The Learning Democracy: The Relationship Between Adult Informal Learning,Lifelong Learning Mindset,and Civic Engagement

Abstract

Using structural regression analyses, data from the U.S. 2017 Program on International Assessment of Adult Competencies (PIAAC) background survey were examined to test the effect of informal learning (observed through everyday activities, such as reading the newspaper or using the Internet to find information about issues) on civic engagement (observed through volunteerism, political self-efficacy, and social trust). Results showed a positive relationship between informal learning and civic engagement with ICT-related activities having the strongest effect. This effect was mediated by presence of a lifelong learning mindset (LLM) observed through traits related to learning motivation and persistence. Although a majority of respondents indicated agreement with LLM attitudes and behaviors, they also reported low frequencies of volunteerism and less agreement with political self-efficacy and social trust statements. The need for governments to provide meaningful opportunities for citizens to contribute their knowledge to the community, thus enabling a learning democracy, is discussed in light of these findings.

Keywords

Informal learning civic engagement lifelong learning learning democracy PIAAC

“ Lifelong learning may not guarantee a learning democracy, but a learning democracy cannot exist without lifelong learning.”

Introduction and Background

It is tempting to define democracy as Abraham Lincoln’s often-quoted closing to the Gettysburg Address: “government of the people, by the people, for the people.” Instead of defining democracy, Gagnon (2018) invited researchers to “drill down into the meaning” (p. 5) of 2234 distinct ways to describe democracy. Gagnon (2018) placed “learning democracy” at number 1088 on his list. A learning democracy is defined as a system or place where citizens are enabled to convert their daily lived experiences (informal learning) into participation in their community’s civic life. Biesta (2011) hypothesized that democracy may only be possible through the presence of a learning democracy. It is our belief that a learning democracy is the natural state of a functioning democracy. The degree to which citizens learn throughout life (lifelong learning), and are equipped to co-create their community (through civic engagement) as a result of this learning, may be how democracies are sustained.

Lindeman (1926) noted connections between learning and stability of a democracy:

“Revolutions are essential only when the true learning process has broken down, failed. We revolt when we can no longer think or when we are assured that thinking has lost its efficacy. Revolution is the last resort of a society which has lost faith in intelligence” (p. 77).

He described the field of adult education as “devoted to the task of training individuals in ‘the art of transmuting. . . experience into influence,’” (p. 57) which built upon Sheffield’s (1922) assertion that “every man [person] with an ambition for self-betterment” could become “spokes[people] for ideas. . . an asset to every organization” (p. v). Thus, a deeper exploration of the meaning of learning democracy benefits from examining the conceptual basis that links adult education to democracy through civic engagement.

While Lindeman (1926) and Sheffield’s (1922) words are longstanding, much continues to be unknown about the mechanisms behind the effect of adult learning on civic engagement (Campbell, 2006). Recent studies have found positive connections between service learning (education integrated with volunteerism or community-focused projects) and positive civic attitudes (Liu & Hui, 2021; Snell et al., 2015). However, Boshier (2018) noted that the bulk of adult learning is informal and effects of this type of learning have not been sufficiently tested, especially in terms of promoting civic outcomes.

To address this gap and build an empirical case for the influence of informal learning on civic behaviors and attitudes, this study used data from the Program for International Assessment of Adult Competencies’ (PIAAC) background survey (OECD, 2009) to test a model of how everyday activities affected three types of civic engagement: frequency of volunteerism, sense of political self-efficacy, and level of social trust through the presence of a lifelong learning mindset (LLM). The intent of this study was to add to the theoretical understanding of how informal learning that occurs from habitual experiences, builds the state of mind that may promote civic engagement and enable a learning democracy.

Literature Review

Rüber et al. (2018) built a conceptual model based on an analysis of 13 empirical studies of the relationship between civic engagement and adult learning to explain “mechanisms through which learning may influence civic participation” (p. 543). This model served as the theoretical basis of our structural equation models. To further support our analysis, we reviewed literature related to the relationships among three core variables: informal learning, LLM, and civic engagement with special attention to studies that used indicators similar to those in our study.

Civic Participation and Adult Learning: The Rüber Model

Rüber et al.’s (2018) model identified five explanatory mechanisms that indicated a relationship between adult learning and civic participation:

Adult learning seems to increase the likelihood of civic participation by (1) generating economic preconditions of civic participation; (2) increasing individual qualifications for civic participation and its perceived benefits; (3) strengthening relevant low-level personality trait characteristics; (4) generating related values and attitudes; and (5) expanding networks and providing access to new communities (p. 557).

Acknowledging that “evidence on the association of civic participation with informal. . . adult learning is relatively weak,” Rüber et al. (2018) limited the influence of informal learning to only one of three conceptual paths (p. 553). This path (adult learning influences low-level personality traits that then increase likelihood of volunteerism) was generalized to all adult learning and is the path this study modeled.

Rüber and Janmaat (2021) used longitudinal data from the United Kingdom to test components of Rüber et al.’s (2018) model. Their results showed that participation in adult education raised volunteerism by nearly four percent. The authors posited that adult education may influence the rate of volunteerism through “expansion of one’s social network and . . . the enhancement of one’s self-efficacy” (Rüber & Janmaat, 2021, p. 66). Rüber et al.’s (2018) model limited measurement of civic participation to volunteerism; however, this subsequent finding lends support to include social trust and political self-efficacy as additional civic engagement indicators in our model.

Informal Learning Indicators

Livingstone (2001) described informal learning as “any activity involving the pursuit of understanding, knowledge or skill which occurs without the presence of externally imposed curricular criteria” (p. 5). Our model defined informal learning as learning which has the potential to happen through the experience of any adult’s everyday life. This type of learning is generally indicated through observation of daily activities, such as those included in PIAAC’s “skills use in everyday life” questions. The PIAAC framework stated these are “factors outside the world of work that can affect the development and retention of competences” (OECD, 2009, p. 25). The PIAAC background survey also collected information on informal learning that occurred inside the workplace; however, because not all adults work we did not consider these indicators as they were not generalizable.

Informal learning may be related to four personality factors: initiative, resourcefulness, persistence, and motivation to learn (Derrick, 2003) and “highly experiential” behaviors (Cerasoli et al., 2018). Measures of informal learning have included reading at work and home (Sulkunen et al., 2021), along with using the Internet, watching educational television, listening to instructional radio, and participating in educational trips (Lai et al., 2011). These definitions and examples align with the types of everyday experiences we selected as indicators of our informal learning latent variable.

While much informal learning research has focused on common life experiences, Pesen and Epçaçan (2017) questioned the validity of this viewpoint and insisted that lifelong learning (LLL) must be intentional and not something that happens “automatically with daily life” (p. 26). However, others reject that requirement (as does this study). Buchczyk and Facer (2018) argued for the importance of looking at learning as an “everyday and embedded practice” (p. 204). Their ethnographic studies found the “learning value of. . . walks and volunteering was intrinsically linked to dealing with the unexpected” and city residents became more capable of “putting things to use” as a result of their daily lived experiences (p. 203). This links the type of informal learning (embedded everyday experience) used in our study to an increased presence of self-efficacy and resiliency, which have also been correlated with LLM (Drewery et al., 2020; Kozikoglu & Onur, 2019).

Lifelong Learning Mindset Indicators

Coşkun and Demirel (2010) defined LLL as the “voluntary and self-motivated pursuit of knowledge for either personal or professional reasons” with the ability to “enhance. . . active citizenship” among its many benefits (p. 2343). Wielkiewicz and Meuwissen (2014) viewed LLL as a “habit of mind” (p. 220) and asserted that LLL is integral to positive civic outcomes. Our use of LLL means learning that happens throughout the course of life through everyday experiences (informal learning). While all humans have everyday experiences, not all exhibit the traits associated with lifelong learners, and thus may or may not possess a lifelong learning mindset (LLM). At least eight distinct instruments have been validated to measure the presence of LLM (also called LLL tendency or simply LLL) (Coşkun & Demirel, 2010; Deakin Crick & Yu, 2008; Drewery et al., 2020; Gür-Erdogan & Arsal, 2015; Kirby et al., 2010; Şentürk, 2019; Tezer & Aynas, 2018; Wielkiewicz & Meuwissen, 2014). Commonly used indicators of LLM focus on learning strategies and attitudes, such as motivation and persistence (Coşkun & Demirel, 2010); willingness to learn and openness to improvement (Gür-Erdogan & Arsal, 2015); creativity and curiosity (Deakin Crick & Yu, 2008), and resilience and strategic thinking (Drewery et al., 2020). These traits are reflected in the PIAAC’s “learning strategies” questions, such as “I like to learn new things,” and “If I don’t understand something, I look for additional information to make it clearer” (OECD, 2009). While much of the research on LLM has occurred in Turkey, the findings are still relevant to a U.S. population because many of the Turkish studies used instruments adapted from North American instruments (Gür-Erdogan & Arsal, 2015).

Civic Engagement Indicators

Jennings and Zeitner (2003) defined civic engagement as a broad range of behaviors and attitudes, including media attentiveness, political involvement, volunteerism, and trust orientation. They found this broadness makes measuring civic engagement difficult. For example, volunteerism characteristics may not be directly applicable to interpreting someone’s attitude towards social trust. Campagna et al. (2020) preferred the term civic “participation” to engagement, describing it as “behaviours and attitudes through which people express their willingness of interacting within the community and contributing to its well-being” (p. 662).

Clearly, civic engagement is a complex, multi-faceted construct. Researchers, like Rüber et al. (2018), frequently choose to focus on single indicators, such as volunteerism, because they are the most prevalent examples and easiest to compare. Others, like Rose et al. (2019), rely on validated items that are readily available in large secondary datasets such as the PIAAC’s civic engagement cluster. However, both approaches can lead to over and under-representation of indicator importance. We kept this limitation in mind when analyzing the civic engagement variable.

The Relationship between Informal Learning, LLM, and Civic Engagement

Christie et al.’s (2015) analysis of transformative learning case studies linked learning to LLM to civic engagement: “If students are given the motivation, means and the knowledge necessary to critically assess, challenge and change their assumptions, they will have the chance to become lifelong learners capable of acting for the best in a rapidly changing world” (p. 22). This implies that LLL develops skills and abilities necessary for critical civic engagement. Akyol (2016) found that motivation, LLM, and self-efficacy were positively related, with LLM the mediator between motivation and self-efficacy. This finding may support our positioning LLM as a mediator between informal learning and civic engagement, especially concerning how those variables relate to political self-efficacy, one of our civic engagement indicators.

Carr et al. (2018) found evidence that informal LLL plays a role in social transformation and personal empowerment. Furthermore, studies have connected everyday activities with increases in civic engagement. Jennings and Zeitner (2003) found that Internet use had a positive, significant correlation with volunteerism, and a limited, positive association with social trust. Kozikoglu and Onur (2019) observed that information literacy level (ability to use information tools in everyday life to solve problems) predicted 32.7% of LLM. They concluded that “information literacy is an important and necessary skill in acquiring LLL habits and in developing LLL tendencies” (Kozikoglu & Onur, 2019, p. 502). Öteleș (2020) identified a significant positive relationship between LLM and digital literacy with about 16% of the variance in digital literacy explained by LLM. Collectively, these studies suggest everyday experiences may be a type of informal learning that could influence LLM, democratic attitudes, and behaviors. However, these relationships have not been sufficiently modeled or tested.

Methods

This study used a two-step structural regression (SR) analysis to answer: What is the effect of informal learning (INFLRN) mediated by lifelong learning mindset (LLM) on civic engagement (CIVENG)? Step one consisted of using confirmatory factor analysis (CFA) to construct latent variables for INFLRN, LLM, and CIVENG. The second step used the best fitting CFA models to test the SR model INFLRN on LLM on CIVENG. The SR model was adapted from part of Rüber et al.’s (2018) conceptual model that applied to all forms of adult learning: the effect of adult learning on civic participation through low-level personality traits. Our study interpreted “low-level personality traits” as LLM because Rüber defined these traits as those likely to be influenced through adult learning activities which assist in mastering new abilities, such as civic participation. We expanded their construct for civic participation (volunteerism) to include political efficacy and social trust, creating a “civic engagement” variable similar to another study using PIAAC data (Rose et al., 2019). Although that study’s CFA model was rejected due to poor global fit, we chose to try the grouping because our data came from different participants and was not a comparative study.

Items from the 2017 PIAAC background survey were used as observable indicators for each latent variable. The PIAAC is a large-scale study that documents knowledge and skills necessary for individuals to be productive members of their countries’ economies (OECD, 2009). In the U.S., the PIAAC is administered by the National Center for Educational Statistics (NCES) and is a validated, nationally representative sample of adults aged 16–74. The U.S. was chosen because it is a long-term democracy. Although the 2017 PIAAC collected information from participants in five additional countries, our focus tested for relationships among our constructs and not on comparing them across different cultures. Based on the literature, we selected PIAAC’s background questionnaire because it collected data that are likely indicators of our model variables and have been used similarly in other studies: informal learning/frequency of skills use in everyday life (Sulkunen et al., 2021), LLM/agreement with learning strategy statements (Gorges et al., 2016), and civic engagement/frequency of volunteerism and agreement with political self-efficacy and social trust statements (Rose et al., 2019). While validated instruments designed to specifically measure these variables exist, statistically representative datasets of entire countries using these other instruments do not. We acknowledge the PIAAC’s limitations: (a) inability to measure the study variables as accurately as uniquely designed instruments could; and (b) a survey design that prevents the dataset from being used to show causality (OECD, 2009). However, we felt the benefits of satisfactory measurement across a large, validated sample outweighed the limitations.

This study used the 2017 U.S. public use data file, a sample (N = 3660) of sufficient size to conduct structural equation modeling (SEM), given 200 cases is a median SEM sample size, although more complex models may require a much larger sample (Kline, 2016). Responses from the background questionnaire’s sections “skills use in everyday life,” “learning strategies,” “cultural engagement,” “political efficacy,” and “social trust” were used as indicators to construct latent variables for INFLRN, LLM, and CIVENG as shown in Tables 1 –3. The criterion for selecting INFLRN indicators was that the activity was a common activity with at least 50% of respondents reporting they engaged in the activity at least weekly. All items from the “learning strategies” section were selected as indicators for LLM because each was relevant to characteristics of LLM, such as “I like to learn new things,” and identical to items on other validated LLM instruments. The CFA and SR models were analyzed using Mplus 8.5. While this study categorized some of the “skills used in everyday life” items as literacy-related, numeracy-related, and information and communication technology (ICT)-related to model a three-factor CFA for INFLRN, it is important to note that only data from the background questionnaire was used and not the scaled PIAAC skills scores for literacy, numeracy, and problem solving in technology rich environments.

Table 1.

Informal Learning Activities Variable Indicators.

PIAAC Identifier	Variable Name	Variable Label	Survey Question
H_Q01A	Read directions	ILA1	Read directions or instructions?
H_Q01B	Read letters	ILA2	Read letters, memos, or e-mails?
H_Q01C	Read newspapers	ILA3	Read articles in newspapers, magazines, or newsletters?
H_Q01G	Read financial statements	ILA5	Read bills, invoices, bank statements, or other financial instruments?
H_Q02A	Write letters	ILA4	Write letters, memos, or e-mails?
H_Q03B	Calculate costs	ILA6	Calculate prices, costs, or budgets?
H_Q05A	Use email	ILA8	Use email?
H_Q05C	Use the Internet to understand issues	ILA9	Use the internet in order to better understand issues related to, for example, your health or illnesses, financial matters, or environmental issues?

All questions in this section begin with the stem “In everyday life, how often do you usually. . .” and the possible responses are Never, Less than once a month, Less than once a week but at least once a month, At least once a week but not every day, Every day, Don’t know, Not stated or inferred, and Refused.

Table 2.

Lifelong Learning Mindset Variable Indicators.

PIAAC Identifier	Variable Name	Variable Label	Survey Question
I_Q04B	Relate new ideas into real life	LS1	When I hear or read about new ideas, I try to relate them to real life situations to which they might apply.
I_Q04D	Like learning new things	LS2	I like learning new things.
I_Q04H	Attribute something new	LS3	When I come across something new, I try to relate it to what I already know.
I_Q04J	Get to the bottom of difficult things	LS4	I like to get to the bottom of difficult things.
I_Q04L	Figure out how ideas fit together	LS5	I like to figure out how different ideas fit together.
I_Q04M	Look for additional information	LS6	If I don’t understand something, I look for additional information to make it clearer.

All questions in this section begin with the stem “To what extent do the following statements apply to you?” and the possible responses are Not at all, Very little, To some extent, To a high extent, To a very high extent, Don’t know, Not stated or inferred, and Refused.

Table 3.

Civic Engagement Variable Indicators.

PIAAC Identifier	Variable Name	Variable Label	Survey Question
I_Q05F	Volunteer	CE1	In the last 12 months, how often, if at all, did you do voluntary work, including unpaid work for charity, political party, trade union or other non-profit organization?
I_Q06A	Political Efficacy	CE2	To what extent do you agree or disagree with the following statement? People like me don’t have any say about what the government does.
I_Q07B	Social Trust1	CE3	To what extent do you agree or disagree with the following statement? If you are not careful, other people will take advantage of you.
I_Q07A	Social Trust2	CE4	To what extent do you agree or disagree with the following statement? There are only a few people you can trust completely.

Potential responses for the volunteer question are Never, Less than once a month, Less than once a week but at least once a month, At least once a week but not every day, Every day, Not stated or inferred, and Refused. Potential responses for the political efficacy and social trust questions are Strongly agree, Agree, Neither agree nor disagree, Disagree, Strongly disagree, Don’t know, Not stated or inferred, and Refused.

The Robust Weighted Least Squares (WLS) estimator was used because the indicators were Likert-type variables that did not have more than five categories, and the responses were not normally distributed. WLS does not make assumptions about how the data are distributed. The usable portion of the dataset (N = 2830), which excluded cases with missing data, met the WLS requirement of needing a large sample size (Kline, 2016).

Final interpretation of each model was based on the combination of global and local fit test results balanced with theory from the literature to aid in making decisions about appropriateness of computer-recommended modifications to the model specifications. The best fitting CFA models for INFLRN (the three-factor LIT NUM TECH), LLM, and CIVENG that could also produce admissible results when entered into the fully latent structural regression model (LIT NUM TECH on LLM on CIVENG) were used to test the SR model.

Results

Frequencies of survey responses showed that 77.4% read letters, memos, or email daily, and 53.2% used the Internet to better understand issues on a daily basis. A majority of participants agreed that all of the learning strategies statements related to them to at least a high extent. Conversely, only 2.3% of participants volunteered daily, while 41% never volunteered at all, and 73.1% agreed that others will take advantage of you and 64.3% felt there are only a few people you can trust.

Of the eight INFLRN indicators, those within the same group (LIT, NUM, or TECH) were generally more correlated with each other than with those in other groups. All six LLM indicators were moderately to strongly correlated. In general, correlations between CIVENG indicators were low. All indicators had positive correlations with other indicators in their respective latent variable group.

Correlations between INFLRN and LLM indicators were moderate [ILA 9—using the Internet, and LS 5—relating new ideas to real life (0.273)] to weak [ILA 5—reading directions, and LS 1—liking to learn new things (0.027)]. Correlations between LLM and CIVENG indicators were also weak. The largest correlation occurred between LS 5—relating new ideas to real life, and CE 1—volunteerism (0.178). The highest correlation between any CIVENG and INFLRN indicator was 0.196 (ILA 4 and CE 1), indicating their greatest relationship may be related to writing letters/email and volunteering. Correlations for all study variables are shown in Tables 4 –8.

Table 4.

Means, Variances, and Correlations of INFLRN Indicators.

Variable	ILA1	ILA2	ILA3	ILA4	ILA5	ILA6	ILA8	ILA9
Mean	3.686	4.623	4.092	3.825	3.708	3.493	4.532	4.236
Variance	1.471	0.696	1.433	1.606	1.254	1.505	0.908	1.040
ILA1	1.000
ILA2	0.275	1.000
ILA3	0.208	0.327	1.000
ILA4	0.208	0.449	0.257	1.000
ILA5	0.109	0.187	0.142	0.144	1.000
ILA6	0.223	0.178	0.137	0.181	0.309	1.000
ILA8	0.166	0.522	0.246	0.474	0.172	0.179	1.000
ILA9	0.203	0.237	0.210	0.295	0.170	0.222	0.381	1.000

Table 5.

Means, Variances, and Correlations of LLM Indicators.

Variable	LS1	LS2	LS3	LS4	LS5	LS6
Mean	4.325	3.911	4.052	4.314	3.663	4.030
Variance	0.620	0.856	0.797	0.589	0.857	0.714
LS1	1.000
LS2	0.534	1.000
LS3	0.441	0.618	1.000
LS4	0.456	0.495	0.457	1.000
LS5	0.469	0.465	0.355	0.354	1.000
LS6	0.503	0.501	0.417	0.377	0.570	1.000

Table 6.

Means, Variances, and Correlations of CIVENG Indicators.

Variable	CE1	CE2	CE3	CE4
Mean	2.106	3.110	2.139	2.398
Variance	1.326	1.499	1.006	1.445
CE1	1.000
CE2	0.156	1.000
CE3	0.100	0.189	1.000
CE4	0.131	0.254	0.506	1.000

Table 7.

Correlations of INFLRN Indicators with LLM and CIVENG Indicators.

Variable	ILA1	ILA2	ILA3	ILA4	ILA5	ILA6	ILA8	ILA9
LS1	0.136	0.116	0.130	0.191	0.027	0.159	0.184	0.245
LS2	0.123	0.094	0.128	0.163	0.041	0.139	0.172	0.243
LS3	0.108	0.087	0.086	0.139	0.087	0.131	0.134	0.187
LS4	0.132	0.138	0.106	0.177	0.068	0.143	0.196	0.244
LS5	0.158	0.172	0.170	0.247	0.062	0.141	0.210	0.273
LS6	0.132	0.119	0.106	0.174	0.063	0.130	0.170	0.203
CE1	0.079	0.125	0.104	0.196	0.016	0.044	0.135	0.106
CE2	0.046	0.095	0.129	0.111	−0.025	−0.005	0.109	0.052
CE3	−0.039	0.047	0.078	0.079	−0.031	−0.084	0.072	0.008
CE4	0.029	0.082	0.134	0.092	−0.037	−0.053	0.081	0.027

Table 8.

Correlations of LLM Indicators with CIVENG Indicators.

Variable	LS1	LS2	LS3	LS4	LS5	LS6
CE1	0.122	0.122	0.078	0.068	0.178	0.125
CE2	0.112	0.083	0.054	0.082	0.156	0.067
CE3	0.047	−0.021	−0.034	−0.014	0.060	0.018
CE4	0.056	0.011	−0.020	0.002	0.091	0.047

CFA testing of INFLRN indicated the three-factor model (LIT, NUM, TECH) demonstrated reasonable global fit (χ² [15] = 83.271, p = 0.000; RMSEA = 0.040; CFI = 0.902; SRMR = 0.052) and was the only specification to produce admissible results. INFLRN indicators related to ICT skills (ILA 8 - using email) and literacy (ILA 2 - reading letters) performed best, explaining 41.6% and 47.9% of the variance, respectively. Newspaper reading, used elsewhere to measure civic participation (Vera-Toscano et al., 2017), did not provide much explanatory value (R² = 0.202), accounting for less of the variance than any of the other INFLRN indicators, except ILA 1—read directions (R² = 0.178). These findings align with Demir-Basaran and Sesli (2019) who did not observe a significant relationship between reading the newspaper and LLM. On the other hand, the good explanatory value of ICT activities supports research that has found positive relationships between ICT, such as blogging, and civic engagement (Harju et al., 2016).

CFA testing of LLM indicated that a one-factor LLM model was the only specification to produce reasonable, admissible results in the SR model (χ² [7] = 92.685, p = 0.000; RMSEA = 0.066; CFI = 0.936; SRMR = 0.044). Likewise, CFA testing of CIVENG demonstrated that a one-factor civic engagement specification should be retained because it passed the exact fit test (χ² [1] = 0.009, p = 0.9236) and other global fit statistics were acceptable (RMSEA = 0.000; CFI = 1.00; SRMR = 0.000). However, local fit of CIVENG was not ideal because indicators showed moderate to poor explanatory value (none of the pattern coefficients was above 0.7). Thus, this model should be used with caution. For example, political self-efficacy may provide good insight as it explained 30% of the variance, but volunteerism only explained 8%. The social trust indicators explained about 12% (CE3) and 21% (CE4).

The fully latent SR model was specified as LIT NUM TECH on LLM on CIVENG (Figure 1) and produced admissible results but did not pass the exact fit test (χ² [124] = 659.570, p = 0.000). Other global fit statistics were mixed. The RMSEA was excellent (0.039), while the CFI (0.810) and SRMR (0.142) were mediocre to poor. The standardized pattern coefficients of LIT, NUM, and TECH when regressed on LLM were 0.192, −0.040, and 0.368, respectively. These values suggest that frequency of engaging in technology-related INFLRN likely had the strongest effect on LLM, and when combined with the literacy-related INFRLN, had a moderate positive effect on LLM. Frequency of numeracy-related INFLRN appeared to have a negative effect on LLM, although this effect was weak. LLM explained 25% of the variance in the final model (finalSR3 R² = 0.250). The standardized pattern coefficient of LLM when regressed on CIVENG was 0.292. The explanatory value of CIVENG in the SR model was weak, accounting for 8.5% of the variance.

Figure 1.

SR model: Path diagram with standardized coefficients.

Discussion

The results of this study demonstrated a positive relationship between the frequency of common informal learning activities and presence of civic engagement attitudes (political self-efficacy and social trust) and behaviors (volunteerism), which is strengthened through LLM. Although the PIAAC background study cannot allow us to demonstrate causality, this finding lends support for the role of informal lifelong learning as a possible enabler of learning democracy, backed by previous qualitative research on the effects of LLL on citizen empowerment (Buchczyk & Facer, 2018).

As frequency of ICT-related informal learning activities seemed to have the greatest impact (TECH standardized pattern coefficient = 0.368), educators may want to focus instruction to help adults better develop skills such as blogging as a means to process and share their learning with others outside of the classroom. Likewise, local governments and neighborhood associations could provide more opportunities for residents to communicate with each other, share their own ideas and learn through videos, blogs, and social media platforms. In fact, Dennis (2015) defined blogs as spaces of public pedagogy where “people go to learn with and through interested others” (p. 6) and Harju et al. (2016) observed that bloggers in Finland viewed the activity as a “form of self-actualization that is driven by a comprehensive need to participate in the world” (p. 13). Adult educators may want to develop partnerships with organizations such as museums, public libraries, community arts groups, and non-profits working on local issues to close gaps between learning and civic action (Hudson et al., 2019; Rhodes et al., 2019). Educators could also find ways to infuse the value of everyday experiences into their curricula. For example, they might ask learners to keep an observational journal, have them identify something they have experienced, relate it to the subject matter they are studying, and complete a project that requires them to take action in the community based on their observation and reflection.

While the SR analysis in this study demonstrated a positive relationship between informal learning and civic engagement variables, it is worth noting that participants also reported extremely low levels of volunteerism, political self-efficacy, and social trust—even though they engaged in high levels of informal learning activities and held strong LLM beliefs. The implication of this finding adds a caveat to the connection between LLL and learning democracy because it suggests that citizens can be engaging in high rates of LLL and possess a LLM, yet still have very low opinions of their government and feel disengaged from their community.

This study demonstrated that political self-efficacy explained most of the variance in civic engagement traits. Perhaps the path to increased civic engagement lies not only in acquiring skills and wealth (Rüber et al., 2018), but also in helping adults convert what they learn through their everyday experiences into opportunities for participating more fully as co-creators of their community (Hudson et al., 2019; Yarnit, 2015). Designing for everyday encounters that build fellowship with others would take us beyond Sheffield’s (1922) “spokespeople for ideas” and could bring us to “advocates for ideas and each other,” enhancing participation in the American experience.

Conclusion

This study provided an empirical test on part of the Rüber et al. (2018) conceptual model (adult learning affects low-level personality traits which result in increased civic participation). This path was modeled using latent variables for informal learning, LLM, and civic engagement and tested using a nationally representative secondary dataset. The SR model used in this study was a step towards transforming a conceptual model into an empirical model for quantitative analyses generalizable to the U.S. population. The current study also expanded the scope of the Rüber et al. (2018) model by including a specific focus on informal adult learning and the role of LLM in shaping not only volunteerism, but also political self-efficacy and social trust.

Jarvis (2008) stated that learning “skills must be undertaken through the act of doing and therefore, experiencing” (p. 12). An increased awareness of how everyday activities influence learning abilities, beliefs, and behaviors can help citizens, policy makers, and adult learning practitioners take a critical look at the habits many may take for granted. These mostly unexamined actions are what create human systems, such as democracies, and are where the opportunities for affecting those systems may be found. The results of this study suggested that a learning democracy is likely one of the systems that can be influenced through LLL.

While Belzer (2017) noted that, “PIAAC data probably cannot be used to point out effective program and policy interventions,” she did allow they could be used to “spur [one] to action by highlighting the failings of the system” (p. 118). Her observation is based on Berwick’s (2003) assertion that it is the design of a system that produces an outcome, “not simply the will, native skill, or attitude of the people who work in that system” (p. 448) and consequently, “the most effective route to improvement is through changing systems, not yelling at them” (p. 449). It may be necessary to analyze current systems present in a community to identify where blockages to civic engagement occur, which could then be targeted with specific learning interventions such as non-formal training or community learning campaigns. Learning options that create meaningful collaboration that allow citizens to use their knowledge on a regular basis are vital to systemic change. Lifelong learning may not guarantee a learning democracy, but a learning democracy cannot exist without lifelong learning.

Footnotes

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.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Linnea M Haren Conely

Leslie A Cordie

Author Biographies

Dr. Linnea M. Haren Conely leads Curriculum Development at AIDT, Alabama’s workforce development agency, and is the founder of social impact consulting firm Questia Corps. Her work focuses on the use of informal lifelong learning to empower citizen-led community change. For more information, visit her website .

Dr. Leslie A. Cordie is an Associate Professor in Adult Education at Auburn University and Affiliate Faculty with the University Writing Center. Cordie is a Fulbright Scholar with active international collaborations in the West Indies, Ireland, the UK and Asia. For more information, visit her website .

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