf

15.

Cansız

Ozbaylanlı

Çolakoğlu

M. H.

(2019). Impact of school type on student academic achievement. Education and Science, 44(197), 275–314. https://dx.doi.org/10.15390/EB.2019.7378

16.

Chen

D. P.

Chang

S. W.

Burgess

Tang

Tsao

K.-C.

Shen

C. R.

Chang

P.-Y.

(2023). Exploration of the external and internal factors that affected learning effectiveness for the students: A questionnaire survey. BMC Medical Education, 23, Article 49. https://doi.org/10.1186/s12909-023-04035-4

17.

Clark

M. A.

Lee

S. M.

Goodman

Yacco

(2008). Examining male underachievement in public education: Action research at a district level. NASSP Bulletin, 92(2), 111–132. https://doi.org/10.1177/0192636508321155

18.

Dong

Jong

M. S.

King

R. B.

(2020). How does prior knowledge influence learning engagement? The mediating roles of cognitive load and help-seeking. Frontiers in Psychology, 11, Article 591203. https://doi.org/10.3389/fpsyg.2020.591203

19.

Epping

K. A.

(2018). The impact of parental involvement on student’s academic achievement, parental well-being, and parent-teacher relationships [Master’s thesis, University of Calgary]. https://dx.doi.org/10.11575/PRISM/33079

20.

Fan

Chen

(2001). Parental involvement and students’ academic achievement: A meta-analysis. Educational Psychology Review, 13(1), 1–22. https://doi.org/10.1023/A:1009048817385

21.

Fang

Feng

(2008). Family background and academic achievements: A study of stratum differences in compulsory education. Zhejiang Social Science, 24(8), 47–55. https://doi.org/10.1186/s40711-018-0083-8

22.

Foner

(1979). Ascribed and achieved bases of stratification. Annual Review of Sociology, 5, 219–242. https://doi.org/10.1146/annurev.so.05.080179.001251

23.

Gamage

K. A. A.

Dehideniya

D. M. S.C. P. K.

Ekanayake

S. Y.

(2021). The role of personal values in learning approaches and student achievements. Behavioral Sciences, 11(7), Article 102. https://doi.org/10.3390/bs11070102

24.

Goldin

Katz

Kuziemko

(2006). The homecoming of American college women: The reversal of the college gender gap (Working Paper No. 12139). National Bureau of Economic Research Inc.

25.

(2023). Factors influencing the satisfaction level of college students in China: Literature analysis based on grounded theory. Frontiers in Psychology, 13, 1–12. https://doi.org/10.3389/fpsyg.2022.1023420

26.

Haegg

(2020). The impact of vocational education on students’ academic outcomes: Evidence from the Dominican Republic (A.B. degree thesis). Princeton University Department of Economics. https://gceps.princeton.edu/wp-content/uploads/2020/10/2020-Thesis_Haegg.pdf

27.

Husaini

Y. N. S.

Shukor

N. S. A.

(2023). Factors affecting students’ academic performance: A review. Res Militaris (resmilitaris.net), 12, 284–288. https://www.researchgate.net/publication/367360842_Factors_Affecting_Students%27_Academic_Performance_A_review

28.

Hyde

J. S.

Lindberg

S. M.

Linn

M. C.

Ellis

A. B.

Williams

C. C.

(2008). Gender similarities characterize math performance. Science, 321(5888), 494–495. https://doi.org/10.1126/science.1160364

29.

Idris

Hussain

Ahmad

(2020). Relationship between parents’ education and their children’s academic achievement. Journal of Arts & Social Sciences, 7(2), 82–92. https://doi.org/10.46662/jass-vol7-iss2-2020(82-92)

30.

Jackman

M. W.

Morrain-Webb

Fuller

(2019). Exploring gender differences in achievement through student voice: Critical insights and analyses. Cogent Education, 6(1), Article 1567895. https://doi.org/10.1080/2331186X.2019.1567895

31.

Jeynes

W. H.

(2007). The relationship between parental involvement and urban secondary school student academic achievement: A meta-analysis. Urban Education, 42(1), 82–110. https://doi.org/10.1177/0042085906293818

32.

Khan

Iqbal

Tasneem

(2015). The influence of parents’ educational level on secondary school students academic achievements in District Rajanpur. Journal of Education and Practice, 6(16), 76–79. https://files.eric.ed.gov/fulltext/EJ1079955.pdf

33.

Kisigot

C. K.

Ogula

Munyua

(2022). Relationships of learning environment, school type and secondary school students’ academic achievement Marakwet East Sub-County, Kenya. International Journal of Humanities Social Sciences and Education, 9(6), 77–88. https://doi.org/10.20431/2349-0381.0906008

34.

Kocak

Goksu

Goktas

(2021). The factors affecting academic achievement: A systematic review of meta-analyses. International Online Journal of Education and Teaching, 8(1), 454–484.

35.

Kuśnierz

Rogowska

A. M.

Pavlova

(2020). Examining gender differences, personality traits, academic performance, and motivation in Ukrainian and Polish students of physical education: A cross-cultural study. International Journal of Environmental Research and Public Health, 17(16), Article 5729. https://doi.org/10.3390/ijerph17165729

36.

Lareau

(2011). Unequal childhoods: Class, race, and family life (2nd ed.). University of California Press. http://www.jstor.org/stable/10.1525/j.ctt1ppgj4

37.

Lawrence

F. B.

Zolna

M. R.

(2016). Declines in unintended pregnancy in the United States, 2008-2011. New England Journal of Medicine, 374(9), 842–852. https://doi.org/10.1056/NEJMsa1506575

38.

Lehti

Erola

Karhula

(2019). The heterogeneous effects of parental unemployment on siblings’ educational outcomes. Research in Social Stratification and Mobility, 64, Article 100439. https://doi.org/10.1016/j.rssm.2019.100439

39.

Kang

Miles

T. P.

Khan

M. M.

(2023). Factors affecting academic performance of college students in China during COVID-19 pandemic: A cross-sectional analysis. Frontiers in Psychology, 14, 1–14. https://doi.org/10.3389/fpsyg.2023.1268480

40.

Qiu

(2018). How does family background affect children’s educational achievement? Evidence from Contemporary China. The Journal of Chinese Sociology, 5, 1–21. https://doi.org/10.1186/s40711-018-0083-8

41.

Lin

M.-J.

(2020). The social and genetic inheritance of educational attainment: Genes, parental education, and educational expansion. Social Science Research, 86, Article 102387. https://doi.org/10.1016/j.ssresearch.2019.102387

42.

Liu

Hau

Liu

Wang

Zheng

(2019). Multiplicative effect of intrinsic and extrinsic motivation on academic performance: A longitudinal study of Chinese students. Journal of Personality, 88(3), 584–595. https://doi.org/10.1111/jopy.12512

43.

Luo

(2024). Achieved status. In Ritzer

(Ed.), The Blackwell encyclopedia of sociology. https://doi.org/10.1002/9781405165518.wbeos1477

44.

Lurie

L. A.

Hagen

M. P.

McLaughlin

K. A.

Sheridan

M. A.

Meltzoff

A. N.

Rosen

M. L.

(2021). Mechanisms linking socioeconomic status and academic achievement in early childhood: Cognitive stimulation and language. Cognitive Development, 58, Article 101045. https://doi.org/10.1016/j.cogdev.2021.101045

45.

Marks

G. N.

(2017). Is SES really that important for educational outcomes in Australia? A review and some recent evidence. The Australian Educational Researcher, 44, 191–211. https://doi.org/10.1007/s13384-016-0219-2

46.

Martinez

N. T.

Xerxa

Law

Serdarevic

Jansen

P. W.

Tiemeier

(2022). Double advantage of parental education for child educational achievement: The role of parenting and child intelligence. European Journal of Public Health, 32(5), 690–695. https://doi.org/10.1093/eurpub/ckac044

47.

Maryanai

Yogaswara

S. M.

Almujab

(2017, October). Analysis of internal and external factors in students achievement study program of economic education faculty of teacher training and education Pasundan University [Conference session]. International Conference: Character Building Through Pricesly International Education.

48.

Mata

M. D. L.

Monteiro

Peixoto

(2012). Attitudes towards mathematics: Effects of individual, motivational, and social support factors. Child Development Research, 2012, Article 876028. https://dx.doi.org/10.1155/2012/876028

49.

McKenzie

Schweitzer

(2001). Who succeeds at university? Factors predicting academic performance in first year Australian university students. Higher Education Research & Development, 20, 21–33. https://doi.org/10.1080/07924360120043621

50.

Munir

Faiza

Jamal

Daud

Iqbal

(2023). The impact of socio-economic status on academic achievement. Journal of Social Sciences Review, 3(2), 695–705. https://doi.org/10.54183/jssr.v3i2.308

51.

Nelson

J. K.

(2009). Impact of parent education on student success. Utah Valley University. https://files.eric.ed.gov/fulltext/ED507263.pdf

52.

Parker

P. D.

Zanden

B. V.

Parker

R. B.

(2018). Girls get smart, boys get smug: Historical changes in gender differences in math, literacy, and academic social comparison and achievement. Learning and Instruction, 54, 125–137. https://doi.org/10.1016/j.learninstruc.2017.09.002

53.

Pascarella

E. T.

Terenzini

P. T.

(2005). How college affects students: A third decade of research. San Francisco: Jossey-Bass.

54.

Pfeffer

F. T.

Danziger

Schoeni

R. F.

(2013). Wealth disparities before and after the great recession. The Annals of the American Academy of Political and Social Science, 650(1), 98–123.

55.

Pinquart

Ebeling

(2020). Parental educational expectations and academic achievement in children and adolescents—A meta-analysis. Educational Psychology Review, 32, 463–480. https://doi.org/10.1007/s10648-019-09506-z

56.

Prato

Kypraios

Ertug

Lee

Y. G.

(2019). Middle-status conformity revisited: The interplay between achieved and ascribed status. Academy of Management Journal, 62(4), 1003–1027. https://doi.org/10.5465/amj.2017.0316

57.

Reardon

S. F.

(2013). The widening income achievement gap. Educational Leadership, 70(8), 10–16. https://stonecenter.gc.cuny.edu/files/2022/09/Conwell-2.pdf

58.

Rhodd

R. G.

Schrouder

S. M.

Allen

M. T.

(2009). Does the performance on principles of economics courses affect the overall academic success of undergraduate business majors? International Review of Economics Education, 8(1), 48–63. https://doi.org/10.1016/S1477-3880(15)30079-7

59.

Richardson

Abraham

Bond

(2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138, 353–387. https://doi.10.1037/a0026838

60.

Ringland

Pearson

S. A.

(2003). Graduate entry to medical school? Testing some assumptions. Medical Education, 38, 778–786. https://doi.org/10.1111/j.1365-2929.2004.01891.x

61.

Rodríguez-Hernández

C. F.

Cascallar

Kyndt

(2020). Socio-economic status and academic performance in higher education: A systematic review. Educational Research Review, 29, Article 100305. https://doi.org/10.1016/j.edurev.2019.100305

62.

Rothstein

(2017). The color of law: A forgotten history of how our government segregated America. New York: W. W. Norton & Co.

63.

Schneider

Preckel

(2017). Variables associated with achievement in higher education: A systematic review of meta-analyses. Psychological Bulletin, 143, 565–600. https://doi.10.1037/bul0000098

64.

Sengonul

(2022). A review of the relationship between parental involvement and children’s academic achievement and the role of family socioeconomic status in this relationship. Pegem Journal of Education and Instruction, 12(2), 32–57. https://files.eric.ed.gov/fulltext/EJ1343324.pdf

65.

Shaheen

Awan

A. G.

(2020). The impact of mother’s education on the academic achievements of her child. Global Journal of Management, Social Sciences and Humanities, 6(4), 735–756.

66.

Simonsmeier

B. A.

Flaig

Deiglmayr

Schalk

Schneider

(2021). Domain-specific prior knowledge and learning: A meta-analysis. Educational Psychologist, 57(1), 31–54. https://doi.org/10.1080/00461520.2021.1939700

67.

Sirin

S. R.

(2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. https://doi.org/10.3102/00346543075003417

68.

Sirin

S. R.

(2010). Socioeconomic status. In Clauss-Ehlers

C. S.

(Eds.), Encyclopedia of cross-cultural school psychology (pp. 911–918). Springer. https://doi.org/10.1007/978-0-387-71799-9_395

69.

Sommer

(2013). Psychosocial factors predicting the adjustment and academic performance of university students [PhD thesis, University of South Africa]. http://uir.unisa.ac.za/handle/10500/13283

70.

Sun

Chen

Yang

(2022). Key influencing factors affecting the student academic performance and student satisfactions ratings: Evidence from undergraduate students in China. Global Research in Higher Education, 5(3), 19–36. https://doi.org/10.22158/grhe.v5n3p19

71.

Tanaka

Kiyokawa

Yamada

Dienes

Shigemasu

(2008). Role of selective attention in artificial grammar learning. Psychonomic Bulletin & Review, 15(6), 1154–1159. https://doi.org/10.3758/PBR.15.6.1154

72.

van Kesteren

M. T. R.

Rijpkema

Ruiter

D. J.

Morris

R. G. M.

Fernandez

(2014). Building on prior knowledge: Schemadependent encoding processes relate to academic performance. Journal of Cognitive Neuroscience, 26(10), 2250–2261. https://doi.org/10.1162/jocn_a_00630

73.

Voyer

S.D.

(2014). Gender differences in scholastic achievement: A meta-analysis. Psychological Bulletin, 140(4), 1174–1204. https://dx.doi.org/10.1037/a0036620

74.

Wang

Dong

Liu

Bai

Zhang

(2020). The effect of parents’ education on the academic and non-cognitive outcomes of their children: Evidence from China. Children and Youth Services Review, 117, Article 105307. https://doi.org/10.1016/j.childyouth.2020.105307

75.

Weis

Trommsdorff

Muñoz

González

(2023). Maternal education and children’s school achievement: The roles of values, parenting, and behavior regulation. Journal of Child and Family Studies, 32, 691–703. https://doi.org/10.1007/s10826-022-02405-y

76.

Westrick

P. A.

Robbins

S. B.

Radunzel

J. M.

Schmidt

F. L.

(2015). College performance and retention: A meta-analysis of the predictive validities of ACT^® scores, high school grades, and SES. Educational Assessment, 20, 23–45. https://doi.10.1080/10627197.2015.997614

77.

Witherby

A. E.

Carpenter

S. K.

(2022). The rich-get-richer effect: Prior knowledge predicts new learning of domain-relevant information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 48(4), 483–498. https://doi.org/10.1037/xlm0000996

78.

Xie

King

R. B.

Luo

(2023). Social motivation and deep approaches to learning: A nationwide study among Chinese college students. Higher Education, 85(3), 669–687. https://doi.org/10.1007/s10734-022-00860-6

Ascribed and Achieved Factors Associated With the Academic Performance: A Longitudinal Study of University Students in China

Abstract

Keywords

Introduction

Literature Review

Ascribed Versus Achieved Factors

Effect of Students’ Ascribed Factor (Gender) and Achieved Factors (High School Background, Study Level) on Academic Performance

Influence of Parents’ Education Level (Both Father’s and Mother’s Education) on Academic Performance

Role of Parents’ Economic Status (Economic Situation and Monthly Income) on Academic Performance

Building a Conceptual Framework: Ascribed and Achieved Factors Roles in Students Performance

Research Methodology

Study Setting

Sample and Sampling Technique

Data Analysis Technique

Results

Discussion

Effect of Gender, High School Background, and Study Level on Academic Performance

Socioeconomic Roots of Academic Performance: The Interplay Between Parental Education, Economic Status, Monthly Income, and Student Outcomes

The Relationship Between Ascribed and Achieved Factors Influences Academic Success

Implications

Theoretical Implications

Practical Implications

Conclusion and Limitations

Footnotes

Acknowledgements

ORCID iD

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

References