When E-Learning Meets Big Data, Cognitive Computing, and Collaborative Environments (original) (raw)
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Collaborative E-learning Environments with Cognitive Computing and Big Data
Journal of Visual Language and Computing, 2019
The actual scenario of e-learning environments and techniques is fast-changing from both the technology side and the users' perspective. In this vein, applications and services as well as methodologies are evolving rapidly, running after the more recent innovations and thus adopting distributed cloud architectures to provide the most advanced solutions. In this situation, two influential technological factors emerge: the former is cognitive computing, which can provide learners and teachers with innovative services enhancing the whole learning process, also introducing improvements in human-machine interactions; the latter is a new wave of big data derived from heterogeneous sources, which impacts on educational tasks and acts as enabler for the development of new analytics-based models, for both management activities and education tasks. Concurrently, from the side of learning techniques, these phenomena are revamping collaborative models so that we should talk about communities rather than classrooms. In these circumstances, it seems that current Learning Management Systems (LMS) may need a redesign. In this respect, the paper outlines the evolutionary trends of Technology-Enhanced Learning (TEL) environments and presents the results achieved within two experiences carried on in two Italian universities.
Editorial note: Learning management systems and big data technologies for higher education
Education and Information Technologies
We are living in a "data-driven era" where new data is continuously being generated by people, businesses, organisations, communities and society each and every moment. This rapidly growing and huge amount of data must be managed through effective way for producing useful information and human decision support for current and future problems solving, planning and practices improvements as well as to generate new knowledge for future generation. As a result, organizations globally are making significant investments to explore how to better utilise the huge data and its diversification to create value and actionable insights (for example, Pardos 2017; Miah et al. 2017, 2019a, b). Big data has been a popular research problem across different academic disciplines. Although this problem has been treated mainly for advancing and innovating technological development (Wang et al. 2017), organisations and business communities are continuously exploring different aspects, perspectives and contextual specifics to find or explore benefits and value adding for improving practices. A lot of existing studies have defined the big data considering large volumes of broadly varied and complexity of datasets that are continuously being generated. The consideration for defining this goes to velocity, volume, value, variety, and veracity, so-called the "five V's of Big Data" (Gandomi and Haider 2015). Organizations such as education institutes have started to treat the issues of big data for reinforcing traditional electronic learning and teaching methods and other relevant products, and services (McAfee et al. 2012). For doing this, opportunity of adopting latest analytics, predictive algorithms and other disruptive technologies are rapidly developed for advancing the traditional e-learning approaches, for example in improving learning management systems (LMS).
Is e-learning ready for big data? And how big data would be useful to e-learning
The paper presents an overview of possible application fields of big data to the technology-enhanced learning (TEL), with the many different facets this could imply. Many are the benefits for e-learning when approaching the collection of data, especially when e-learning is delivered in a lifelong learning perspective. All these benefits could impact the future of eLearning, by revolutionizing the way we analyze and assess the eLearning experience. On one side, we present our experience in enriching the persistence layer of an LMS with a deeper log system on users' actions, in the perspective of collecting volumes of data compatible with big data tools and technologies, while highlighting some related issues. On the other hand we will deal with the first applications of cognitive systems that are responsible for catalysing the big data in analytics aimed at e-learning activities.
Taking Advantage of the “Big Data” in Learning Management Systems
Proceedings of the Canadian Engineering Education Association (CEEA)
This work presents a novel learning management system (LMS), named Catalyseur, which allows the students to easily access pedagogic contents as well as allowing them to directly complete the exercises on the web. Meanwhile, the LMS acquires data about the students’ completion status of exercises and lessons and the time before the examinations these exercises and lessons were completed. An artificial intelligence (AI) predicting student performance at an examination depending of their completion status is continuously updated with new data acquired by the LMS. This AI model is then used to automatically send messages to students about how they are expected to perform at the next examination.
IJERT-The Relation between Big Data Technology and E-Learning
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/the-relation-between-big-data-technology-and-e-learning https://www.ijert.org/research/the-relation-between-big-data-technology-and-e-learning-IJERTV3IS041525.pdf Big Data has been an emerging topic especially when it is considerably linked with E-learning. In today's era, most of the people are interested in fetching major part of the information like to buy or sell (i.e. e-commerce transactions), are all performed through cloud which is the other name of internet. People are learning and even developing new sources of information and income through e-learning. But the problem is that web has a humongous amount of structured and even un-structured data which can be given a systematic presentation in E-learning and users may get maximum information through interfaces. This signifies the reason that this literature may boost the upcoming learning techniques and technologies when we combine the benefits of Big Data to increase velocity and volume of E-learning and all its types. E-learning needs different sources to extract insights. And through this study help can be taken that how, the complex nature of Big Data can be taken a benefit of with using it in all different modes of E-learning and can promote the usage though many institutes are unaware of the usage of Big Data. A further research can also be conducted in which learning analytics can be extracted with the help of Big Data information in this study and new research in Artificial Intelligence and easy learning can be promoted through this study.
Big Data & Learning Analytics: A Potential Way to Optimize eLearning Technological Tools
International Association for Development of the Information Society, 2013
In the information age, one of the most influential institutions is education. The recent emergence of MOOCS is a sample of the new expectations that are offered to university students. Basing decisions on data and evidence seems obvious, and indeed, research indicates that data-driven decision-making improves organizational productivity. The most dramatic factor shaping the future of higher education is Big Data and analytics. Big Data emphasizes that the data itself is a path to value generation in organizations and it is, also, a critical value for higher education institutions. The emerging practice of academic analytics is likely to become a new useful tool for a new era. Analytics and big data have a significant role to play in the future of higher education. This paper attempts an analytical practice about the use of e-learning technological tools to generate relevant information, for the teacher and the students who try to optimize their learning process.This combination of ...
Learning Management System 2.0
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The recent adoption of cloud computing, Web 2.0 (web as a platform), and Big Data technologies have become the main driver of the paradigm shift. For higher education, choosing the right platform for a next generation of Learning Management System (LMS) namely LMS 2.0 is becoming more important than choosing a tool in the new paradigm. This chapter discusses factors for higher institution in determining a future direction for its LMS to take advantage of pervasive knowledge management, efficiency and effectiveness of operations. Literature studies have deployed for this study to portray the state of future LMS initiative. We found that the trends of cloud computing and big data will be predominant factor in viewing future LMS adoption and implementation. LMS 2.0 can be a solution to make learning systems in a higher education is flexible in terms of resources adoption, quality of learning, knowledge management, and implementation.
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The aim of this paper is to present revolutionizing education through e-learning and big data. In the olden times, when technology has not progressed far enough, there is only one type of teaching and learning that was known: a chalk and blackboard, with the teacher and student being the main factor. Now, as in the 21st century, teaching can be taught without a teacher and learning can be done in various ways, either we are connected to the Internet or not, and also have the drive to strive for that certain knowledge. E-learning is referred to the delivery of learning, training or education via electronic means. It happened because of the revolutionizing of technology and increased in standard of living. The technology includes many types of mass media, for example photos, voice, animation and many more. This application will react efficiently with the connection of the Internet. In addition, e-learning can occur inside or outside the classroom. In fact, people nowadays use e-learning as distance learning or blended learning, which blended learning means the use of technology during face-to-face interaction.
Improving Online Education Using Big Data Technologies
The Role of Technology in Education, 2020
In a world in full digital transformation, where new information and communication technologies are constantly evolving, the current challenge of Computing Environments for Human Learning (CEHL) is to search the right way to integrate and harness the power of these technologies. In fact, these environments face many challenges, especially the increased demand for learning, the huge growth in the number of learners, the heterogeneity of available resources as well as the problems related to the complexity of intensive processing and real-time analysis of data produced by e-learning systems, which goes beyond the limits of traditional infrastructures and relational database management systems. This chapter presents a number of solutions dedicated to CEHL around the two big paradigms, namely cloud computing and Big Data. The first part of this work is dedicated to the presentation of an approach to integrate both emerging technologies of the big data ecosystem and on-demand services of the cloud in the e-learning field. It aims to enrich and enhance the quality of e-learning platforms relying on the services provided by the cloud accessible via the internet. It introduces distributed storage and parallel computing of Big Data in order to provide robust solutions to the requirements of intensive processing, predictive analysis, and massive storage of learning data. To do this, a methodology is presented and applied which describes the integration process. In addition, this chapter also addresses the deployment of a distributed e-learning architecture combining several recent tools of the Big Data and based on a strategy of data decentralization and the parallelization of the treatments on a cluster of nodes. Finally, this article aims to develop a Big Data solution for online learning platforms based on LMS Moodle. A course recommendation system has been designed and implemented relying on machine learning techniques, to help the learner select the most relevant learning resources according to their interests through the analysis of learning traces. The realization of this system is done using the learning data collected from the ESTenLigne platform and Spark Framework deployed on Hadoop infrastructure.
In the past decade, we have witnessed a tremendous rise in the use of electronic devices in education. Starting from nursery classes at the preschool level to the postgraduate programs at the universities, electronic devices are being used extensively to enhance and facilitate quality of education. Although the use of computer networks is an inherent feature of online learning, the traditional schools and universities are also making extensive use of network-connected electronic devices such as mobile phones, tablets, and computers. However, it is humanly impossible to analyze enormous volume of data generated from the active usage of devices connected through a large network. The educators and academic administrators can benefit from their counterparts in business and service industries where a complex system of methods and techniques, usually referred as data analytics or data mining, are being used to analyze a large influx of real-time data in decision-making. Researchers have started paying attention to the application of data mining and data analytics to handle big data generated in the educational sector. In the context of education, these techniques are specifically referred to as Educational Data Mining (EDM) and Learning Analytics (LA). Generally, EDM looks for new patterns in data and develops new algorithms and/or new models, while LA applies known predictive models in instructional systems. This chapter starts by describing major EDM and LA techniques used in handling big data in commercial and other activities. It will also provide a brief description of how EDM and LA are affecting the typical stakeholders of a higher education institution. Furthermore, the chapter will provide a detailed account of how these techniques are used to analyze the learning process of students, assessing their performance and providing them with detailed feedback in real-time. The technologies can also assist in planning administrative strategies to provide quality services to all stakeholders of an educational institution. Not all the stakeholders involved in providing education are experts of big data. However, in order to meet their analytical requirements, the researchers have developed easy-to-use data mining and visualization tools. In this chapter, we have provided the necessary details of some of these tools. The institutions/governments across the world are adopting EDM/LA to frame strategic policies, to understand the students learning behaviors etc. As case studies, we have also discussed some implementation of EDM and LA techniques in universities in different countries.