Keywords

1 Introduction

Teaching students to be lifelong learners, to study at their own pace, pursue their own goals, and monitor their own progress is the major change in public education. We have to rethink the role of education and educators, curriculum and the future of work, collaboration and work integrated learning, vocational and professional practices, employability skills and qualities for the 21st-century world of work. Under this context, Personal Learning Environments (PLEs) and Personalized Learning (PL) are gradually receiving attention from higher education and applied degree education. The number of papers published on PLEs research has grown exponentially since 2019. The topics on PLE are becoming more diverse. Most papers focus on teaching guidelines or the latest technologies in higher education, such as learning analytics and augmented reality (Lin et al., 2013). Some are about the academic achievements of PLEs and their relationship to standardized tests. However, authors such as Attwell (2021) showed their concern about the slow implementation of PLEs after two decades of development. This study believed one of the major reasons was the little efforts devoted in the implementation of PLEs in primary and secondary education. Since PLEs represent new teaching approach, to have its potential into full play, kindergarten and primary school should be the starting point.

A few studies investigated K-12 personalized practices and how they related to students’ academic performance. The increasing demand for education reform has pushed a good number of schools to move toward PL systems (Basham et al., 2016; Bingham et al., 2016). Education systems in the United Kingdom and the United States were making efforts to realize it, with the goal of addressing the increasingly diverse student population and providing quality education for all (Peterson, 2016). The research in PL mainly covered two themes, including the investigation of (a) the role of various technologies and (b) contextual factors that impacted the implementation of PL (Zhang et al., 2020). Few studies have examined the effects of PL on student learning outcomes. Due to the difficulty in obtaining large-scale data (Underwood et al., 2007), there are only two studies regarding the relationship between personalized learning and students’ academic performance at the national-scale level in the UK and the US, respectively. (Gross et al., 2018; McCarthy & Schauer, 2017). Meanwhile, when implementing PLEs, many challenges pop up, including the failure to define what PLEs and Personalized Learning mean and why they should be adopted; Besides, there is an utterly wrong assumption that PLEs are all about putting digital devices in students’ hands, while PLEs actually represent a learning paradigm shift, and effective personalized learning demands major shifts in teachers’ roles and practices.

2 Concepts of PLEs and Personalized Learning

2.1 Concepts of PLEs

Personal Learning Environments (PLEs) is generally seen as a collection of individual systems and external services. It can also be broadly defined from the perspective of knowledge management as well as from a technical perspective. From the perspective of knowledge management, PLEs is seen as a virtual learning ecosystem (Attwell, 2007). Harmelen (2006) treated PLE as a personal e-learning system that allows users to set a mixture of their learning objectives and content and monitor their whole learning process. PLEs are viewed as technology-based methods for learners to select the proper tools and resources to improve their studies. FitzGerald (2006) defined PLEs as “a set of free, distributed, web-based tools, usually blog-centric, that aggregate content together using RSS (Really Simple Syndication) feeds and simple HTML scripts”. Downes (2005) considered PLEs as “a personal learning center where learners reuse and remix learning content according to their needs and interests”. It is a collection of interoperable applications, an environment rather than a system. Malamed (2014) considered PLEs as self-regulating and developing environments that consist of tools, services, and resources where learners can get a convenient way to acquire knowledge, communicate with others who have similar interests, and set lifelong learning objectives by recreating their own PLEs. PLEs are used to gather the resources learners need to solve their learning problems and obtain and share information for individual and group learning. Downes (2005) defined Personal Learning Environments as “a node in a content network that connects to nodes and content creation services used by other learners”.  “It is more than an institutional or corporate application, as the content is reused and remixed for the user’s own needs and interests”. “PLEs aim to bring all types of learning, including informal learning, workplace learning, home learning, problem-solving-driven learning, and personal interest-driven learning, together by participation”. The above definitions and descriptions indicate that PLEs can be seen as a technical as well as a pedagogical concept. This study defines PLEs as “knowledge-based, learner-centered teaching assistance platform, where users can customize resources they need in a virtual community to share and co-construct knowledge.”

2.2 Concepts of Personalized Learning

Personal Learning Environments should be distinguished from Personalized Learning. The Oxford Online Dictionary defines “personalized” as “the design or production of (something) to meet the requirements of one's personality,” while “personal” is defined as “done or produced by a particular person; involving the actual presence or conduct of a particular individual”. Personalized learning is a new global education policy on learning and curriculum that supplies more freedom, personal responsibility, and customized knowledge for learners rather than the acquisition of specific formal knowledge. Personal Learning Environments are “how people build environments for themselves: the tools they choose, the communities they join, the resources they collect, and what they write about”.

As a learning model, personalized learning aims to adapt itself to the learners’ strengths, needs, and interests by using interactive digital resources and data (Bill & Melinda Gates Foundation, 2014; Pane et al., 2015). Personalized learning aims to provide learners and teachers with high-level, diverse choices via digital resources and online courses (Bill & Melinda Gates Foundation, 2014). Learners can use the digital platform to browse different content at different speeds, getting learning outcomes to demonstrate their understanding of learning. Some proponents argue that personalized learning has the potential to help learners with high or low talent (U.S. Department of Education, 2016). Nonetheless, personalized learning aimed at improving learners’ academic performance has gained widespread attention in high-tech schools as a model of action theory. (Bill & Melinda Gates Foundation, 2014). Increased opportunities or promotions to a specific group of universities and vocations. The concept of personalized learning has recently become a major goal of the education system. Historically, personalized learning can be traced back to the early twentieth century, when John Dewey put the learner at the center of teaching and doing some long-term work (Keefe & Jenkins, 2008; Redding, 2016). Later, the concept of personalized learning began to appear when educational reformers began to denounce the standardized methods of industrialized education systems and sought ways to enrich the learning methods (Redding, 2016). The Personalized Instruction System (PSI) introduced by Keller (1968), which emphasizes the learning monitor and learning groups tutored, is usually regarded as the predecessor of personalized learning (Keefe & Jenkins, 2008; Keller, 1968). Personalized learning is applied in some areas (e.g., special education, personalized instruction, educational technology). However, the future of its large-scale use is unknown (Basham et al., 2016).

2.3 Relationship Between PLEs and PL

Personalized learning is a teaching method as well as a kind of educational philosophy (Sebba et al., 2007). The Personal Learning Environments can be seen as a system, a platform, an idea, or a path. Personalized learning is a part and a goal of the Personal Learning Environments. The primary purpose of PLEs is to “create an environment for handling learning in the hands of the learner”. The goal of personalized learning is to make learners know how to learn. Customizing learning to each student’s strengths, needs, and interests—including giving students a self-chosen mode on what, how, when, and where they learn—to provide flexibility and support for their high-quality learning. Students choose the resources and tools that are suitable for them and build a network of peers, teachers, and experts to support and guide their learning. The main difference between the two is that the PLEs emphasize the construction and application of platforms and systems, with the path and method to realize personalized learning. It involves not only technology but also teaching concepts, scholarly literacy, social interaction, and the coordination of various stakeholders. Schaffert and Hilzensauer (2008) proposed seven foundations that compose the PLEs:

  • The role of the learner: PLEs focused on the learner as an active, autonomous participant who was also responsible for creative learning, evolving from a mere "consumer" to a "producer and prosumer" of learning (p. 4).

  • Personalization: The goal of personalization in PLEs was defined as “access to information about learning opportunities and content from multiple communities and services tailored to learners’ interests” (p5). tools, materials, appearance, etc., and the source of information chosen by the learner.

  • Content: The centralized use of “Learning Opportunities and Content” (p. 6) was what differentiates PLEs from traditional learning management systems. That was, PLEs contained not only teacher-created content but also content from experts and a broad “peer” community.

  • Social Engagement: PLEs acknowledged the importance of social engagement as "PLEs always need and build on the community" (p. 7) to find contributors, collaborators, sources, and suggestions for learning content.

  • Ownership: Access to information, database creation, and acquisition in traditional learning management systems were often in a “sealed” system that can only be used by authorization. PLEs pursued the opposite extreme, where all available data and information were "almost completely open to the whole world" (p. 7).

  • Education and Organizational Culture: It involved not only the change of individuals but also the change of the whole society and culture, including the various stakeholders of PLEs.

  • Technical aspects: PLEs were not designed to replace previous systems, but rather to enable coupling: (1) integrating PLEs into existing learning management systems; or (2) deploying loosely coupled tools within existing learning management systems (p. 8).

2.4 Policies Related to the Development of PLEs

The widespread implementation of Personalized Learning and PLEs was being driven by Government through policy changes. For instance, in China, the Outline of the National Medium- and Long-Term Program for Education Reform and Development (2010–2020) pointed out, “Information technology has a revolutionary impact on the development of education and must be given high priority.” In recent years, with the rapid development of information technology such as big data, cloud computing, and mobile internet, the internet has stimulated profound changes in the field of education. Internet technology provides great potential for the innovation and development of education. It respects each student’s individual characteristics, supports students’ differential learning with technology, and then realizes each student’s all-around development. The Ten-Year Development Plan for Informatization of Education (2011–2020) stated that “We should provide each learner with an information-based environment and service for personalized learning”. The construction of Personal Learning Environments has been put forward in the development plan of education informatization all around China.

Abroad, the Every Student Succeeds Act (ESSA) promulgated in the United States in 2015, provided sufficient policy support and financial guarantee for the individualization of learning. Jenkins et al. (2019) designed the State Policy Framework for Personalized Learning to help states and stakeholders define and navigate their pathway from the exploratory phase of system design to statewide transformation. In the UK, personalized education emerged as an official government policy in 2004. Publications such as “Pedagogy and Personalization” (DCSF, 2007b) emphasized the need to establish a shared language for discussing pedagogy to transform learning and teaching, while major research programs like ‘The Making Good Progress’ pilot (DCSF, 2007a) were trialing new personalized teaching and learning strategies to raise rates of progression throughout the Key Stages.

Recent advancements in Information and Communications Technology (ICT), data science, and machine learning help to boost the popularity of PLEs. To have a general understanding of the PLEs and PL development in primary and secondary schools the world around, the next section would discuss the status quo of PLEs implementation in China, the U.S., and the U.K.

3 The Implementation of PLEs and PL in Primary and Secondary Schools

The implementation of PLEs and PL in China, the U.S., and the U.K. have reported some benefits to students’ learning achievement, learning autonomy, etc., however, challenges and problems also existed. The following sections would focus on reporting the status quo of PLEs and PL implementation in the above-mentioned three contexts.

3.1 The Implementation of PLEs in Primary and Secondary Schools

Proponents of PLEs argued that personalization could help close achievement gaps and promote student success and positive outcomes because it allowed students to become masters of learning (Pane et al., 2015). However, some early research on PL and PLEs models suggested that schools and teachers may face challenges in implementing PLEs, hindering their effectiveness in pedagogical applications (Bingham et al., 2016). For example, research indicated that teachers and schools may have difficulty transitioning to new practices in individual learning environments. Bingham et al. (2016) also found that the optionality of digital and online resources in an individual learning environment could facilitate or limit the orderly delivery of instructions. When online courses were poorly designed or did not provide useful data, students said they were overwhelmed and dissatisfied with the patterns of their individual learning environments. This was because learner autonomy, control, and choice were also key components of the Personal Learning Environments, which posed challenges for the application of PLEs. Most of the experimental results on the use of PLEs in teaching showed that assisted classroom teaching could improve students' academic performance to a certain extent. However, applying learning models to the classroom required a lot of practice and long-term research.

In China, using the keywords “Personal Learning Environment”, “personalized learning”, and “primary and secondary school” to search in CNKI, regardless of the year, we retrieved 13 academic papers, of which only 8 are in line with the topic of the Personal Learning Environment. The searched keywords are analyzed and grouped using topic analysis. The results obtained were shown in Fig. 13.1.

Fig. 13.1.
A table of 2 columns and 2 rows. The column headers read, keywords and number. The entries read as follows, personalized learning 6, personal learning environment 2, digital bag 1, and web 2.0 1.

Searched keywords results

Full size image

In general, there were few studies on the PLEs of middle schools in mainland China, leaving a huge space for exploration in this field. The research on PLEs in Chinese primary and secondary schools mainly focused on the following three aspects: (1) how to promote Personalized Learning in the secondary education system; (2) how to construct a model of PLEs in junior high schools; and (3) how to promote Personalized Learning. The application of PLEs software and platforms in middle schools was rarely mentioned, and the interpretation of the PLEs in middle schools was still unclear.

Although the vision of PLEs is based on the collaborative design of teachers and students for responsive learning opportunities, few empirical studies have explored the application of Personalized Learning in practice. A series of studies commissioned by the Bill and Melinda Gates Foundation (Pane et al., 2015, 2017; RAND Corporation, 2014) found that compared with the matched control group, students in the PLEs setting had higher reading and mathematics levels and greater progress. However, the researchers also noted that "considerable variation exists in the details of school instruction models," and the schools in the study did not adopt "a single standardized model of personalized learning" (Pane et al., 2015, p. 3). Indeed, follow-up studies focusing on PLEs implementation found that students rarely choose teaching materials or classroom focus topics independently, which indicated that teachers and students in these schools had relatively little co-construction (Pane et al., 2017). Zeiser et al. (2014) found that the personalized practice network has a positive impact on many aspects, including interpersonal and personal achievements, cognitive development, complex problem-solving strategies, and the university admission rate of middle school students. However, they found that the results varied greatly across schools.

3.2 The Implementation of PL in Primary and Secondary Schools

Both continuous research and new understandings have supported the ability to develop and scale systems that implement PL across diverse student populations in a variety of settings (Arroyo et al., 2014; Basham et al., 2016; Robinson & Sebba, 2010; Walkington, 2013). School districts in the United States were increasingly turning to personalized learning (PL) as a way to improve academic performance and meet the diverse needs and interests of their learners (Bingham et al., 2016). The "Top-District competition" proposed by the U.S. Department of Education (DOE) in 2013 encouraged schools to implement personalized learning environments.

School districts in the United States were increasingly turning to personalized learning (PL) as a way to achieve better academic performance and meet the diverse requirements and interests of learners in the districts (Bingham et al., 2016). The “Top-District Competition” proposed by the U.S. Department of Education (DOE) in 2013 encouraged schools to put PLEs into effect. Personalized Learning emphasized transferable or interdisciplinary development skills such as goal setting, metacognition, problem-solving ability, and self-directed learning.

With support from the U.S. Department of Education, McCarthy and Schauer (2017) conducted a national study of the practice of PLEs in charter high schools by using a case study methodology. This study was not only conducted to investigate the practices of Personalized Learning in these schools themselves, but also some common issues found in schools practicing personalized learning. Based on the advisory panel's recommendations and a review of achievement data, they whittled down the 70 charter schools to 26, all of which showed excellent academic performance and rising grades. Besides, most of the students from the 26 schools came from low-income families or ethnic minority families. At the end of the study, merely eight schools were selected with demographic differences, promising practices, geography, and achievement data taken into consideration. At each school, informal observations and interviews were conducted among school-wide learners, parents, teachers, board members, administrators, and school partners. In addition, data including school timetables, sample assessments, instructional planning sheets, teacher planning agreements, newsletters, application forms, brochures, charter plans, and transcripts were analyzed. McCarthy and Schauer (2017) identified four prominent characteristics of Personalized Learning in schools: (1) providing authentic experiences to learners; (2) teaching for mastery; (3) developing independent learning capability and metacognition skills; and (4) assessing data-driven instruction. Research has found that in schools, learners were highly encouraged to enrich their experience and actively learn something outside the classroom. Learners engage in a variety of real-world experiences, including internships, trips to various places, school activities, and university lectures, that provided meaningful learning experiences. In addition, the study employed learning assessments to provide norms and guidance for personalized instruction. Mastery-based instruction required continuous assessment and data-driven instruction. In schools, learners’ needs were identified based on assessments, and the curriculum was continuously updated. Another US study funded by the Bill and Melinda Gates Foundation investigated the general picture of personalized learning in K-12 education in the US (Gross et al., 2018). 908 teachers from 38 schools responded to the survey. The study's findings showed that nearly 80% of primary and secondary school teachers reported that competency-based teaching was practiced less. Only one-third of learners reported setting learning goals at least half the time. This was consistent with only 19%-29% of teachers reporting that the learning goals and instructional requirements set by their learners were consistent. Moreover, most teachers were reluctant to let learners control their own learning pace, content, and learning activities. In the interviews, teachers mentioned that the pressure to meet learning standards was the main reason hindering the development of personalized learning.

Overall, Personalized Learning was showing a growing trend in thousands of classrooms in the U.S. and beyond. However, there were not many studies on different models of personalized learning and their effectiveness (Bingham et al., 2016; Bowles, 2019). Romano (2013) described experiential teaching in which learners enhance their understanding of mathematics by establishing their own Personal Learning Environments. In building the PLEs, learners needed to develop and strengthen their critical analytical skills as well as overcome personal difficulties and misunderstandings about mathematics presented by their individual learning environment. The UK Department for Education launched a five-year strategy for UK children and learners in 2004 to promote personalized learning practices from primary to secondary school. Funded by the UK Department of Education, Sebba et al. (2007) conducted two separate national studies. The two studies used large-scale national data to reveal academic testing and achievement in personalized learning. Sebba et al. (2007) investigated personalized learning methods in K-12 schools and proposed five components of Personalized Learning based on previous research: (1) learning assessment; (2) effective teaching and learning; (3) curriculum rights and choices; (4) school organizations; and (5) beyond the classroom. Using a representative sample of K-12 schools across the United Kingdom, Sebba et al. (2007) investigated how schools implemented the Personalized Learning Initiative launched by the Five-Year Strategy for Children and Learners. His research found that many schools used the following learner-centered teaching methods: collaborative learning (88%), inquiry-based learning (69%), teaching in a preferred learning style (66%), encouraging self-directed learning, and self-directed learning (64%) and classes by ability (41%). In terms of assessment, more than 80% of schools indicated that they routinely provided personal feedback (94%), conducted personal goal setting (92%), self and peer assessment (86%), and academic tracking (81%). Additionally, 45% of schools utilized technology to assess learners. In contrast, their case study found that assessment was not consistently embedded in learning in most schools. He further explained that across all types of schools, teachers reported that the following factors had a positive impact on academic performance: continuous learning assessment, individual learning goal setting, tracking learner progress, and self and peer assessment. Primary teachers put forward group-based, targeted intervention strategies such as catch-up and reinforcement programs. Some secondary school teachers emphasized the importance of career or alternative pathways and flexibility in the curriculum. Research by Sebba et al. (2007) helped to identify effective teaching and assessment strategies for personalized learning. Another UK national study on Personalized Learning commissioned by the UK Educational Communication and Technology Agency was “Impact 2007: Personalized Learning and Technology”. The purpose of this study was to collect perception information about the role of the five-year strategy in promoting the individualization progress of teachers and students. Underwood et al. (2007) conducted a survey of 67 schools, where the number of primary and secondary schools and high and low technological sophistication were relatively evenly distributed in both rural and urban areas. This study applied multilevel modeling to examine the relationship between personalized learning practices and other factors. Results showed that compared with the teachers working in middle school, those working in primary school had a higher degree of recognition and implementation of personalized practical learning and responded more positively. Compared with teachers who teach other subjects, math teachers show more negative views on the implementation of personalized learning. High achievers were often unaware of their strong tendency toward personalized innovation in school. In addition, researchers observed that teaching activities mainly include computer-based activities, project-based learning, collaborative learning, multimedia teaching, and learning beyond the national curriculum. Moreover, assessment activities include learners’ online self-assessment, individual goal setting, support, and monitoring of individual performance. What’s more, a similar study found that individualized practices had a negative or uncertain impact on student’s test scores, but the study did not control the implementation degree of specific practices, nor did it coordinate individualized assessment principles with the established standardized assessment design (Zimmerman & Kuhlmann, 2019). In short, there were still some common and key deficiencies in the existing research on the impact of individualized practice on students’ performance: The commonly used measurement standards of students’ performance are often inconsistent with the expected results of personalized learning; significant differences in the implementation of different research sites have weakened people's confidence in the research findings. Therefore, there was still much to learn from the implementation of personalized teaching at the classroom and school levels to expand these projects.

4 Effectiveness of PLEs and PL in Primary and Secondary Schools

Advocates for PL have argued that students, including those with diverse learning needs and disabilities, could achieve higher levels of learning if they received personalized instruction and supports that were tailored to their specific needs and capitalized on their strengths (Jones & Casey, 2015). Basham et al. (2016) found that PL environments across an entire reform district supported better than expected outcomes in student growth. Positive findings were also reported in engagement (Arroyo et al., 2014), attitude toward learning (Hwang et al., 2012), and meta-cognitive skills (Chen, 2009). Nevertheless, studies also reported negative positions toward certain aspects of PL (Beach & Dovemark, 2009). Lee et al. (2021) made a comparison between learner-centered high-performing and low-performing schools (as determined by their standardized test results), based on five characteristics of personalized learning (such as personal learning plans, competency-based progress, standard-referenced assessments, project-or problem-based learning, and multi-year mentoring) and the use of technology in planning, learning, assessments, and record-keeping. In general, teachers in high-performing schools were able to carry out personalized learning more fully and complete more tasks via technology than in low-performing ones. When making individual learning plans, sharing project outcomes with the community, and evaluating non-academic outcomes, teachers in high-performing schools paid more attention to learners’ career goals, spent more time with them, and conducted more interaction with them to establish close relationships. They also made use of technology more frequently to share resources and boast stronger technology systems than teachers in low-performing schools. In addition, they found that in the UK, high-performing schools were reluctant to shift to a personalized learning system (Underwood et al., 2007). According to a national survey (Gross et al., 2018), in the United States, most teachers have yet to implement competency-based teaching or allow learners to manage their own learning under the pressure to meet academic requirements. However, some teachers in the UK believed that certain characteristics of competency-based teaching were positively correlated with academic performance, e.g., continuous learning assessment, goal setting for individual learning, tracking learners’ progress, as well as self-assessments and peer assessments. As was shown by research reports, Personalized Learning practices were among the critical characteristics of high-performing schools, including providing authentic experiences to learners, teaching based on mastery, and developing self-directed learning skills (McCarthy & Schauer, 2017). As for the effectiveness of PLEs, many institutions have developed specific solutions for the development of the individual learning environment on campus, such as customized portals that help learners organize research and resources as well as express their views. However, because the principles of institutional engagement conflict with the individual learning environment, many educators preferred to use free applications such as iGoogle and MyYahoo, which provided solid platforms for a learner-centered environment for individual learning. Despite learning new online tools and computer applications rapidly, many high school learners still lacked the necessary information literacy. Thus, we needed a model that could balance full openness with access to resources and tools, providing useful guidance. Basham et al. (2016) found that PLEs supported better than expected outcomes in student growth. Arroyo et al. (2014) stated that the learning outcomes, motivation, and meta-cognitive skills were improved after using PLEs. Zhao et al. (2020) stated the personalized learning platform could effectively promote teachers’ dynamic and layered personalized teaching, solve the learning difficulties of students, and provoke their learning motivation. It helped teachers get data on the learning processes of students and improve teaching performance. Meanwhile, it freed students from traditional problem-solving strategies and provided personalized practice materials, which could improve students’ study efficiency within the limited practice time, thereby achieving mutual learning in teaching.

Yang and Zhang (2015) believed that Personal Learning Environments with a social network model as a resource aggregation and recommendation framework could more effectively promote personalized learning. The personalized learning environment could aggregate personalized resources from the learning environment inside and outside, analyzing learning behavior data, recommending personalized learning resources, providing support for teachers, and improving the optimization of the learning process. The development of artificial intelligence technology has enabled more intelligent human–computer interactions. The learning space in human–computer interaction was also called the personalized learning space (Zhang et al., 2017). The core of the personalized learning space was to use the personalized learning engine to provide learners with support services.

Conceptually, the very strength of PL and PLEs was to focus on designing learning experiences at the individual point of learning, accepting student variability and individuality (Rose & Ogas, 2018).

5 Challenges of PLEs and PL in Primary and Secondary Schools

Abbott et al. (2014) pointed out that there were five areas that required further research to advance personalized learning. Summit attendees found that, as a field, there was a need to learn how educators and researchers use data, how to design technology to support learners and related teaching practices, how to educate people who are prepared to work in personalized environments, and how to explore curriculum to support personalized learning. These fields should be supported by the research and development agenda that promoted the practice. Partnerships between practitioners, industry and researchers can be leveraged to support further understanding of these areas (Basham et al., 2013). Given the current growth in online and Personalized Learning, investments should be made to get a further understanding of interactive dynamics and potential outcomes in these environments. In future research, it is important to put personalized learning outcomes beyond standardized academic tests. Developing PLEs to organize learners' academic performance is different from creating a transformative environment that helps learners make greater progress. To fully understand the potential of Personalized Learning, research efforts need to be invested in system-wide reforms rather than single or limited variable interventions (e.g., personalized LMS, self-directed learning).

In addition, to have the potential of PLEs and PL into full play, all the stakeholders involved in PLEs should reach a consensus on the meaning of PLEs and be fully aware of its values. From a technology perspective, individual learning environments should respond to the vast differences in student identities and needs (e.g. cultural, cognitive, physical, social, emotional, and moral) and focus on prioritizing the development of 21st-century essential skills such as citizen consciousness, problem-solving ability, and so forth. From the users’ perspective, learners, teachers, and PLEs designers need to have a consistent understanding of PLEs and PL, be equipped with the necessary competencies/skills, and collaborate to design and implement PLEs and PL. From an administration perspective, schools’ academic administrations need to support and guide the implementation of PLEs, including political support, financial support, staff training, etc.

6 Future Trends of PLEs and PL in the Education Field

The application of artificial intelligence in Personalized Learning Environments is mainly reflected in answering questions, learning emotion capture, learning resources, intelligent thrust, and rapid positioning of learning methods as well as learning content. AI boasts functions such as intelligent cognitive algorithms, pattern recognition, and intelligent sensing technology. The adaptive education model supported by AI technology can realize a virtuous circle between the domain model and the teaching model, thus giving learners a better experience. Within PL research, it is important to consider the moderating interactions in relation to the final goal and the variability within a given experience. The emergence of more sophisticated mobile devices, including wearables, faster wireless, and more feasible data connections, along with technologies such as augmented reality, virtual reality, and improved data models, should not only enhance feasibility and interest but also the variability in ubiquitous PL research (Bhattacharjee et al., 2018). PL is growing by leaps and bounds with the integration of advanced technologies so that it can achieve better student interactions with the learning environment. However, the lack of consistency in the conceptualization of PL is likely to result in confusion and misunderstanding in implementation and poses challenges in conducting research that can optimize the practice. To establish an effective PL ecology, it would be beneficial to contain all the factors identified in the current studies, all acting together under a unified research-based framework. The research documented in this review (Abawi, 2015; Basham et al., 2016), U.S. policy (ESSA, 2015), and educational guidance documents (NETP, 2016) have often associated PL with the UDL framework. UDL might serve as a starting point to begin the development of a unified framework that could foundationally build PL through research, practices, and policies. The operationalization of PL and PLEs is such a complex process that it requires leveraging many different components of the education system to satisfy the needs of each individual student (Abbott et al., 2014). Research remains to be conducted on the foundations of learning, human growth, variability, and measurement to support PL model development. As more schools gravitate toward PL, research in the field should be ultimately focused on examining the positive effects as well as side effects of practices (Zhao, 2017).

Furthermore, as for applied degree education, significant change and transformation are occurring with the above-mentioned blended learning, artificial intelligence, and PLEs, along with curriculum renewal with industries and professions. We initially find that there is a strong need for the employment of Culturally Responsive Teaching to boost self-directed learning for learners receiving applied degree education. Culturally responsive teaching is a research-based pedagogy that makes meaningful connections between what learners learn in school and their culture, language, and work experiences. These connections are conducive to the learning of course content, develop learners’ high-level academic skills, and interconnect classroom content with learners’ lives and work.