Plan a dashboard for energy measuring, improve overview of energy consumption, and increase energy recovery​

This thesis is written on behalf of a manufacturing company, focusing on energy consumption, recovery, and management. ​ The energy sector continuously changes through carbon emission targets and laws demanding action from companies in the transition to renewable energy resources. Therefore, companies target more innovative manufacturing solutions by measuring, controlling, and visualising energy consumption. Furthermore, the unstable and fluctuating energy situation, rising energy costs, and customers demanding sustainably produced products have enhanced the interest in energy questions at the company. Accordingly, the desire is to improve the overview of energy consumption, improve energy efficiency, and enable energy recovery through storage. Currently, energy measurements are limited to monthly reports based on historical data. This thesis attempts to overcome this by presenting a system providing all stakeholders access to real-time operational data. The energy management system with a dashboard visualising energy consumption and performance indicators could be used to plan production cycles, adjust product prices, and perform predictive maintenance more accurately. The method used in this thesis is qualitative research through interviews with stakeholders at the company. Based on the interview results, a dashboard design is developed through three different layouts, customised for all stakeholder groups. In addition, the proposed energy management system enables visualising collected real-time data in dashboards. The theoretical framework in this thesis is a literature review of scientific research in energy management, dashboard design, energy recovery, and storage. Previous research in energy management presents several implemented technologies improving efficiency, reliability, and stability in the energy supply. The thesis result includes an interview analysis, an energy management system, a dashboard design, and an energy storage system. The interview gives comprehensive knowledge to identify significant performance measures, experience, and interest from stakeholders in the field. The resulting energy management system is an IoT system with collecting assets, an edge platform, a database, and dashboard visualisation. The proposed energy storage system uses thermal energy storage technology with sand as a storage medium. This solution could be driven by renewable energy resources as primary energy resources and implemented to store recovered energy as secondary energy resources improving energy efficiency. In conclusion, this thesis proves that an energy management system with a dashboard visualising collected energy data could be implemented. Furthermore, this thesis concludes that involved stakeholders effectively provide knowledge and experience in the development process of customised dashboard designs

Phase 2: Investigation of Leading Indicators for Systems Engineering Effectiveness in Model-Centric Programs

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsThis technical report summarizes the work conducted by Massachusetts Institute of Technology under contract award HQ0034-20-1-0008 during the performance period May 22, 2020 – July 31, 2021. Digital engineering transformation changes the practice of systems engineering, and drives the need to re-examine how engineering effectiveness is measured and assessed. Early engineering metrics were primarily lagging measures. More recently leading indicators have emerged that draw on trend information to allow for more predictive analysis of technical and programmatic performance of the engineering effort. By analyzing trends (e.g., requirements volatility) in context of the program’s environment and known factors, predictions can be forecast on the outcomes of certain activities (e.g., probability of successfully passing a milestone review), thereby enabling preventative or corrective action during the program. Augmenting a companion research study under contract HQ0034-19-1-0002 on adapting and extending existing systems engineering leading indicators, this study takes a future orientation. This report discusses how base measures can be extracted from a digital system model and composed as leading indicators. An illustrative case is used to identify how the desired base measures could be obtained directly from a model-based toolset. The importance of visualization and interactivity for future leading indicators is discussed, especially the potential role of visual analytics and interactive dashboards. Applicability of leading edge technologies (automated collection, visual analytics, augmented intelligence, etc.) are considered as advanced mechanisms for collecting and synthesizing measurement data from digital artifacts. This research aims to provide insights for the art of the possible for future systems engineering leading indicators and their use in decision-making on model-centric programs. Several recommendations for future research are proposed extending from the study.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

A framework for strategic planning of data analytics in the educational sector

The field of big data and data analysis is not a new one. Big data systems have been investigated with respect to the volume of the data and how it is stored, the data velocity and how it is subject to change, variety of data to be analysed and data veracity referring to integrity and quality. Higher Education Institutions (HEIs) have a significant range of data sources across their operations and increasingly invest in collecting, analysing and reporting on their data in order to improve their efficiency. Data analytics and Business Intelligence (BI) are two terms that are increasingly popular over the past few years in the relevant literature with emphasis on their impact in the education sector. There is a significant volume of literature discussing the benefits of data analytics in higher education and even more papers discussing specific case studies of institutions resorting on BI by deploying various data analytics practices. Nevertheless, there is a lack of an integrated framework that supports HEIs in using learning analytics both at strategic and operational level. This research study was driven by the need to offer a point of reference for universities wishing to make good use of the plethora of data they can access. Increasingly institutions need to become ‘smart universities’ by supporting their decisions with findings from the analysis of their operations. The Business Intelligence strategies of many universities seems to focus mostly on identifying how to collect data but fail to address the most important issue that is how to analyse the data, what to do with the findings and how to create the means for a scalable use of learning analytics at institutional level. The scope of this research is to investigate the different factors that affect the successful deployment of data analytics in educational contexts focusing both on strategic and operational aspects of academia. The research study attempts to identify those elements necessary for introducing data analytics practices across an institution. The main contribution of the research is a framework that models the data collection, analysis and visualisation in higher education. The specific contribution to the field comes in the form of generic guidelines for strategic planning of HEI data analytics projects, combined with specific guidelines for staff involved in the deployment of data analytics to support certain institutional operations. The research is based on a mixed method approach that combines grounded theory in the form of extensive literature review, state-of-the-art investigation and case study analysis, as well as a combination of qualitative and quantitative data collection. The study commences with an extensive literature review that identifies the key factors affecting the use of learning analytics. Then the research collected more information from an analysis of a wide range of case studies showing how learning analytics are used across HEIs. The primary data collection concluded with a series of focus groups and interviews assessing the role of learning analytics in universities. Next, the research focused on a synthesis of guidelines for using learning analytics both at strategic and operational levels, leading to the production of generic and specific guidelines intended for different university stakeholders. The proposed framework was revised twice to create an integrated point of reference for HEIs that offers support across institutions in scalable and applicable way that can accommodate the varying needs met at different HEIs. The proposed framework was evaluated by the same participants in the earlier focus groups and interviews, providing a qualitative approach in evaluating the contributions made during this research study. The research resulted in the creation of an integrated framework that offers HEIs a reference for setting up a learning analytics strategy, adapting institutional policies and revising operations across faculties and departments. The proposed C.A.V. framework consists of three phases including Collect, Analysis and Visualisation. The framework determines the key features of data sources and resulting dashboards but also a list of functions for the data collection, analysis and visualisation stages. At strategic level, the C.A.V. framework enables institutions to assess their learning analytics maturity, determine the learning analytics stages that they are involved in, identify the different learning analytics themes and use a checklist as a reference point for their learning analytics deployment. Finally, the framework ensures that institutional operations can become more effective by determining how learning analytics provide added value across different operations, while assessing the impact of learning analytics on stakeholders. The framework also supports the adoption of learning analytics processes, the planning of dashboard contents and identifying factors affecting the implementation of learning analytics

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

Practitioner Track Proceedings of the 6th International Learning Analytics & Knowledge Conference (LAK16)

Practitioners spearhead a significant portion of learning analytics, relying on implementation and experimentation rather than on traditional academic research. Both approaches help to improve the state of the art. The LAK conference has created a practitioner track for submissions, which first ran in 2015 as an alternative to the researcher track. The primary goal of the practitioner track is to share thoughts and findings that stem from learning analytics project implementations. While both large and small implementations are considered, all practitioner track submissions are required to relate to initiatives that are designed for large-scale and/or long-term use (as opposed to research-focused initiatives). Other guidelines include: • Implementation track record The project should have been used by an institution or have been deployed on a learning site. There are no hard guidelines about user numbers or how long the project has been running. • Learning/education related Submissions have to describe work that addresses learning/academic analytics, either at an educational institution or in an area (such as corporate training, health care or informal learning) where the goal is to improve the learning environment or learning outcomes. • Institutional involvement Neither submissions nor presentations have to include a named person from an academic institution. However, all submissions have to include information collected from people who have used the tool or initiative in a learning environment (such as faculty, students, administrators and trainees). • No sales pitches While submissions from commercial suppliers are welcome; reviewers do not accept overt (or covert) sales pitches. Reviewers look for evidence that a presentation will take into account challenges faced, problems that have arisen, and/or user feedback that needs to be addressed. Submissions are limited to 1,200 words, including an abstract, a summary of deployment with end users, and a full description. Most papers in the proceedings are therefore short, and often informal, although some authors chose to extend their papers once they had been accepted. Papers accepted in 2016 fell into two categories. • Practitioner Presentations Presentation sessions are designed to focus on deployment of a single learning analytics tool or initiative. • Technology Showcase The Technology Showcase event enables practitioners to demonstrate new and emerging learning analytics technologies that they are piloting or deploying. Both types of paper are included in these proceedings

RAMPVIS: Answering the Challenges of Building Visualisation Capabilities for Large-scale Emergency Responses

The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses

Towards highly informative learning analytics

Among various trending topics that can be investigated in the field of educational technology, there is a clear and high demand for using artificial intelligence (AI) and educational data to improve the whole learning and teaching cycle. This spans from collecting and estimating the prior knowledge of learners for a certain subject to the actual learning process and its assessment. AI in education cuts across almost all educational technology disciplines and is key to many other technological innovations for educational institutions. The use of data to inform decision-making in education and training is not new, but the scope and scale of its potential impact on teaching and learning have silently increased by orders of magnitude over the last few years. The release of ChatGPT was another driver to finally make everyone aware of the potential effects of AI technology in the digital education system of today. We are now at a stage where data can be automatically harvested at previously unimagined levels of granularity and variety. Analysis of these data with AI has the potential to provide evidence-based insights into learners’ abilities and patterns of behaviour that, in turn, can provide crucial action points to guide curriculum and course design, personalised assistance, generate assessments, and the development of new educational offerings. AI in education has many connected research communities like Artificial Intelligence in Education (AIED), Educational Data Mining (EDM), or Learning Analytics (LA). LA is the term that is used for research, studies, and applications that try to understand and support the behaviour of learners based on large sets of collected data