Smart Learning Systems Research Articles

Empowering Education Through Teacher-Technology Collaboration

The emergence of information systems integrated with artificial intelligence (AI) technology has led to great changes in education. It provides strong support and guarantees for the education field in adapting to the transformation of the digital environment. As end users and managers of information system and technology, teachers' innovative leadership contributes to this rapid digitalization transformation in education. The current study highlights the leading role of teachers' innovative leadership in the teaching process involving the application of smart learning systems. In the smart learning environment, teachers who practice more innovative leadership improve the engagement of end user learners in collaborative activities and further improve campus organizational culture. This study further presents feasible measures for practicing innovative teacher leadership in class and conducting blended management for teacher–AI collaboration in a smart environment.

Ethical enactivism for smart and inclusive STEAM learning design

Current global challenges of the 21st century promote STEAM (science, technology, engineering, arts and mathematics) education and digitalization as a means for humans to be the central actors in the construction of a sustainable society that favors a sense of worth and global wellbeing. In this scenario, new educational technology tools and immersive learning affordances (possibilities), offer unprecedented potential for the design of smart and dynamic learning systems and contexts that can enhance learning processes across varied audiences and educational settings. However, current STEAM education practice lacks attention to equipping all citizens with the necessary skills to use digital technologies in an ethical, critical and creative way. This gap calls for attention in design processes, principles and practices that are attentive to ethical considerations and values-based approaches. On the other hand, in its formulation STEAM as an educational approach is framed in four fundamental pillars: creativity, inclusion, citizenship and emerging technologies, which also put attention on the inclusion of disadvantaged and underrepresented social groups during STEAM education design. Following an apparent need to explore ethical and inclusive design in STEAM education, and inspired in the 4E cognition framework, ethical enactivism and embodied and ecosomaesthetics experience design, here we propose a theoretical framework grounded on systems thinking for the design of smart and dynamic STEAM learning systems and settings. The framework is aimed at STEAM educational psychologists, educational technologists, learning designers and educational practitioners who wish to address the global challenges of 21st century education by means of creative, innovative and inclusive education design.

A Comparative Study of Intelligent Online e-Learning Systems

In recent decades there has been extremely rapid development in the area of Information Technology (IT), with different IT applications altering significantly as well. Online teaching and learning is one of the most popular IT applications. The primary objective of this essay is to examine and then compare the different e-learning designs. Some recommendations on the limits identified based on the analytical comparative analyses of these different designs have been offered as context of this research. This study highlights a number of research and design difficulties to enhance smart online learning systems to offer the cultural elements of online classrooms with a productive effect. The study also highlights the concerns that need to be addressed.

Smart systems of open science in teachers’ education

The project announced by the authors is focused on the smart systems of open science, the issues of elaboration of methodological, methodic, and organizational support for the use of these emerging ICT in teaching and research, increasing the level of ICT competence of educators, including teachers. Given the significant pedagogical potential and novelty of existing approaches to the use of smart learning systems meaning first of all the cloud-based adaptive open science systems, their design and use in educational institutions, these issues still need theoretical and experimental research, refinement of approaches, models, methods and techniques, possible ways of implementation. In the process of studying leading experience, the following advantages of using the cloud-based services for teachers’ training were identified: resource savings, access mobility, elasticity, and others. The cloud-based methodological system for educational personnel training at different levels was proposed and considered: for the training of students for the Master level for educational sciences; for the in-service teachers’ training. The methods and tools of smart systems design and implementation were considered and evaluated. The recommendations and suggestions for the advisable ways for smart systems of open science introduction into the learning process were provided.

Artificial Intelligence and Its Role in Education

The objective of this study is to explore the role of artificial intelligence applications (AIA) in education. AI applications provide the solution in many ways to the exponential rise of modern-day challenges, which create difficulties in access to education and learning. They play a significant role in forming social robots (SR), smart learning (SL), and intelligent tutoring systems (ITS) to name a few. The review indicates that the education sector should also embrace the modern methods of teaching and the necessary technology. Looking into the flow, the education sector organizations need to adopt AI technologies as a necessity of the day and education. The study needs to be tested statistically for better understanding and to make the findings more generalized in the future.

Internet of Things for education: A smart and secure system for schools monitoring and alerting

The education system is one of the mechanisms and aspiration to build the society and contribute to human capital, well-being, and wealth. Security and privacy concerns in the educational organization is always significant due to various violent and terrorist activities. Technologies have been adopted for smart learning systems to improve the learning experience whereas security has been neglected inside or outside the institutions. The new integrated technologies have been adopted by using smart monitoring and sensing devices. The main objective of this paper is to analyze the Internet of Things (IoT) solutions specially designed for schools to provide smart and secure systems for educational settings. This paper also proposes a Secure system for the Internet of Schools Things (S-IoST) for smart schools based on a new advanced communication system integrated with 5 G cellular systems, sensing technologies, intelligent transportation systems, and IoT networks. The proposed system provides a more secure alert mechanism and facilitates the users at school and during mobility to the school or home. The proposed system evaluates in terms of data delivery, time, and response alert parameters .

Student behavior analysis to measure engagement levels in online learning environments.

After the COVID-19 pandemic, no one refutes the importance of smart online learning systems in the educational process. Measuring student engagement is a crucial step towards smart online learning systems. A smart online learning system can automatically adapt to learners’ emotions and provide feedback about their motivations. In the last few decades, online learning environments have generated tremendous interest among researchers in computer-based education. The challenge that researchers face is how to measure student engagement based on their emotions. There has been an increasing interest towards computer vision and camera-based solutions as technology that overcomes the limits of both human observations and expensive equipment used to measure student engagement. Several solutions have been proposed to measure student engagement, but few are behavior-based approaches. In response to these issues, in this paper, we propose a new automatic multimodal approach to measure student engagement levels in real time. Thus, to offer robust and accurate student engagement measures, we combine and analyze three modalities representing students’ behaviors: emotions from facial expressions, keyboard keystrokes, and mouse movements. Such a solution operates in real time while providing the exact level of engagement and using the least expensive equipment possible. We validate the proposed multimodal approach through three main experiments, namely single, dual, and multimodal research modalities in novel engagement datasets. In fact, we build new and realistic student engagement datasets to validate our contributions. We record the highest accuracy value (95.23%) for the multimodal approach and the lowest value of “0.04” for mean square error (MSE).

Enhanced learning resource recommendation based on online learning style model

Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences. This paper introduces a learning style model to represent features of online learners. It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style (AROLS), which implements learning resource adaptation by mining learners' behavioral data. First, AROLS creates learner clusters according to their online learning styles. Second, it applies Collaborative Filtering (CF) and association rule mining to extract the preferences and behavioral patterns of each cluster. Finally, it generates a personalized recommendation set of variable size. A real-world dataset is employed for some experiments. Results show that our online learning style model is conducive to the learners' data mining, and AROLS evidently outperforms the traditional CF method.

Enabling smart learning systems within smart cities using open data

Enabling smart learning systems within smart cities using open data

Exploration of Knowledge Engineering Paradigms for Smart Education: Techniques, Tools, Benefits and Challenges

Knowledge engineering paradigms (KEPs) deal with the development of intelligent systems in which reasoning and knowledge play pivotal role. Recently, KEPs receive increasing attention within the fields of smart education. Researchers have been used the knowledge engineering (KE) techniques, approaches and methodologies to develop a smart tutoring systems (STSs). The main characteristics of such systems are the ability of reasoning, inference and based on static and heuristic knowledge. On the other side, the convergence of artificial intelligence (AI), web science (WS) and data science (DS) is enabling the creation of a new generation of web-based smart systems for all educational and learning tasks. This paper discusses the KEPs techniques and tools for developing the smart educational and learning systems. Four most popular paradigms are discussed and analyzed namely; case-based reasoning, ontological engineering, data mining and intelligent agents. The main objective of this study is to determine and exploration the benefits and advantages of such computational paradigms to increase the effectiveness and enhancing the efficiency of the smart tutoring systems. Moreover, the paper addresses the challenges faced by the application developers and knowledge engineers in developing and deploying such systems. In addition to institutional and organizational aspects of smart educational technologies development and application

Does Context Matter for Value Co-Creation in Smart Learning Services?

Smart learning services offer customized learning solutions by systematically considering the context of the learner. However, it is still unclear how the value co-creation within smart learnings is affected by context considerations. Therefore, it is important to understand how the context can influence the value co-creation potential in smart learning services. To investigate how context shapes value co-creation smart learning services, we first conduct a systematic literature review to investigate context factors within learning processes. Afterwards, we develop a conceptual model to explain how context in smart learning systems can be used to enhance the co-creation potential in learning processes. Overall, we provide a better understanding of context considerations in service systems as a theoretical contribution of our conceptual investigation. Finally, we provide practical implications for educational service providers for designing smart learning services under the specific consideration of different contexts

Exploiting the knowledge engineering paradigms for designing smart learning systems

Knowledge engineering (KE) is a subarea of artificial intelligence (AI). Recently, KE paradigms have become more widespread within the fields of smart education and learning. Developing of Smart learning Systems (SLS) is very difficult from the technological perspective and a challenging task. In this paper, three KE paradigms, namely: case-based reasoning, data mining, and intelligent agents are discussed. This article demonstrates how SLS can take advantage of the innovative KE paradigms. Therefore, the paper addresses the pros of such smart computing approaches for the industry of SLS. Moreover, we concentrate our discussion on the challenges faced by knowledge engineers and software developers in developing and deploying efficient and robust SLS. Overall, this study introduces the reader the KE techniques, approaches and algorithms currently in use and the open research issues in designing the smart learning systems.

Evolve systems using incremental clustering approach

Incremental learning, software engineering, data mining, incremental clustering etc. are the major research areas and have received great attention in recent years. The intelligent system building cannot be possible without efficient knowledge management and machine learning. Since traditional machine learning approaches do not consider systemic interactions, they fail in complex decision scenarios. Increasing competition and the availability of multitudes of information and diminishing traditional entry barriers have left next generation businesses with the requirement to compete on knowledge grounds. This motivated my research in incremental learning. The effective reuse of organization data, fast and pragmatic learning based on context and augmentation of knowledge are some of the major research outcomes. This work introduces a new paradigm for machine learning and propose new framework for incremental clustering. Learning in dynamic scenarios, exploration and smart decision making are a few other facets of this work. With the information explosion, numerous information systems and complex decision scenarios there is need of truly smart learning systems and effective knowledge management. This work definitely builds the platform for dynamic and incremental learning. The stability of method is proved by various ways: it can handle all possible inputs—further mathematically prove that it converges—also prove same thing with experiments.