South Korean Adult Learning Participation in Relation to Employment Characteristics and Quality of Life

I. Introduction

Adult education in South Korea (Korea hereafter) has not matched the country’s well-known strength in the educational achievement of the school-aged population, resulting in a gap between school education and adult education. Although Korea’s recent higher education completion rate is significantly high at 56.2% for ages 25-64, surpassing the OECD average of 41.9% (Korean Educational Development Institute, 2025), the adult learning participation rate remains low at only 33.1% (Korean Educational Development Institute, 2024). More concerning than the low participation rate is the severe imbalance in adult learning participation for different groups of people. For example, the participation rate in job-related adult education among the unemployed or economically inactive population is lower than that of employed individuals (Statistics Korea, 2021). This imbalance is a factor in the deepening gap between socioeconomic classes and serves as a barrier to social integration (Jang, 2017; Kim, 2021; Kim & Park, 2020).

In this regard, the status and characteristics of economic activities in the Korean context are assumed to affect Koreans’ engagement in adult learning. Previous studies on the motivations for participating in adult learning and engagement in learning among adults who are active in the labor market have identified the possible influence of several job characteristics. For example, the higher level of specialization required in their roles, such as managerial and professional positions, was correlated with higher adult learning participation rates (Han, 2016; Lee, Lee, & Park, 2016; Panitsides, 2023; Shim, 2018). In addition, individuals engaged more in adult learning activities if they had more stable employment (Ferreira, de Grip, & van der Velden, 2018; Fouarge et al., 2012; Kyndt & Baert, 2013; Lee, Lee, & Park, 2016; Ryu & Jun, 2017; Yoon & Park, 2017), higher education levels (Han, 2016; Hwang & Gil, 2017; Jang, Jeon, & Kim, 2023), and worked in larger companies (Jang, Jeon, & Kim, 2023; Kyndt & Baert, 2013).

Some studies have also shown that higher levels of participation in adult learning are associated with more positive perceptions of quality of life (QoL hereafter) (Byun & Kim, 2011; Escuder-Mollon, 2012). Or, individuals who participated more in adult learning tended to report higher levels of happiness (Goian, 2014; Koh & Lee, 2014; Kwon, 2013; Li, 2019; Panitsides, 2013). However, this has not been closely examined in research and no research has confirmed whether adult education participants’ perception of QoL is affected by their employment characteristics.

The purpose of this study is to investigate whether there is a difference in the level of participation in adult learning and the perceived level of QoL based on individuals’ economic activity status, employment stability, and occupational skill level. In this study, we attempt a detailed analysis of the dimensions that constitute QoL. We aim to understand in greater detail the individual differences in QoL perceptions among adult learning participants based on their individual employment circumstances. The findings can provide insights into the impact of adult learning on participants' perceived QoL and diversifying tailored adult learning programs.

Ⅱ. Literature Review

Ⅲ. Methods

The Ministry of Education and the Korean Educational Development Institute (KEDI) have conducted the Korean Adult Lifelong Learning survey annually since 2007. The survey collects data through a systematically stratified sampling method at the national level based on a face-to-face household interview from adults aged 25 to 79 to determine their level of participation in adult learning and their perceptions of adult learning. It includes respondents’ participation in formal and non-formal education, accessibility to adult learning, non-participation factors, and learning outcomes. From the data set of 9,968 (KEDI, 2022), we selected 2,817 respondents who participated in adult learning at least once in 2021.

The dependent variables in this study were the frequency of adult learning participation and participants’ perceived QoL associated with their engagement in adult learning. The frequency of adult learning participation was assessed by the number of times participants engaged in adult learning programs. QoL was measured based on responses to four items, including mental health, physical health, social participation satisfaction, and a sense of economic stability. These dimensions were selected based on the multifaceted nature of QoL, which encompasses various aspects of an individual's well-being (Bowling & Windsor, 2001; Cella, 1994). Although this simplified measurement does not fully capture the multidimensional nature of QoL as conceptualized in the WHOQOL framework, some prior studies (Jang, 2021; Kil & Kim, 2019) have assessed quality of life using four brief items addressing mental health, physical health, social participation, and perceived economic stability. All items were rated on a 5-point Likert scale (1 = not helping at all; 5 = very helpful) by asking how helpful adult learning was to their QoL. The Cronbach’s coefficient for the QoL items was .632. Despite a moderate level of internal consistency, it is acceptable for exploratory research when the data were obtained from a large-scale national survey (Nunnally & Bernstein, 1994).

The independent variables in this study are economic activity status, employment stability, and occupational skill level. Economic activity status was measured based on the question, “Are you working for the purpose of income?” Employment stability was measured based on the response to a regular job or temporary job, excluding non-wage workers, which was originally included as a response option. In this study, regular workers were operatively defined as employment types with high employment stability (e.g., full-time, salaried, or continuous), and temporary and daily workers were operatively defined as employment types with low employment stability (e.g., part-time, seasonal). Occupational skill refers to the level of skills needed for job performance. In this study, occupational skill level was divided into three groups based on the type of occupation. The survey presented nine categories of occupations according to the Korean Standard Classification of Occupations (KSCO), which were divided into three skill levels: (1) the first skill level includes elementary workers; (2) the second skill level includes clerks, service workers, sales workers, skilled agricultural, forestry and fishery workers, craft and related trades workers, plant, machine operators, and assemblers; and (3) the third skill level includes managers, professionals, and related workers. Additionally, since educational level has been identified as a factor associated with both participation in adult learning (Han, 2016; Hwang & Gil, 2017; Jang, Jeon, & Kim, 2023) and QoL (Ban et al., 2015; Han, 2016; Kim, 2013), it was included as a covariate along with age in the analysis.

The data were analyzed using jamovi version 2.6.44. The analysis of covariance (ANCOVA) was conducted to examine whether there were significant differences in how many times they participated in adult learning and the perceived QoL level based on respondents’ economic activity status, employment stability, and occupational skill level, while using the age and education level as the covariates. Although the assumption of normality and homogeneity of variances was not fully met, the ANCOVA results were considered robust due to the large sample size and were interpreted in conjunction with effect size estimates (Yoo, 2013). Post-hoc analyses were performed when the ANCOVA results showed significant differences to identify which groups differed. Post hoc comparisons were conducted based on estimated marginal means, adjusting for the covariates. Although the assumption of homogeneity of variances was violated, pairwise comparisons were interpreted with caution, focusing on effect sizes rather than solely on statistical significance.

The sociodemographic characteristics of the 2,817 respondents were reported in Table 1. The percentages of males and females were similar. The largest age group was 30-39 years old, and the smallest group was 70-79 years old. In terms of education level, the largest group was those with a bachelor’s degree or higher. Most of the respondents were economically active, and among them, the proportion of regular workers was higher than that of temporary and daily workers. When classifying the occupations of economically active individuals based on skill levels, the largest proportion was the second skill level group. The average of adult learning participation frequency per participant was 1.24 times (Table 2). The largest group participated one time, accounting for 82.8% of the total. The average score for their perceived QoL among adult learning participants was 3.69 on a 5-point Likert scale.

<Table 1>

Characteristics of Participants

<Table 2>

Adult Learning Participation Frequency and Perceived QoL among Participants

Ⅳ. Results

RH 1-1: An ANCOVA was conducted to determine if there was a difference in the number of times they participated in adult learning based on their economic activity status while controlling for age and education level. The results (Table 3) indicated that economic activity status had a statistically significant effect on adult learning participation after controlling for age and education level, F(1, 2813) = 4.07, p = .044. Age, F(1, 2813) = 4.59, p = .032, and education level, F(1, 2813) = 31.37, p < .001, were also significant covariates. However, the effect sizes were small, indicating limited practical significance.

<Table 3>

ANCOVA Results for Economic Activity Status, Employment Stability, and Occupational Skill Level

RH 1-2: An ANCOVA was conducted to determine if there was a difference in the number of times they participated in adult learning based on employment type while controlling for age and education level. The results (Table 3) indicated a statistically significant effect of employment type on adult learning participation after controlling for age and education level, F(1, 1708) = 9.80, p = .002. Age, F(1, 1708) = 7.79, p = .005, and education level, F(1, 1708) = 13.21, p < .001, were also significant covariates. Although the effect sizes were small, the findings indicate that employment type remains significantly associated with differences in adult learning participation.

RH 1-3: An ANCOVA was conducted to determine if there were differences in the number of times they participated in adult learning based on occupational skill level while controlling for age and education level. The results (Table 3) indicated a statistically significant effect of occupational skill level on adult learning participation after controlling for age and education level, F(3, 2811) = 5.52, p < .001. Age showed a marginal effect, F(1, 2811) = 3.83, p = .050, whereas education level had a strong and statistically significant effect, F(1, 2811) = 22.56, p < .001. Although the effect sizes were small, the findings indicate that occupational skill level remains significantly associated with differences in adult learning participation. Post hoc comparisons (Table 4) using the Bonferroni test revealed that individuals in the highest occupational skill level participated in adult learning significantly more frequently than those in the first and second skill levels, whereas no significant difference was observed between the first and second skill levels. Thus, hypotheses 1-1, 1-2, and 1-3 are all supported.

<Table 4>

Post Hoc Comparisons by Occupational Skill Level Based on Estimated Marginal Means (Bonferroni-adjusted)

RH 2-1: An ANCOVA was conducted to determine if there was a difference in the perceived QoL level of adult learning participants based on economic activity status while controlling for age and education level. The results (Table 5) indicated that economic activity status did not have a statistically significant effect on the perceived QoL level after controlling for the covariates, F(1, 2813) = 2.94, p = .086. Age was not a significant covariate, F(1, 2813) = 0.93, p = .336, whereas education level had a significant effect on the perceived QoL level, F(1, 2813) = 12.41, p < .001, although the effect size was small (partial η² = .004). Thus, Hypothesis 2-1 is not supported.

<Table 5>

ANCOVA Results for QoL by Economic Activity Status

However, after breaking down the QoL perception scores, statistically significant differences were observed across the dimensions of emotional health, physical health, and a sense of economic stability. In terms of emotional health, the economically active group had a lower perception of QoL compared to the economically inactive group. The results (Table 6) indicated that economic activity status had a statistically significant effect on emotional health after controlling for the covariates, F(1, 2813) = 27.89, p < .001. Age, F(1, 2813) = 2.16, p = .142, and education level, F(1, 2813) = 2.72, p = .099, were not statistically significant covariates. In contrast, in terms of their sense of economic stability, the economically active group had a higher perception of QoL compared to the economically inactive group. Economic activity status had a statistically significant effect on the sense of economic stability, after controlling for the covariates, F(1, 2813) = 10.31, p = .001. Age was not a significant covariate, F(1, 2813) = 2.30, p = .130, whereas education level had a significant effect on the sense of economic stability, F(1, 2813) = 15.52, p < .001, although the effect size was small (partial η² = .005). These findings suggest that the impact of economic activity status on the emotional health of adult learning participants may be different from the impact on their sense of economic stability.

<Table 6>

ANCOVA Results for QoL Subdomain by Economic Activity Status

RH 2-2: An ANCOVA was conducted to determine if there were differences in the level of perceived QoL based on employment stability while controlling for age and education level. The results (Table 7) indicated that employment stability did not have a statistically significant effect on QoL after controlling for the covariates, F(1, 1708) = 3.40, p = .065. Age was not a significant covariate, F(1, 1708) = 1.97, p = .161, whereas education level had a significant effect on QoL, F(1, 1708) = 19.18, p < .001, with a small effect size (partial η² = .011). Thus, Hypothesis 2-2 is not supported.

<Table 7>

ANCOVA Results for QoL by Employment Stability

However, breaking down the QoL perception scores (Table 8), statistically significant differences were found in the area of emotional health. In terms of emotional health, the regular employees group had a lower perception of QoL compared to the temporary and daily workers group. Employment stability had a statistically significant effect on emotional health after controlling for the covariates, F(1, 1708) = 9.62, p = .002. Age was not a significant covariate, F(1, 1708) = 1.44, p = .230, whereas education level had a significant effect on emotional health, F(1, 1708) = 10.01, p = .002, although the effect size was small (partial η² = .006). In contrast, for perceived economic stability, regular employees showed significantly higher QoL scores than temporary and daily workers before controlling for age and education level. However, this difference was no longer statistically significant after adjusting for the covariates. These results suggest that the observed differences in perceived economic stability are attributable to educational differences rather than employment stability per se.

<Table 8>

ANCOVA Results for QoL Subdomain by Employment Stability

RH 2-3: An ANCOVA was conducted to determine if there were differences in the QoL perception scores based on occupational skill level while controlling for age and education level. The results (Table 9) indicated that skill level did not have a statistically significant effect on QoL after controlling for the covariates, F(3, 2811) = 2.43, p = .063. Age was not a significant covariate, F(1, 2811) = 0.85, p = .357, whereas education level had a significant effect on QoL, F(1, 2811) = 8.63, p = .003, although the effect size was small (partial η² = .003). Thus, Hypothesis 2-3 is not supported.

<Table 9>

ANCOVA Results for QoL by Skill Level

V. Discussions and Conclusion

The results reveal important findings and implications. First, the results indicated differences in adult learning participation levels across groups based on the Korean data. This finding is well aligned with international research indicating that individuals participate more in adult learning if they are economically active (Dincer, Tekin-Koru, & Askar, 2016; Hwang & Gil, 2017; Rubenson, 2018), have stable employment (Ferreira, de Grip, & van der Velden, 2018; Fouarge et al., 2012; Kim, 2001; Kyndt & Baert, 2013; Lee, 2006; Son, 2004; Yoon & Park, 2017), and are engaged in high skill-level occupations (Boeren et al., 2012; Choi, Kim, & Lee, 2008; Han, 2016; Lee, Lee, & Park, 2016; Rubenson, 2018). It seems that people who have better job conditions (e.g., more stable work, higher level of skilled work) are more likely to participate in adult learning activities.

Although groups that are economically inactive and those with low employment stability also participate in adult learning to overcome economic instability (Radovan, 2024), this is not enough to reverse the overall imbalance in adult learning participation. The cause of this imbalance needs to be examined to identify organizational and societal barriers and, if any, promote public policies or programs to overcome this disparity. On the one hand, the relatively low participation in adult learning among economically inactive populations partly explains the overall low level of adult learning participation in Korean society. While workplaces provide education and training, including mandatory education, those outside the workplace are not widely participating in adult learning. It is necessary to explore specific factors that hinder participation in learning through follow-up studies.

Consistent with prior research, differences by skill level were associated with variations in participation levels. Job-related education and training participation varies significantly across different economic sectors, with a notable disparity between jobs at high risk of automation and others (Ioannidou & Parma, 2022). This disparity underscores a concerning trend, whereas workers in jobs most vulnerable to technological displacement are less likely to receive training that could help them adapt to changing job markets (OECD, 2023). The pace of technological change demands continuous skill upgrading for currently employed people (Dincer, Tekin-Koru, & Askar, 2016; OECD, 2025), requiring interventions to ensure that low-skilled workers receive the necessary training and education in a timely manner.

The findings suggest that no statistically significant differences in QoL were observed according to the employment characteristics of adult learning participants. Although preliminary analyses appeared to show variations in QoL by occupational skill level, these differences were no longer statistically significant after controlling for education level, suggesting that the observed differences by skill level were spurious. Likewise, when economic activity status and employment stability were considered, variations in perceived QoL appeared to be more closely associated with differences in education level than with employment characteristics. These patterns are broadly consistent with previous studies indicating that education level is associated with QoL through both direct and indirect pathways (Ban et al., 2015; Han, 2016; Kim, 2013). In the Korean context, education has been widely emphasized as an important means of facilitating individual social mobility and contributing to economic development (Ban et al., 2015).

Educational level has also been frequently identified as a factor associated with participation in adult learning. The present findings are in line with earlier research (Han, 2016; Hwang & Gil, 2017; Jang, Jeon, & Kim, 2023), suggesting that higher levels of education tend to be associated with a greater likelihood of participating in adult learning. Taken together, these results suggest that individuals with higher levels of education are more likely to participate in adult learning and to report relatively higher levels of QoL. These findings indicate the need for increased policy and institutional attention to adults with lower levels of education. Although adult learning is often framed as a mechanism for reducing inequalities in learning opportunities (Song, 2025), observed disparities in both participation and QoL by education level suggest the possibility that adult learning may not fully offset, and may in some cases reflect, existing social inequalities (Jang, Jeon, & Kim, 2023).

There was no difference in the perception of overall QoL based on employment stability, but the perceived QoL in terms of emotional health was lower for regular workers than for their counterparts. This pattern may be interpreted in consideration of Koreans’ challenging working life. Korea is known for high work demands and longer working hours under competitive social pressure with harsh organizational practices (Kim et al., 2021, 2023). As an example, in a study of Korean women’s job transitions (Kim, Kim, & Baek, 2024), personal health problems were found to be one of the most troubling reasons for their career transitions. Additionally, since the possibility of job mobility of temporary workers has increased as the labor market has recently become more flexible, their emotional stress may have declined in response to such social changes (Yoo et al., 2016). According to Fontinha, Van Laar, & Easton (2018), this may imply that temporary workers have lower expectations for their working life and tend to be easily satisfied with given conditions compared to regular workers. Presumably, these contextual factors could be considered to partly explain why temporary workers reported relatively better emotional health. Nevertherless this calls for rigorous future research to fully understand the underlying mechanisms.

The phenomenon that economically inactive participants feel relatively less economically secure through their participation in adult learning makes us reconsider the differences in participation rates in adult learning according to economic activity status. As we have seen, economically active people tend to participate more in adult learning. Economically inactive people, by contrast, tend to have limited access to learning opportunities that could enhance their economic stability. In this sense, it can be interpreted that the utility of adult learning itself is tilted toward emotional health rather than economic stability. According to national statistics, the perceived outcomes of adult learning participation follow a hierarchical pattern: psychological satisfaction and happiness, increased income, and employment stability (Statistics Korea, 2022). Therefore, in designing adult learning policies, greater emphasis should be placed on promoting participants’ subjective well-being and happiness rather than focusing solely on economic returns.

This study on Korean workers suggests an inverse relationship between economic stability and emotional well-being among economically active people, contrasting with U.S. research (Prati, 2024; Ryu & Fan, 2023) that generally report positive reciprocal relationships between economic status and mental health. This disparity highlights how cultural and economic contexts could influence the interplay between financial factors and psychological well-being. The findings underscore the need for culturally informed approaches in both research and workplace interventions, recognizing that the impact of economic stability on mental health varies across cultures.

Based on the findings of this study, several policy implications can be suggested. First, it appears important to implement practical support programs that enable adult learning participants with low socioeconomic status, including lower levels of educational attainment, to sustain their participation in learning activities. Such efforts may include the expansion of vocational skills development programs aimed at enhancing employment stability among older workers (Oh & Sung, 2023), as well as the expansion of lifelong learning voucher schemes targeting socially and economically vulnerable groups (Byun & Seo, 2021). Second, adult education programs could be informed by the diverse characteristics of learners, including differences in educational background and work experience. This study suggests that economically active adult learners report relatively lower levels of emotional health. While mandatory workplace training—such as industrial safety and health education—continues to be implemented, there may be a need to broaden institutional support for mental health–related programs, particularly within small and medium-sized enterprises. In this regard, greater emphasis could be placed on the role and responsibility of employers in promoting workers’ emotional well-being (Kim, 2025).

We acknowledge some limitations of our study. First, because we selected rather clear control variables (age and education level) missing out other potential control variables such as income level, it is cautious when interpreting the result that professional and managerial occupations (i.e., third skill level) with higher occupational skill levels exhibited a statistically significant higher perception of QoL compared to elementary workers (i.e., first skill level). Second, this study relied on self-reported measures of QoL, which are not free from response bias, even though a form of self-report on QoL is commonly used in research (Cummins, 2005; Diener, 1994). In the future, QoL can be explored with different data collection methods. Third, there are some concerns about disparities in some sample sizes. For example, nine occupations were re-categorized based on their skill level and analyzed across three tiers, which resulted in the uneven distribution of sample sizes for each level. Also, substantial differences exist in sample sizes between the economically active and economically inactive groups, as well as between regular employees and temporary and daily workers. Furthermore, considering that unequal group sizes may affect the stability of parameter estimates, future research could address these limitations by employing stratified sampling designs to achieve more balanced group distributions.

As additional recommendations for future research, the general adult population, both adult learning participants and non-participants, can be recruited to directly confirm whether employment characteristics influence participation in adult learning. In addition to targeting the general population, future research may examine the associations between employment characteristics, adult learning participation, and perceived QoL through longitudinal research designs, which can identify causal relationships among the variables, given that our cross-sectional data at a single point has a limitation.

In conclusion, the need for continuing education among working adults is increasing due to rapid changes in the economic environment, but the opportunities are not equal or evenly distributed. Therefore, it is necessary to study the factors in the economic approach that impact adult learning and subsequent performance. The significance of this study lies in identifying the links among employment characteristics of working adults, their motivations, and outcomes of participation in adult learning, and how these factors impact their perceived QoL.

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