Spatial computing goes to education and beyond
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Spatial big data (SBD) has been utilized in many fields and we propose SBD analytics to apply to education with semantic trajectory data of undergraduate students in Songdo International Campus at Yonsei University. Higher education is under a pressure of disruptive innovation, so that colleges and universities strive to provide not only better education but also customized service to every single student, for a matter of survival in upcoming drastic wave. The entire research plan is to present a smart campus with SBD analytics for education, safety, health, and campus management, and this research is composed of four specific items: (1) to produce 3D mapping for project site; (2) to build semantic trajectory based on class attendance records, dorm gate entry records, etc.; (3) to collect pedagogical and other parameters of students; (4) to find relationship among trajectory patterns and pedagogical characteristics. Successful completion of the research would set a milestone to use semantic trajectory to predict student performance and characteristics, even further to go to proactive student care system and student activity guiding system. It can eventually present better customized education services to participating students.Keywords:
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Learning Analytics
The emergence of massive open online courses (MOOCs) has caused a major impact on online education. However, learning analytics support for MOOCs still needs to improve to fulfill requirements of instructors and students. In addition, MOOCs pose challenges for learning analytics tools due to the number of learners, such as scalability in terms of computing time and visualizations. In this work, we present different visualizations of our "Add-on of the learNing AnaLYtics Support for open Edx" (ANALYSE), which is a learning analytics tool that we have designed and implemented for Open edX, based on MOOC features, teacher feedback, and pedagogical foundations. In addition, we provide a technical solution that addresses scalability at two levels: first, in terms of performance scalability, where we propose an architecture for handling massive amounts of data within educational settings; and, second, regarding the representation of visualizations under massiveness conditions, as well as advice on color usage and plot types. Finally, we provide some examples on how to use these visualizations to evaluate student performance and detect problems in resources.
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Learning analytics is receiving increased awareness because it helps educational institutions in growing student retention, enhancing student fulfillment, and easing the burden of accountability. Although those massive-scale issues are worthy of attention, schools may additionally be inquisitive about how they can use learning analytics in their personal guides to assist their students. In this chapter, the authors define learning analytics, the way it has been used in educational establishments, what learning analytics tools are available, and how college can make use of facts in their publications to reveal scholar overall performance. Finally, the authors articulate several problems and uncertainties with the usage of learning analytics in higher education.
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This paper describes the configuration, setup and initial analysis ex- amples of an experience for introducing learning analytics in a MOOC of maths for adult high school education, using the flipped the classroom methodology. An overview of the MOOC of maths is provided, as well as an overview of the ANALYSE learning analytics tool which is used to analyze the learning pro- cess. We describe how the learning analytics tool can be useful in this setup and methodology and we illustrate specific examples of conclusions in the MOOC of maths.
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The aim of learning analytics is to apply available facts from different systems and databases if you want to assist students and instructors in learning and teaching processes. In order to ensure that important groups of users get what they need the most with the aid of the usage of getting to know analytics, it may be vital to broaden possible needs evaluation methodology as well as to perform the wishes evaluation in step with it. There is much less research into the role and implementation of analytics in college training than in higher schooling. This chapter focuses on primary and secondary schools. Final results are presented in the form of the most relevant questions posed by students and teachers that a learning analytics system is supposed to answer.
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The emergence of platforms to support MOOCs (Massive Open Online Courses) strengthens the need of a powerful learning analytics support since teachers cannot be aware of so many students. However, the learning analytics support in MOOC platforms is in an early stage nowadays. The edX platform, one of the most important MOOC platforms, has few learning analytics functionalities at present. In this paper, we analyze the learning analytics support given by the edX platform, and the main initiatives to implement learning analytics in edX. We also present our initial steps to implement a learning analytics extension in edX. We review technical aspects, difficulties, solutions, the architecture and the different elements involved. Finally, we present some new visualizations in the edX platform for teachers and students to help them understand the learning process.
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There is growing rhetoric about the potential of learning analytics in higher education. There is concern about what the growing hype around learning analytics will mean for the reality. Will learning analytics be a repeat of past mistakes where technology implementations fail to move beyond a transitory fad and provide meaningful and sustained contributions to learning and teaching? How can such a fate be avoided? This paper identifies three paths that learning analytics implementations might take, with particular consideration to their likely impact on learning and teaching. An ongoing learning analytics project – currently used by hundreds of teaching staff to support early interventions to improve student retention - at a regional Australian university is examined in relation to the three paths, and some implications, challenges and future directions are discussed.
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This chapter presents the role of learning analytics in global higher education, thus illustrating the theoretical and practical overview of learning analytics; learning analytics and educational data mining (EDM); learning analytics and learning management system (LMS); learning analytics and Course Signals; learning analytics and knowledge perspectives; learning analytics and social networking sites; and the significance of learning analytics in global higher education. The application of learning analytics is critical in global higher education that seeks to serve the school administrators and students, increase educational performance, sustain competitiveness, and fulfill expected accomplishment in global higher education. The chapter argues that applying learning analytics has the potential to improve educational performance and reach strategic goals in the information age.
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Learning analytics is a key knowledge area for the improvement of education that proposes the use of educational data to improve decision making. In the last years, the Iberoamerican region has made a lot of efforts to introduce learning analytics and take advantage of its advantages. In this Special Issue, a set of proposals of learning analytics in this region are presented. The Special Issue includes four articles that cover a wide range of topics of this area, including adoption at the institutional level, analytics applied to academic improvement, video analysis, or visual analytics in learning management systems.
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Education for the twenty-first century continues to promote discoveries in the field through learning analytics. The problem is that the rapid embrace of learning analytics diverts educators' attention from clearly identifying requirements and implications of using learning analytics in higher education. Learning analytics is a promising emerging field, yet higher education stakeholders need to become further familiar with issues related to the use of learning analytics in higher education. This chapter addresses the above problem and design of learning analytics implementations: the practical shaping of the human tactics involved in taking on and using analytic equipment, records, and reviews as part of an educational enterprise. This is an overwhelming but equally essential set of design choices from the ones made within the advent of the learning analytics structures themselves. Finally, this chapter's implications for learning analytics teachers and students and areas requiring further studies are highlighted.
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