Smart Learning Environment (SLE) in the Fourth Industrial Revolution (IR 4.0)

This paper is attempted to examine the explanatory approach in dealing with SLE by advancing online learning sources. The systematic approach of searching for the relevant articles on SLE in IR 4.0 has been widely identified through two electronic databases, Scopus and Web of Sciences. Through adopting such digitally systematic search program, identification was made on the various elements in terms of online learning resources (OLR). This attempts to propose the SLE framework model with an innovative approach in enhancing the learning through incorporating IR 4.0 platform to utilize the variety of information sources together with knowledge attribution in the higher education (HE). The contribution provides theoretical framework with the guideline of well-adapted performance in the educational activities as the new normal trend. In achieving this attainment, the readiness of both instruction facilities and accessibility procedure is significantly the main basis in ensuring the process flow in enlarging the digital learning.

Teaching through urban sensorium: urban spatiality as a smart learning environment

This paper qualitatively analyses the implication of urban sensorium as a pedagogic mode in the teaching of Urban Studies. Underpinned by the frames of smart learning environments, the paper reiterates experiencing urban ontologies as spatial learning environments. By drawing from a range of transdisciplinary and experiential modes of learning, this paper maps how an undergraduate course on Bangalore city in India served learners to critically engage with and experience spatial urban ontologies both digitally, and in real-world experiences of learning, furthering learner autonomy and reflection. The methodological prisms of this paper are autoethnography and critical reflection. It is organised around enabling learners recognize the experiential, embodied urban spaces through the urban sensorium via real-life engagements with urban spaces, and creation of digital portfolios that map this learning. Findings from the learners’ knowledge of sensory learning, the city’s intersectional aspects, and the student’s embodied and emplaced self in built environments and digital spaces are analysed via cognitive and affective-reflection levels; the course instructor's reflection is analysed via a process-reflection level. These reflections hold implications for the pedagogy of urban studies in undergraduate classrooms by foregrounding spatiality and urban sensorium as significant critical and affective pedagogic tools. The paper has also accommodated critical engagement with an external faculty member as a co-author, in order to manage any bias or researcher subjectivity in the design.

Personalized Smart Learning Recommendation System for Arabic Users in Smart Campus

The advancement of technologies has modernized learning within smart campuses and has emerged new context through communication between mobile devices. Although there is a revolutionary way to deliver long-term education, a great diversity of learners may have different levels of expertise and cannot be treated in a consistent manner. Nevertheless, multimedia documents recommendation in Arabic language has represented a problem in Natural Language Processing (NLP) due to their richness of features and analysis ambiguities. To tackle the sparsity problem, smart learning recommendation-based approach is proposed for inferring the format of the suitable Arabic document in a contextual situation. Indeed, the user-document interactions are modeled efficiently through deep neural networks architectures. Given the contextual sensor data, the suitable document with the best format is thereafter predicted. The findings suggest that the proposed approach might be effective in improving the learning quality and the collaboration notion in smart learning environment

An Artificial Neural Network (ANN)-Based Learning Agent for Classifying Learning Styles in Self-Regulated Smart Learning Environment

The increasing development in smart and mobile technologies are transforming learning environments into a smart learning environment. Students process information and learn in different ways, and this can affect the teaching and learning process. To provide a system capable of adapting learning contents based on student's learning behavior in a learning environment, the automated classification of the learners' learning patterns offers a concrete means for teachers to personalize students' learning. Previously, this research proposed a model of a self-regulated smart learning environment called the metacognitive smart learning environment model (MSLEM). The model identified five metacognitive skills-goal settings (GS), help-seeking (HS), task strategies (TS), time-management (TM), and self-evaluation (SE) that are critical for online learning success. Based on these skills, this paper develops a learning agent to classify students' learning styles using artificial neural networks (ANN), which mapped to Felder-Silverman Learning Style Model (FSLSM) as the expected outputs. The receiver operating characteristic (ROC) curve was used to determine the consistency of classification data, and positive results were obtained with an average accuracy of 93%. The data from the students were grouped into six training and testing, each with a different splitting ratio and different training accuracy values for the various percentages of Felder-Silverman Learning Style dimensions.

Determinants of Active Online Learning in the Smart Learning Environment: An Empirical Study with PLS-SEM

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.

Review on self-regulated learning in smart learning environment

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.