Exploring the Contribution of Social and Economic Status Factors (SES) to the Development of Learning Cities (LC)

Abstract

Learning cities can help to reinforce the socio-economic well-being of residents in deprived areas contributing to the sustainability of cities and also provide them with learning and working opportunities. Diverse learning providers should be inclusively designed to meet all citizens’ needs, opportunities, and aspirations. Understanding the different social and economic characteristics of a city enables the proposition of appropriate development strategies to truly meet all citizens’ needs. Thus, this study examines the relationship between social and economic status (SES) and the perceptions of the development of learning cities (LC) in peri-urban development. To understand the significance of different social factors affecting the development of a learning city so that appropriate development guidelines and responses to people’s needs in outskirt areas can be recommended, data were collected from 400 participants through questionnaires in Thanyaburi District, Pathum Thani province, Thailand. This study applied nonparametric statistics through the use of the Chi-Square and Kruskal–Wallis H test to explore the differences in the variables of each classification and pairwise, including exploring the correlation between independent and dependent variables. The results revealed that different SES characteristics were significantly associated with different learning-enhancing activities (p-value < 0.05). The development of a learning city is therefore recommended to respond to the diverse citizens’ needs while contributing to several societal objectives with great potential for sustainable urban development.

1. Introduction

Cities are expanding, and the population is increasing due to urban development in many countries around the world. Over half of humanity lives in cities, and this is predicted to grow to 5.2 billion people living in cities by 2030 [1]. Cities in several parts of the world have been growing in both population and infrastructures in response to human activities. However, each area has a different level of development, and this creates differences in development in each area, especially in urban and rural areas. Rapid development in urban areas has resulted in the migration of rural populations to urban areas or better-developed areas with high density, where a wide range of jobs and activities can be found [2]. This migration is partly due to the increasing appeal of these urban areas to people of all ages from diverse socioeconomic and cultural backgrounds, searching for better lives, more safety, essential services, and job opportunities. However, the negative result of this global urbanization is the challenges of managing its rapid growth. Cities are now faced with inadequacy of housing and infrastructure to support the well-being of the population living within the city. These challenges must be addressed in all parts of the city to prevent local inequalities, particularly in the outskirt areas with a long distance to services and facilities. The environmental impact of urban sprawl is a huge challenge for cities and towns, particularly in rural areas.

Based on the points mentioned above, creating such learning opportunities in the city requires that such learning city must be of high quality and incorporates the diverse backgrounds of all learners so as to provide them with opportunities continuously throughout their lives. Currently, many existing urban ideas, such as smart cities, compact cities, liveable cities, healthy cities, etc., were conceptualized, erected, and developed to solve the problems and develop the cities [3]. Learning city (LC), as a matter of fact, is one of the approaches or urban conceptions deployed to solve the problem of the city, which allows it to develop through the provision of learning opportunities for the diverse groups in the city. There has been interest in the role of learning in responding to the challenges of demographic, technological, ecological, and economic change. Various outcomes of urban development have led to many changes in urban issues arising from urbanization. For instance, Wei and Ewing (2018) showed linkages between urban sprawl/expansion and spatial inequality [4]. The urbanization process reshapes economic/income [5,6], social (in terms of education, mobility, health, access to employment opportunities, and public services) [7,8], and environmental inequalities [9,10]. Most urban projects of recent decades, no matter whether in developed or developing countries, have contributed to a physical reinforcement of inequality and segregation in multiple dimensions [11]. LC is one of the concepts with drivers of inclusion and sustainability that promote equity [12]. This includes access to flexible and quality infrastructures and basic services for city residents that creates local economic opportunities such as good job creation and social harmony [13].

LC, as an educational concept along with lifelong learning, has emerged and is realized as a relatively recent addition to the international development agenda, and its concept has evolved in a long history to become more widespread during the last decade of the 20th century. The LC concept has evolved historically, and it was defined geographically in the 1970s. It was defined on the basis that the cities concerned placed education at the heart of their strategies. Then, in the 1980s and 1990s, there was a tendency to emphasize the agency of social and economic actors [14]. Subsequent developments mean that lifelong learning is now at the heart of the learning city concept, emphasizing its continuing and lifelong learning responsibilities [15,16]. The LC concept has been interpreted in many ways in relation to uniting all the diverse providers of learning to meet the needs and aspirations of its citizens [17,18]. All citizens, including particular groups, should have access to educational opportunities irrespective of their social status, race and ethnicity, gender, disability, or geographical location. This history may be traced back to work regarding the learning city of the Organization for Economic Cooperation and Development (OECD), such as “City Strategies for Lifelong Learning” prepared in 1992 [19] and “Learning Cities: The New Recipe in Regional Development” [20]. Furthermore, it may also be traced back to work about ideas of the learning society in UNESCO [21,22].

Nowadays, learning cities respond mainly to two of the UN Sustainable Development Goals: SDGs 2030, particularly SDG 4 (Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all) and SDG 11 (Making cities and human settlements inclusive, safe, resilient and sustainable) [23]. The UNESCO Institute for Lifelong Learning (UIL) defines a learning city as one that effectively mobilizes its resources in every sector to promote inclusive learning from basic to higher education. It also includes the revitalization of learning in families and communities, facilitating learning for and in the workplace, extending the use of modern learning technologies, enhancing quality and excellence in learning, and fostering a culture of learning throughout life [23,24]. Learning cities thus offer numerous benefits for people in the communities, towns, and cities. To further consolidate these benefits, UNESCO has established the Global Network of Learning Cities (GNLC) to be a channel for cities that want to develop and promote lifelong learning to share experiences and progress as well as create a knowledge support system and working network at an international level.

Thailand has continuously supported the concept of a learning city, and this is evident in the number of her cities that are members of the UNESCO GNLC, including Chiang Rai Municipality, Phuket Municipality, Chiang Mai Municipality, and Chachoengsao Municipality [24]. Emphasis is placed on empowering citizens with new knowledge, skills, and attitudes in various contexts to adapt to the world’s rapid social, economic, and political changes to promote social reconciliation, economic sustainability, and cultural and environmental development. In the province of Pathum Thani in Thailand, there has been a policy for urban development related to the promotion of people’s learning in the city. This policy, which focused on building a network of educational cooperation among several stakeholders, has made the government sector, educational institutions, and agencies relentless in promoting learning at all levels. This is achieved by promoting innovation and technological knowledge through human resource development in terms of education with quality standards and learning skills, as well as labor potential development following the needs of the labor market. This policy also aims to develop and promote the quality of life for all citizens, including children and youth, women, and the elderly, by promoting the safety of life and property of the people, social work, health care and sports, education, and acknowledgment of public information. However, in applying the principles of urban development to promote learning or any other developmental aspect, it is necessary to consider the basic information on the social and economic status (SES) of citizens (e.g., demographic characteristics, socioeconomic characteristics, etc.). Based on this comprehensive view, it can reflect the needs, attitudes, and perceptions of the local people in order to be able to implement the policy and respond to the needs of the people in the city. Thus, this study examines the relationship between SES and perceptions of the residents regarding the development of learning cities by focusing on the study area and its potential demonstrated in Figure 1. The objective is to understand the significance of different social and economic characteristics affecting the development of a learning city in order to suggest appropriate development guidelines and respond to people’s needs.

The original contribution of this paper is hence to prove the importance and correlation of people’s social and economic characteristics to learning activities in a given area. This is due to the basic preconditions for the development of a Learning City, which need to be understood in relation to the context of the area as each area has different citizens, activities, and physical characteristics. To establish a learning city in the peri-urban Thanyaburi district, Pathum Thani, it is necessary to understand the significance of different social and economic factors affecting the development of such a city in the context of Thailand in order to suggest appropriate development guidelines and respond to people’s needs.

2. Literature Review

2.1. Learning City (LC)

The LC concept is a way not only to promote the integration of disadvantaged groups but also to promote the development of learning infrastructure to generate internal investment and facilitate business development that emphasizes economic, social, and globally competitive dimensions [25]. This is basically a vehicle to drive place-based lifelong learning through formal, non-formal, and informal means. Such that it is not confined to formal schooling or to the classroom but rather takes place throughout life across a variety of situations such as at home, the workplace, in the community, through mass media, problem-solving, social participation, in leisure activities, etc. [26,27]. LC enhances individual empowerment, social inclusion, economic development, cultural prosperity, and sustainable development of cities [19,28]. Critical aspects of this learning approach include: (1) flexibility of the learning offer in terms of location (e.g., within formal institutions, workplaces, communities, and virtual means), (2) mode (full-time, part-time, blended), (3) means of accreditation, and (4) the extent to which learners become active co-constructors of their learning programs based on their demands and self-identified needs [29]. The LC initiative can be distinguished not only by its implied geographical boundaries but also by stakeholder consideration, activities, and broader objectives that extend beyond economic instruments [30]. In addition, the development of the learning city not only promotes various activities within the area but also extracts resources from outside (national and international) to invest within the area, such as knowledge exchange, fundraising, internal investment, foreign direct investment, etc. However, all operations are joint operations of stakeholders on the basis of resource capital, and the measures implemented must be considered appropriate for sustainable development.

2.2. Socioeconomic Status (SES)

Socioeconomic status (SES) is one of the main factors limiting achievements, and the perception of the people differs socioeconomically. Socioeconomic status (SES) presents a variable expressing the position held by every person or a group of people in the structure of a society (on the social ladder) [31]. The quality of life associated with SES also reflects the needs, attitudes, perceptions, etc. of the people as it relates to the opportunities and privileges afforded to them within a society [32]. Socioeconomic status (SES) refers to the rank or prestige of an individual in relation to others or the individual’s access to material as well as social resources and goods [33], encompassing both subjective and objective aspects [34,35]. In a nutshell, socioeconomic status is a notion that incorporates concepts such as financial/economic well-being, prestige, and power [36,37,38]. Moreover, as urban spatial segregation, particularly in disadvantaged neighborhoods, could lead to the perpetuation of stigmatization against the poor [39,40], it is imperative that urban development needs to consider socioeconomic factors and potentially negative perceptions against the marginalized so as to alleviate these issues in tomorrow’s cities [41].

SES is commonly conceptualized as an individual or group’s social standing or class. It can be measured by several factors such as education, income, labor market position, the profession’s prestige, social class, poverty, financial distress, and wealth [42,43]. Moreover, it is often measured as a combination of education, income, and occupation [37,44,45,46,47,48,49]. Education in this context presents an essential indicator for SES since it determines income and opportunity in life [31,50]. SES is typically broken into three categories, high SES, middle SES, and low SES (American Psychological Association, 2022). Furthermore, it has been observed that different SES can affect access to different opportunities. For instance, those of lower socioeconomic status often face additional challenges, including a dearth of learning resources, difficult learning conditions, and poor motivation, which has an impact on their performance. The SES level of the family has a positive correlation with the student’s success as well as other demographic factors, which effects on SES are still much prevalent at the individual level [51]. However, some studies, such as in Creţan and O’Brien (2019), showed the argument about the basic assumptions of the theory of stigmatization, that such stigma is limited to the poor, and highlight the need to understand mobilization against particular minority groups, regardless of socioeconomic status [52]. Thus, understanding the people’s needs through their SES would enable the proposition and development of appropriate policies and sustainable urban solutions relating to the LC concept.

3. Methodology

3.1. Study Area

This study is based on a quantitative approach, using a case study of Thanyaburi District in Pathum Thani as a representative case for peri-urban areas in Thailand, as depicted in Figure 2. The district spans over an area of 112.124 sq. km. with a population of about 214,091 people as of 2021 [53]. Thanyaburi District is a district of Pathum Thani province that appears like a long line parallel to the Rangsit Prayoonsak canal located towards the end of the province.

Thanyaburi district is a peri-urban area that is more prosperous than Muang Pathum Thani. Although it is not a central district of the province, its high-level potential for a major highway network allows important connectivity as a main gateway to the north and the northeast of the country. Recently, this area has experienced additional mass transit development that connects the province to Bangkok, the capital city of Thailand. In this study area (Thanyaburi district), there are four component municipalities (Rangsit city municipality, Bueng Yitho town municipality, Thanyaburi subdistrict municipality, and Sanan Rak town municipality) which are connected by the main canal of Rangsit Prahyoonsak. Going by the development of peri-urban areas in Pathum Thani province from the past to the present, there has been a noticeable pattern of rapid development which brings both positive and negative effects with the continued expansion of communities in the surrounding areas [54,55]. Pathum Thani province is considered an important strategic point when it comes to developing clean industrial areas. As part of its developmental support for industrial and technological expansion, as well as educational services, its speedy housing estate expansion has undoubtedly covered the main part of the study area [56]. Thanyaburi district is now facing a phenomenon of sprawling areas that turns to support the development expansion along the boundary between the capital city and the outskirt area of Pathum Thani, seen particularly in the Rangsit municipality. In addition, the potential of attractive employment opportunities in populated zones makes this sprawling area the main node for urban development that meets a wide range of citizens’ needs. The four municipalities of the Thanyaburi district are focused on and elaborated on because of their unique characteristics, discussed as follows:

• Rangsit municipality represents a highly urbanized area, considering the capacity and variety of activities taking place within the area. However, due to the long history of the area, it is characterized by activities that reflect some lifestyles, such as Rangsit boat noodles, Rangsit floating market, longtail boat races, temples, and shrines, as well as arts and sculptures, giving the municipality outstanding characteristics in terms of gastronomy, culture, and traditions, including the arts.
• Bueng Yithomunicipality is another highly urbanized area as determined by the capacity of the settlement activities within the area and the buildings’ utilization characteristics with economic activities. There are also several activities interspersed within the area, such as temples and Dream World amusement park, that attract people to the area.
• Thanyaburi subdistrict municipality is mainly concentrated in residential areas with relatively low agricultural activities, and its settlement density is less than that of Rangsit municipality and Bueng Yitho municipality. A distinctive feature of this area is the places dedicated to science and nature education, such as the Lotus Museum, the Geological Museum, the Science Museum, as well as a new zoo that is still under construction, which, when completed, will become a new landmark for the area.
• Sanan Rak municipality area has the least concentration of buildings and is located further away from the city, with most of the activities being residential. In this area, the proportion of agricultural land use is more significant than that of other municipalities. This made the area’s characteristics comprise of activities related to agriculture and some lifestyle such as orange orchards, flower gardens, perm plantations, tie-dye fabrics, etc.

3.2. Data Collection

To examine, through quantitative analysis, the relationship between SES and perceptions for the purpose of developing LC in the study area of the Thanyaburi district, the sample size was determined by using the sampling error of 0.05, with 400 samples as the minimum number of sample groups determined as adopted by the calculation of Krejcie and Morgan [57]. Each municipality was divided into spatial grids, and questionnaires were distributed to each grid in order to cover the samples in each area (see Figure 2). Data were collected by random distribution of sampling technique from participants through the use of face-to-face questionnaires in Thanyaburi district, Pathum Thani province, Thailand, between January to February 2022. An exclusion process was implemented on incomplete answers for questions within each set of questionnaires, such that they were excluded from consideration in further steps of the research. However, the data collection has limitations, which include how the random sampling technique does not depict an accurate representation of the population due to time of day (relating to issues of working hours, people not working in the area, etc.).

The questionnaire was approved by the Human Research Ethics Committee of Thammasat University Social Sciences (certificate of approval number 128/2021). This questionnaire was divided into two major parts, namely: socioeconomic background data and residents’ perception of LC development. The first part was obtained on the empirical distributions of selected socioeconomic factors considered as independent variables, e.g., gender (male, female, LGBT), religion (Buddhist, Christian, Islam, others), residential period (years), marital status (married, single, divorced, others), education (primary school, junior high school, high school, vocational college, bachelor’s degree, postgraduate), occupation (government, private agency, students, trading/personal business, general employee, farming/garden, other), average monthly income (THB) (THB < 5000, 5001–10,000, 10,001–15,000, 15,001–20,000, 20,001–25,000, 25,001–30,000, 30,001–35,000, 35,001–40,000, and >40,000). The second part centered on the participation in learning activities factor, and questions about the people’s involvement in social activities within the study area were asked. The list of activities consisted of nine factors, namely: (LC₁) engage in adult learning and education, (LC₂) participate in supporting underprivileged groups, (LC₃) get involved in supporting the elderly, (LC₄) access to learning spaces in the community, (LC₅) participate in community learning, (LC₆) participate in family learning, (LC₇) participate in training activities for the unemployed, (LC₈) participate in training activities for ICT, (LC₉) participate in learning through the internet. The perceptions of these individuals were assessed based on the Likert scale, where 1 represents never participated in the activity and 5 means normally participated in the activity.

3.3. Measures and Data Analysis

This study examines the relationship between social and economic status (SES) and the perceptions of the development of learning cities (LC) in peri-urban areas. It is hypothesized that there is a correlation between the different socioeconomic factors to learning activities which could affect LC development and implementation. The analysis applied both descriptive and inductive methods for the statistical analysis of the study, as demonstrated in Figure 3. Descriptive analysis was used to provide a general data overview of variables illustrated in terms of mean, standard deviation, proportion, and quantity. Since the variable distributions were not normal, this study applies nonparametric statistics of the Chi-Square and Kruskal–Wallis H test to explore the differences in variable of each classification and pairwise, including the exploration of the correlation between independent and dependent variables.

4. Results of Analysis

This study focuses on examining the correlation between SES and the development of LC. The details of the analysis are given as follows:

4.1. Socio-Economic Status

Table 1. Socioeconomic profile of participants.

Variables		Total		Thanyaburi Subdistrict Municipality		Bueng Yitho Town Municipality		Rangsit City Municipality		Sanan Rak Town Municipality
		n	%	n	%	n	%	n	%	n	%
Social Aspects
Gender	Male	213	53.25	56	47.06	25	45.45	91	57.23	41	61.19
	Female	178	44.50	61	51.26	30	54.55	64	40.25	23	34.33
	Others (LGBTQ)	9	2.25	2	1.68	0	0.00	4	2.52	3	4.48
Religion	Buddhist	381	95.25	118	99.16	55	100.00	149	93.71	59	88.06
	Christian	9	2.25	1	0.84	0	0.00	5	3.14	3	4.48
	Islam	8	2.00	0	0.00	0	0.00	3	1.89	5	7.46
	Others	2	0.50	0	0.00	0	0.00	2	1.26	0	0.00
Residential period (years)	1–10	192	48.00	31	26.05	24	43.64	98	61.64	39	58.21
	11–20	106	26.50	40	33.61	19	34.55	31	19.50	16	23.88
	21–30	44	11.00	16	13.45	7	12.73	16	10.06	5	7.46
	31–40	28	7.00	15	12.61	1	1.82	7	4.40	5	7.46
	41–50	22	5.50	12	10.08	3	5.45	5	3.14	2	2.99
	>50	8	2.00	5	4.20	1	1.82	2	1.26	0	0.00
Marital status	Married	214	53.50	90	75.63	31	56.36	68	42.77	25	37.31
	Single	67	16.75	15	12.61	7	12.73	28	17.61	17	25.37
	Divorced	109	27.25	13	10.92	16	29.09	57	35.85	23	34.33
	Others	10	2.50	1	0.84	1	1.82	6	3.77	2	2.99
Economic Aspects
Education level	Primary school	22	5.50	8	6.72	8	14.55	6	3.77	0	0.00
	Junior high school	28	7.00	6	5.04	5	9.09	14	8.81	3	4.48
	High school	76	19.00	22	18.49	8	14.55	35	22.01	11	16.42
	Vocational college	70	17.50	22	18.49	8	14.55	27	16.98	13	19.40
	Bachelor’s degree	172	43.00	59	49.58	25	45.45	58	36.48	30	44.78
	Postgraduate	32	8.00	13	10.92	2	3.64	19	11.95	8	11.94
Occupation	Government	42	10.50	13	10.92	2	3.64	19	11.95	8	11.94
	Private agency	110	27.50	30	25.21	20	36.36	41	25.79	19	28.36
	Students	42	10.50	1	0.84	3	5.45	28	17.61	10	14.93
	Trading/personal business	136	34.00	48	40.34	19	34.55	49	30.82	20	29.85
	General employee	47	11.75	19	15.97	4	7.27	17	10.69	7	10.45
	Farming/garden	6	1.50	5	4.20	0	0.00	0	0.00	1	1.49
	Others	17	4.25	3	2.52	7	12.73	5	3.14	2	2.99
Average monthly income per person (THB)	Less than 5000	22	5.50	4	3.36	6	10.91	10	6.29	2	2.99
	5001–10,000	32	8.00	7	5.88	4	7.27	17	10.69	4	5.97
	10,001–15,000	80	20.00	19	15.97	9	16.36	31	19.50	21	31.34
	15,001–20,000	86	21.50	26	21.85	9	16.36	39	24.53	12	17.91
	20,001–25,000	62	15.50	32	26.89	8	14.55	14	8.81	8	11.94
	25,001–30,000	45	11.25	15	12.61	5	9.09	21	13.21	4	5.97
	30,001–35,000	41	10.25	13	10.92	11	20.00	13	8.18	4	5.97
	35,001–40,000	32	8.00	0	0.00	3	5.45	6	3.77	5	7.46
	More than 40,000	22	5.50	3	2.52	0	0.00	8	5.03	7	10.45

describes the SES factors among respondents in each municipality in the data collection of 400 sets of face-to-face questionnaires regarding the potential and perception of the development of learning cities in the study area. It was found that the number of male participants accounted for 53.3 percent, followed by females (44.5 percent) and LGBTQ (2.3 percent). In terms of education, most groups of respondents have a bachelor’s degree, which amounts to 43.0 percent, followed by high school/vocational education (19.0 percent) and a bachelor’s degree/vocational certificate (17.5 percent). Considering their occupation, it was found that trade/personal business accounted for 34.0 percent, followed by private employees, which accounted for 27.5 percent, and civil servants/state enterprises and students, which accounted for 10.5 percent, respectively. Most of the sample groups had an average monthly income per person in the range of THB 15,001–20,000, representing 21.5 percent, followed by THB 10,001–15,000, representing 20.0 percent, and THB 20,001–25,000, which accounted for 15.5 percent. The overall income range is THB 10,000–35,000.

When the association between socioeconomic status and learning activities of participants was considered (

Table 2. Social aspects in correlation to participation in learning activities.

Variables	LC1		LC2		LC3		LC4		LC5		LC6		LC7		LC8		LC9
	n	%	n	%	n	%	n	%	n	%	n	%	n	%	n	%	n	%
Gender (G)
G1	30	57.69	41	56.94	33	47.83	38	56.72	44	56.41	57	52.78	25	59.52	22	59.46	38	53.52
G2	21	40.38	30	41.67	35	50.72	27	40.30	33	42.31	50	46.30	16	38.10	14	37.84	31	43.66
G3	1	1.92	1	1.39	1	1.45	1	1.49	1	1.28	1	0.93	1	2.38	1	2.70	2	2.82
p-values	0.965		0.062		0.239		0.420		0.886		0.463		0.260		0.625		0.782
Religion®
R1	50	96.15	70	97.22	73	97.33	64	95.52	76	97.44	105	97.22	40	95.24	36	97.30	70	98.59
R2	0	0.00	0	0.00	0	0.00	0	0.00	0	0.00	1	0.93	0	0.00	0	0.00	0	0.00
R3	2	3.85	2	2.78	2	2.67	2	2.99	2	2.56	2	1.85	2	4.76	1	2.70	1	1.41
R4	0	0.00	0	0.00	0	0.00	0	0.00	0	0.00	0	0.00	0	0.00	0	0.00	0	0.00
p-values	0.831		0.652		0.960		0.936		0.892		0.716		0.585		0.878		0.990
Residential Period (Years) (RP)
RP1	24	46.15	34	47.22	32	42.67	24	36.36	28	35.90	36	33.33	24	57.14	22	59.46	34	47.89
RP2	15	28.85	18	25.00	19	25.33	21	31.82	22	28.21	32	29.63	12	28.57	11	29.73	21	29.58
RP3	6	11.54	10	13.89	11	14.67	11	16.67	11	14.10	17	15.74	3	7.14	2	5.41	6	8.45
RP4	1	1.92	1	1.39	3	4.00	4	6.06	6	7.69	7	6.48	1	2.38	1	2.70	5	7.04
RP5	4	7.69	7	9.72	6	8.00	5	7.58	9	11.54	12	11.11	1	2.38	1	2.70	4	5.63
RP6	2	3.85	2	2.78	4	5.33	1	1.52	2	2.56	4	3.70	1	2.38	0	0.00	1	1.41
p-values	0.301		0.207		0.005		0.607		0.209		0.005		0.262		0.592		0.557
Marital Status (M)
M1	29	55.77	47	65.28	48	64.00	30	53.57	51	65.38	78	72.22	18	42.86	14	37.84	35	49.30
M2	17	32.69	19	26.39	19	25.33	18	32.14	19	24.36	19	17.59	18	42.86	17	45.95	21	29.58
M3	6	11.54	6	8.33	8	10.67	8	14.29	8	10.26	10	9.26	6	14.29	6	16.22	14	19.72
M4	0	0.00	0	0.00	0	0.00	0	0.00	0	0.00	1	0.93	0	0.00	0	0.00	1	1.41
p-values	0.011		0.001		0.007		0.081		0.003		0.001		0.001		0.001		0.160

Remark: Significance level at p < 0.05 and questionnaire is 400 sets. G = Gender; G₁ = Male; G₂ = Female; G₃ = Others; R = Religion; R₁ = Buddhist; R₂ = Christian; R₃ = Islam; R₄ = Others; RP = Residential period (years); RP₁ = 1–10; RP₂ = 11–20; RP₃ = 21–30; RP₄ = 31–40; RP₅ = 41–50; RP₆ > 50; M = Marital status; M₁ = Married; M₂ = Single; M₃ = Divorced; M₄ = Others. LC₁ = Engage in adult learning and education; LC₂ = Participate in supporting underprivileged groups; LC₃ = Get involved in supporting the elderly; LC₄ = Access to learning spaces in the community; LC₅ = Participate in community learning; LC₆ = Participate in family learning; LC₇ = Participate in training activities for the unemployed; LC₈ = Participate in training activities for ICT; LC₉ = Participate in learning through the internet.

and

Table 3. Economic aspects in correlation to participation in learning activities.

Variables	LC1		LC2		LC3		LC4		LC5		LC6		LC7		LC8		LC9
	n	%	n	%	n	%	n	%	n	%	n	%	n	%	n	%	n	%
Education Level (E)
E1	1	1.92	2	2.78	3	3.61	2	2.70	3	3.85	5	4.31	0	0.00	0	0.00	1	1.41
E2	2	3.85	5	6.94	4	4.82	2	2.70	4	5.13	5	4.31	2	3.57	2	3.92	2	2.82
E3	8	15.38	6	8.33	9	10.84	5	6.76	9	11.54	13	11.21	1	1.79	1	1.96	6	8.45
E4	6	11.54	9	12.50	10	12.05	16	21.62	16	20.51	20	17.24	6	10.71	5	9.80	15	21.13
E5	32	61.54	40	55.56	43	51.81	36	48.65	41	52.56	59	50.86	30	53.57	26	50.98	42	59.15
E6	1	1.92	5	6.94	6	7.23	5	6.76	5	6.41	6	5.17	3	5.36	3	5.88	5	7.04
p-values	0.049		0.042		0.100		0.008		0.007		0.161		0.048		0.060		0.017
Occupation (O)
O1	10	19.23	14	19.44	15	18.07	11	14.86	12	15.38	17	14.66	8	14.29	6	11.76	10	14.08
O2	25	48.08	30	41.67	30	36.14	26	35.14	28	35.90	37	31.90	20	35.71	21	41.18	32	45.07
O3	1	1.92	0	0.00	0	0.00	0	0.00	0	0.00	0	0.00	0	0.00	0	0.00	1	1.41
O4	12	23.08	19	26.39	23	27.71	17	22.97	22	28.21	34	29.31	9	16.07	7	13.73	17	23.94
O5	2	3.85	6	8.33	5	6.02	6	8.11	8	10.26	12	10.34	2	3.57	2	3.92	5	7.04
O6	0	0.00	0	0.00	0	0.00	4	5.41	5	6.41	4	3.45	0	0.00	0	0.00	4	5.63
O7	2	3.85	3	4.17	2	2.41	2	2.70	3	3.85	4	3.45	1	1.79	1	1.96	1	1.41
p-values	0.001		0.003		0.003		0.001		0.001		0.001		0.039		0.003		0.005
Average Monthly Income (THB) (I)
I1	1	1.92	1	1.39	1	1.20	1	1.35	1	1.28	2	1.72	0	0.00	0	0.00	1	1.41
I2	0	0.00	1	1.39	1	1.20	0	0.00	0	0.00	2	1.72	0	0.00	0	0.00	1	1.41
I3	7	13.46	9	12.50	8	9.64	10	13.51	14	17.95	16	13.79	5	8.93	4	7.84	11	15.49
I4	16	30.77	17	23.61	20	24.10	16	21.62	20	25.64	22	18.97	13	23.21	11	21.57	18	25.35
I5	8	15.38	13	18.06	12	14.46	12	16.22	13	16.67	24	20.69	8	14.29	8	15.69	13	18.31
I6	10	19.23	13	18.06	12	14.46	11	14.86	12	15.38	20	17.24	8	14.29	6	11.76	9	12.68
I7	5	9.62	12	16.67	12	14.46	8	10.81	9	11.54	13	11.21	3	5.36	3	5.88	8	11.27
I8	3	5.77	3	4.17	4	4.82	3	4.05	3	3.85	4	3.45	3	5.36	3	5.88	5	7.04
I9	2	0.5	3	4.17	13	15.66	13	17.57	6	7.69	13	11.21	16	28.57	16	31.37	5	7.04
p-values	0.108		0.001		0.001		0.012		0.002		0.001		0.101		0.043		0.001

Remark: Significance level at p < 0.05 and total number of questionnaire is 400 sets; E = Education level; E₁ = Primary school; E₂ = Junior high school; E₃ = High school; E₄ = Vocational college; E₅ = Bachelor’s degree; E₆ = Postgraduate; O = Occupation; O₁ = Government; O₂ = Private agency; O₃ = Students; O₄ = Trading/personal business; O₅ = General employee; O₆ = Farming/garden; O₇ = Others; I = Average monthly income (THB); I₁ = less than 5000; I₂ = 5001–10,000; I₃ = 10,001–15,000; I₄ = 15,001–20,000; I₅ = 20,001–25,000; I₆ = 25,001–30,000; I₇ = 30,001–35,000; I₈ = 35,001–40,000; I₉ = More than 40,000. LC₁ = Engage in adult learning and education; LC₂ = Participate in supporting underprivileged groups; LC₃ = Get involved in supporting the elderly; LC₄ = Access to learning spaces in the community; LC₅ = Participate in community learning; LC₆ = Participate in family learning; LC₇ = Participate in training activities for the unemployed; LC₈ = Participate in training activities for ICT; LC₉ = Participate in learning through the internet.

), it was found that careers and average monthly income per person (THB) are mostly significant at a p-value of 0.05. However, gender was found to be insignificant among different types of learning activities.

Ten aspects pertaining to the level of satisfaction with the community environment were also considered, with different values in each municipality.

Table 4. The level of satisfaction with the community environment.

Variables	Total		Thanyaburi Subdistrict Municipality		Bueng Yitho Town Municipality		Rangsit City Municipality		Sanan Rak Town Municipality
	Mean	St.d.	Mean	St.d.	Mean	St.d.	Mean	St.d.	Mean	St.d.
S1	2.50	0.88	2.55	0.82	2.89	1.01	2.38	0.88	2.36	0.79
S2	2.59	0.93	2.94	0.90	2.87	1.06	2.35	0.86	2.31	0.78
S3	2.39	0.90	2.39	0.84	2.42	0.90	2.35	0.92	2.45	0.96
S4	2.19	0.78	2.07	0.52	1.73	0.65	2.32	0.84	2.46	0.89
S5	2.53	0.90	2.63	0.85	2.65	1.02	2.40	0.88	2.54	0.91
S6	2.70	0.97	2.81	0.91	2.73	0.97	2.63	0.99	2.64	1.00
S7	2.68	0.94	2.74	0.89	2.73	0.93	2.65	0.99	2.60	0.91
S8	2.61	0.95	2.73	0.91	2.67	0.90	2.52	0.95	2.57	1.02
S9	2.52	0.88	2.59	0.84	2.62	0.89	2.38	0.87	2.63	0.95
S10	2.57	0.93	2.71	0.92	2.80	1.01	2.42	0.88	2.51	0.94
Total	2.52		2.62		2.61		2.44		2.51

Remark: S₁ = Environment, living, travel, nature of the community; S₂ = Neighbors in your community are friendly and dependable; S₃ = Feeling part of and participating in community development; S₄ = Theft, crime and accident problems; S₅ = Safety of life and property; S₆ = Physical health; S₇ = Mental health; S₈ = Feeling of trust in neighbors; S₉ = A sense of trust in community leaders; S₁₀ = The comfort of living in the community.

demonstrates these aspects comprise of (1) environment, living, travel, and nature of the community (S₁), (2) neighbors in your community are friendly and dependable (S₂), (3) feeling part of and participating in community development (S₃), (4) theft, crime and accident problems (S₄), (5) safety of life and property (S₅), (6) physical health (S₆), (7) mental health (S₇), (8) feeling of trust in neighbors (S₈), (9) a sense of trust in community leaders (S₉), and (10) the comfort of living in the community (S₁₀).

The data showed that most satisfaction came from the health condition of the participants (S₆ = 2.70), followed by the feeling of trust in neighbors (S₈ = 2.61), affirmation of whether neighbors in the community are friendly and dependable (S₂ = 2.59), and the comfort of living in the community (S₁₀ = 2.57), respectively. Thanyaburi Subdistrict municipality showed the highest satisfaction level with a mean of 2.62 (${\overline{S}}_{Thanyaburi Subdistrict Municipality}$), followed by Bueng Yitho Town municipality (${\overline{S}}_{Bueng Yitho Town Municipality}$ = 2.61), Sanan Rak Town municipality (${\overline{S}}_{Sanan Rak Town Municipality}$ = 2.51), and Rangsit City municipality (${\overline{S}}_{Rangsit City Municipality}$ = 2.44), respectively. When participation in various learning activities is considered (see Figure 4), the result showed that more than 80 percent of respondents never attended learning activities.

Furthermore, it was discovered that only about two percent frequently attended learning activities. Most of the learning activities attended by the people were conducted through the internet (LC₉ = 1.44), followed by participation in community learning (LC₅ = 1.41) and involvement in supporting the elderly (LC₃ = 1.40), respectively. The detailed comparison among each municipality can be demonstrated in average value of levels of participation in learning activities ($\overline{LC}$) as seen in Figure 5. It revealed that Sanan Rak Town municipality (${\overline{LC}}_{Sanan Rak Town Municipality}$ = 1.42) has the most average value of activities participation, followed by Thanyaburi Subdistrict municipality (${\overline{LC}}_{Thanyaburi Subdistrict Municipality}$ = 1.40), Bueng Yitho Town municipality (${\overline{LC}}_{Bueng Yitho Town Municipality}$ = 1.28), and Rangsit City municipality (${\overline{LC}}_{Rangsit City Municipality}$ = 1.19), respectively. However, the level of activity participation was generally low.

4.2. Socio-Economic Status and Its Correlation

When the relationship between the levels of socioeconomic characteristics is considered, the range of analysis was classified into three levels, which are low SES, medium SES, and high SES. Each range is determined by the Market Research Association of Thailand (TMRS), which sets standards for socioeconomic status (SES: Standardization of Socio-Economic Status). This standard is applied to standardize the household income identification; divided into eight groups consisting of: A+ (THB >160,000), A (THB 85,001–160,000), B (THB 50,001–85,000), C+ (THB 35,001–50,000), C (THB 24,001–35,000), C− (THB 18,001–24,000), D (THB 7500–18,000), E (THB < 7500). Based on this classification, this study is therefore regrouped into three levels: A for high SES, B to C for medium SES, and D to E for low SES.

Table 5. Socio-economic status and its correlation with participation in learning activities.

Variables		Low SES		Medium SES		High SES		p-Value
Engage in adult learning and education (LC1)	No	53	13.25	290	72.50	5	1.25	0.028
	Yes	1	0.25	51	12.75	0	0.00
Participate in supporting underprivileged groups (LC2)	No	52	13.00	271	67.75	5	1.25	0.015
	Yes	2	0.50	70	17.50	0	0.00
Get involved in supporting the elderly (LC3)	No	52	13.00	268	67.00	5	1.25	0.011
	Yes	2	0.50	73	18.25	0	0.00
Access to learning spaces in the community (LC4)	No	53	13.25	276	69.00	5	1.25	0.007
	Yes	1	0.25	65	16.25	0	0.00
Participate in community learning (LC5)	No	53	13.25	264	66.00	5	1.25	0.002
	Yes	1	0.25	77	19.25	0	0.00
Participate in family learning (LC6)	No	50	12.50	237	59.25	5	1.25	0.003
	Yes	4	1.00	104	26.00	0	0.00
Participate in training activities for the unemployed (LC7)	No	54	13.50	299	74.75	5	1.25	0.018
	Yes	0	0.00	42	10.50	0	0.00
Participate in training activities for ICT (LC8)	No	54	13.50	304	76.00	5	1.25	0.028
	Yes	2	0.50	69	17.25	0	0.00
Participate in learning through the internet (LC9)	No	52	13.00	272	68.00	5	1.25	0.011
	Yes	2	0.50	69	17.25	0	0.00
Total		54	13.50	341	85.25	5	1.25

Remark: Significance level at p < 0.05 and questionnaire is 400 sets; LC₁ = Engage in adult learning and education; LC₂ = Participate in supporting underprivileged groups; LC₃ = Get involved in supporting the elderly; LC₄ = Access to learning spaces in the community; LC₅ = Participate in community learning; LC₆ = Participate in family learning; LC₇ = Participate in training activities for the unemployed; LC₈ = Participate in training activities for ICT; LC₉ = Participate in learning through the internet.

shows the socio-economic status and its correlation with participation in learning activities. The results of the analysis demonstrated that all variables become significant when p-value is less than 0.05. The details are as follows: Engage in adult learning and education (LC₁) (p-value = 0.028), participate in supporting underprivileged groups (LC₂) (p-value = 0.015), get involved in supporting the elderly (LC₃) (p-value = 0.011), access to learning spaces in the community (LC₄) (p-value = 0.007), participate in community learning (LC₅) (p-value = 0.002), participate in family learning (LC₆) (p-value = 0.003), participate in training activities for the unemployed (LC₇) (p-value = 0.018), participate in training activities for ICT (LC₈) (p-value = 0.028), and participate in learning through the internet (LC₉) (p-value = 0.011). When the level of socioeconomic characteristics is considered, it was found that the majority of the sample falls under medium SES, accounting for 85.3 percent, followed by low SES (13.5 percent) and high SES (1.3 percent).

Although the majority of the participants were found to be classified as medium SES, differences in the socioeconomic levels were nevertheless significant for participation in learning-enhancing activities within the area. This implies that the socioeconomic traits for most intra-city activities progressed from low SES to medium SES. However, because of the relatively low sample, it may not be possible to determine the characteristics of high SES.

4.3. Relationship between SES and Perceptions for the Development of Learning Cities (LC)

When the relationship between SES and perceptions for the development of LC is considered, Kruskal–Wallis H (KWH) test was applied to explore the differences of each classification variable and pairwise, to understand the correlation between independent and dependent variables. The result in

Table 6. Relationship between SES and perceptions for the development of Learning Cities (LC).

Variables	Kruskal–Wallis H	Sig.	Monte Carlo Sig. 99% Confidence Interval
			Lower	Upper
Engage in adult learning and education (LC1)	7.524	0.023	0.005	0.045
Participate in supporting underprivileged groups (LC2)	9.571	0.008	0.000	0.027
Get involved in supporting the elderly (LC3)	10.189	0.006	0.000	0.027
Access to learning spaces in the community (LC4)	10.918	0.004	0.000	0.019
Participate in community learning (LC5)	13.760	0.001	0.000	0.011
Participate in family learning (LC6)	14.654	0.001	0.000	0.011
Participate in training activities for the unemployed (LC7)	8.072	0.018	0.006	0.049
Participate in training activities for ICT (LC8)	7.020	0.030	0.015	0.065
Participate in learning through the internet (LC9)	10.136	0.006	0.000	0.023

Remark: Kruskal–Wallis H Test; Significance level at p < 0.05 and questionnaire is 400 sets; LC₁ = Engage in adult learning and education; LC₂ = Participate in supporting underprivileged groups; LC₃ = Get involved in supporting the elderly; LC₄ = Access to learning spaces in the community; LC₅ = Participate in community learning; LC₆ = Participate in family learning; LC₇ = Participate in training activities for the unemployed; LC₈ = Participate in training activities for ICT; LC₉ = Participate in learning through the internet.

showed that all variables become significant when p-value is less than 0.05. The details are as follows: Engage in adult learning and education (LC₁) (KWH = 7.524, p-value = 0.023), participate in supporting underprivileged groups (LC₂) (KWH = 9.571, p-value = 0.008), get involved in supporting the elderly (LC₃) (KWH = 10.189, p-value = 0.006), access to learning spaces in the community (LC₄) (KWH = 10.918, p-value = 0.004), participate in community learning (LC₅) (KWH = 13.760, p-value = 0.001), participate in family learning (LC₆) (KWH = 14.654, p-value = 0.001), participate in training activities for the unemployed (LC₇) (KWH = 8.072, p-value = 0.018), participate in training activities for ICT (LC₈) (KWH = 7.020, p-value = 0.030), participate in learning through the internet (LC₉) (KWH = 10.136, p-value = 0.030). The results clearly showed that different socioeconomic characteristics were associated with different learning-enhancing activities.

5. Discussion

This research attempted to demonstrate that learning city development acknowledges the interconnectedness of environmental, economic, community, cultural, health, and well-being factors as they impinge on learning. This view is supported by UNESCO, which claims that learning cities offer numerous benefits, ranging from promoting individual empowerment and social cohesion to economic development, cultural prosperity, and, perhaps most broadly, sustainable development [23,28]. LC drives inclusion and sustainability by the principle of advanced policies and practices about lifelong learning programs that promote equity, economic development, cohesion, and peace. Many member cities of the UNESCO Global Network of Learning Cities contribute to lifelong learning development and sustainability. For instance, the Danish city of Sønderborg, whose ‘4–17–42′ strategy comprises the city’s four political commitments (environmental, economic, social, and cultural), the 17 SDGs [12]. Given the necessity for urgent intervention to secure a sustainable future, there needs to be a concrete action plan on how sustainable transformation can be enforced at the local level by pursuing a lifelong learning approach [28]. Overall, sustainable development and the Learning City concept are relevant for the everyday life of its citizens to lead life in a sustainable way.

The concept of an LC from the discovery of this research is to think and act beyond the walls of established educational institutions and infrastructures. The results of the analysis above highlighted the importance of people’s social and economic characteristics as they relate to learning activities within the study area. As can be seen in this place-based study situated in Pathum Thani province in Thailand, different socioeconomic characteristics were associated with different learning activities with a significance level at p-value of 0.05. This implies that all stakeholders, including the important local and city authorities, are unanimous in the mobilization of all the ‘players’ involved in the search for learning needs, providing learning opportunities for people of all ages, ensuring the quality of education, and providing training. Furthermore, LC is a procedure for making sure that people are credited for their knowledge, skills, and abilities, no matter where they come from [58].

Several studies demonstrated that socioeconomic status (SES) encompasses not just income but also includes educational attainment, financial security, and subjective perceptions of social status and social class. Moreover, this study has revealed that different levels of socioeconomic status were associated with different opportunities, needs, attitudes, perceptions, etc. For instance, research findings indicated that children from low-SES households and communities develop academic skills slower than children from higher SES groups [59]. However, low SES in childhood is related to poor cognitive development, language, memory, and socioemotional processing as a consequence of poor income and health in adulthood. The school systems in low-SES communities are often under-resourced, and this has negative effects on the students’ academic progress and outcomes [60]. Furthermore, learning opportunities for older adults were found to be more critical in the face of poverty, deprivation, and poor access to healthcare [61]. Diemer and Blustein (2007) found that racial, ethnic, and socioeconomic barriers generally hinder individuals’ vocational development [62]. Career barriers are significantly higher amongst those from poor backgrounds, people of color, women, those who are disabled, and LGBTIQ-identified individuals [63]. Another interesting point identified from this study is the participation in ICT-related learning activities, as online learning plays a very important role nowadays. Online or E-learning is conducive for people to achieve lifelong learning and sustainable development [64,65,66,67]. Learning city is, therefore, an urban development approach toward people-centered and learning-focused cities that provides a collaborative, action-orientated framework that can address the increasingly diverse challenges relating to inequality and sustainable development that most cities face [28,68].

Therefore, further research on the correlation between SES and education is essential so that suburban or peri-urban rapid development across cities can be managed in a better and more sustainable way. This can suggest appropriate development guidelines and respond to people’s needs by planning and providing access to appropriate learning opportunities, which can give benefits that address a wide variety of lifestyles, contexts, and other issues such as job, marginality, formal education, etc. In addition to every action plan, there should be basic conditions with strong political leadership as well as steadfast commitment, participation, and involvement of all stakeholders.

6. Conclusions

The Learning City concept no doubt promotes lifelong learning and lays a strong foundation for sustainable social, economic, and environmental development. The learning zone is not confined to the scope of formal schooling but extends to other activities within the city capable of promoting and providing learning opportunities. However, development guidelines for learning cities should reflect the nature of the population it serves.

This study showed that different socioeconomic characteristics were associated with different learning-enhancing activities. This is reflected in the idea that education systems and institutions should be fair in terms of access, provide proper support upon entry and create a path for equal outcomes from the learning they provide. A fair and equitable system should reflect the nature of the population it serves. This study showed the significance of different social and economic characteristics to LC development, as understanding the needs of a population through these characteristics will enable the proposition of appropriate development strategies that can truly meet all citizens’ needs. Therefore, understanding the LC ecosystem contains vast opportunities, resources, and potential for enabling people to learn and develop themselves in ways that can meet their needs, interests, and ambitions of both residents and commuters. This, in turn, helps to sustain their families, communities, and the city, which is the best approach for all contexts of urban development. In this sense, the learning cities concept encompasses other approaches to sustainable development at the local level by integrating existing concepts of urban development (e.g., healthy cities, child-friendly cities, smart cities, age-friendly cities, resilient cities, etc.). LC interestingly covers all ages of citizens with different needs whose individual data are related to the factors of demographic, socioeconomic, etc.

The concept of an LC from the discovery of this research is to think and act beyond the walls of established educational institutions and infrastructures, which point out the need to promote learning outside the classroom for all groups/social class of people, including family learning, learning at work, learning at all ages, etc. However, there were some limitations in this study occasioned by the inadequacy of socioeconomic status descriptive inputs that could grade the socioeconomic traits with the most commonly used factors (e.g., income, education, and occupation). Moreover, the development of learning cities is based on the extraction of local resource capital to drive development, where each city has different resource capital. This also includes the potential to attract people to participate in learning activities from the outstanding capital of various resources. For example, the local wisdom resource capital of Phayao City Municipality is one of the members of the Global Network of Learning Cities in Thailand. LC has been developed as a platform to create learning, generate income, and sustainable happiness for the Phayao people. It is also presented as concrete evidence for a multicultural resource capital of Phuket Municipality, etc. Thus, further studies can extend this understanding and explore this relationship in other case study areas. The possibility of future research can also be broadened to identify further factors influencing the development of LC, as through a variety of formal, non-formal, and informal learning modalities, the inclusivity of LC development is capable of meeting a wide range of learning needs and demands.