Systems Thinking Skills and the ICT Self-Concept in Preschool Teachers for Sustainable Curriculum Transformation

Abstract

The importance of early learning is even greater today if we are to prepare children for the 21st century by developing several lifelong competencies. With the advent of digitalization, some educators already integrate information communication technology (ICT) into early childhood, whereas others also have concerns about early implementation. However, the introduction of digital competencies from the perspective of systems thinking among preservice and in-service preschool teachers has not yet been demonstrated. The purpose of this study is to identify preschool teachers’ systems thinking skills and their ICT self-concept and to develop a pathway model for developing an ICT self-concept for sustainable and digital preschool education using systems thinking. An empirical research design with advanced statistical analysis and structural equation modeling was used. The sample consists of 172 preschool teachers. The results showed small differences between the preservice and in-service preschool teachers in the areas of “sequence of events” and “communication” in favor of preservice preschool teachers. Regardless of the group of preschool teachers, systems thinking develops self-concept in relation to ICT equally. The pathway diagram shows that “understanding the relationships between patterns” is the strongest predictor of the ICT self-concept, that only perceiving and understanding the inter-relationships of factors influence problem solving, and that understanding variations of different types (random/specific) and causal sequences alone has predictive power for “process and store” in the context of the ICT self-concept. The developed model will help educators, researchers, and curriculum designers to improve preschool education practices and transform the curriculum in a sustainable way.

1. Introduction

Our lives are increasingly intertwined with technology and digital systems, affecting natural, social, economic, and political environments. People need to be able to deal with all technologies while being aware of all influences; therefore, a more holistic understanding of the complexity is needed [1,2]. The need for a more comprehensive understanding of complex systems was recognized as early as the 1950s [3].

The trend in education is to prepare children, pupils, and students across the educational chain for the needs of the 21st century. In addition to course content, according to the Framework for 21st Century Learning [4], other important interdisciplinary areas that affect most of the 21st century must be included, such as civic literacy, global awareness, health literacy, financial, economic, business, entrepreneurial literacy, environmental literacy, etc. Among the necessary 21st century skills, we find critical thinking, problem solving, creativity, collaboration, cross-cultural skills, life and career skills, flexibility, initiative, self-direction, productivity, and leadership as well as information, media, and technology skills [4]. The latter is extremely important in today’s world; according to UNESCO [5], the number of learners who used a massive open online course (MOOC) in 2021 was at least 220 million, and the number of internet users has increased and reached 66% in 2022 (ranging from 26% in low-income countries to 92% in high-income countries), with an even higher percentage of internet users found among young people, etc. In comparison, these people accounted for 16% of all internet users in 2005. In addition, 90% of countries are working on digital skills development, and 54% have already developed digital skills standards [5]. By 2030, at least 80% of the population in the European Union is expected to achieve basic digital skills, and the Global Education Monitoring Report [5] indicated that by 2021, 54% of the population had already achieved that level. In addition, countries need to define digital skills and find ways to increase them among citizens, as digital systems and their complexity are constantly increasing, and the digital literacy of the world population is still low, although it is recognized as very important [5,6].

The complexity and rapid change of the times require the modern child to take a more comprehensive view and use systems thinking [7]. The latter, as one of the most important engineering practices for the inevitable need for understanding and use of technological products and systems, includes digital devices and digitally related systems [1,8]. Without qualified educators and teachers, students cannot obtain and develop such approaches [7,9]. Systems thinking refers to a holistic view of a problem, including the elements, the whole, the influencing factors, and the influenced factors [1]. Systems thinking enables individuals to cope with the complexity of the 21st century. It encourages individuals to analyze situations, look at things from different perspectives, identify elements, look for causes and patterns, etc. With a comprehensive and interconnected perspective, it promotes critical thinking, problem solving, collaboration, creativity, communication, adaptability, and other 21st century skills [10]. The goal of systems thinking is focused on sustainable development and aims to develop competencies for the 21st century. Sustainable development and sustainable education, among others, have been pointed out for several decades, but the world still faces many problems that are interconnected and strongly influence each other [3]. Wolff et al. [11] reported that it is not enough to simply include sustainable development content in the curriculum. They emphasized the proper training of teachers who would be able to translate such a way of working into practice, but at the same time, education for sustainable development in higher education is challenging due to its interdisciplinary nature [11]. The education system plays a key role in providing knowledge and skills, across the vertical, from kindergarten to higher education [1]. Addressing the problems of the 21st century requires solving so-called ill-structured or non-routine problems [12]. In addition to the skills of solving (routine) problems, where typically only one solution is sought, it is necessary to equip children with skills such as creative thinking and critical thinking because through creative training and open-ended thinking in combination with problem-based learning, we develop creative and critical thinking and enable trial and error and thus the acquisition of new ideas in solving complex problems [12,13,14].

In Slovenia, kindergarten teachers and assistants are guided by the key document for working with preschool children, “Kurikulum za vrtce” (Curriculum for Kindergartens) [15], the latest edition of which dates from 1999 and is extremely outdated given the speed of change in today’s world. Over the years, supplements to the curriculum have been issued for work with children with special needs, long-term patients, Roma, in adjusted programs, etc. It is designed to give educators more autonomy in their work, but after several decades, it has become necessary to review it professionally and update it according to the latest knowledge about education and learning in the preschool period in order to educate competent people who can independently face the problems and situations of the 21st century [16].

In 2016, guidelines were issued in Slovenia for the use of ICT in kindergarten to achieve digital literacy as part of the general right of individuals to education. In addition to the need to prepare children for life in the real world, the development of digital technologies has led to the need to train children in the virtual world as well. The guidelines for the use of ICT in kindergarten consist of the principle of equal opportunities and respect for the differences between children and the principle of multiculturalism, the principle of respect for privacy and intimacy, the principle of cooperation with parents, the principle of cooperation with the environment, and the principle of active learning and ensuring the possibility of verbalization and other means of expression [17]. Despite the advantages of using ICT in preschool, which allows for an even greater variety of activities, different representations, etc., there are also concerns that strongly hinder both the usefulness of introducing ICT in kindergarten and articulation for targeted use [18,19,20]. In the guidelines for the use of ICT in kindergartens, the authors define the situations in which ICT can be used appropriately (achieving set goals, safe use, promoting a higher opinion formation of the child, etc.) and emphasize that the use of ICT as a filler in the preschool period is inappropriate and unjustified [17].

As a first step, the Ministry of Education of the Republic of Slovenia, through the Office of Education of the Republic of Slovenia and with the help of EU funds, has started a thorough renovation of the curricula for the years 2022–2026. In parallel, the University of Ljubljana has also started the renovation of the preschool education study program, with a focus on digitalization and sustainable development, which is described in the following section/chapter.

1.1. Study Context

This research forms part of a larger 3-year national research project called “Developing the twenty-first-century skills needed for sustainable development and quality education in the era of rapid technology-enhanced changes in the economic, social, and natural environment”. This study was supported by the Slovenian Research and Innovation Agency (ARIS), grant no. J5-4573 [21]. In parallel with the aforementioned project, the Ministry for Education of the Republic of Slovenia, in 2022, launched a project named “The renewal of education programs through the renewal of key curriculum documents (kindergarten curricula, curricula, and catalogs of knowledge and skills)” [22], which is co-financed by the European Union—NextGeneration EU [23] and by the Republic of Slovenia’s Ministry of Education. The investment is part of the actions of the plan, which is funded by the Recovery and Resilience Facility and organized by the National Education Institute Slovenia, which is the main national research, development, and consultancy institution in the field of preschool, primary, and general secondary education [24]. The project is implemented in accordance with the plan under the development area “Smart, Sustainable, and Inclusive Growth”, which is a component for strengthening competencies, in particular, digital competencies and those required by new professions and the green transition [22]. The project will cover the curricular renewal of the entire education sector, from preschool to higher education. The Ministry of Education has set up working groups of experts to analyze the situation and propose improvements as part of the project. The goal is to create a national education program for the period 2023–2033, which will provide guidelines for a high-quality, sustainable, efficient, and inclusive education system in the Republic of Slovenia, equipping students and teachers with the current competencies that the new age requires, such as digital competencies, entrepreneurial competencies, and competencies for sustainable development and health care mechanisms [25]. Aligned with the project goals, an expert group, which consists of experts from the Faculty of Education at the University of Ljubljana, the University of Maribor, and the University of Primorska (all in Slovenia), has overseen members from the National Education Institute and practitioners from kindergartens and schools in preparation of a document called “Starting points for the reform of the curriculum” [16]. An expert group determined a professional content basis for reform, which is the current valid curriculum for kindergarten from 1999 [15]. Hereafter, Antic et al. [16] emphasized that a quality kindergarten curriculum must follow theoretical insights and innovations based on research findings on toddler/child development and learning, but it must also keep pace with societal developments and changes that affect both the lives of families and children and institutional upbringing and education. Updating the curriculum is, therefore, a professionally based necessity to ensure that it remains in line with scientific and dynamic adaptability to rapid societal change. Moreover, due to the rapid social and technological changes in our country and in the world, such as globalization, digitalization, immigration, intergenerational cooperation, and natural and social crises, which affect the lives of children and families and the functioning of kindergarten when taking into account the fundamental tasks of the kindergarten (which are to help parents to care for their children in a holistic way, improve the quality of life of families and children, and create the conditions for the development of children’s physical and mental abilities), these changes must be recognized and incorporated into an updated and improved curriculum document. Antic et al. [16] gave reasons for curriculum reform, which include (1) system reasons (organization of work, inclusion ratio, compensation role of kindergarten, children with special needs, etc.) and (2) conceptual and content reasons (contemporary theoretical insights into toddler/child development and learning, the short-term and the long-term impact of kindergarten on children’s development and learning, quality in kindergarten, social crisis, inclusion in kindergarten, and readiness to be enrolled in school).

In the everyday practice of preschool education in kindergartens, there are particularly recognized active methods and approaches (e.g., design thinking, project-based learning, experiential learning, systems thinking, etc.) in the work and learning of children, which lead them via different pathways to new knowledge, skills, and attitudes [16]. Especially after the COVID-19 pandemic, the ICT-enhanced approaches gained importance and popularity in preschool education and training. The implementation of ICT and digital tools in preschool education needs special attention, and their targeted use is foreseen as digitally enhanced learning, for which it makes sense to be integrated thoughtfully and professionally into work with children but only when the use of modern technology also adds value to the use of other approaches to promote children’s development and learning, which is also noted in Starting points for the reform of the curriculum [16]. This also requires greater digital literacy and competence among kindergarten professionals and preservice teachers, who can use modern technology at different stages of the educational process, including in the ongoing monitoring of educational work. Moreover, using a systems thinking approach in the preschool education curriculum may be crucial for a student gaining competence for ESD, especially in terms of enhancing their critical thinking and problem-solving skills [26]. Several authors claim that using systems thinking in educational settings (from preschool to higher education) is still in the early stage, and even well-educated adults have insufficient abilities in systems thinking [26,27,28,29], which is also true for the non-educational area [30].

The University of Ljubljana has already begun developing and implementing targeted ICT strategies, methods, and tools, which started in 2022 with the pilot project Educational ecosystem for the acquisition of digital competencies of educators and university students [31]. The project aims to improve the existing support system for the use of ICT to promote the digital knowledge and competencies of educators and university students. Educators and students are empowered through the thoughtful and safe, didactic use of ICT. By introducing smart classroom models, educators and university students are actively involved in the pedagogical process and problem-solving situations, participating in the assessment of digital competencies, etc. [31].

Thus, new and solid insights into the articulation of the development of systems thinking ability in both preservice and in-service teachers, including its predictive value analysis towards technology-enhanced learning, are needed [26].

1.2. Theoretical Background

1.2.1. Digital Competencies

Digital competencies are part of the key competencies for lifelong learning, which represent the necessary competencies of every citizen for a healthy and sustainable life, social inclusion and activity, employability, and personal development [32]. They were first defined in 2006. Digital competencies are not just about knowing how to use a cell phone or computer. They encompass much more, such as the responsible use of digital technology, digital content, and program creation as well as safe use, cybersecurity skills, media, information, data literacy, problem solving, and critical thinking [33]. UNICEF [6] uses the term digital literacy and explains that it refers to the skills, knowledge, and attitudes that enable children to succeed in, protect, and empower the digital world in a culturally, contextually, and age-appropriate way [6]. It defines it as part of the holistic skills of quality education and learning. Different terminology can be found to describe digital competencies, such as digital skills, digital literacy, or even digital citizenship [6]. “UNICEF calls for a holistic approach to digital literacy, in terms of skills … stakeholders … and connection with traditional literacy.” [6] (p. 30). The challenge is to promote holistic and systemic interventions and to recognize support for digital literacy in both formal and informal settings [6]. However, the development of society requires an ever-higher level of competency in individuals, so the definition needs to be updated [33]. Over time, the definition evolved from an appropriate instrumental view to a more comprehensive approach to digital competencies [34]. The Digital Competence Framework for Citizens (DigComp 2.2) [32] document plays an important role in this, providing a common understanding of digital competencies for the purpose of standardization. Digital competence consists of five areas: information and data literacy, communication and collaboration, digital content creation, security, and problem solving. In particular, the last two areas relate to all other competencies but are emphasized in the digital domain to enable the adoption of digital practices and the latest technologies [32]. DigComp 2.2. plays a key role in achieving the set EU goals of having 80% of the population equipped with basic digital skills and 20 million specialists in ICT by 2030 [35]. Digital competencies are also included in Sustainable Development Goal indicator 4.4.1, which addresses the quality of education [36]. The introduction of artificial intelligence, virtual and augmented reality, and robotization at the same time as the increase in disinformation makes new digital competencies necessary. Another important document [33] on key competencies describes eight equally important competencies that contribute to everyone’s life. Digital competencies are treated equally and are as important as literacy, personal, social, and learning competencies as well as mathematical competence and competence in science, technology, engineering, entrepreneurship, etc. [33].

The definition of digital competencies (DigComp 2.2) was updated in 2018 by the Council of the European Union [33]. The updated definition includes responsible use, learning, and collaboration; intellectual property issues; and critical thinking and problem solving as opposed to the first definition, which refers more to the use of digital tools in terms of content creation and communication [33]. Digital competencies include knowledge (facts, principles, theories, etc.), skills (the ability to apply knowledge, divided into cognitive or practical), and attitudes (values, priorities, and aspirations) [32]. The European Commission and the Council of Europe created key competencies with reference frameworks and terminology to support their conceptualization. The competencies are inter-related, and the development of one competence promotes and supports the development of others (media literacy as part of the definition of digital competencies also plays an important role in citizenship competencies, etc.) [32].

Since today’s children will live in a world in which the use of ICT will be inevitable and present to an even greater extent, it is necessary to begin a sensible and critical approach to ICT at a young age. Preschool teachers know the digital world to varying degrees. Consciously or unconsciously, they transfer their own attitudes toward ICT to preschool children. Preschool teachers who refuse to work with ICT may harm the children and deepen the differences between them. The guidelines for the use of ICT [17] suggest a thoughtful use of ICT and thus also the development of digital competences for the benefit of preschool teachers and, consequently, of the children [17]. With the increase in technological progress and the development of ICT, these aspects are also increasingly used in schools, kindergartens, and other educational institutions [37,38,39]. Nevertheless, Ramirez-Rueda et al. [40], in their research, stated that neither parents nor teachers give ICT the same importance as textbooks, and therefore, they believe that ICT will not fully take over the role and replace “traditional” tools [40]. However, the increasing use of ICT for educational purposes has raised questions not only about content but also about methods, e.g., how to use ICT effectively for positive effects in terms of child development [39]. These findings confirm Tezci’s statement that educators, especially preservice teachers, need to understand how to effectively incorporate technology into classrooms and not just support traditional teaching methods [41].

In a systematic review by Herodotou [39], both positive and negative effects of ICT use were found, with the positive ones manifesting in different areas (literacy, math, science skills, improved problem-solving skills, social interaction and growth, etc.). Nikolopoulou [18] also noted positive effects on a child’s learning and development in relation to the use of ICT but, at the same time, pointed out that the non-use or resistance to the use of ICT is related to limited resources, the problem of online safety, and the impact on children’s concentration [18]. Resistance to ICT use may be related to the various constraints of educators that are also perceived by other authors, such as the potential misuse of ICT, passive learning experiences [42], personal beliefs, limited resources and knowledge [43], etc. Mertala [19] found that there are no significant differences between older and younger preschool educators regarding the benefits of ICT use and related concerns, with younger educators considered more digitally literate than their older colleagues [19].

Children are very susceptible to developing an addiction to electronic devices at a young age. Therefore, it is extremely important that parents are aware of this and work with preschool teachers and other professionals in kindergartens. The use of ICT in preschool is highly dependent on the perceptions of parents and educators towards these devices and tools, as Park and Park [44] stated in their research. Regarding the collaboration between teachers and parents, Botturi’s case study [45] noted a kind of tension regarding the responsibility of teaching digital literacy to children. However, after a course on digital and media literacy, the perception of the participating preschool and elementary school teachers changed toward the idea that they share the responsibility for teaching digital skills equally with the children’s parents [45]. The perceptions of educators are also crucial to the level and effectiveness of ICT use in working with children [18,42]. As noted in the Mertala study [19], educators’ perceptions of ICT use in early childhood are shaped by education, care, and socialization. Educators’ beliefs are shaped by cultural context, national policy, early childhood traditions, education and training, and personal experiences, among other factors. This is consistent with the findings of Tezci [41], who found that the use of ICT in the education of preservice preschool teachers is influenced by both internal (knowledge and experience) and external factors (school climate and support) [41]. Several authors have found in their research a positive attitude of preschool educators towards the use of ICT [19,40,43], especially when it comes to diversifying activities in kindergarten. Educators use ICT in visualization, the preparation of materials, watching videos, etc. [38]

Hatzigianni and Kalaitzidis [20] found that only about one-third of the early childhood educators involved in the study felt confident using ICT in groups with children under three years of age, with 51% of the educators personally believing that the youngest children should not use technology. The results also support the findings of Mertala [19]. Despite the majority saying that children are young explorers, they do not feel that the inclusion of touchscreen technologies enhances free play and the fundamental values of early childhood education. At the same time, the authors conclude that educators do not reject the use of ICT but rather need proper training and support in the area [20]. The use of ICT points in favor of preschool teachers attending in-service training/courses on the mentioned topic [38]. It was found that more hands-on training for educators is needed for successful ICT implementation [43,45,46]. On the other hand, Masoumi’s research [47] showed a discrepancy between educators and their instructors about ICT. The educators believed that they did not have enough opportunities during their training to develop digital competencies for the later use of ICT in practice [47]. In a systematic review, Johar et al. [48] noted that in the field of online learning platforms, not all five domains of student engagement are used simultaneously (cognitive, collaborative, behavioral, social, and emotional). The study [48] addressed the importance of using learning analytics adapted to a specific online learning tool (e.g., MOOC) to provide important information for student improvement. Learning analytics is a process that is used for the purpose of prediction, analysis, and feedback to improve online learning and sustainable education [48]. Only through professionally trained and aware educators can the trend of children being mere consumers of new technologies be countered [17].

In providing more and more digital technology in education, it is important to see the larger context [49]. The suitability for qualitatively assessing the impact of modern instructional technologies has been demonstrated using the so-called activity theory [50]. Activity theory has been widely applied in assessing the impact of educational innovations. It requires a holistic approach because certain changes can disrupt activities and affect other activities. The basic element represents an activity or a system of activities that consists of different elements (subjects, instruments, objects, community, rules, and division of labor) and their interactions to achieve the desired outcomes. All elements that are part of the activity are interconnected, and changes in individual aspects affect all other components [49,50,51]. Similarly, problems and systems issues are addressed with systems thinking.

1.2.2. Systems Thinking

In 2015, the 2030 Agenda for Sustainable Development [52] was adopted, which includes as its foundation 17 Sustainable Development Goals (SDGs) that humanity must pursue to adopt a sustainable path. The goals cover several areas: climate change, poverty and hunger, mutual respect and a reduction in inequality, responsible consumption, clean energy production, quality education, etc. They are based on systemic solutions to the problems facing our society today. UNESCO [2] has identified Education for Sustainable Development (ESD) as the main instrument to achieve these goals [2]. With the goal of achieving sustainable development and a sustainable society, UNESCO defines the key competencies for ESD as the following: systems thinking competency, anticipatory competency, normative competency, strategic competency, collaboration competency, critical thinking competency, self-awareness competency, and integrated problem-solving competency. Competencies refer to the ability to recognize and understand inter-relationships, see multiple possible outcomes, evaluate the consequences of actions, think about norms and values as the basis for an individual and his or her actions, implement innovative actions, learn from others, resolve conflicts, question norms, take one’s own point of view, to reflect, to use different ways to solve problems with a focus on sustainable solutions, etc. [2].

UNESCO [2], therefore, defines systems thinking as a key competency that includes understanding structures and inter-relationships as well as analyzing systems and dealing with uncertainty. The definition of systems thinking is also found in the Standards for Technological and Engineering Literacy, where it is defined as a technique. These standards refer to looking at a problem as a whole and considering all the social and technological variables that affect the system or how the system affects them [1]. In their systematic review, Camelia et al. [53] noted that different authors define systems thinking in different ways, e.g., science, method, approach, skill, and conceptual framework, and proposed a view of systems thinking “as a mental construct for thinking and learning about systems” [53] (p. 116). Arnold and Wade [54] also addressed the problem of the abundance of different systems thinking definitions (such as art and science, discipline, skill, etc.) and the problem that some authors do not even give a clear definition or avoid explicitly saying what systems thinking is. To this end, they put forward the so-called systems test for defining systems thinking as a necessary but not sufficient condition for an adequate definition. In order to pass the systems test successfully, a definition must include information about the function or a clear description of the purpose of systems thinking, the elements that appear as features of that thinking, and their inter-relationships. Arnold and Wade [54] gave their own definition that passes the systems test and defines systems thinking as a set of synergistic analytical skills, the use of which improves one’s recognition, understanding of systems, prediction of behavior, and planning for change [54]. This definition is also used in other research [55], whereas there are also studies [56,57] in which the authors used definitions of systems thinking incoherently, e.g., as an ability, way of thinking, approach, skills, etc. Grohs et al. [58] emphasized that the development of different academic domains makes it difficult to accept a single definition, which is a point noted by many authors [7,10,53,54].

In their work, Cabrera and Cabrera [7] defined four waves of systems thinking, beginning with the first in the 1950s, when researchers were primarily concerned with “hard systems” because systems were understood primarily as physical concepts. In the second wave, the focus shifted to “soft systems”, with a shift to social metaphors. In the third wave, which began around 1990, critical systems thinking came to the fore. The pursuit of pluralism has led to a blurring of what systems thinking is. Because of the breadth of different methods and concepts, it became clear that a coherent, clear, and simple definition was increasingly difficult to find. Along with this, Cabrera and Cabrera [7] came to what they call the fourth wave, which promotes pluralism and supports the idea of unification. The authors see this unification as the potential of the new DSRP theory (distinctions, systems, relationships, and perspectives). According to Cabrera and Cabrera [7], there are four essential capabilities of systems thinking: making distinctions, organizing systems, recognizing relationships, and taking multiple perspectives [7].

Systems thinking was recognized as useful and necessary for sustainability education. In the area of ecological sustainability, Palmberg et al. [56] found that preservice teachers did not exhibit excessive systems thinking, suggesting that systems thinking has not yet been effectively integrated into education. It is also interesting to note that older preservice teachers scored statistically significantly higher for systems thinking than younger preservice teachers, which might also imply that systems thinking may be the result of life experiences rather than education, as noted by the authors [56]. This is contrary to the findings of several authors [27,28,59,60] who developed systems thinking through interventions/courses in their studies. Karaarslan, Semiz, and Teksöz [28] developed systems thinking in preservice science teachers through outdoor ESD courses. Systems thinking was measured using semi-structured interviews, and they found that preservice science teachers reached the highest level of their systems thinking skills in all areas after implementing the last module, except for the aspect related to adapting the perspective of systems thinking to personal life [28]. Hiller Connell et al. [27] also conducted research on the development of systems thinking through the implementation of two interventions. Students’ systems thinking scores improved after the first, single intervention, but progress was even greater after the second, longer intervention. Similarly, Ateskan and Lane [59], through systems thinking workshops, achieved an increase in the level of agreement regarding the importance of sustainability and the application of systems thinking of in-service teachers, which was already relatively high in the self-reported pretest [59]. Interestingly, Green et al. [55] determined the effects of simulation and systems thinking on sustainability education, showing that simulation had a positive effect on sustainability education, whereas systems thinking showed no statistically significant effect. The authors sought reasons for the result in terms of possible cognitive overload, as the additional complex material may have overwhelmed learners in such a short time (only one learning unit) [55]. In their study, Ben-Zvi-Assaraf and Orion [60] examined individual differences among students, the impact on their learning, and their perceptions of learning in relation to systems thinking. They found that in addition to students’ conceptual knowledge and awareness, learning and change over time are influenced by learning patterns that prove to be quite resilient and important in the context of developing systems thinking skills [60].

1.3. Goals and Research Questions of the Study

Both systems thinking and self-concept related to ICT play important roles for preschool teachers. Systems thinking in the context of work in preschool education, when topics are treated holistically, it is necessary to see the whole picture, individual parts, the functioning of relationships, etc. [15] Self-concept related to ICT, on the other hand, is important in the age of digitalization, as self-perception influences feelings and behavior towards digital technologies, which in turn affects their (mis)use in preparing content, learning activities, and other work [61]. Since systems thinking develops throughout life, study, and career, it is useful to examine the differences in its levels and its influence on self-concept related to ICT. Moreover, both self-concept related to ICT and systems thinking are multidimensional, as the literature suggests [61,62]. Systems thinking is reflected in a variety of skills (understanding processes, patterns of relationships, recognizing factors and possible causes, etc.), so it is useful to examine which of these elements of systems thinking can predict which of the dimensions of self-concept are related to ICT (communication, safe use, problem solving, etc.) [61,62].

The aim of this study is two-fold. Firstly, it attempts to identify and describe preschool teachers’ system thinking skills and self-concept related to ICT. Secondly, a predictive pathway model was developed, which could be more easily handled by lecturers, curriculum designers, and experts in the field to recognize and use prediction rules with influential pathways to develop ICT self-concept for sustainable, digitally related preschool education using a systems thinking approach.

The following hypotheses were raised in this study (H):

• H1a: There are significant differences between pre-service and in-service preschool teachers in their perceptions of systems thinking in at least one of factors;
• H1b: There are significant differences in at least one of the self-concept dimensions related to ICT dimensions of preservice preschool teachers compared to in-service preschool teachers;
• H2: The effect of different levels of systems thinking on ICT self-concept is moderated by the type of enrollment of preschool teachers;
• H3: The dimensions of systems thinking have a varying influence on the dimensions of self-concept related to ICT, with the greatest influence on the dimension of problem solving.

Specifically, we addressed the following three research questions (RQs):

• RQ1: How do preschool preservice and in-service teachers perceive the system thinking skills and self-concept related to ICT?
• RQ2: How do different levels of system thinking skills affect ICT self-concept moderated by the type of enrollment of preschool teachers?
• RQ3: Which dimensions of systems thinking are statistically significant to ICT self-concept dimensions in a pathway model, and what is the effect size of those relationships?

2. Methods

For the purpose of this study, a cross-sectional empirical research design was used with a quantitative approach. Because we were interested in how systems thinking, as a promising approach to teacher education, including preschool education [1,26], might affect self-concept to ICT, we collected data from both preservice and in-service teachers involved in preschool education. Representing the relationships between each part of the system and the interactions with the social and natural environments in which the technologies function, systems thinking is used in many aspects of daily life and may be of particular interest to technology and engineering practice in teacher education. The present study was designed towards the goals of the project Developing the twenty-first-century skills needed for sustainable development and quality education in the era of rapid technology-enhanced changes in the economic, social, and natural environment, granted by the Slovenian Research and Innovation Agency. A holistic role of systems-oriented thinking can emphasize several variables and thus relate social and technological features well [1]. The implications of this study will contribute to the following project objectives: (1) determining the factors for the quality of future teachers’ education in achieving the goals of technology-driven sustainable development and 21st century skills; (2) developing, implementing, and evaluating learning models based on multi-criteria decision-making models for pre-service teachers; and (3) the development, implementation, and evaluation of transfer learning models for student skill development. This study was conducted according to the code of ethics for researchers at the University of Ljubljana [63] and approved by the Institutional Review Board at the Faculty of Education, University of Ljubljana.

2.1. Sample and Procedures

This study is based on a voluntary national survey of preservice and in-service teachers in Slovenia. The participants for this study (n = 172) were recruited via an online classroom Moodle (preservice teachers) and via the Faculty of Education at the University of Ljubljana co-operation network (in-service teachers). The preservice teachers’ sample consists of 83 students from the University of Ljubljana (the largest university), which educates and trains nearly half of all preschool students in the country [64]. The students were of average age of 21.6 years (SD = 0.8) and were surveyed in the academic years 2021/22 and 2022/23 after the course on design and technology education was finished. The total possible sample of students engaged in the study of design and technology was 178 students. Thus, a recruited sample of 83 students (46.6%) was a satisfactory response rate collected by online means [65]. When surveying the in-service preschool teachers, the response rate was low, with only 89 teachers effectively finishing the survey out of 321 who entered the online survey (27.7%). The average age of in-service teachers was 37.3 years (SD = 8.3), and they were surveyed from January 2023 to June 2023. The online survey was delivered to all teachers who gained access through the 1KA’s portal at https://1ka.arnes.si, which complies with all GDPR requirements and is recommended by the University of Ljubljana. All involved participants were informed about the study on the survey front page before proceeding with the questionnaires; there were also clear instructions on how to fill out both questionnaires. All concerns and questions raised by the participants were addressed to the author’s email address and were promptly resolved before they took part. Informed consent was given by the participants for the collection of personal information, e.g., gender, age, and type of enrolment. All consent forms were collected and are archived in the online portal 1KA. As this was a voluntary activity, the participants were free to withdraw from the study at any time and were not stimulated by any means to provide responses. Since the preschool education area is predominantly a female domain, the sample size consists of 94.8% females and only 5.2% males.

The quality of the sample size was reviewed according to the main statistics and tests used in the study.

The G*Power software v.3.1 (Heinrich Heine Universität, Düsseldorf, Germany) is easy to use for calculating sample size and power for various statistical methods [66]. The power (1-β) was set at 0.90, and α = 0.05 indicated that a total sample of 171 participants would be needed to detect moderate effects (F² = 0.25) for the F-test using the factorial ANOVA with 1 degree of freedom and four groups.

Since the sample size in confirmatory factor analysis (CFA) depends on a number of features, e.g., study design, the number of relationships among the indicators, indicator reliability, etc., a determination of sample size is approximated by using power analysis [67]. For statistical power and precision, structural equation modeling (SEM) needs large samples, where 150–200 samples represents the minimum acceptable to obtain reasonable results [67,68,69,70]. We used Soper’s [71] online sample size calculator (https://www.analyticscalculators.com/calculator.aspx?id=89 (accessed on 29 July 2023)), with a desired statistical power of 0.80 [72] and a probability of detecting an existing specified effect of 90% (minimum probability is 0.50 [72]); for the model with 9 latent variables and 38 observed variables, the recommended minimum sample size is 165.

2.2. Measures

2.2.1. Systems Thinking Skills

Over the last decade, many researchers and teams have been engaged in developing measures for systems thinking for use in specific contexts [59,73,74,75]. However, to date, no specific measures of systems thinking have been developed to assess the systems thinking of preschool teachers for use in rapidly changing and developing digital-related preschool curricula as a part of quality improvement initiatives and innovation learning.

For the purpose of the present study, we adapted a measure of systems thinking, the systems thinking scale (STS), developed by Dolansky et al. [74], which was aimed for use in healthcare systems. The original STS consists of 20 items, with a five-point Likert assessment scale, ranging from 0—never to 4—most of the time. Moreover, the original STS supports a uni-dimensional model representing a one-factor solution despite seven conceptual domains identified in systems thinking: (1) sequence of events, (2) causal sequence, (3) multiple causations possible, (4) variation of different types, (5) feedback, (6) inter-relationships of factors, and (7) patterns of relationships [62]. In our study, we retained all 20 items that were transformed for the Slovene language and preschool education context, whereas for the assessment, a six-point Likert scale was used, ranging from 1—never to 6—always, which obligates the respondents to choose the positive or negative end of the scale, resulting in better data quality [76]. Thus, the neutral middle category that occurred in the original scale was omitted to reduce the response tendency towards the middle.

In order to identify the hypothetical constructs of the adapted STS, an exploratory factor analysis (EFA) was used. The sampling adequacy was assessed by examining the Kaiser–Meyer–Olkin (KMO), which was 0.89, while Bartlett’s test of sphericity was significant (p = 0.00 < 0.05). A KMO correlation above 0.6–0.7 can be considered adequate for analyzing the EFA output [68]. The commonalities of all the items were above 0.6 (all fell between 0.6 and 0.8). The results of the EFA suggested that the one-factor solution explained 37.6% of the variance, whereas when using Kaiser’s criterion [77] to examine eigenvalues and consider those that are greater than 1, a five-factor solution can be favorited. Velicer’s minimum average partial (MAP) test was also performed [78], but this pointed toward a one-factor solution. Unfortunately, no method was found to be correct in all situations [79,80,81]; thus, we carefully judged each plausible solution in the context of the hypothesized predictive power of systems thinking by using a different factor retention method and evaluated them by selecting the smallest and largest number of factors.

In addition to this, we conducted EFA using Oblimin with the Kaiser normalization rotation method and enforcing a five-factor structure in order to compare the fit of the theoretical dimensionality. A principal component analysis (PCA) extraction method was used, which extracted five factors, wherein the items explain 68.3% of the variance, which is above the threshold of 0.5–0.6 suggested by Hair et al. [82]. Items with loadings >0.5 (loadings smaller than 0.5 were excluded, as suggested by Field [83]) loaded on five factors and the number of items with an item example are shown in

Table 1. Systems thinking scale factor structure.

Factor	Number of Items	Item Example
Factor 1	5	I propose solutions that affect the work/learning environment, not specific individuals.
Factor 2	3	I think that systems are constantly changing.
Factor 3	4	I think more than one or two people are needed to have success.
Factor 4	4	I seek everyone’s view of the situation.
Factor 5	4	I think of the problem at hand as a series of connected issues.

.

2.2.2. Self-Concept Related to ICT

In recent years, several scales to assess self-concept (SC) related to ICT have been developed [61]. The scales have different features that might reduce the usability and validity of the measure, such as being tool-specific, measuring uni-dimensional self-concept, integrating self-concept with other constructs (motivation, self-efficacy, etc.), and conceptualizing for a specific target group [61]. Thus, Schaufel et al. [61] developed a new scale that overcame the aforementioned shortcoming and captured the entire structure of self-concept; this also has the ability to be used across ages and in different life contexts. An original ICT-SC has been validated in English and German, and this points to the possibility of its valid accommodation in some other language, too. The term self-concept related to ICT refers to the evaluation of one’s own perception of ICT competence in general and in specific competence areas. These are related to well-being, motivation, performance, etc. Self-concept related to ICT also has a significant influence on people’s feelings and behavior when confronted with ICT [61].

For the purpose of the present study, we used a 25-item ICT-SC scale that accommodated the Slovene language and preschool education context and also considered both groups of preservice and in-service teachers having different levels of work experience and age. For the assessment, we also chose a six-point Likert scale, ranging from 1—strongly disagree to 6—strongly agree. A 25-item ICT-SC with dimensions, the number of items, and an example of the item are shown in

Table 2. A structure of self-concept related to ICT.

Component	Number of Items	Item Example
General ICT-SC	5	I can very easily operate digital systems.
Communicate	4	It is easy for me to spread information through digital systems.
Process and store	4	I am very good at assessing the relevance of digital data, information, and content.
Generate content	4	I can easily create digital data, information, and content on my own.
Safe application	4	It is easy for me to handle digital systems responsibly.
Solve problems	4	I quickly learn to solve content problems with the help of digital systems.

.

2.3. Data Analysis

Data analysis was performed using different software carefully chosen to fit the phenomena best.

The descriptive statistics, normality tests, EFA, and 2 × 2 factorial analysis of variance were performed using IBM SPSS Statistics software (version 25), whereas structural equation modeling (SEM) was conducted using IBM Amos (version 24). The convergent and discriminant validity of the constructs was conducted using the ADANCO 2.3 software (https://www.composite-modeling.com/ (accessed on 31 July 2023)), and for reliability estimation, McDonald’s omega was calculated using Hayes’ omega macro for SPSS, downloaded from www.afhayes.com (accessed on 12 July 2023). For empirical research (as is our case), McDonald’s omega is favored since several assumptions can be violated (equal factor loadings, uncorrelated errors, uni-dimensionality, etc.), and omega is a better alternative than Cronbach’s alpha [84,85].

For the effect size, different measures were used. As the measure of effect size needed to take into account the between-group effect, eta squared (η²) was used with the following interpretation size: from 0.01 to 0.05 = a small effect, 0.06 to 0.14 = medium effect, and 0.14 and higher = large effect [86]. In the pathway model, a measure of effect size, Cohen’s f², was used. Cohen categorized effect size as small (≥0.02), medium (≥0.15), or large (≥0.35) [87].

3. Results

3.1. Preliminary Analysis of Validation and Reliability Evidence

3.1.1. Common Method Bias

The present study obtained data from the same source, meaning that both endogenous and exogenous variables were captured by the same response method. Thus, the possibility and source for common method bias were present, which may affect the reliability of the study items, the validity of the results, and the parameter estimates of the hypothesized relationships among constructs [88]. In order to prevent method biases, we implemented some procedural controls in the survey design (giving clear instructions, ensuring anonymity of responses, avoiding complex and ambiguous items, and keeping surveys concise) and used temporal separation when collecting data from the same source. As a statistical control, we performed a widely used Harman’s single-factor test by using EFA. The result indicated that a single-factor solution accounted for less than 50% (39.2%). This shows that the dataset was not contaminated by common method bias [89,90]. In addition to Harman’s test, we conducted a full collinearity test, where the occurrence of all variance inflation factors (VIFs) at lower than 3.3 indicated that the model could be considered free of common method bias [89]. In this study, all latent factors had a VIF value of less than 3.3 (3.1 and less).

3.1.2. Convergent and Discriminant Validity

Both of the questionnaires that we adapted and used for the purpose of this study (in a new context and for target samples) were validated to ensure that they measure the topic that they aim to measure and that they do this in a reliable way.

Systems thinking scale. The convergent validity of the constructs is indicated by the significant factor loadings of each of the measures on the appropriate trait or stage [91], and the final structure of the model of systems thinking skills demonstrated convergent validity for all measures. As is shown in

Table 3. Reliability of McDonald’s ω, composite reliability (CR), the square root of the average variance extracted (AVE) (in bold), and the correlations among the systems thinking constructs (off-diagonal).

Latent Constructs	ω	CR	AVE	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5
Factor 1	0.81	0.83	0.57	0.76
Factor 2	0.74	0.75	0.65	0.61	0.80
Factor 3	0.72	0.81	0.55	0.55	0.54	0.74
Factor 4	0.76	0.84	0.56	0.58	0.47	0.55	0.75
Factor 5	0.78	0.83	0.58	0.61	0.59	0.51	0.62	0.77

, all five measures were significantly loaded onto the contemplation trait and are reliable enough, whereas McDonald’s ω and the composite reliability (CR) of the constructs were >0.70 [92,93].

As is shown in Table 3, all AVE values are above the threshold of 0.5, whereas the square root of AVE is larger than 0.7 (bold diagonal), which is the threshold suggested by Hair et al. [92]. Moreover, the interconstruct correlation values (off-diagonal) range from 0.47 to 0.62, and this indicates that the measures have medium to large convergence, according to the strength of correlation (represented as large ≥0.5, medium 0.3–0.5, and small 0.1 to 0.29, as suggested Carlson and Herdman) [94]. Thus, our results suggest convergent validity for the adapted constructs, and high convergent validity suggests the retainment of all dimensions of systems thinking.

Next, we also examined discriminant validity by using the heterotrait–monotrait (HTMT) approach proposed by Hensler et al. [95] and Shaffer et al. [96].

Table 4. Heterotrait–monotrait ratio of correlations (HTMT) results for the systems thinking scale.

Latent Constructs	Factor 1	Factor 2	Factor 3	Factor 4
Factor 1
Factor 2	0.78
Factor 3	0.70	0.71
Factor 4	0.74	0.61	0.73
Factor 5	0.76	0.74	0.68	0.80

shows that the HTMT ratio of the correlations is less than the threshold of 0.85 [96].

Discriminant validity was also evaluated against the Fornell and Larcker criterion [97] as a control that is commonly employed [98,99]. This criterion suggests that the AVE (see Table 3) is greater than the shared variance. When examining the results of the validation generated by ADANCO software v. 2.3 [100], it was found that all shared correlation values are markedly lower than the AVE of each factor.

Our results suggest that all variables of systems thinking demonstrate evidence of discriminant validity.

Self-concept related to ICT. As a part of the present study, we also validated the adapted ICT-SC scale, although it has been validated in some other studies in different language versions and heterogeneous samples. The convergent validity of the ICT-SC scales was examined through analyses in AVE, factor loadings, and inter-factor correlations (

Table 5. Reliability of McDonald’s ω, composite reliability (CR), the square root of the average variance extracted (AVE) (in bold), and the correlations among the ICT-SC constructs (off-diagonal).

Latent Constructs	ω	CR	AVE	General ICT-SC	Communicate	Process and Store	Generate Content	Safe Application	Solve Problems
General ICT-SC	0.95	0.96	0.86	0.93
Communicate	0.94	0.95	0.85	0.87	0.92
Process and store	0.92	0.95	0.83	0.83	0.84	0.91
Generate content	0.93	0.95	0.84	0.78	0.76	0.83	0.91
Safe application	0.89	0.91	0.74	0.72	0.69	0.69	0.77	0.86
Solve problems	0.92	0.94	0.82	0.71	0.68	0.66	0.81	0.75	0.90

).

The AVE ranged between 0.74 and 0.86, thus exceeding the 0.5 threshold, while the square root of the AVE values is also higher than the threshold of 0.7, supporting the convergent validity of their latent constructs. All interconstruct correlations are higher than 0.5, which suggests high convergence [94]. All the constructs of ICT-SC show strong evidence of reliability, whereas both the ω and CR values are higher than the threshold of 0.7 (0.89–0.96), which denotes excellent reliability.

Furthermore, our results suggest discriminant validity (

Table 6. Heterotrait–monotrait ratio of correlations (HTMT) results for the ICT-SC scale.

Latent Constructs	General ICT-SC	Communicate	Process and Store	Generate Content	Safe Application
General ICT-SC
Communicate	0.90
Process and store	0.87	0.90
Generate content	0.82	0.81	0.88
Safe application	0.78	0.76	0.77	0.83
Solve problems	0.75	0.73	0.71	0.86	0.82

). The HTMT criterion was used as the first criterion to evaluate discriminant validity. The HTMT ratio of correlations was compared with a more liberal threshold value of 0.90 [101], and all values met this criterion. Moreover, Henseler et al. [95] stated that even when the interconstruct correlations were as high as the 0.95 HTMT given, it failed to detect discriminant validity violations.

As the second criterion for assessing discriminant validity, the Fornell and Larcker decision rule was used; discriminant validity holds for two scales if the AVEs for both are higher than the squared factor correlation between the scales (

Table 7. Fornell–Larcker criterion results for the ICT-SC scale with AVE in the diagonal.

Latent Constructs	General ICT-SC	Communicate	Process and Store	Generate Content	Safe Application	Solve Problems
General ICT-SC	0.86
Communicate	0.76	0.85
Process and store	0.69	0.71	0.83
Generate content	0.61	0.58	0.69	0.84
Safe application	0.52	0.48	0.48	0.59	0.74
Solve problems	0.51	0.47	0.44	0.65	0.56	0.82

). This criterion did not reveal any violations of the discriminant validity of the constructs. No concerns are raised.

All variables demonstrate evidence of discriminant validity. The correlations are low enough for the factors to be regarded as distinct constructs.

3.2. Descriptive and Inferential Statistics

Descriptive statistics include the systems thinking ability and self-concept related to ICT, as reported for preservice and in-service teachers, and in total, inferential statistics was used to compare the mean scores and express them in terms of statistical significance with effect size.

3.2.1. Systems Thinking in Preservice and In-Service Preschool Teachers

Table 8. Preschool preservice and in-service teachers’ self-reported average scores expressed as the mean (M) and standard deviation (SD) across the subscales of systems thinking, along with a measure of skewness (S) and kurtosis (K) (n = 172).

Latent Construct	Preservice Teachers				In-Service Teachers				Total Sample
	M	SD	S	K	M	SD	S	K	M	SD	S	K
Factor 1	4.72	0.76	−0.21	−0.68	4.68	0.65	0.06	−0.86	4.70	0.70	−0.09	−0.76
Factor 2	4.60	0.82	−0.32	−0.26	4.35	0.76	−0.25	−0.34	4.47	0.79	−0.23	−0.33
Factor 3	4.30	0.78	−0.24	−0.68	4.54	0.74	−0.11	−0.33	4.43	0.77	−0.18	−0.47
Factor 4	4.70	0.68	−0.46	−0.19	4.78	0.77	−0.23	−0.77	4.74	0.72	−0.30	−0.53
Factor 5	4.26	0.74	0.05	−0.51	4.21	0.67	0.40	−0.09	4.23	0.70	0.21	−0.35
Total	90.45	12.48	−0.18	−0.81	90.62	11.14	0.31	−0.36	90.54	11.71	0.03	−0.61

shows the descriptive statistics for both the preservice and in-service teachers involved in the study. Together with the measure of the central tendency mean (M) and standard deviation (SD), measures of skewness (S) and kurtosis (K) are also reported. For the total score, the questionnaire assessment items are summed. Thus, a total score of systems thinking can range from 20 to 120 when no reverse-coded items exist.

Normality was checked using the Shapiro–Wilk test, which showed that all constructs follow a normal distribution (p > 0.05). Based on this outcome, parametric tests were used to compare the groups and reveal whether differences occurred. In order to identify differences among the groups of teachers regarding their systems thinking ability and its constructs, a MANCOVA test was used. The type of teachers (preservice or in-service) was used as an independent variable, whereas age was used as the control variable. The main effects of the groups were compared with Bonferroni corrections for confidence interval adjustment. First, we tested the interaction effect of age and group, which was found to be nonsignificant (p = 0.36 > 0.05) when using Wilk’s lambda criterion, and Levene’s test of equality of error variances was also nonsignificant for all constructs of systems thinking and the total score (p > 0.05).

The test of the between-subjects effects revealed differences for Factor 2 (p = 0.008 < 0.05), where an effect size of a partial η² = 0.05 is regarded as a small effect. The group of preservice teachers slightly outperformed the group of in-service teachers. When comparing the total score to the existing mean scores of healthcare professionals measured with an original STS [74], our scores are comparable with quality improvement specialists (only a 2.5% difference), whereas for other professionals in the field and medical and public health students, the difference can be estimated to be less than 10% [62].

3.2.2. Self-Concept Related to ICT in Preservice and In-Service Preschool Teachers

Self-concept related to ICT has increased in importance, especially due to the rapid digitalization of all areas, and education is not exempt. Self-concept may influence affective, cognitive, and behavioral outcomes in different areas of life [61]; thus, capturing and researching the ICT-SC in preschool teachers might also encourage research in other disciplines and contexts.

Table 9. Preschool preservice and in-service teachers’ self-reported average scores expressed as the mean (M) and standard deviation (SD) across the subscales of ICT-SC, along with a measure of skewness (S) and kurtosis (K) (n = 172).

Latent Construct	Preservice Teachers				In-Service Teachers				Total Sample
	M	SD	S	K	M	SD	S	K	M	SD	S	K
General ICT-SC	4.15	1.08	−0.10	−0.87	3.88	1.03	0.15	−0.36	4.01	1.06	−0.09	−0.76
Communicate	4.54	1.08	−0.43	−0.62	3.95	1.00	0.21	−0.68	4.23	1.08	−0.07	−0.88
Process and store	4.08	1.07	0.02	−0.78	3.81	1.11	−0.14	−0.58	3.94	1.09	−0.08	−0.62
Generate content	3.62	1.14	0.31	−0.28	3.40	1.02	0.12	0.24	3.50	1.08	0.25	−0.02
Safe application	3.73	1.06	0.13	−0.64	3.45	0.98	0.52	−0.61	3.59	1.02	0.33	−0.69
Solve problems	3.60	1.11	0.36	−0.37	3.14	0.99	0.81	0.18	3.36	1.07	0.58	−0.21
Total ICT-SC	98.98	24.42	0.11	−0.62	90.52	23.13	0.35	−0.22	94.62	24.01	0.24	−0.49

shows the descriptive statistics for both the preservice and in-service teachers involved in the study. Together with the measure of the central tendency of the mean (M) and standard deviation (SD), measures of skewness (S) and kurtosis (K) are also reported. For the total score, the questionnaire assessment items are summed. Thus, the total score of ICT-SC can range from 25 to 150 when no reverse-coded items exist.

The Shapiro–Wilks test for normality was used to detect all departures from normality. All the p-values were >0.05, which indicates that the dataset was normally distributed. Next, we tested whether statistically significant differences in the ICT-SC between preservice and in-service teachers occurred, and for this purpose, we used MANCOVA with the variable of age as a covariate.

When we conduct a MANCOVA, we always first test the hypothesis regarding the interaction effect. If the null hypothesis of no interaction is rejected, we do not interpret the results of the hypotheses involving the main effects. In our case, the interaction effect of group and age was not significant (p > 0.05); thus, we examined the main effects. MANCOVA yielded a main effect only for a subscale of communicate: F (1170) = 5.51, p = 0.02 < 0.05. An effect size is regarded as small if η² = 0.03.

3.3. Relationship between Systems Thinking, Self-Concept Related to ICT, and Engagement in Preschool Education

When we examined how different levels of systems thinking affect the self-concept of preschool teachers who are in education or in service, we predicted that the type of enrollment would moderate the effects of systems thinking in ICT-SC.

A standard median split was used on the continuous variable of the systems thinking total score, and we turned it into a dichotomous variable. The cases were put into a low- and high-level systems thinking group. The visualization of ICT-SC reported those lower vs. higher in system thinking, with teachers’ enrollment type as a condition, and this is shown in Figure 1.

As shown in Figure 1, those higher on systems thinking reported similar levels of ICT-SC across enrollment types, with a modest trend toward greater ICT-SC reported by preservice teachers. For those lower in systems thinking, the trends look similar.

After reviewing the mean trends, a 2 × 2 factorial ANOVA was used to understand the effects of two independent variables (systems thinking and type of enrollment) on a single dependent variable (ICT-SC) (

Table 10. Self-concept related to ICT by systems thinking (lower vs. higher) and different enrolment types (n = 172).

Source	Type III Sum of Squares	df	Mean Square	F	Sig. p	Partial η2
Corrected model	22,526.97 a	3	7508.99	16.47	0.000	0.23
Intercept	1,527,203.12	1	1,527,203.12	3350.44	0.000	0.95
Group	2701.82	1	2701.82	5.92	0.016	0.04
Systems thinking	19,425.18	1	19,425.18	42.61	0.000	0.22
Group x systems thinking	0.04	1	0.04	0.00	0.993	0.00
Error	76,577.92	168	455.82
Total	1,638,701.00	172

^a Adjusted R² = 0.26.

).

The main effect of enrolment type yielded an F ratio of F (1, 168) = 5.92, p = 0.016 < 0.05, indicating that the mean change in score was significantly greater for preservice teachers (M = 98.98, SD = 24.42) than for in-service teachers (M = 90.52, SD = 23.13). The main effect of systems thinking yielded an F ratio of F (1, 168) = 42.61, p < 0.001, indicating that the mean change in score was significantly higher in high-level systems thinking (M = 104.97, SD = 23.60) than in low-level systems thinking (M = 83.49, SD = 19.21). The interaction effect was nonsignificant: F (3, 168) = 0.01, p > 0.05. Thus, we can argue that the effect of enrollment type on the dependent variable ICT-SC is not conditional on the level of systems thinking.

3.4. Structural Relationship between Systems Thinking and Self-Concept Related to ICT

The structural relationships were analyzed using structural equation modeling (SEM) using IBM Amos software v.24. When performing SEM, we carefully followed the steps determined by Kline [69], Byrne [102], and Henseler [103]. In the first step of performing SEM (model specification), it was hypothesized that teachers’ systems thinking may affect their self-concept related to ICT in preschool education settings. We also hypothesized that the constructs of self-directed learning as exogenous variable effects would be significantly correlated with both positive and negative outcomes. Next, we checked model identification; if the model is feasible for deriving a unique solution for every parameter, and if the model is just-identified or over-identified, we can estimate the model coefficients. Next, we evaluated model performance or fit, with the quantitative indices calculated for the overall goodness of fit. First, the model hypothesized that all systems thinking factors would have predictive value in ICT-SC, but the model evaluation indices suggested a poor fit. Thus, after several attenuation corrections and modifications to the model, the factor structure became solid, and we found a good model fit. During model improvement, we eliminated two factors in the systems thinking scale (factors 2 and 4) and the nonsignificant (p > 0.05) path coefficients (Figure 2).

We also tested multivariate normality to find if the given groups of latent variables came from a normal distribution or not. In the context of SEM, kurtosis is more relevant as a measure for this since it impacts variances and covariances tests, as argued by Byrne [102]. The value of kurtosis was 6.22, which is larger than the critical ratio (2.90) [99]. Next, we also assessed the presence of multivariate outliers in the data by using the Mahalanobis distance measure. Kline [69] recommended a more conservative p-value, such as p < 0.001, when testing for significance; the test revealed no cases that might have departed from the others in the dataset.

In order to manage nonnormality, bootstrapping as a strategy for assessing model fit and correcting the standard errors was proposed [67,104,105]. The procedure obtained 5000 usable bootstrap samples, which is sufficient for the bootstrapping procedure advised by Byrne [102], Kline [69], and Streukens and Leroi-Werelds [105]. In order to evaluate the hypothesized model after data transformation, we used Bollen–Stine bootstrapping [106], which yielded a nonsignificant p-value (0.43), indicating that the model tested via the bootstrapping procedure was not significantly different from the hypothesized model.

The model fit of SEM was acceptable for the entire sample (CFI = 0.988, TLI = 0.981, RMSEA = 0.014, and SRMR = 0.047). The PCLOSE was greater than 0.05 (0.70). The probability level of the test of close fit was also higher than the proposed threshold level of 0.50 for a good model fit [92,102]. In the final model using nine constructs (Figure 2), we observed a nonsignificant p-value (0.40) in the chi-square (χ²) test (14.3) with nine degrees of freedom (df). The chi-square was divided by the degrees of freedom (χ²/df) as a measure of model fit, with values between 1 and 5 being a common benchmark for a good model fit [107].

An analysis of path coefficients in the model (Figure 2) assessed the comparative strength of different effects on an outcome, and the relationships between the variables are expressed in terms of correlations expressed with β-weights (ranges from −1 to +1) and represent the proposed hypotheses [108]. In order to test our hypothesis on the relationship between systems thinking and ICT-SC, a single-group analysis was conducted using maximum likelihood estimation (Figure 2). For the sample in the study, adjusted R² values for the endogenous variables were significant at the p < 0.001 level for all constructs of ICT-SC, ranging from 0.13 to 0.27, which represents a weak effect size, according to Chin [109]. According to Falk and Miller [110] and van Tonder and Petzer [111], R² values equal to or greater than 0.1 are acceptable for studies in social science in order for the explained variance of a particular endogenous construct to be deemed adequate.

The path coefficient values that indicated the direction of the relationship between the systems thinking and ICT-SC variables and the significant magnitude of each variable’s effect were assessed and regarded as weak to moderate (Cohen f² from 0.05 to 0.25), according to Cohen [87]. All three factors of systems thinking included in the model (factors 2 and 4 were excluded) have positive path coefficients, whereas factor 3 (patterns of relationships) has predictive power in the majority of ICT-SC dimensions. Factor 5 (casual sequence) only has predictive power in the process and store dimension of ICT-SC, whereas factor 1 (multiple causations possible and inter-relationships of factors) might be decisive in enhancing problem-solving ability in preschool teachers.

4. Discussion

The following subsections present the results of our research on the self-reported systems thinking and self-concept related to ICT of preservice and in-service preschool teachers in relation to RQs and Hs as well as insights from the literature on similarities or differences.

4.1. Perceptions of ICT-Related Systems Thinking Skills and the Self-Concept of Preschool Preservice and In-Service Teachers

Hypothesis H1a predicts that there will be significant differences in at least one of the systems thinking factors among preschool teachers. The results show that, for the most part, there are no statistically significant differences between the preservice and in-service preschool teachers for the five factors of systems thinking identified in the study. An exception with a statistically significant difference of a small effect (partial η² =0.05) occurs for factor 2 in favor of preservice preschool teachers (M = 4.6, SD = 0.82) in contrast to in-service preschool teachers (M = 4.35, SD = 0.76). Therefore, we confirm the hypothesis. Factor 2 contains three items, which are “I think that systems are constantly changing”, “I recognize system problems are influenced by past events”, and “I consider that the same action can have different effects over time, depending on the state of the system” [62]. We named factor 2 after the main dimension, “sequence of events”, which is derived from the content of the items. The name was chosen from the set of dimensions given in the questionnaire source [62]. The latter can be argued by the fact that preservice teachers are significantly more subject to the analysis of the sequence of events as part of systemic thinking than in-service preschool teachers in their work. This could be due to the fact that preservice preschool teachers are actively involved in the educational system, so more algorithmic thinking or analytical synthesis is required. For in-service preschool teachers, algorithmic thinking may be less important because they are not involved in the system as part of their studies.

Regarding self-concept in relation to ICT, there was also a slight difference between the dimensions, with only the communication dimension being statistically significant. The small effect (η² = 0.03) in favor of the preservice preschool teachers (M = 4.54, SD = 1.08) in contrast to in-service preschool teachers (M = 3.95, SD = 1.00) suggests confirming the hypothesis since H1b states that there are significant differences in self-concept related to ICT dimensions of preschool teachers compared to regular preschool teachers. This might be due to the fact that the preservice preschool teachers are, in general, much younger and considered an ICT generation. The results of Stockless et al.’s study [112] showed that preservice teachers rated the communication category as “good” when assessing the mastery of digital tools (furthermore, only the “office suite” category was rated “good”; all other categories were rated somewhere between “poor” and “novice”) [112]. Another reason why there is no statistically significant difference in self-concept related to ICT dimensions might be that the preschool program at the university currently does not include an ICT-related compulsory subject but only an elective for future preschool teachers [113]. Additionally, there are several in-service training courses on ICT that can be taken by working preschool teachers [114].

4.2. The Effect of Different Levels of Systems Thinking on ICT Self-Concept, Moderated by the Type of Enrollment of Preschool Teachers

The results show that preservice preschool teachers who ranked lower for the systems thinking self-assessment also scored lower for self-concept in relation to ICT and vice versa. Figure 1 shows that in the area of self-concept related to ICT, preservice preschool teachers scored higher than in-service teachers in both the lower- and higher-scoring groups for systems thinking, implying that preservice preschool teachers (M = 98.98, SD = 24.42) generally scored higher in the area of self-concept related to ICT in contrast to in-service teachers (M = 90.52, SD = 23.13). The results are consistent with those of the study by Schweizer and Horn [115]. The research findings of Lohbeck et al. [116] support this, with the difference that both elementary and secondary school teachers were involved in the study, and the findings on self-concept in relation to media use speak for preservice teachers compared to in-service teachers.

In contrast, Stockless et al. [112] found that preservice teachers (including preschool and elementary teachers) rated their ICT skills surprisingly low. The authors explain the lower self-assessment preservice teachers are still in the training phase, and it can be assumed, on the one hand, that they do not rate themselves and their own ICT skills highly compared to in-service teachers. In contrast, the young generation is often referred to as the ICT generation/internet generation/digital natives [5,112,117], which could lead to the logical expectation that they are more competent in using ICT, but that is, on the other hand, not necessarily the case [112]. In addition, preservice teachers today face an educational shift in which ICTs are increasingly integrated into the teaching/learning process, requiring different teaching and learning methods [117].

Statistically insignificant differences in the interaction between the self-assessment of systems thinking and the type of enrollment (preservice and in-service) imply the independence of self-assessment of systems thinking in relation to the self-concept of preschool teachers in relation to ICT. This means that a preschool teacher who rates his or her systems thinking higher also has a higher self-assessment of ICT self-concept and that the enrollment type (preservice/in-service), therefore, has no statistically significant influence. Hypothesis 2 predicts that the effect of different levels of systems thinking on ICT self-concept will be moderated by the way preschool teachers are enrolled. Given the aforementioned, the hypothesis cannot be confirmed. The perception of systems thinking has an even influence on the development of self-concept in relation to ICT. Developed learning models to promote systems thinking or with the approach in question would therefore work equally and develop self-concept related to ICT in all groups of preschool teachers (preservice and in-service). Statistically significant differences in the average of the results obtained indicate that the self-assessment of systems thinking could be one of the predictors of self-concept in relation to ICT. It would be interesting to further investigate the actual systems thinking as well as the developed digital competencies since self-assessments can be subject to perception errors (underestimation or overestimation) and deviations from actual capabilities, which Kruger and Dunning [118] have already written about and which is known as the Dunning–Kruger effect.

4.3. The Impact of The Dimensions of Systems Thinking on the Dimensions of ICT Self-Concept in a Path Model

As for the relationship between systems thinking and self-concept related to ICT, the path model visually shows very clearly the connection between the factors of one and the dimensions of the other. It can be seen that all dimensions of self-concept related to ICT are influenced by factors of systems thinking, while factor 2 and factor 4 of systems thinking do not have a statistically significant effect on self-concept related to ICT dimensions and are, therefore, excluded from the representation for reasons of clarity. In contrast, factor 3 has a visible impact on several dimensions of self-concept related to ICT. This is followed by factor 1 and, finally, factor 5. In order to facilitate interpretation, given the content of the individual items expressed within the factors and the dimensions of systems thinking already indicated [62], we suggest naming the factors with the most appropriate proposed dimensions that relate to most of the items: factor 1: inter-relationships between the factors and multiple possible causations; factor 2: sequence of events; factor 3: patterns of relationships; factor 4: feedback; factor 5: variation of different types (random/special) and causal sequence. In this sense, we see that the sequence of events factor has no predictive value for any dimension of the ICT self-concept, nor does feedback. Since feedback is a means to improve performance and make increasingly sophisticated judgments, etc. [119], it is interesting that there is no predictive value for self-concept related to ICT dimensions. On the other hand, feedback is considered to be inefficiently developed and mispronounced and therefore quite problematic in education [119]. In their study, Carless and Winstone [120] found a correlation between teacher and student feedback and pointed out the importance of research on teacher feedback as well. Carless and Boud [119] suggested that feedback should be further researched because it is complex and important as a core skill for lifelong learning.

As for the size of the effect, the numbers suggest a small to moderate effect. The largest effect is found in the problem solving (Cohen f² = 0.25) and general SC-ICT dimensions (Cohen f² = 0.19), with the latter influenced by recognizing patterns of relationships and the problem-solving dimension influenced by understanding interrelated factors associated with multiple possible causations.

Intermediate influences are found in recognizing relationship patterns, which have an effect size on communication (Cohen f² = 0.15) and content generation (Cohen f² = 0.13), the latter also influenced by understanding inter-relationships between the factors and multiple possible causations.

The effect size is smallest in the processing and storage dimension and in safe application. Here, recognizing patterns of relationships has the smallest effect on safe application (Cohen f² = 0.05), while understanding interrelationships between factors associated with multiple possible causations has a slightly larger effect (Cohen f² = 0.10). The latter also has an influence on the processing and storage dimension (Cohen f² = 0.08). For processing and storage, the influence was also shown by the variation factor of different types (random/special) and causal sequences.

It is interesting to note that the influence of the dimensions of systems thinking on problem solving is relatively small, as only the factor inter-relationships of factors and multiple possible causations had a statistically significant effect (Cohen f² = 0.25) albeit the largest. According to the theory and definition of systems thinking as an approach to problem solving [1,2,7,26], one would expect it to promote problem solving in most dimensions. On the other hand, the authors [38] report the use of ICT in preschool education based on the preparation of materials and the display of images, sounds, videos, etc., whereas the use of ICT to solve problems is not mentioned. According to [112], preservice teachers often use ICT to prepare activities in which students do not actively engage but only consume the content. On the other hand, it is to be expected that preservice teachers are more involved in problem solving during their studies than in-service teachers, but Slovenian university programs for preschool education do not include regular subjects for the successful and useful integration of ICT for preschools or subjects with ICT specifically and purposefully used for problem solving [113].

Nevertheless, we can confirm the hypothesis that the dimensions of systems thinking have a different influence on the self-concept related to ICT dimension, with the greatest influence on the problem-solving dimension.

4.4. Limitations, Implications, and Future Work

There are some limitations to the studies conducted. The results of the study might have been different if the sample size had been larger. The sample of preservice preschool teachers was large enough but included only students from the Faculty of Education at the University of Ljubljana. Therefore, it would be advisable to include preservice preschool teachers from other Slovenian universities in further research. Moreover, the sample of in-service preschool teachers was small, and to obtain better results, it would be necessary to gather even more responses from preschool professionals. In addition, due to the nature of the work, most preschool teachers are women, so the sample was not analyzed by gender.

It is important to note that both questionnaires addressed the self-assessment and self-concept of preschool teachers. In further research, it would be useful to use a different, less subjective measurement approach to measure systems thinking using multiple qualitative and quantitative instruments along with questionnaires and interviews [121], video analysis [122], etc., for the necessary elaboration of preschool teachers’ systems thinking. The same applies to the questionnaire on self-concept in relation to ICT. The latter was otherwise used because it provides the most support for later behavior since an individual’s behavior in the environment is closely related to his self-concept and is largely influenced by his perception of himself as part of the system as a whole [123]. However, the questionnaire proved to be of little distinction, possibly due to an overly broad focus on digital systems, making the questionnaire insufficiently differentiated for targeted use. In further research, it would be useful to update the self-assessment questionnaire on systems thinking according to the proposed dimensions and, as suggested in [62], add items on personal effort and reliance on authority.

Systems thinking has implications for performance in variety of areas, including preschool education. Preschool teachers, both preservice and in-service, believe that if they can master the material in ICT-enhanced learning, it can impact educational performance. As such, identifying the dimensions of systems thinking that influence self-concept related to ICT is critical. As more advanced ICT solutions are used in study or work in preschool settings, educators develop beliefs about their ability to use these ICTs, with or without work experience or practice in kindergarten. By assessing preschool teachers’ competence in their current systems thinking skills, this study has shown that there is a positive and significant relationship between systems thinking and preschool teachers’ confidence that they can successfully learn/work using different ICT. The dimensions of systems thinking were found to lack predictive power, both at the structural level (identifying and characterizing feedback loops) and at the behavioral level (responding to change over time, describing past and predicting future system behavior). Next, implications also describe how three of the five established dimensions of systems thinking need to be changed to best shape a self-concept related to ICT. In particular, we suggest fostering patterns of relationships, exploring multiple perspectives, using mental modeling and abstraction, distinguishing and assessing each part of a system, and using variations of different types (random/special). Our study makes valuable contributions to both educators and practitioners. On the one hand, this study emphasizes the need for educators to adjust to the context of active meaningful learning and, therefore, consider ICT-enhanced learning. The results are useful for redesigning curricula of university subjects in preschool education program and for designing training courses to improve self-concept related to ICT of preservice and in-service preschool teachers. It is necessary to consider the factors of systems thinking that have shown significant predictive value for self-concept related to ICT, such as recognizing inter-relationships, multiple possible factors, patterns of relationships, and causal sequences. With systems thinking development courses that focus specifically on the relational pattern dimension, we can also almost fully develop self-concept related to ICT, as it has predictive value for most of its dimensions. The models developed can be applied to both preservice and in-service preschool teachers, with a small difference in the emphasis on the communication dimension, which should be correspondingly larger in the in-service preschool teacher group, as the results are in favor of the pre-service preschool teachers. Systems thinking has been shown to be fairly universal in that it develops self-concept related to ICT equally regardless of the group of preschool teachers participating. Therefore, it has the potential to be used with a variety of groups. Our results underline that the success of ICT-supported learning depends not only on the availability of ICT in the course of study or in the workplace but also on the willingness and ability of preschool teachers to gear their learning/teaching to it.

Finally, it is important to realize that systems thinking is not the only factor that has predictive power for the dimensions of self-concept related to ICT. In further studies, it would, therefore, be useful to investigate the influence of other factors, such as self-regulation, self-direction, engagement (behavioral, cognitive, emotional, etc.), optimal experiences with ICT, etc. Considering the insignificant predictive value of the feedback dimension, it is necessary to pay more attention to its study, as it plays an extremely important role in education. It would also be useful to investigate the influence of self-concept related to ICT on the dimensions of systems thinking as one of the most important factors for sustainable development. In addition, we propose to investigate the relationship between systems thinking and other factors, such as engagement at different levels or the impact of applying systems thinking on different learning outcomes, etc.

5. Conclusions

Systems thinking and digital competencies have become extremely important in today’s world. For now, the use of both is not yet included in the Slovenian kindergarten curriculum. Preservice preschool teachers also lack formal education on the effective and safe use of ICT in an educational context since there is no regular subject on incorporating ICT into preschool education.

Our research supports the findings [112] that educational settings should include tasks where individuals are encouraged to communicate and solve problems. The solution to achieving competencies for the 21st-century is to develop the competencies of teachers and future teachers so that they can transfer their knowledge to early childhood, contributing to the comprehensive development of individuals and successful development for today. The study showed that there were no significant differences between preservice and in-service preschool teachers in their perceptions of systems thinking (except for the “sequence of events” factor) and self-concept related to ICT (apart from the “communication” dimension). The results indicate that self-concept related to ICT develops to the same extent as the self-assessment of systems thinking. Even though the questionnaires are self-reported and self-concepts, the potential predictive value of each dimension of systems thinking in self-concept related to ICT is evident from the research. In this context, it would be useful to consider the findings and emphasize the importance of recognizing the inter-relationships among the factors, possible multiple causations, patterns of relationships, variations of different types, and causal sequences when revising curricula for digital literacy development. The results and findings of the study are useful not only for curriculum developers and educational policymakers but also for those designing courses to promote and develop ICT as well as for higher education teachers and future teachers.