E-learning Methodologies: Fundamentals, technologies and applications
2: Department of Computer Science and Engineering, National Institute of Technology (NIT), Hamirpur, India
E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote learning. ICT and computational intelligence techniques are being used to design more intelligent and adaptive systems. However, the art of designing good real-time e-learning systems is difficult as different aspects of learning need to be considered including challenges such as learning rates, involvement, knowledge, qualifications, as well as networking and security issues. The earlier concepts of standalone integrated virtual e-learning systems have been greatly enhanced with emerging technologies such as cloud computing, mobile computing, big data, Internet of Things (IoT), AI and machine learning, and AR/VT technologies. With this book, the editors and authors wish to help researchers, scholars, professionals, lecturers, instructors, developers, and designers understand the fundamental concepts, challenges, methodologies and technologies for the design of performant and reliable intelligent and adaptive real time e-learning systems and platforms. This edited volume covers state of the art topics including user modeling for e-learning systems and cloud, IOT, and mobile-based frameworks. It also considers security challenges and ethical conduct using Blockchain technology.
Inspec keywords: Big Data; mobile learning; data analysis; Internet of Things; augmented reality; human factors; bioinformatics; artificial intelligence; computer aided instruction; cloud computing; teaching
Other keywords: smart campus; mnemonics; artificial intelligence; IoT workshop course; active learning; student performance prediction; node-RED; elliptic curve cryptosystem; intelligent techniques; authentication protocols; virtual laboratory environment; learning analytics model; bioinformatics algorithms; resource constrained RFID-sensor integrated mobile devices; e-learning systems; computational algorithms; mobile learning; pedagogies; student behavioural engagement prediction; augmented reality; smart e-learning transition; cloud computing; goal-oriented adaptive e-learning; big data; COVID-19; microlearning
Subjects: Computer-aided instruction; Ergonomic aspects of computing; Information networks; Knowledge based systems; Mobile, ubiquitous and pervasive computing; Data handling techniques; General and management topics; Internet software
- Book DOI: 10.1049/PBPC040E
- Chapter DOI: 10.1049/PBPC040E
- ISBN: 9781839531200
- e-ISBN: 9781839531217
- Page count: 352
- Format: PDF
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Front Matter
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Part I: Introduction and pedagogies of e-learning systems with intelligent techniques
1 Introduction
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B-learning in the twenty-first century is a learning platform where learning materials are shared for learners using smart e-learning applications. Different technologies and techniques are used to handle the teaching activities and assist the student to learn the content in an effective manner. In the last few years, the field of education gets MOOCs that is an excellent innovation of the e-learning concept used in largely populated countries from which learners can increase their knowledge. One of the main advantages of e-learning is that the learners get the chance to attend lectures of excellent professors of their fields. E-learning can be made successful with the help of a positive attitude and interaction between learners and instructors. This book explores the different technologies and techniques of e-learning systems that may handle the retention and satisfaction of learners with socioeconomic well-equipped systems. It also explores the methods and techniques toward the e-learning system for the quality of the curriculum, flexibility of the course, course usefulness, and ease of use of the system.
2 Goal-oriented adaptive e-learning
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Adaptive learning has a lot of potential as it provides personalized information and learning material for learners of various fields and areas. The adaptive e-learning systems are now an ingrained vehicle of modern education. It accommodates an extensive range of learners with varied backgrounds who register with their specific learning objectives. The main challenge in the current situation is to produce adaptive learning paths so that learners can attain their goals successfully. Previous works used static features such as learners' level of knowledge, browsing preferences, grasping ability, and learning styles to determine personalized learning materials.
3 Predicting students' behavioural engagement in microlearning using learning analytics model
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The student-centred learning trend is one of the e-learning service factors in universities and schools that have been improved with added values. Now, students can access the e-learning platform on a cloud server with their mobile devices. Several ways and practices in e-learning today include learning management systems (LMS), blended learning, microlearning, mobile learning, open learning, self-learning, and virtual learning. Microlearning refers to the micro perspective in learning contact, education, and exercise. Student engagement is one of the key indicators of a successful implementation of e-learning. Those studies were carried out based on the educational data mining technique, which is widely used in analysing the various patterns of online learning behaviour and predicting learning outcomes. Another popular technique that uses a similar approach but with different focus is learning analytics.
4 Student performance prediction for adaptive e-learning systems
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The growth of e-learning systems has changed current learning behavior and tries to present a new framework for the learners. E-learning platforms have become common and approachable for a vast set of audiences. The COVID-19 pandemic in 2020 has triggered the application of these online learning platforms. The number of e-learning platforms has been increasing rapidly to fulfill the requirement. This chapter tries to estimate the three factors consisting of learner's personality, learning style and knowledge level in order to recommend the content that is best suited to the learner. An ensemble approach to solving this problem has been used, which utilizes a genetic algorithm and KNN to find the content appropriate for the learner.
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Part II: Technologies in e-learning
5 AI in e-learning
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This current research chapter focuses on the different areas of e-learning where AI can be implemented to make e-learning a better experience. E-learning is a 24/7 platform where learners can gain knowledge at the convenience of their home and timeframe. AI can help such learners with different adaptive technologies in clarifying the doubt, identifying the problem area of the learner and providing them a customized learning solution. Adaptive learning suggested that the learning pace is different for different learners. It must be made sure that the educational supplies and amenities provided must fit the requirement of each learner; else, it will lose its essence. There are different AI features to enhance the learning experience of e-learning. The providers must keep this in mind that the acquired information about learners must be wisely used while implementing the AI technology to e-learning mode so that the blended model can provide an enriching experience to the end-user. Cognitive learning can be a key to constructive, collaborative and contextualized execution of AI-enabled learning processes. Maximization of AI effectiveness as a tool of e-learning can be brought only when it is implemented to overall program pedagogy and is monitored for continuous improvement.
6 Mobile learning as the future of e-learning
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B-learning plays a crucial role in modern-day education. In traditional learning, a successful teacher can teach at one location in a given time. B-learning complements modern learning and an effective tool for the individual's intellectual development and helps one to create is an intelligent society. Among the various new trends in e-learning, mobile learning (m-learning) is the revolutionary measure and has lots of scopes and potential to transform India's educational system. Millennial generation heavily depends on the smartphone for its entertainment and educational needs. Changing technological advancement also provides an opportunity for higher education institutions to explore the various online educational methods to engage a large number of students. This chapter aims to provide the significant importance of m-learning, its opportunities, and challenges in the Indian higher education system.
7 Smart e-learning transition using big data: perspectives and opportunities
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E-learning is providing education through a computing platform that encourages the learners to learn from anywhere at anytime. Web 5.0 will be able to map the emotions of the people when they are interacting with computers. The users can interact with content with the help of a headphone. While interacting with the content, their emotions are captured by the facial recognition system to bring real classroom learning into the existing e-learning system. Any e-learning approach will lead to the explosion of different types of information such as text, videos, and images, results in different data types that are not used in traditional data management systems. Analytical operations cannot be applied directly to these data. The online learning platforms generate enormous learner behavioral data, and educational big data plays an important role in transforming the data obtained from online learning platforms into useful information for the improvement of academic activities. The teachers can develop the content for personalized learning analyzing the current knowledge level of the students. The students have the opportunity to learn at their own pace. The key issue here is the effective analysis and utilization of the data to improve the e-learning features. Big data technology provides the capability of analytics to enhance the e-learning process. This chapter presents the outline of the big data techniques such as prediction, clustering, relationship mining, structure discovery, and various tools used for big data analytics in e-learning.
8 E-learning using big data and cloud computing
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E-learning is a learning system in which educational content and technologies are combined to facilitate the learner. The unprecedented increase in the number of students and educational contents and services provided makes the e-learning method grow at an exponential rate. Many challenges such as optimized resource utilization, storage necessities along with management of dynamic parallel requests concurrently necessitate the use of a platform that demands cost control to be satisfied by the environment of cloud computing. Cloud-based e-learning architecture provides an enriched learning experience, including interactive features to challenge the user's depth of understanding and level of preservation. Big data, in the context of e-learning, is the data that are gathered from learners during the learning process from many platforms such as the progress of the learners, evaluation results, discussion forums, messages, feedback, collaborations with the learner communities, teaching interactions and any other data generated related to the learning process. The data collected by these mediums are still large and difficult to manage. Hence big data in e-learning helps in a better analysis and management of these data. Using big data technologies, the enormous amount of data generated by e-learning can be processed, analyzed, organized, filtered, which can be visualized efficiently in less time. The useful information can be extracted from a large volume of data that help one to take better, smarter, and fast decisions. Hence, it is better to make use of big data for a better quality of e-learning systems. This chapter provides insight into the drawbacks of the conventional e-learning model, e-learning using the technology of cloud computing and big data. It also shows how big data and cloud computing are integrated to provide e-learning support. Moreover, it uncovers some of the case studies in e-learning industries and concludes with challenges.
9 E-learning through virtual laboratory environment: developing of IoT workshop course based on Node-RED
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At present, there are tremendous growth and opportunities in the field of the “Internet of Things” (IoT). Further, various application systems, such as smart city, smart grid, smart healthcare system, smart transportation, require the adaptation of IoT within it to enhance the smartness of such systems. In addition, several industrial organizations are focusing on the generation of revenues out of these IoT-based services. Hence, in this perspective, there is an extensive demand for human knowledge power on IoT technologies. There exists an increase in demand for professionals in IoT technology. Need for preparing engineering graduates to face this demand of IoT technology is the key focuses of educational institutions. In the revised course curriculums, IoT specialized courses remain the main focuses to be framed and offered to students. This chapter discusses one such course named IoT workshop, which was offered to the sixth semester students of undergraduate engineering. This chapter presents the requirements of offering IoT course, Node-RED as programming environment, course contents delivered, learning outcomes, course assessments, projects, and results achieved through this course by students.
10 Mnemonics in e-learning using augmented reality
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AR has great potential to affect the learning experience. Developments in AR technology have motivated the researchers to develop applications using hardware, software and the authoring of content. A summary of the main findings of this chapter are as follows: Published surveys show that AR in education has progressively increased year by year intensively and mainly in the last 8 years. AR has been mostly applied in professional education as well as higher education. Location-based AR along with marker-based AR is very widely used.
11 E-learning tools and smart campus: boon or bane during COVID-19
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The sudden spread of novel coronavirus COVID-19 across the world has been leading to the drastic changes in complete structural, organizational and social aspects of every sector, including the education system. The quick closure of universities and schools for public health safety during COVID-19 pandemic has become a catalyst for searching innovative solutions within a short span of time. In the context of this new and challenging situation, e-learning tools have become the new educational policy and practice for virtual classrooms. This chapter presents an analysis of various e-learning tools for synchronous and asynchronous learning. It also focuses on the various health issues arising due to the excessive exposure of everyone to screens with the growing adoption of online learning tools and technologies.
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Part III: Case studies
12 Bioinformatics algorithms: course, teaching pedagogy and assessment
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This chapter is a case study for presenting various modes of in-class lecture delivery, student-instructor interaction, and topic discussion. The aim of using numerous forms of teaching-learning pedagogy is for justifying and achieving the learning outcomes of the course. We have tried to incorporate and change strategies of having instructor-led training (ILT) materials to student-centric learning. It explores various learning styles and dimensions so that the course content may be delivered to its fullest. Adaptation of different types of learning styles is implemented to promote flexibility with the instructor and help the students perceive a topic in various flavours. The chapter also puts forth topic-wise teaching-learning pedagogy availed, students' motivations, as justified from their informal feedbacks, recommended actions that have a positive influence in topic delivery and understanding and usage in exploring the subject in relation to other domain studies. Blooms' cognitive level is also mentioned to give a concise idea of the topic depth that would be followed in this particular course delivery. The chapter also discusses the concept development and exploration, courserelated material design and development, and evaluation and analysis. The measurement framework is developed on the basis of the following criteria of intuitive capability levels, in-class response, topic understanding (based on student's informal and formal feedback), and marks-based evaluations. This inherently incorporates certain evaluation practices followed in this course. Having a high cohesion with bioinformatics, the course helps in offering computational solutions to sustainability-related issues. Further, based on NBA requirements, the course outcomes are also measured as per their given directives. Based on student's interactions, the course was found to be popular and useful to students. The computer science and engineering and information technology (CSE and IT) students could easily relate the understanding of data structures and algorithms captured in the interdisciplinary course, whereas the biotechnology students could relate their core knowledge in bioinformatics, genes, genetics, protein, and other domain knowledge to the various algorithms that can help in addressing solutions. The subject presents a win-win situation for students as they get to work in a domain with a vast dataset and that can have a huge impact on the human lifestyle and lifespan understanding.
13 Active learning in E-learning: a case study to teach elliptic curve cryptosystem, its fast computational algorithms and authentication protocols for resource constraint RFID-sensor integrated mobile devices
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Elliptic curve cryptosystem (ECC) is a public key-based cryptosystem. All algorithms in ECC are based on point addition and point doubling arithmetic operations. This explains the active learning process and its usages in teaching elliptic curve cryptography-based contents to undergraduate and graduate students. Here, the emphasis is given to mathematical contents that make the cryptography concepts easy to understand. Mathematical concepts mainly include point addition and doubling with examples. Further, technical observations are made while teaching these contents. For example, to reduce computation cost, there is a need for fast computations method in point addition and doubling operations. Thus, the fast computational algorithms are important to understand with examples for reducing the cost in ECC arithmetic operations. These algorithms reduce the number of steps required to perform cryptography operations by reducing compute addition and doubling operations. Group authentication is one of the major application domains of ECC. This work presents the ECC-based authentication protocols that provide authentication using encryption/decryption, digital signature, and other cryptography primitives. In observations, it is found that the proposed approach is much better quantitatively and quantitatively compared to traditional teaching.
14 Conclusion
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This book addresses techniques, technologies, pedagogies, and issues in implementing an e-learning system. The sustainability of any system depends on the four-pillar model of sustainability such as human, social, economic, and technical. Human sustainability allows us to protect the individual need and support the system in such a way that it improves the quality of human life. The book explores the adaptive e-learning systems and accommodates an extensive range of learners with the varied background to fulfill their specific aims. Behavioral engagement in microlearning, one of the techniques that help the instructor to develop the e-learning applications. Artificial intelligence (AI) is another technique that supports a more personalized e-learning environment. AI injected e-learning platforms that can adapt from the previous learning pattern of the users and empower them with a customized pedagogy for better understanding. Personalized learning and recommender systems can help widely in mining the required information for the learner from the information overload. Use of AI in e-learning is gaining a lot of importance and is becoming a wide area of research to improve learning experience with advanced technology.
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Back Matter
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