Digital learning ecosystem at educational institutions: A content analysis of scholarly discourse

Abstract

This paper explored the characteristics of the digital learning ecosystem (DLE) in educational institutions based on the analysis of English scholarly discourse from various sources between 2002 and 2021. The content analysis method was used to examine core conceptual elements from the existing models. Researchers used Google Scholar and other databases to collect sources using relevant keywords. In total, 35 publications were collected and analyzed to provide a general picture of themes, including (1) components/models of DLE, (2) roles of components in DLE, and (3) enabler and barrier factors on DLE. The findings showed that DLE included different components, and the interaction between them played important roles in enhancing the educational quality. Besides, the internal and external factors have influenced the development of a DLE. However, there are some limitations from previous research of the lack of the analysis of (1) the teacher–supporter interaction in learning process, (2) the emerging technologies (i.e., artificial intelligent (AI), augmented reality (AR), virtual reality (VR) and Internet of things (IoT) applied in the DLE, (3) and some issues pertaining the copyright, intellectual properties of learning contents and the integration of digital contents into learning management system. This study could be considered basic background for future research to select and design a DLE and conduct appropriate studies. In addition, this study has important implications for policy-makers, administrators, teachers, and other staff for educational institutions with respect to using DLEs to deploy effective solutions.

Keywords:

• digital learning ecosystem
• learning and teaching
• content analysis
• scholarly discourse
• educational institutions

1. Introduction

The strong development and application of information technology in education have introduced opportunities for educators, learners, and administrators to create and use effective tools and ways for learning and teaching (Thuy, 2019). The technology-driven revolution was helping individuals with different incomes and in different countries extract effective technologies to create a better future that is human-centered. This revolution in digital learning raised the effectiveness of educational organizations by improving students’ learning performance and faculty members’ capacity and enhancing the quality of teaching, administration, and the working environment (Abdulrahim & Mabrouk, 2020).

The COVID-19 pandemic situation has impacted not only health systems but also educational systems in many countries. Under the influence of this pandemic, leaders and educators must find holistic solutions to relieve the pressure of the pandemic on the educational situation. Educational institutions must adapt their teaching and learning methods to overcome the resulting challenges (Alhumaid et al., 2020). Digital learning was a logical approach worldwide in this situation to facilitate adaptation to a new normal and enhance educational quality (Humayun, 2020). A survey of Organization for Economic Co-operation and Development (OECD) on educational situations under the influence of COVID-19 in 98 countries showed that “the availability of technological infrastructure, addressing student emotional health, addressing the right balance between digital and screen free activities and managing the technological infrastructure” were challenges in most countries (Reimers & Schleicher, 2020, p. 17). Consequently, changes to adapt to the new situation were indispensable. Most of the surveyed countries used various ways to adapt teaching and learning activities; for example, delivering materials, resources, and courses on social media and websites in China, Costa Rica, Czech Republic, Estonia, Japan, and Romania; and using digital workspaces, electronic mail and creating virtual classrooms, online learning pages, video tutorials, and digital content in France, Hungary, and Italy. Some countries, such as Italy, Latvia, Australia, and Estonia, capitalized on e-learning platforms and webinar and available technology tools to deliver teaching and learning guidelines to teachers, students, and parents. In addition, Belgium, Israel, and Latvia used national television channels to broadcast educational programs for learners (Reimers & Schleicher, 2020).

Digital learning created fruitful opportunities for educational institutions; however, there were some challenges relating to technology, courses, instructors and learners (Händel et al., 2020; Shehzadi et al., 2020). The limitations of technology platforms, the quality of the internet, learner-teacher interaction, and the limited training of teachers and learners regarding the online learning system influenced the effectiveness of learning in a digital environment. In addition, the ability to adapt to immediate changes in the new situation would affect the future of online learning (Dinh & Nguyen, 2020).

In the context of learning and teaching in the new normal age, the digital learning ecosystem (DLE) was important in enhancing the quality and effectiveness of teaching and learning. According to Reyna (2011), this model was useful to understand how teachers use learning technologies to support their teaching activities, how to improve teaching, assess students’ achievement and recognize challenges and disadvantages of teaching. It also helped explore how students learn and communicate to peers, teachers and others, how to enhance students’ experiences and create a learning community. The DLE could be used as a methodology to assess and rank “the online units based on the following elements: design layout, navigability, accessibility, content and interactivity, quality of assessments, and user experiences” (Reyna, 2011, p. 1086).

Therefore, this paper drew a general picture of the DLE in educational institutions based on the analysis of collected data from different sources which used English language and ranged from 2002 to 2021. It posed three questions:

• What are the components/models and roles of the DLE characterized by scholars?

• Which factors influence teaching and learning activities in the DLE?

• How do educational institutions create a new learning ecosystem to deal with the educational crisis?

This study could be considered as a basic background and springboard for scholars, educators, and practitioners to select and design a DLE and conduct future studies based on the understanding of characteristics and factors influencing the quality and effectiveness of educational activities in a digital environment.

2. Methods

2.1. Data collection

To select and identify accurate and meaningful publications, we used Google Scholar to search for relevant literature. The following keywords were used: “digital learning ecosystem”, “digital ecosystem”, “online learning ecosystem”, “e-learning ecosystem”, “learning ecosystem”, “factors”, “governance”, “educational institutions”, and “schools”. In addition, the ScienceDirect and Emerald databases were also used to search for other appropriate literature.

We carried out the screening process through two stages, (1) examining the title and abstract to choose materials mentioned digital learning ecosystem, (2) reading the full text of papers to select reliable data. The appropriate sources were identified relied on the accurate, up-to-date, relevant and reliable as follows:

• Papers written by English language; translated language sources were excluded;

• Time ranging from 2002 to 2021;

• Duplication of data were removed;

• Appropriate sources regarding journal and conference papers, theses, book chapter were selected by identifying purpose, scope, authority, audience and format of literature.

• Research documents were prioritized through considering the research methods, population, measurement, results, discussion, limitations, etc.;

• The scopes of papers mentioned digital learning ecosystem in educational sector.

Consequently, 35 appropriate publications were selected for deployment in the next steps.

• According to the data sources, about 39 papers were eliminated because of irrelevant contents of above criteria in educational sector. The documents must present the definition, components and factors influencing on the digital learning ecosystem;

• The papers were published within 20 years between 2002 and June 2021. In the light of this, we collected 35 relevant documents and removed 12 publications.

As a result, Table 1 shows that most sources were from journal articles and conference papers. Book chapters and theses accounted for a small percentage.

Table 1. Data listed by source, author and frequency

Source	Author	Frequency
Journals		14
Afterschool Matters	Allen et al. (2020)	1
Asia Pacific Education Review	Davidson et al. (2019)	1
Computer Science and Information Systems	Laanpere et al. (2014)	1
International Journal of Information and Education Technology	Kummanee et al. (2020) Sarnok et al. (2019, 2020	3
Journal of Higher Education Research	Beggan (2020)	1
Journal of the Learning Sciences	Hecht and Crowley (2020)	1
Interaction Design and Architecture(s) Journal	Jeladze et al. (2017) Väljataga et al. (2020)	2
Smart Learning Environments	Benita et al. (2021) Giannakos et al. (2016)	2
Tekhnê	Ospina and Galvis (2017)	1
TEM Journal	Andryukhina et al. (2021)	1
Conference proceedings		19
Advanced Learning Technologies	Dong et al. (2009)	1
Changing demands, changing directions	Reyna (2011)	1
Data engineering and management	Gütl and Chang (2008)	1
Digital Ecosystems and Technologies	Atif et al. (2010) Chang and Guetl (2007) Chang and Uden (2008) Ficheman and de Deus Lopes (2009) Leong and Miao (2008) Uden et al. (2007)	6
Education and New Learning Technologies	Quaicoe et al. (2016)	1
Education Technology and Research	Tong (2019)	1
E-learning	Geertshuis and Liu (2016)	1
Information and Knowledge Technology	Lohmosavi et al. (2013)	1
Internet Technology and Secured Transactions	Eswari (2011)	1
Interaction design and children	Ficheman and de Deus Lopes (2008)	1
Interactive Collaboration Learning	Gütl (2008)	1
Management of Digital EcoSystems	Ali et al. (2017)	1
New Trends and Issues Proceedings on Humanities and Social Sciences	Markoska (2017)	1
Smart Information Systems and Technologies	Wolff et al. (2021)	1
Book chapters		1
Modernizing Learning: Building the Future Learning Ecosystem	Giattino and Stafford (2019)	1
Thesis		1
The Structure and Components for the Open Education Ecosystem-Constructive Design Research of Online Learning Tools	Põldoja (2016)	1
Total		35

In addition, publications were studied by many researchers from different countries. The United States and Estonia had the most publications, followed by Australia, New Zealand and Thailand (Table 2). Documents concerning the digital learning ecosystem were the most published in 2019 and 2020 (Figure 1).

Figure 1. Year of publication.

Table 2. List of countries with publications

No.	Country	Frequency
1	Estonia	4
2	USA	4
3	Australia	3
4	New Zealand	3
5	Thailand	3
6	China	2
7	Singapore	2
8	Belgium	1
9	Brazil	1
10	Bulgaria	1
11	Colombia	1
12	France	1
13	Germany	1
14	Ghana	1
15	India	1
16	Iran	1
17	Malaysia	1
18	Norway	1
19	Russia	1
20	U.A.E	1
21	UK	1
Total		35

2.2. Data analysis

Content analysis was used to interpret knowledge and understanding in scholarly studies by systematically coding and identifying topics, themes and content. Hsieh and Shannon (2005) determined three qualitative approaches for coding text data, namely, conventional, directed and summative content analysis. Collected data were coded manually. Regarding this, the coding process of data is shown in Figure 2. Data were analyzed by reading selected papers, capturing and highlighting main concepts and prominent results; then categorizing data and coding themes. The reliability and validity of data were considered by considering the appropriate research methods to solve the research objectives and questions, assessing the accuracy of the results, systematic results’ presentation, profound and logic discussions. Finally, appropriate results were analyzed and synthesized following the themes, sub-themes and figures.

Figure 2. The coding process of collected data.

In terms of the definition of DLE, text data were coded and listed into various keywords. The definition was recognized by paying attention to wording; for instance, “has been used to describe … ”, “the term refers to … ”, “it is used to … ”, “has been cited … ”, “has been proposed … ”, “is defined as … ”, “DLE is … ”, “is a link … ”, and “refers to … ”. Therefore, we identified that the DLE was the most common term used, followed by the e-learning ecosystem, learning ecosystem, digital ecosystem, digital educational ecosystem, and learning and teaching ecosystem (Figure 3).

Figure 3. Different terms of digital learning ecosystem.

The DLE was variously defined by scholars from 2002 to 2021. The results from previous studies show the different terms were coded from the analyzed data (Ali et al., 2017; Chang & Guetl, 2007; Ficheman & de Deus Lopes, 2008, 2009; Giannakos et al., 2016; Gütl & Chang, 2008; Hecht & Crowley, 2020; Kummanee et al., 2020; Laanpere et al., 2014; Leong & Miao, 2008; Markoska, 2017; Quaicoe et al., 2016; Tong, 2019; Wolff et al., 2021). However, we divided and analyzed the terms into four groups based on the frequency of terms, namely, digital learning ecosystem, digital (educational) system, e-learning ecosystem and learning ecosystem. Other terms were considered as a part of and belonged to other words; for instance, digital teaching and learning ecosystem belonged to digital learning ecosystem, learning ecosystem included learning and teaching ecosystem, and smart learning ecosystem and open educational ecosystem belonged to digital (educational) ecosystem. These ecosystems were studied under different background with the use of various framework, organizations’ features and different objects.

2.2.1. Digital learning ecosystem

DLE was like a natural ecosystem in which biotic and abiotic components interact with each other and with their social, economic and cultural environment (Ficheman & de Deus Lopes, 2008, 2009; Kummanee et al., 2020; Laanpere et al., 2014; Quaicoe et al., 2016). Teachers, students, educational institutions and stakeholders could share learning resources and tools to boost the learning process (Sarnok et al., 2019).

2.2.2. Digital (educational) ecosystem

A digital (educational) ecosystem referred to the e-learning, mobile learning (Leong & Miao, 2008), educational processes, software entities, and changes (Markoska, 2017). This ecosystem could be used anytime and anywhere and facilitated to develop educational resources and provide assessment methods to enhance learners’ competences (Wolff et al., 2021).

2.2.3. E-learning ecosystem

An e-learning ecosystem centered on user-centered educational methods, the design of programs and content, learning tools, process and approaches, the innovation of technologies, learning management system, the consideration of environmental, social and cultural characteristics to solve and respond to problems, new situations and unresolved issues (Chang & Guetl, 2007; Gütl & Chang, 2008; Leong & Miao, 2008).

2.2.4. Learning ecosystem

The learning ecosystem not only focused on similarity components of DLE but also emphasized information systems, learners’ profile (Ali et al., 2017), needs, motivations (Giannakos et al., 2016), and relevant processes inside and outside school (Hecht & Crowley, 2020), as well as the combination of microsystem (participants, resources, modules of curriculum), middle system (parents, resources managers, curriculum structure) and macro system (organizational environment and social-cultural environment; Tong, 2019).

It is clear that the digital learning ecosystem was explored in various perspectives. The nature of terms showed the characteristics and components of a learning ecosystem in different environment. Some studies mentioned a DLE with two main components (biotic and abiotic), others extended or narrowed the sub-components of this ecosystem relied on the features of educational institutions, stakeholders, and environment. For instance, some definitions focused on digital platforms, methods, technologies, educational resources, learning tools; others centered on systems, process, changes, educational resources and evaluation.

Looking at the results of used terms, it can be seen that “digital learning ecosystem” was used commonly in most of papers; thus, we used this term in this study. Despite that the difference and past research suggesting that DLE could be reviewed as a learning community in educational institutions with the interaction between components of living things (i.e., learners, teachers and stakeholders) and non-living things (information technology infrastructure; content; and economic-social and cultural environment) to enhance the effectiveness of education in digital sphere and was influenced by different factors.

In other words, the issues and themes of components and enablers and barriers of the DLE were categorized and coded. These characteristics showed the various approaches of studies in different circumstances and environments. Next, concepts, patterns and themes were expressed and divided into small topics. The results of the data analysis are displayed in the next section.

3. Results

From the results of data analysis, this section presented three parts as follows: (1) the components/models of the DLE, (2) roles of components in the DLE, and (3) enablers and barriers affecting the DLE.

3.1. Components/models of the DLE

There are indications from previous research that the DLE included different components depending on the environment, objects, goals of the ecosystem, and organizational characteristics (Figure 4).

Figure 4. Components of digital learning ecosystem.

Different perspectives from previous studies mentioned DLE components. Reyna (2011) designed a digital teaching and learning ecosystem based on an ecological approach with two components, namely, a biotic component (i.e., lecturer, tutor, online learning staff and students) and abiotic components (i.e., technology devices, the internet, the interface or e-learning portal, communication tools, collaboration tools and content). However, other studies added users, educators, peers, family, guardians, learner supporters, system administrators, content vendors, designers, experts, learning stakeholders in living things (Gütl, 2008; Kummanee et al., 2020; Markoska, 2017; Põldoja, 2016; Sarnok et al., 2020) and hardware, software, databases, networks, media, pedagogical theories, open educational resources, learning environments and open assessment arrangements in nonliving things (Kummanee et al., 2020; Põldoja, 2016).

Ali et al. (2017) emphasized that a learning ecosystem not only included learners but also pertained to learning tools using tablets, notepads, and other resources (e.g., processes, materials, methods, instructions); learning space relating to information, digital resources (e.g., slides, lecture recordings, forums, discussions, blogs), traditional materials (Giannakos et al., 2016), parties (e.g., peers, teachers, technology professionals, colleagues), and the participation of other stakeholders (i.e., schools, government agencies, funding agencies, services providers and pedagogical institutes; Benita et al., 2021). In addition, knowledge must be exchanged, shared and distributed in this ecosystem (Ali et al., 2017). Individuals and groups must adapt to the conditions of the learning environment, and learning utilities must be ready for use (Giannakos et al., 2016).

Additionally, the e-learning ecosystem of Dong et al. (2009) included architecture with three layers (i.e., infrastructure, content, and application) and four modules (i.e., monitoring, policy, arbitration, and provision) and four mechanisms to guarantee educational activities; or it may consist of content providers, consultants, and infrastructure, as suggested by Lohmosavi et al. (2013), and Uden et al. (2007).

Notably, other studies indicated that a learning ecosystem could include four, five or six components: for example, people, places, activities/resources and intangibles (Hecht & Crowley, 2020); tools, subject, rules, community and labor division (Quaicoe et al., 2016); learning content, stakeholders, roles, content repositories, reports and assessments, and the collaboration process of stakeholders (Eswari, 2011); technologists, learning models, governance elements (i.e., processes, policies, guidelines, research and development management, and strategic plans), technology, competences of components in an ecosystem, and human capital and infrastructure (Ospina & Galvis, 2017).

According to Quaicoe et al. (2016), electricity, physical devices, teachers’ digital resources, ICT ability, training and networking openings are important tools used in a DLE. Similarly, Davidson et al. (2019) suggested a conceptual framework with five parts, namely, personality, student approaches to learning, intrinsic motivation, perceptions of course experience, and current and previous academic achievement. However, another model given by Eswari (2011) centered on the learning environment and the interactions of components to enhance learning performance, in which abundant activities were implemented such as “content creation, courseware planning and delivery, collaboration, assessment and performance tracking.”

3.2. Roles of components in the DLE

3.2.1. Biotic elements

The biotic elements (i.e., teachers, students, tutors, e-learning staff) primarily performed task education and supported learning activities to help learners gain knowledge, skills, and experience (Sarnok et al., 2019). Teachers transferred knowledge, directly taught courses and effectively delivered them to students (Reyna, 2011), and took responsibility for planning and delivering course content, managing learners’ participation and learning achievements (Eswari, 2011). Technological staff were liable for managing and maintaining the e-learning system, controlling the stability of the e-learning interface during the teaching and learning process (Reyna, 2011), and guiding and providing equipment and materials for students’ learning (Sarnok et al., 2019). Students also relied on the lectures, guidelines of learning and recommendations of teachers and tutors to capture and understand learning content and to meet requirements to pass tasks and examinations (Reyna, 2011).

A tutor completed tasks based on suggestions from teachers (Reyna, 2011) and provided learning support (Sarnok et al., 2019). Teaching practicum advisors/school mentor teachers, consultants, and experts could help teachers and schools support hands-on teaching experience and learning content and technologies (Reyna, 2011). A group of supporters (i.e., peers, family, guardian) provided the support, gave advice, or encouraged learning for learners. Administrators managed and maintained the stability of the learning ecosystem and created and determined users’ roles (Eswari, 2011). All biotic units “were linked together as a learning community within the digital ecosystem” (Sarnok et al., 2019, p. 21).

Other stakeholders participated in the development of an ecosystem. For example, government agencies could draw programs for smart learning and control, navigate and build action plans for schools. Educational institutes managed the content and structure of ecosystems, including the design, development, supervision, and implementation of learning activities, content, and learning outcomes evaluation. Finally, funding agencies set the budget for educational activities (Benita et al., 2021).

According to Sarnok et al. (2019), learners joined in the learning process and showed their work. They were equipped with skills pertaining to information-seeking, data analysis and synthesis, communication, presentation, organizing ideas, improving teamwork and lifelong learning skills. They used many types of technologies to link skills, knowledge and understanding. “Learners accessed learning content, notifications, assessment, performance reports and collaboration tools to communicate with peers, instructors and experts” (Eswari, 2011, p. 403). Ali et al. (2017) revealed that learner profiles must be designed in the learning ecosystem to facilitate identifying and responding to learners’ needs (i.e., personal information, preferences, and knowledge level).

3.2.2. Abiotic elements

Infrastructure contributed to growing successfully and maintaining the stable development of an ecosystem because of its resource and service delivery and storage (Dong et al., 2009). Leong and Miao (2008) asserted that infrastructure, such as technologies, services, models, and interfaces, was necessary for educational institutions to provide instruction, use resources and access systems. Additionally, digital media and technology were used to convey stories, content and emotions through video games, sounds, animations, images, and other media formats (Sarnok et al., 2019). Portals supported to stakeholders in accessing, using, collaborating and communicating with others (Eswari, 2011).

In addition, digital devices were connected to the internet and supported objects to access digital content (Eswari, 2011; Ficheman & de Deus Lopes, 2009; Giattino & Stafford, 2019; Reyna, 2011). Internet connections played essential roles in supporting students, teachers and other stakeholders to access content and e-learning systems. The e-learning interface was viewed as a virtual environment and used by users to browse knowledge resources (Gütl, 2008; Reyna, 2011). Tools supporting communication and collaboration could be used to interact during online learning and create interactive sites to support e-learning and students’ teamwork (Reyna, 2011).

According to Beggan (2020), collaboration during the learning process could support learners in creating new knowledge and exploring new ideas. Therefore, digital environment was designed to deliver and encourage collaboration and communication in learning and teaching. Reyna (2011, p. 1085) mentioned that the evaluation process was performed with the online interface if teachers used online quizzes or onsite teaching. This element was important to transform “information (static and dynamic content) and interaction (communication and collaborative tools) into knowledge”. Learning tools and resources were used to share offerings together and to boost sustainable development in learning (Sarnok et al., 2019).

Notably, the design of a DLE must ensure the features of delivering and sharing resources to other systems and partners, support collaboration and confirm heterogeneity of resources. In addition, course content must also be defined and shown so that learners, teachers and stakeholders could understand, exchange and transfer knowledge (Ali et al., 2017). Tech-based learning systems were tools and platforms to support teachers in implementing appropriate teaching methods. Therefore, a learning management system must facilitate learning resource sharing, upload functions, communication, collaboration, and discussion (Laanpere et al., 2014). Furthermore, e-examination systems must be developed to consider features, processes, and collaboration between groups and to improve the quality of e-assessments in digital ecosystems (Chirumamilla & Sindre, 2021).

3.2.3. The interaction between components in the DLE

The primary feature of an ecosystem was expressed in the interaction between components to transform information into knowledge. Interaction played a crucial role in changing the understanding, perspective, and cognition of students, improving communication skills, enhancing the learning experience and cultural knowledge (Lawrence & Lorraine, 2021), helping to better shape learners’ quality and effective learning experience, boosting teaching methods and enhancing the dynamics of learning to meet the needs of learners (Giannakos et al., 2016), and adapting and adjusting behaviors (Gütl, 2008). Giannakos et al. (2016) indicated six main interactions in DLE (Figure 5); however, from the online collaboration perspective, student-student interaction played the most important role in all interactions (Reyna, 2011).

Figure 5. Types of interrelationships based on the triangle diagram of Giannakos et al. (2016).

The study of Laanpere et al. (2014) indicated three principles of the DLE. The first principle addressed the interactions between species and abiotic parts. It is related to changing information into knowledge through teaching and learning activities (Reyna, 2011). “The permeability of a digital learning ecosystem to export and/or import information and knowledge depends on the nature of the ‘architecture’ of the components in the system (e.g., connectivity, clustering), the characteristics of species, and their diversity and distribution, and interactions between them” (Laanpere et al., 2014, p. 243). The second principle encompassed the reaction, change, and feedback of species to adapt to their environment. In addition, services were activated by users (learners, supporters) and their learning purposes. The third principle expressed the interaction between species to share resources and was associated with the first principle of “energy and matter exchanges in the network” (Laanpere et al., 2014, p. 243).

In addition, five different interactions in the ecosystem were mentioned by Ficheman and de Deus Lopes (2009). The first interaction occurred when information was sent to actors (teachers, learners, and others), and then actors used an authoring tool and create content, increasing its population (interaction 2). In the third interaction, actors interacted and stimulated changes in content. Collaboration between individuals occurred in the fourth interaction. Finally, technology elements cooperated together.

3.3. Enablers and barriers influencing the DLE

The literature provided a detailed picture of factors affected the digital learning ecosystem, highlighting internal and external elements.

Chang and Uden (2008) revealed that the governance of an ecosystem helped maximize the effectiveness of components. Success was expressed through commitment, understanding, holistic solutions in line with an organization’s characteristics, and the satisfaction and balance of diverse needs among individuals, organizations, and the community (Giattino & Stafford, 2019). Therefore, it is important for practitioners, educators and administrators to identify characteristics of the learning community, stakeholders, and learning utilities and drawbacks of the learning ecosystem to maximize enablers and reduce the impacts of barriers (Gütl, 2008).

Quaicoe et al. (2016) indicated that infrastructure helped to improve learning and teaching activities and that learning facilitation (i.e., national curriculum requirements, e-learning platforms, applications, resources, and teaching and learning activities) support the development of students’ ICT competence, lecture design, and organization of teaching processes. Additionally, schools should focus on the responsibilities and roles of staff, policies, and teachers’ participation in training. However, the limited budget for infrastructure, limited change management, and traditional teaching styles could impede the development of digital services in schools.

Väljataga et al. (2020) acknowledged that infrastructure, digital environments and materials, tools, and an internet connection were crucial conditions for learning and teaching effectively. The digital transformation in the teaching environment required teachers to adapt to new environments and develop a sustainable learning ecosystem. Technological adoption impacted performance, changing the teaching methods, motivation and innovation of teachers. Thus, teaching staff and technologists should work together to exchange information and explore priorities in educational activities (Geertshuis & Liu, 2016), use keystones for resource allocation, and focus on developing learners’ interest to enhance the quality of educators (Hecht & Crowley, 2020).

Challenges relating to the design, boosting learners’ motivation, community establishment, learning tools, learners’ competence, self-learning and learning, and establishing partnerships (Põldoja, 2016) and the educational budget and policies, and sustainability must be considered in planning the conditions of the ecosystem (Gütl, 2008; Lohmosavi et al., 2013 as cited in Ospina & Galvis, 2017). Besides, appropriate workloads, understandings of evaluation, courses and circumstances, and teaching and learning styles played important roles in forming educational goals, performance and collaboration skills, interests and learning content (Andryukhina et al., 2021; Davidson et al., 2019).

Other studies have also found that the success and challenges of the learning ecosystem depend on financial resources, sustainable relationships, the quality of programs, experiences, data use for continuous development, learning content and inquiry (Allen et al., 2020), and the privacy and security of the ecosystem (i.e., processes, policies, technologies, procedures, design, contents; Eswari, 2011; Uden et al., 2007).

4. Discussions

4.1. Components/models and roles parts, and factors having influence on the DLE

Relied on the results of data analysis, it can be indicated that the DLE included two important components with living things and non-living things. However, the parts of a DLE depended on the features of an educational institution, and purposes of the design of a DLE. Some DLEs emphasized the design of an ecosystem to ensure the accessibility, process, methods and learning tools to boost learning and teaching effectiveness at institutions; others focused on technology platforms to boost online learning, and the interaction between components to maximize the communication, presentation, teamwork and collaboration.

In addition, the roles of each sub-components in the DLE were analyzed to enhance the deeply understanding of elements to suggest ideas and solutions to boost strong points and eliminated barriers in the DLE. The interaction relationships of components were clarified to improve the process of transforming information into knowledge at educational institutions.

Past research explored the internal and external factors having influence on the development of a DLE. In the light of this, some internal elements, such as, governance, communication and relational mechanisms, technical design, learning contents and tools, teacher’s competence and learners, resources; and external factors (i.e., educational policies, competency framework, community gravity, and cultural-social factors) strongly influenced the sustainability of DLE. It is interesting to say that the understanding of factors in a DLE could help design an appropriate DLE to promote the role of factors comprehensively.

However, there are some following limitations of DLE in educational sector based on the collected data from previous studies:

• Lack of the analysis of the teacher-supporter interaction in learning process, especially in the current Covid-19 outbreak, the interaction between teachers and family is necessary for students’ learning;

• The emerging technologies, such as, artificial intelligence (AI), augmented reality (AR), virtual reality (VR) and Internet of things (IoT) are less mentioned and applied in the DLE;

• Some issues pertaining the copyright, intellectual properties of learning contents and the integration of digital contents into learning management system;

• Intrinsic and extrinsic factors strongly impacted the development of DLE; however, the limited studies deeply analyzed these elements and their roles in ecosystem.

4.2. How educational institutions create a new learning ecosystem to deal with the educational crisis

Under the serious influence of the Covid-19 pandemic, it is important for educational institutions to consider and change learning ways to overcome these challenges (Alhumaid et al., 2020). Pinet et al. (2021, p. 1) emphasized the need for developing the “resilient and sustainable systems and economies that leverage digital technologies as a tool” for learners. However, there are some problems that need to be solved regarding evaluative standards, security, and authenticity guarantee (Humayun, 2020); the financial limitations (Chandio, 2020). Therefore, creating and developing a new learning ecosystem in the digital environment would facilitate to deal with the educational emergence and uncertain situation. In the light this, the readiness for digital learning was essential to improve learning performance. Some research has pointed that the readiness was expressed through digital knowledge and skills, technology, digital platforms, attitudes, time management, pedagogy, motivation, competencies, online communication, self-directed learning, and materials to gain educational aims and expectations (Giovannella & Marcello, 2020; Hong & Jeong Kim, 2018; Kirmizi, 2015; Pinet et al., 2021; Topal, 2016); digital devices, internet, technology use tools and information sharing (Händel et al., 2020); the interaction between teachers and learners, environment design, the content of course, teaching process, materials and communication tools; policy, technology, financial, human resources, and infrastructures (Saekow & Samson, 2011).

AI, AR, VR and IoT have influenced the smart learning in schools. Southgate et al. (2019) indicated that it is essential to increase the capacity of IoT in schools with the equipment of wireless system, digital devices, sensors, platforms, learning spaces, new services and the applications of technologies in non-learning spaces (Siripongdee et al., 2020). The use of IoTs helped teacher explore learning behavior of students, create and adjust learning climate in the classroom, develop pedagogical methods, assess academic performance (Southgate et al., 2019), and predict and estimate the sustainability of educational issues (Mohammadian et al., 2020).

Furthermore, AI opened new opportunities and brought many benefits in designing the teaching content, learning outcomes and strategies in curricula (Chiu, 2021); quality education products (i.e., teaching tools, learning systems, smart classroom, software, simulations, programs, etc.; Gocen & Aydemir, 2021). However, educational institutions should consider the ethical issues when applying AI by identifying “the norms, values and assumptions reflected in AI systems so that these enhance human potential, creativity and well-being rather than foreclose or homogenise it” (Southgate et al., 2019, p. 39); and identify relevant factors to use AI in the learning process, know how they work and apply in daily life.

Other technologies, such as, AR/VR were also used in schools to motivate and boost effective learning, increase interactivity and learning experience by converting content from text to visual, interactive using mobile devices, computers, and projectors (Alkhattabi, 2017; Southgate et al., 2019). Nevertheless, the use of AR/VR in learning required the necessary elements pertaining to IT infrastructure, skills, readiness and change resistance (Alkhattabi, 2017). When using AR/VR in the teaching process, thus, the students’ physical, cognitive, linguistic, legal and ethical issues, and risks should be considered to adjust students’ approaches in the learning process (Southgate et al., 2019).

Based on the results mentioned above, the development of new learning ecosystem with the adequate equipment of components concerning policies, procedures, technologies, readiness of schools, teachers, learners and stakeholders, and digital learning content was considered as an important tool to solve crisis problems and increase the resilience of educational institutions.

5. Limitations and conclusion

This paper was considered a review paper of digital learning ecosystems; therefore, there are some limitations as follows:

1. Analyzed sources based on the searching results on Google Scholar, ScienceDirect, Emerald databases that could not cover all publications of DLE by other languages in many countries. Other databases, such as, Scopus and Web of Science have not searched for and analyzed to identify other features of a digital learning ecosystem;

2. Specific contextual and sociocultural elements have not been used to explore the differences between ecosystems;

3. A digital learning ecosystem was focused on educational sector; thus, the results could not compare the similarity and difference characteristics of this ecosystem in different sectors to have deeply understanding to set up a well-rounded and adequate DLE.

The findings of this study mentioned a general picture of DLE in educational institutions and have important implications for policy-makers, administrators, teachers, and other staff in educational institutions to (1) consider and build educational strategy plans, policies and competence frameworks to enhance educational quality; (2) obtain a general picture of the digital learning ecosystem to invest in and boost its sustainable development; (3) identify learning goals, pedagogical aspects, and learning technologies and content and manage the learning process to boost learning achievement; and (4) design appropriate learning and management systems in line with an organization’s context and conditions.

Acknowledgements

This research is supported by the Postdoctoral Training Scholarship, Khon Kaen University, Thailand.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author received no direct funding for this research.

Notes on contributors

Lan Thi Nguyen

Lan Thi Nguyen (Ph.D. in Information Studies, Khon Kaen University, Thailand), a postdoc trainee at Khon Kaen University, Thailand. She is also a lecturer at the Department of Information Science, Faculty of Humanities and Social Sciences, Khon Kaen University, Thailand. Her research interests are educational information, digital learning, digital literacy, information services, and information management. Email: [email protected]

Kulthida Tuamsuk

Kulthida Tuamsuk (D.A. in Library and Information Science, Simmons University, USA), Professor and senior researcher of the Faculty of Humanities and Social Sciences, and a Director of Smart Learning Innovation Research Center (SLIRC), Khon Kaen University (KKU), Thailand. Her research interests are knowledge management, digital humanities, metadata, ontology, digital learning, and smart learning. Corresponding author. Email: [email protected]