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Smart devices and intelligent technologies are enabling a smart learning environment to effectively promote the development of personalized learning and adaptive learning, in line with the trend of accelerating the integration of both. In this regard, we introduce a new teaching method enabled by a smart learning environment, which is a form of personalized adaptive learning. In order to clearly explain this approach, we have deeply analyzed its two pillars: personalized learning and adaptive learning. The core elements of personalized adaptive learning and its core concept are explored as well. The elements are four: individual characteristics, individual performance, personal development, and adaptive adjustment. And the core concept is referred to a technology-empowered effective pedagogy which can adaptively adjust teaching strategies timely based on real-time monitoring (enabled by smart technology) learners’ differences and changes in individual characteristics, individual performance, and personal development. On this basis, A framework of personalized adaptive learning is also constructed. Besides, we further explored a recommendation model of the personalized learning path. To be specific, personalized adaptive learning could be constructed from the following four aspects, namely, learner profiles, competency-based progression, personal learning, and flexible learning environments. Last, we explored a form of learning profiles model and a generative paths recommendation pattern of personal learning. This paper provides a clear understanding of personalized adaptive learning and serves as an endeavor to contribute to future studies and practices.

Advances in technology in recent years have changed the learning behaviors of learners and reshaped teaching methods. This had resulted in several challenges faced by current educational systems, such as an increased focus on informal learning, a growing gap of prior knowledge among students in classrooms and a mismatch between individual career choices and the development of the work force. This paper looks at these challenges with a view towards revolutionizing current learning environments to smart learning environments and provides new suggestions for technological solutions. Furthermore, this paper argues for a transformation from the current learning environments to smart learning environments. This is to be achieved by reengineering the fundamental structure and operations of current educational systems to better integrate these new technologies with the required pedagogical shift. The future perspectives of smart learning environments are reviewed and shared, through examples of emerging innovations such as the flipped classroom, game based learning, gesture based learning, along with pedagogical shifts, such as life-long learning portfolio maintenance, team teaching, and separation of learning and competency assessment.

This study examines the research landscape of smart learning environments by conducting a comprehensive bibliometric analysis of the field over the years. The study focused on the research trends, scholar’s productivity, and thematic focus of scientific publications in the field of smart learning environments. A total of 1081 data consisting of peer-reviewed articles were retrieved from the Scopus database. A bibliometric approach was applied to analyse the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of smart learning environments. The result from this bibliometric analysis indicates that the first paper on smart learning environments was published in 2002; implying the beginning of the field. Among other sources, “Computers & Education,” “Smart Learning Environments,” and “Computers in Human Behaviour” are the most relevant outlets publishing articles associated with smart learning environments. The work of Kinshuk et al., published in 2016, stands out as the most cited work among the analysed documents. The United States has the highest number of scientific productions and remained the most relevant country in the smart learning environment field. Besides, the results also showed names of prolific scholars and most relevant institutions in the field. Keywords such as “learning analytics,” “adaptive learning,” “personalized learning,” “blockchain,” and “deep learning” remain the trending keywords. Furthermore, thematic analysis shows that “digital storytelling” and its associated components such as “virtual reality,” “critical thinking,” and “serious games” are the emerging themes of the smart learning environments but need to be further developed to establish more ties with “smart learning”. The study provides useful contribution to the field by clearly presenting a comprehensive overview and research hotspots, thematic focus, and future direction of the field. These findings can guide scholars, especially the young ones in field of smart learning environments in defining their research focus and what aspect of smart leaning can be explored.

Despite the increasing use of the self-regulated learning process in the smart learning environment, understanding the concepts from a theoretical perspective and empirical evidence are limited. This study used a systematic review to explore models, design tools, support approaches, and empirical research on the self-regulated learning process in the smart learning environment. This review revealed that there is an increasing body of literature from 2012 to 2020. The analysis shows that self-regulated learning is a critical factor influencing a smart learning environment’s learning process. The self-regulated learning components, including motivation, cognitive, metacognitive, self-efficiency, and metacognitive components, are most cited in the literature. Furthermore, self-regulated strategies such as goal setting, helping-seeking, time management, and self-evaluation have been founded to be frequently supported in the literature. Besides, limited theoretical models are designed to support the self-regulated learning process in a smart learning environment. Furthermore, most evaluations of the self-regulated learning process in smart learning environment are quantitative methods with limited mixed methods. The design tools such as visualization, learning agent, social comparison, and recommendation are frequently used to motivate students’ learning engagement and performance. Finally, the paper presents our conclusion and future directions supporting the self-regulated learning process in the smart learning environment.

In the contemporary digital age, education is no longer limited to traditional educational environments. Many educational institutions shifted to depend on the smart learning process but expressed concern about this solution due to its various challenges in securing the learning process and learners’ data. By virtue of the most recent technologies like blockchain and artificial intelligence, which played a significant role in solving many challenges that faced the educational sector and overcoming issues like fake certificates, manipulation, tracking learners’ activities, and predicting learners’ academic performance. The study proposed a smart framework based on blockchain and deep learning to enhance smart learning processes and provide solutions for challenges in the field. The framework is intended to store the learner’s data on the blockchain through the interplanetary file system and reap the benefits of securing the learner’s data and ensuring its integrity, as well as ensuring the confidentiality and authentication of the users through the wallets that are created on the Ethereum private blockchain platform. Then apply the deep learning model to this secured data to predict the learner’s performance. The smart contract functions also play a role in enabling the university to issue learners’ certificates that are stored on the blockchain to be available and verifiable by all the nodes in the network. Based on the experimental results, deep neural networks were used to model the encrypted data that was stored on the blockchain and predict the learner’s performance and achieved a high degree of accuracy (91.29%) and low loss (about 0.18) in comparison to other studies that depended on the centralized nature of the data. As well, the university blockchain’s functionality was tested, and it successfully returned all the functional requirements and showed its legitimacy.

This methodological-theoretical synergy provides an integrative framework of learning analytics through the development of the human-and-machine symbiotic reinforcement learning. The framework intends to address the challenges of the current learning analytics model, including a lack of internal validity, generalizability, immediacy, transferability, and interpretability for precision education. The proposed framework consists of a master component (the brain) and its four subsuming components: social networking, the smart classroom, the intelligent agent, and the dashboard. The brain component takes in and analyzes multimodal streams of student data from the other components with the model-based reinforcement learning, which forms policies of adequate actions that maximize the long-term rewards for both the human and machine in the seamless learning environment. An example case plan in advanced statistics was demonstrated to illustrate the course description, data collected in each component, and how the components meet different features of the smart learning environment to deliver precision education. An empirical demonstration was provided using some selected mulitmodal data to inform the effectiveness of the proposed framework. The human-and-machine symbiotic reinforcement learning has theoretical and practical implications for the next-generation learning analytics models and research.

This paper discusses the key characteristics of smart learning and the main challenges to be overcome when designing smart educational environments to support personalisation. In order to integrate smart learning environments into the learning ecosystem and educational contexts, innovative uses and new pedagogical approaches need to be implemented to orchestrate formal and informal learning. This contribution describes the main characteristics of smart learning and smart learning environments and sustains the relevance of taking the participation of future users into account during the design process to increase knowledge of the design and the implementation of new pedagogical approaches in smart learning environments.

A smart learning environment, featuring personalization, real-time feedback, and intelligent interaction, provides the primary conditions for actively participating in online education. Identifying the factors that influence active online learning in a smart learning environment is critical for proposing targeted improvement strategies and enhancing their active online learning effectiveness. This study constructs the research framework of active online learning with theories of learning satisfaction, the Technology Acceptance Model (TAM), and a smart learning environment. We hypothesize that the following factors will influence active online learning: Typical characteristics of a smart learning environment, perceived usefulness and ease of use, social isolation, learning expectations, and complaints. A total of 528 valid questionnaires were collected through online platforms. The partial least squares structural equation modeling (PLS-SEM) analysis using SmartPLS 3 found that: (1) The personalization, intelligent interaction, and real-time feedback of the smart learning environment all have a positive impact on active online learning; (2) the perceived ease of use and perceived usefulness in the technology acceptance model (TAM) positively affect active online learning; (3) innovatively discovered some new variables that affect active online learning: Learning expectations positively impact active online learning, while learning complaints and social isolation negatively affect active online learning. Based on the results, this study proposes the online smart teaching model and discusses how to promote active online learning in a smart environment.

Personalized learning is a learning approach that aims to personalize the learning experience according to the unique needs, goals, and skills of individuals which can be achieved by using current instructional technology that provides unique learning experiences in different learning environments. Technology is the main component that will enable and enrich personalized learning experience; however, even though technology is available to personalize the learning experience, there is still a lack of unified agreement on what components need to be considered for a dynamic personalized learning approach that is to be able to provide a unique and effective learning experience to each learner. To address this need, this study aims to analyze and synthesize different personalized learning approaches that consider different learning components, so that we have an evolving agreement on personalized learning models and approaches. The findings of this research identified the following main components: learner profiles and attitudes, previous knowledge and beliefs, personalized adaptive learning paths, and flexible self-paced learning environments that are generated by learning analytics. These prominent characteristics imply that a personalized learning environment (PLE) would need to be dynamic to maintain a current record of learner interests and attitudes, past experiences and performance, and activities and interactions likely to match a particular learner and learning goal.

This paper describes the design, implementation and research of the Cyber Sleuth Science Lab (CSSL), an innovative educational program and supporting virtual learning environment, that combines pedagogical theory, gender inclusive instruction strategies, scientific principles/practices, gamification methods, computational thinking, and real-world problem solving. This program provides underrepresented youth, especially girls, with digital forensic knowledge, skills and career pathways, challenging them to explore complex social issues related to technology and to become cyber sleuths using real-world digital forensic methods and tools to solve investigative scenarios. Students also learn about related careers while improving their cyber street smarts. The CSSL incorporates additional “outside of the computer” activities to strengthen students’ engagement such as structured in-classroom discussions, mock trials, and in-person interactions with practitioner role models. The CSSL was piloted in various forms to assess the suitability for in-school and out-of-school settings, and the students predominantly represented racial minorities. Research in this project relied on a mixed methods approach for data collection and analysis, including qualitative and quantitative methods, reinforced using learning analytics generated from the students clicking through the interface and interacting with the system. Analysis of gathered data indicate that the virtual learning environment developed in this project is highly effective for teaching digital forensic knowledge, skills, and abilities that are directly applicable in the workplace. Furthermore, the strategies for gender inclusive STEM instruction implemented in CSSL are effective for engaging girls without being harmful to boys’ engagement. Learning STEM through digital forensic science taps into girls’ motivations to address real-world problems that have direct relevance to their lives, and to protect and serve their community. After participating in the educational program, girls expressed a significantly greater increase in interest, relative to boys, in learning more about careers related to digital forensics and cybersecurity.