Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Learning Analytics to Support Experiential Learning

Experiential learning is increasing in prominence within higher education institutions. There is a lack of technology explicitly designed to support experiential learning pedagogies. This paper presents research that brings together learning analytics and learning theory to explore how technology could better support experiential learning programs. The research outcomes will be followed by a reflexive discussion about how insights from the research could impact the practice of experiential learning

Digital analytics in professional work and learning

In a wide range of fields, professional practice is being transformed by the increasing influence of digital analytics: the massive volumes of big data, and software algorithms that are collecting, comparing, and calculating that data to make predictions and even decisions. Researchers in a number of social sciences have been calling attention to the far-reaching and accelerating consequences of these forces, claiming that many professionals, researchers, policy makers and the public are just beginning to realise the enormous potentials and challenges these analytics are producing. Yet, outside of particular areas of research and practice, such as learning analytics, there has been little discussion of this to date in the broader education literature. This article aims to set out some key issues particularly relevant to the understandings of professional practice, knowledge and learning posed by the linkages of big data and software code. It begins by outlining definitions, forms and examples of these analytics, their potentialities and some of the hidden impact, and then presents issues for researchers and educators. It seeks to contribute to and extend debates taking place in certain quarters to a broader professional education and work audience

An Evaluation of Undergraduate Advisors Experience Using Learning Analytics to Support First-year Students

Higher education institutions are now serving post-traditional students. With the ever-increasing diversity and complex needs of these post-traditionals, institutions are striving to design policies, programs, and institutional supports to best support their diverse needs. Many are venturing into the world of learning analytics to gain deeper insights into the student academic experience and leveraging data to improve student success and retention. Previous research has centered on the institutional level impact of learning analytics on student success and rarely gives representation to the experience of specific individual sub-groups of organizational stakeholders. This summative evaluation sought to capture the experiences of 5 undergraduate advisors who participated in a three-year pilot of Civitas Inspire, a learning analytics system, to support first- year students. The Comprehensive Mixed Methods Participatory Evaluation model served as a conceptual framework allowing for an in-depth exploration of advisors’ perspectives on six evaluation components: acceptability, social validity, program integrity, program outcomes, implementer competence, sustainability, and institutionalization. An examination of previous research identified capacity building, data integrity, messaging, and privacy/ethics as common challenges faced by institutions who have adopted learning analytics systems. Evaluation results found advisors encountered similar challenges. Prominent throughout the advisors narrative was the effects of shadow-culture on technology adoption efforts. Advisors expressed the need for greater stakeholder inclusivity; for institutions to acknowledge and understand stakeholder workflow, and the necessity for a connect the dots approach towards institutionalization efforts

Using moodle analytics for continuous e-assessment in a financial mathematics course at Polytechnic of Porto

The relevance of electronic learning, commonly called e-learning, has been growing exponentially in the last decade. Virtual learning environments (VLEs) disclosed new paths for interactions and motivation promotion, offering basic learning analytics functions and are becoming progressively popular. Moodle (acronym for Modular Object Oriented Dynamic Learning Environment) is one of the most used VLEs, it is a free learning management system distributed as Open Source. The VLE Moodle gives professors access to an “endless” use and performance database like the number of downloads for each resource, participation of students in courses, statistics of performed quizzes, among others. The data stored by Moodle offers a good and handy source for learning analytics. One popular definition, from the First International Conference on Learning Analytics and Knowledge in 2011, states that “Learning Analytics is the measurement, collection, analysis and reporting of data about students and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”. Thus, using appropriate learning analytics methods and techniques, it would be helpful to analyze what particular learning activities or tools were practically used by students in Moodle, and to what extent. Considering the importance of the student engagement and the benefits of continuous assessment in higher education, as well as the impact of information and communications technology (ICT) on educational processes, it is important to integrate technology into continuous assessment practices. Since student engagement is connected to the quality of the student experience, increasing it is one way of enhancing quality in a higher education institution. In this study, will be demonstrated how the use of several educational resources and a low-stakes continuous weekly e-assessment in Moodle had a positive influence on student engagement in a second year undergraduate Financial Mathematics Course. Students felt that their increased engagement and improved learning was a straight result of this method. Furthermore, this suggests that wisely planned assignments and assessments can be used to increase student engagement and learning, and, as a result, contribute to improving the quality of student experience and success.info:eu-repo/semantics/publishedVersio

How Engaged are our Students? Using Analytics to Identify Students-at-Risk

Learning Management System (LMS) analytics have become an area of increasing interest and development. The potential to better understand our students’ levels of engagement provided by the systems have, to date, has been underutilized information resources. The study reported here looks at the relationship of student and staff engagement in the LMS and considers the levels of predictability in student behavior leading to failure. Also considered is the impact of the lecturer on the student engagement of poor and high performing students

Predictive Analytics for Roadway Maintenance: A Review of Current Models, Challenges, and Opportunities

With the pressing need to improve the poorly rated transportation infrastructure, asset managers leverage predictive maintenance strategies to lower the life cycle costs while maximizing or maintaining the performance of highways. Hence, the limitations of prediction models can highly impact prioritizing maintenance tasks and allocating budget. This study aims to investigate the potential of different predictive models in reaching an effective and efficient maintenance plan. This paper reviews the literature on predictive analytics for a set of highway assets. It also highlights the gaps and limitations of the current methodologies, such as subjective assumptions and simplifications applied in deterministic and probabilistic approaches. This article additionally discusses how these shortcomings impact the application and accuracy of the methods, and how advanced predictive analytics can mitigate the challenges. In this review, we discuss how advancements in technologies coupled with ever-increasing computing power are creating opportunities for a paradigm shift in predictive analytics. We also propose new research directions including the application of advanced machine learning to develop extensible and scalable prediction models and leveraging emerging sensing technologies for collecting, storing and analyzing the data. Finally, we addressed future directions of predictive analysis associated with the data-rich era that will potentially help transportation agencies to become information-rich

Social Media Analytics in Disaster Response: A Comprehensive Review

Social media has emerged as a valuable resource for disaster management, revolutionizing the way emergency response and recovery efforts are conducted during natural disasters. This review paper aims to provide a comprehensive analysis of social media analytics for disaster management. The abstract begins by highlighting the increasing prevalence of natural disasters and the need for effective strategies to mitigate their impact. It then emphasizes the growing influence of social media in disaster situations, discussing its role in disaster detection, situational awareness, and emergency communication. The abstract explores the challenges and opportunities associated with leveraging social media data for disaster management purposes. It examines methodologies and techniques used in social media analytics, including data collection, preprocessing, and analysis, with a focus on data mining and machine learning approaches. The abstract also presents a thorough examination of case studies and best practices that demonstrate the successful application of social media analytics in disaster response and recovery. Ethical considerations and privacy concerns related to the use of social media data in disaster scenarios are addressed. The abstract concludes by identifying future research directions and potential advancements in social media analytics for disaster management. The review paper aims to provide practitioners and researchers with a comprehensive understanding of the current state of social media analytics in disaster management, while highlighting the need for continued research and innovation in this field.Comment: 11 page

Are We Living in LA (P)LA Land? Reporting on the Practice of 30 STEM Tutors in Their Use of a Learning Analytics Implementation at The Open University

Most higher education institutions view their increasing use of learning analytics as having significant potential to improve student academic achievement, retention outcomes, and learning and teaching practice but the realization of this potential remains stubbornly elusive. While there is an abundance of published research on the creation of visualizations, dashboards, and predictive models, there has been little work done to explore the impact of learning analytics on the actual practice of teachers. Through the lens of social informatics (an approach that views the users of technologies as active social actors whose technological practices constitute a wider socio-technical system) this qualitative study reports on an investigation into the practice of 30 tutors in the STEM faculty at Europe’s largest distance learning organization, The Open University UK (OU). When asked to incorporate learning analytics (including predictive learning analytics) contained in the Early Alert Indicator (EAI) dashboard during the 2017–2018 academic year into their practice, we found that tutors interacted with this dashboard in certain unanticipated ways and developed three identifiable “shadow practices”

Innovating for Learning: Designing for the Future of Education

Teaching has moved online as the world has moved online and learning is losing its sense of physical location with the availability of many different options from mobile to MOOC (Massive Open Online Course). The impact of online learning is not confined to distance learning; when a student attends a campus university they are now as likely to meet with their fellow learners virtually as face to face. The education sector has yet to fully adapt to what this means, and indeed there strong signs of a built in resilience from providers, employers and students themselves which may mean an apparent evolution is more likely than a revolution. At the same time, there are some quiet changes underway that mean we should be preparing to innovate for the revolution to come. Some of those changes are considered in work undertaken at The Open University that has been disseminated in a series of Innovating Pedagogy reports. These reports allow the academic authors to be more speculative than is usual practice and engage in considering the future, while remaining based on a view of what is happening in the sector. In particular they adopt a position focused on pedagogy that balances technology-based futurology that can dominate yet fail to resonate with those actually involved in the teaching process. The annual Innovating Pedagogy reports cover 10 topics each, with some deliberate overlap from year to year and development of themes that show innovations moving into teaching practice. This is illustrated by two cases, the impact of MOOCs and the application of learning design and analytics. The development of MOOCs demonstrates the value of reviewing pedagogy that aligns with technology. While the use of learning design and learning analytics demonstrates how improvements in the way we describe our learning processes and the way we understand learner behaviour is helping determine how choices in pedagogy impact on student satisfaction, progression and success