System Design and Architecture of an Online, Adaptive, and Personalized Learning Platform

The authors propose that personalized learning can be brought to traditional and nontraditional learners through a new type of asynchronous learning platform called Guided Learning Pathways (GLP). The GLP platform allows learners to intelligently traverse a vast field of learning resources, emphasizing content only of direct relevance to the learner and presenting it in a way that matches the learner’s pedagogical preference and contextual interests. GLP allows learners to advance towards individual learning goals at their own pace, with learning materials catered to each learner’s interests and motivations. Learning communities would support learners moving through similar topics. This report describes the software system design and architecture required to support Guided Learning Pathways. The authors provide detailed information on eight software applications within GLP, including specific learning benefits and features of each. These applications include content maps, learning nuggets, and nugget recommendation algorithms. A learner scenario helps readers visualize the functionality of the platform. To describe the platform’s software architecture, the authors provide conceptual data models, process flow models, and service group definitions. This report also provides a discussion on the potential social impact of GLP in two areas: higher education institutions and the broader economy

SciTech News- 69(1)-2015

Columns and Reports From the Editor.....5 SciTech News Call for Articles....5 Conference Report, Momentum Press SLA Annual Conference Grant Recipient.. 20 Division News Science-Technology Division...6 Chemistry Division... 11 Engineering Division... 17 Aerospace Section of the Engineering Division.. 23 Architecture, Building Engineering, Construction and Design Section of the Engineering Division.. 24 Call for Nominations & Applications Sparks Award for Professional Development... 16 Reviews Sci-Tech Book News Reviews.... 25 Advertisements Annual Reviews.....3 IEEE....

Bridging the Geospatial Education-Workforce Divide: A Case Study on How Higher Education Can Address the Emerging Geospatial Drivers and Trends of the Intelligent Web Mapping Era

The purpose of this exploratory collective case study is to discover how geospatial education can meet the geospatial workforce needs of the Commonwealth of Virginia, in the emerging intelligent web mapping era. Geospatial education uses geographic information systems (GIS) to enable student learning by increasing in-depth spatial analysis and meaning using geotechnology tools (Baker & White, 2003). Bandura’s (1977) self-efficacy theory and geography concept of spatial thinking form an integrated theoretical framework of spatial cognition for this study. Data collection included in-depth interviews of twelve geospatial stakeholders, documentation collection, and supporting Q methodology to determine the viewpoints of a total of 41 geospatial stakeholders. Q methodology is a type of data collection that when used as a qualitative method utilizes sorting by the participant to determine their preferences. Data analysis strategies included cross-case synthesis, direct interpretation, generalizations, and a correlation matrix to show similarities in participants\u27 preferences. The results revealed four collaborative perceptions of the stakeholders, forming four themes of social education, technology early adoption, data collaboration, and urban fundamentals. Four strategies were identified for higher education to prepare students for the emerging geospatial workforce trends. These strategies are to teach fundamentals, develop agile faculty and curriculum, use an interdisciplinary approach, and collaborate. These strategies reflect the perceptions of stakeholders in this study on how higher education can meet the emerging drivers and trends of the geospatial workforce

Widening Access to Applied Machine Learning with TinyML

Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.Comment: Understanding the underpinnings of the TinyML edX course series: https://www.edx.org/professional-certificate/harvardx-tiny-machine-learnin

System design and architecture of an online, adaptive, and personalized learning platform

Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 76-82).The author proposes that personalized learning can be brought to traditional and nontraditional learners through a new type of asynchronous learning platform called Guided Learning Pathways (GLP). The GLP platform allows learners to intelligently traverse a vast field of learning resources, emphasizing content only of direct relevance to the learner and presenting it in a way that matches the learner's pedagogical preference and contextual interests. GLP allows learners to advance towards individual learning goals at their own pace, with learning materials catered to each learner's interests and motivations. Learning communities would support learners moving through similar topics. This thesis describes the software system design and architecture required to support Guided Learning Pathways. The author provides detailed information on eight software applications within GLP, including specific learning benefits and features of each. These applications include content maps, learning nuggets, and nugget recommendation algorithms. A learner scenario helps readers visualize the functionality of the platform. To describe the platform's software architecture, the author provides conceptual data models, process flow models, and service group definitions. This thesis also provides a discussion on the potential social impact of GLP in two areas: higher education institutions and the broader economy.by Cole J. Shaw.S.M.in Technology and Polic

Arctic Domain Awareness Center DHS Center of Excellence (COE): Project Work Plan

As stated by the DHS Science &Technology Directorate, “The increased and diversified use of maritime spaces in the Arctic - including oil and gas exploration, commercial activities, mineral speculation, and recreational activities (tourism) - is generating new challenges and risks for the U.S. Coast Guard and other DHS maritime missions.” Therefore, DHS will look towards the new ADAC for research to identify better ways to create transparency in the maritime domain along coastal regions and inland waterways, while integrating information and intelligence among stakeholders. DHS expects the ADAC to develop new ideas to address these challenges, provide a scientific basis, and develop new approaches for U.S. Coast Guard and other DHS maritime missions. ADAC will also contribute towards the education of both university students and mid-career professionals engaged in maritime security. The US is an Arctic nation, and the Arctic environment is dynamic. We have less multi-year ice and more open water during the summer causing coastal villages to experience unprecedented storm surges and coastal erosion. Decreasing sea ice is also driving expanded oil exploration, bringing risks of oil spills. Tourism is growing rapidly, and our fishing fleet and commercial shipping activities are increasing as well. There continues to be anticipation of an economic pressure to open up a robust northwest passage for commercial shipping. To add to the stresses of these changes is the fact that these many varied activities are spread over an immense area with little connecting infrastructure. The related maritime security issues are many, and solutions demand increasing maritime situational awareness and improved crisis response capabilities, which are the focuses of our Work Plan. UAA understands the needs and concerns of the Arctic community. It is situated on Alaska’s Southcentral coast with the port facility through which 90% of goods for Alaska arrive. It is one of nineteen US National Strategic Seaports for the US DOD, and its airport is among the top five in the world for cargo throughput. However, maritime security is a national concern and although our focus is on the Arctic environment, we will expand our scope to include other areas in the Lower 48 states. In particular, we will develop sensor systems, decision support tools, ice and oil spill models that include oil in ice, and educational programs that are applicable to the Arctic as well as to the Great Lakes and Northeast. The planned work as detailed in this document addresses the DHS mission as detailed in the National Strategy for Maritime Security, in particular, the mission to Maximize Domain Awareness (pages 16 and 17.) This COE will produce systems to aid in accomplishing two of the objectives of this mission. They are: 1) Sensor Technology developing sensor packages for airborne, underwater, shore-based, and offshore platforms, and 2) Automated fusion and real-time simulation and modeling systems for decision support and planning. An integral part of our efforts will be to develop new methods for sharing of data between platforms, sensors, people, and communities.United States Department of Homeland SecurityCOE ADAC Objective/Purpose / Methodology / Center Management Team and Partners / Evaluation and Transition Plans / USCG Stakeholder Engagement / Workforce Development Strategy / Individual Work Plan by Projects Within a Theme / Appendix A / Appendix B / Appendix