This presentation define network structure and commonly used sources for data collection in social network analysis. The presentation is prepared for DALMOOC by Dragan Gasevic.
Divya Kothari worked as a research assistant for Professor Hans Jochen Scholl during the fall of 2015 on a project studying the response to the 2014 landslide disaster near Oso, Washington. Over thirty incident managers and responders were interviewed, with interviews ranging from 55 minutes to over 4 hours. Divya coded several of the interviews using a pre-established codebook, and her coding was found to be of very high quality and highly reliable when checked against other coders. Professor Scholl attests that Divya is a proficient, fast, accurate, and caring research assistant.
Presentation about T-Space, University of Toronto's institutional repository, by Gabriela Mircea, Scholarly Communications Publishing Coordinator, in KMD1001 class (http://1001.kmdi.utoronto.ca)
It's 2015. Do You Know Where Your Data Are?Patricia Hswe
This document summarizes a presentation on research data management. It discusses definitions of research data and why data should be shared. It provides tips for best practices in file naming, description standards, formats and storage. Tools, resources and services for research data management from Penn State and beyond are presented, including ScholarSphere and DMPTool. The importance of having an online presence and sharing research is discussed.
Outline of the UCSF approach to Research Networking, which focuses on rapid iterations of adding new data sources and features to see what works, and abandon what doesn't work.
Sallans RDAP11 NSF Data Management Plan Case StudiesASIS&T
NSF Data Management Plan Case Studies
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
This document lists and describes several large network dataset collections for research purposes. It includes social networks, communication networks, citation networks, collaboration networks, web graphs, product networks, road networks, and more. Sources provided include the Stanford Large Network Dataset Collection, a Twitter dataset, leaked Facebook pages, UCIrvine Datasets, and additional results. The datasets cover a wide range of network types and can be used to study interactions in online social networks, information cascades, and networked communities.
Jordan, K. (2015) Characterising the structure of academics’ personal networks on academic social networking sites and Twitter. Presentation at the Computers and Learning Research Group (CALRG) annual conference, The Open University, Milton Keynes, UK, 17th June 2015.
Divya Kothari worked as a research assistant for Professor Hans Jochen Scholl during the fall of 2015 on a project studying the response to the 2014 landslide disaster near Oso, Washington. Over thirty incident managers and responders were interviewed, with interviews ranging from 55 minutes to over 4 hours. Divya coded several of the interviews using a pre-established codebook, and her coding was found to be of very high quality and highly reliable when checked against other coders. Professor Scholl attests that Divya is a proficient, fast, accurate, and caring research assistant.
Presentation about T-Space, University of Toronto's institutional repository, by Gabriela Mircea, Scholarly Communications Publishing Coordinator, in KMD1001 class (http://1001.kmdi.utoronto.ca)
It's 2015. Do You Know Where Your Data Are?Patricia Hswe
This document summarizes a presentation on research data management. It discusses definitions of research data and why data should be shared. It provides tips for best practices in file naming, description standards, formats and storage. Tools, resources and services for research data management from Penn State and beyond are presented, including ScholarSphere and DMPTool. The importance of having an online presence and sharing research is discussed.
Outline of the UCSF approach to Research Networking, which focuses on rapid iterations of adding new data sources and features to see what works, and abandon what doesn't work.
Sallans RDAP11 NSF Data Management Plan Case StudiesASIS&T
NSF Data Management Plan Case Studies
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
This document lists and describes several large network dataset collections for research purposes. It includes social networks, communication networks, citation networks, collaboration networks, web graphs, product networks, road networks, and more. Sources provided include the Stanford Large Network Dataset Collection, a Twitter dataset, leaked Facebook pages, UCIrvine Datasets, and additional results. The datasets cover a wide range of network types and can be used to study interactions in online social networks, information cascades, and networked communities.
Jordan, K. (2015) Characterising the structure of academics’ personal networks on academic social networking sites and Twitter. Presentation at the Computers and Learning Research Group (CALRG) annual conference, The Open University, Milton Keynes, UK, 17th June 2015.
RDAP 16: Building the Research Data Community of PracticeASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Presenters:
Sherry Lake, University of Virginia
Brianna Marshall, University of Wisconsin-Madison
Regina Raboin, University of Massachusetts Medical School
Andrew Johnson, University of Colorado
Brian Westra, University of Oregon
Panel lead:
Cynthia Hudson-Vitale, Washington University in St. Louis
In collaboration with participating faculty at California State University Northridge, Oviatt Library Research Fellows have developed a pilot that facilitates both course and information literacy objectives. Using Scalar, an open source semantic Web-authoring program, the Guided Resource Inquiry Tool (GRI) combines assignment prompts, digitized primary sources and digital information literacy media into a single online interface. The GRI leverages renewed pedagogical interest in ‘knowledge synthesis’ by placing documentary evidence as the point of departure for critical inquiry. The tool incorporates materials from archival and digital collections repositories to supply content for course assignments. The GRI contains links to existing archives and information literacy resources that teach students as they complete the assignment. Specific benefits include: 1) archives and information literacy instruction within an applied research context; 2) course learning through critical analysis of primary documents, 3) extended outreach for digital collections and archival repositories; 4) 24/7 access to the GRI assignment online; 5) no limits on class size, 6) integrated exposure to finding aid and secondary source databases as well as Special Collections patron protocols.
Presenter: Steve Kutay
Mass Edu Data Challenge - Kick-off & Data Divecscranto
The document provides information about the Mass EduData Challenge, including details about the agenda, timeline, rules, prizes, and available education data sets from the Massachusetts Department of Education. The challenge kicks off on May 28th with an introduction and data deep dive. Teams have until June 30th to submit their projects. Winners will be announced on July 8th. The available datasets include student enrollment, graduation rates, MCAS scores, educator data, and finance data from 1995-2014. Questions about the data can be directed to the Department of Education.
10 simple ways UCSF Profiles has been used to win funding, find collaborators...lesliey
UCSF Profiles has been used in 10 ways to help researchers, clinicians, and the university:
1) It connects students and trainees with potential faculty mentors based on shared interests.
2) It saves staff and faculty time by allowing campus websites to automatically update information from researcher profiles.
3) Administrators use profile data to generate reports that recognize researchers' achievements and new publications in top journals.
This document summarizes the history and evolution of scholarly publishing, including:
- Commercial publishers began acquiring journals in the 1960s, dominating the market with high profit margins.
- Journal prices skyrocketed in the 1990s-2000s beyond what libraries could afford, known as the "serials crisis".
- The rise of open access emerged due to the internet and high journal costs, allowing unrestricted online access to peer-reviewed research.
- Publishers were seen as only caring about profits, fueling a backlash against traditional models.
RDAP 16: Building Sustainable Services at the Small(er) Scale (Panel 4, Measu...ASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Part of “Panel 4, Measuring Up: How Are We Defining Success for Research Data Services?”
Presenter:
Ryan Clement, Middlebury College
1. The document discusses research networking profiles created by the Clinical and Translational Science Institute at the University of California, San Francisco (CTSI at UCSF).
2. It notes that most universities have their own research networking profiles, like LinkedIn for researchers, to provide credibility and allow customization.
3. However, the document advocates connecting local profiles into a global research network using Linked Open Data, OAuth authentication, and OpenSocial technologies to facilitate collaboration between researchers across institutions.
Kaleidoscope conference slides - Academic networkingKaty Jordan
Jordan, K. (2013) Reshaping the Higher Education network? Analysis of academic social networking sites. Presentation given at the Kaleidoscope Conference, Faculty of Education, University of Cambridge, 31st May 2013.
E-Learn 2014 Abstract: Today digital footprints are left all over the Internet for others to find. This article reviews the means through which scholars can organize research and connect digital scholarship for increased visibility and impact. A survey of the literature on scholarship tools to provide connections for publishing records, academic citations, and digital identity management was done. The authors reviewed Researcher ID, ORCID, and Google Scholar Citations. The numbers of portals for synthesizing research output and related identity management platforms are increasing; however, understanding what this research impact might look like in the digital age can provide questions for assessment for understanding these traces of scholarship online.
This document discusses how digital tools and platforms can help researchers measure the impact of their work. It explores developing a digital footprint and identity through platforms like ORCID, ResearcherID, Scopus, Google Scholar Citations, Academia.edu and Mendeley. These tools allow researchers to track citations, collaborations and the broader influence of their work beyond traditional publications. The document advocates that researchers should utilize emerging social media and online platforms to increase the reach of their research and better develop their overall research identity and impact.
Would you like to be my friend: Patron responsiveness to academic library Fac...parfitt123
A Masters student presentation - presented by Suzanne Parfitt (Master of Information Studies student at Charles Sturt University, Australia) at the MMIT 2015 Conference, Sheffield University, UK in September 2015
Presentation and workshop notes from session on how to apply the Researcher Development Framework to library and information service provision for research/e support
Uses case studies of different types of researchers.
Workshop notes integrated into the presentation
Social Network Analysis - an Introduction (minus the Maths)Katy Jordan
This document provides an overview of social network analysis concepts without advanced mathematics. It defines social network analysis as conceptualizing social relationships as links between nodes, which can be visualized and analyzed using graph theory. It discusses frequently used network metrics like degree, density, and betweenness centrality. It summarizes classic social network studies by Milgram on "six degrees of separation" and Granovetter on "the strength of weak ties." It also discusses considerations for social network analysis and tools like Gephi for visualizing networks.
Trailblazing in the Wilderness of Data ManagementStephanie Wright
The document discusses trailblazing in research data management. It defines key terms like data, data management, and big data. It outlines why various stakeholders like funding agencies, universities, researchers, and libraries are venturing into research data management. It reviews assessments of data management needs conducted at various universities, examples of existing research data management programs, and available tools and resources. Finally, it discusses how institutions can blaze their own trail in research data management by identifying needs, partners, priorities, and potential services and policies to develop.
This document summarizes the work of UC BRAID (UC Biomedical Research Acceleration, Integration, and Development Network) over the past 4 years to integrate resources and accelerate clinical and translational research across the University of California system. Key accomplishments include establishing IRB reliance across campuses, creating the UC Research eXchange clinical data repository and recruitment tool, and convening innovators through the UC Center for Accelerated Innovation. The document discusses opportunities to further develop informatics, expand data services, pursue industry partnerships, and leverage the network to get to shared goals by 2024.
This presentation was provided by Jan Fransen of the University of Minnesota - Twin Cities during the NISO virtual conference, Research Information Systems: The Connections Enabling Collaboration, held on August 16, 2017.
Yueshen Xu is a fifth-year Ph.D. student in computer science at Zhejiang University in China. He has published papers in several international conferences and journals on topics related to recommender systems, text mining, and natural language processing. He was a visiting student at the University of Illinois at Chicago from 2014-2015 and has worked as an intern developing recommendation algorithms.
This presentation discusses measuring and improving drive performance through analyzing average access time, data transfer rate, optimizing performance through defragmentation and file compression. It explains that defragmentation consolidates fragmented files to improve performance and should be done monthly, while file compression reduces file sizes, frees up disk space, and increases reading and writing speeds by using tools like PKZip, WinZip, and WinRAR. The presentation aims to increase disk space, speed up data transfers and I/O, and optimize overall disk performance.
A storage device stores information on a computer and retains it when the computer is switched off. The most common internal storage device is a hard disk, which can store 60+ gigabytes of data on a circular, magnetized surface that spins rapidly. Externally, floppy disks and CDs are often used, with floppy disks holding up to 1.4 megabytes and CDs holding up to 650 megabytes of data. DVDs have largely replaced CDs due to being able to store much larger amounts of data, up to 17 gigabytes.
RDAP 16: Building the Research Data Community of PracticeASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Presenters:
Sherry Lake, University of Virginia
Brianna Marshall, University of Wisconsin-Madison
Regina Raboin, University of Massachusetts Medical School
Andrew Johnson, University of Colorado
Brian Westra, University of Oregon
Panel lead:
Cynthia Hudson-Vitale, Washington University in St. Louis
In collaboration with participating faculty at California State University Northridge, Oviatt Library Research Fellows have developed a pilot that facilitates both course and information literacy objectives. Using Scalar, an open source semantic Web-authoring program, the Guided Resource Inquiry Tool (GRI) combines assignment prompts, digitized primary sources and digital information literacy media into a single online interface. The GRI leverages renewed pedagogical interest in ‘knowledge synthesis’ by placing documentary evidence as the point of departure for critical inquiry. The tool incorporates materials from archival and digital collections repositories to supply content for course assignments. The GRI contains links to existing archives and information literacy resources that teach students as they complete the assignment. Specific benefits include: 1) archives and information literacy instruction within an applied research context; 2) course learning through critical analysis of primary documents, 3) extended outreach for digital collections and archival repositories; 4) 24/7 access to the GRI assignment online; 5) no limits on class size, 6) integrated exposure to finding aid and secondary source databases as well as Special Collections patron protocols.
Presenter: Steve Kutay
Mass Edu Data Challenge - Kick-off & Data Divecscranto
The document provides information about the Mass EduData Challenge, including details about the agenda, timeline, rules, prizes, and available education data sets from the Massachusetts Department of Education. The challenge kicks off on May 28th with an introduction and data deep dive. Teams have until June 30th to submit their projects. Winners will be announced on July 8th. The available datasets include student enrollment, graduation rates, MCAS scores, educator data, and finance data from 1995-2014. Questions about the data can be directed to the Department of Education.
10 simple ways UCSF Profiles has been used to win funding, find collaborators...lesliey
UCSF Profiles has been used in 10 ways to help researchers, clinicians, and the university:
1) It connects students and trainees with potential faculty mentors based on shared interests.
2) It saves staff and faculty time by allowing campus websites to automatically update information from researcher profiles.
3) Administrators use profile data to generate reports that recognize researchers' achievements and new publications in top journals.
This document summarizes the history and evolution of scholarly publishing, including:
- Commercial publishers began acquiring journals in the 1960s, dominating the market with high profit margins.
- Journal prices skyrocketed in the 1990s-2000s beyond what libraries could afford, known as the "serials crisis".
- The rise of open access emerged due to the internet and high journal costs, allowing unrestricted online access to peer-reviewed research.
- Publishers were seen as only caring about profits, fueling a backlash against traditional models.
RDAP 16: Building Sustainable Services at the Small(er) Scale (Panel 4, Measu...ASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Part of “Panel 4, Measuring Up: How Are We Defining Success for Research Data Services?”
Presenter:
Ryan Clement, Middlebury College
1. The document discusses research networking profiles created by the Clinical and Translational Science Institute at the University of California, San Francisco (CTSI at UCSF).
2. It notes that most universities have their own research networking profiles, like LinkedIn for researchers, to provide credibility and allow customization.
3. However, the document advocates connecting local profiles into a global research network using Linked Open Data, OAuth authentication, and OpenSocial technologies to facilitate collaboration between researchers across institutions.
Kaleidoscope conference slides - Academic networkingKaty Jordan
Jordan, K. (2013) Reshaping the Higher Education network? Analysis of academic social networking sites. Presentation given at the Kaleidoscope Conference, Faculty of Education, University of Cambridge, 31st May 2013.
E-Learn 2014 Abstract: Today digital footprints are left all over the Internet for others to find. This article reviews the means through which scholars can organize research and connect digital scholarship for increased visibility and impact. A survey of the literature on scholarship tools to provide connections for publishing records, academic citations, and digital identity management was done. The authors reviewed Researcher ID, ORCID, and Google Scholar Citations. The numbers of portals for synthesizing research output and related identity management platforms are increasing; however, understanding what this research impact might look like in the digital age can provide questions for assessment for understanding these traces of scholarship online.
This document discusses how digital tools and platforms can help researchers measure the impact of their work. It explores developing a digital footprint and identity through platforms like ORCID, ResearcherID, Scopus, Google Scholar Citations, Academia.edu and Mendeley. These tools allow researchers to track citations, collaborations and the broader influence of their work beyond traditional publications. The document advocates that researchers should utilize emerging social media and online platforms to increase the reach of their research and better develop their overall research identity and impact.
Would you like to be my friend: Patron responsiveness to academic library Fac...parfitt123
A Masters student presentation - presented by Suzanne Parfitt (Master of Information Studies student at Charles Sturt University, Australia) at the MMIT 2015 Conference, Sheffield University, UK in September 2015
Presentation and workshop notes from session on how to apply the Researcher Development Framework to library and information service provision for research/e support
Uses case studies of different types of researchers.
Workshop notes integrated into the presentation
Social Network Analysis - an Introduction (minus the Maths)Katy Jordan
This document provides an overview of social network analysis concepts without advanced mathematics. It defines social network analysis as conceptualizing social relationships as links between nodes, which can be visualized and analyzed using graph theory. It discusses frequently used network metrics like degree, density, and betweenness centrality. It summarizes classic social network studies by Milgram on "six degrees of separation" and Granovetter on "the strength of weak ties." It also discusses considerations for social network analysis and tools like Gephi for visualizing networks.
Trailblazing in the Wilderness of Data ManagementStephanie Wright
The document discusses trailblazing in research data management. It defines key terms like data, data management, and big data. It outlines why various stakeholders like funding agencies, universities, researchers, and libraries are venturing into research data management. It reviews assessments of data management needs conducted at various universities, examples of existing research data management programs, and available tools and resources. Finally, it discusses how institutions can blaze their own trail in research data management by identifying needs, partners, priorities, and potential services and policies to develop.
This document summarizes the work of UC BRAID (UC Biomedical Research Acceleration, Integration, and Development Network) over the past 4 years to integrate resources and accelerate clinical and translational research across the University of California system. Key accomplishments include establishing IRB reliance across campuses, creating the UC Research eXchange clinical data repository and recruitment tool, and convening innovators through the UC Center for Accelerated Innovation. The document discusses opportunities to further develop informatics, expand data services, pursue industry partnerships, and leverage the network to get to shared goals by 2024.
This presentation was provided by Jan Fransen of the University of Minnesota - Twin Cities during the NISO virtual conference, Research Information Systems: The Connections Enabling Collaboration, held on August 16, 2017.
Yueshen Xu is a fifth-year Ph.D. student in computer science at Zhejiang University in China. He has published papers in several international conferences and journals on topics related to recommender systems, text mining, and natural language processing. He was a visiting student at the University of Illinois at Chicago from 2014-2015 and has worked as an intern developing recommendation algorithms.
This presentation discusses measuring and improving drive performance through analyzing average access time, data transfer rate, optimizing performance through defragmentation and file compression. It explains that defragmentation consolidates fragmented files to improve performance and should be done monthly, while file compression reduces file sizes, frees up disk space, and increases reading and writing speeds by using tools like PKZip, WinZip, and WinRAR. The presentation aims to increase disk space, speed up data transfers and I/O, and optimize overall disk performance.
A storage device stores information on a computer and retains it when the computer is switched off. The most common internal storage device is a hard disk, which can store 60+ gigabytes of data on a circular, magnetized surface that spins rapidly. Externally, floppy disks and CDs are often used, with floppy disks holding up to 1.4 megabytes and CDs holding up to 650 megabytes of data. DVDs have largely replaced CDs due to being able to store much larger amounts of data, up to 17 gigabytes.
This document provides an overview of the key concepts and terminology related to the internet and world wide web. It discusses the major services of the internet like the web, email, file transfer, and chat. It explains how the web works using HTML, hyperlinks, browsers, and URLs. It also covers ways to access websites by typing or clicking URLs, as well as search techniques using keywords, boolean operators, and directories versus search engines.
A computer is an electronic device that accepts data as input, processes it, and produces output. It has four main functions: input, processing, output, and storage. Hardware refers to the physical components like keyboards, monitors, and hard drives. Software gives computers their functionality and includes operating systems, applications, and utilities. The central processing unit controls the main components and allows data and instructions to flow through the computer.
This document discusses hackers and network intruders. It begins by defining key hacking terms and providing a brief history of notable hacks. It then outlines the main threats posed by hackers, including denial of service attacks, data theft, and reputation damage. The document categorizes different types of hackers and their motivations. It describes common methods for gaining unauthorized access, such as exploiting software vulnerabilities or using password cracking. Finally, it discusses approaches for detection and prevention of intrusions, as well as some legal and ethical issues surrounding hacking.
This document discusses input and output devices for computers. It describes common input devices like keyboards, mice, and touch screens. For output, it covers monitors, printers, and interfaces. Monitors display images through scanning lines from left to right and top to bottom. Printers mentioned include inkjet, laser, and dot matrix printers. The document explains how serial, parallel, and SCSI ports facilitate communication between input/output devices and other computer components.
This chapter describes different types of storage devices used in computers. It discusses magnetic storage devices like hard disks and floppy disks, which use magnetism to store data. Optical storage devices like CDs and DVDs are also covered, which use lasers to read and write data. Finally, solid-state storage options like flash memory, smart cards, and solid-state drives are introduced. The document provides details on how each storage type works and its common uses. It also discusses topics like formatting disks, finding data locations, and measuring/improving drive performance.
Fiber optic networks transmit light through optical fibers to communicate over long distances at high bandwidth. An optical fiber consists of a cylindrical glass core surrounded by a cladding layer of glass or plastic with a lower refractive index. This structure allows light to propagate along the core through total internal reflection. Fibers have a small single-mode core or larger multimode core. Single-mode fiber has lower attenuation over long distances while multimode can transmit more data over shorter runs.
This document summarizes a study that examined how culture, class, and context influence the structure of social networks and the acquisition of social capital. The study found that: 1) Cultural norms around different social classes' use of settings shaped how ties were maintained over time. 2) Material resources impacted one's ability to socialize within settings, with those lacking resources having less geographically dispersed networks. 3) There are "periodic friends" with whom the original context is lost but the relationship endures through low contact but high commitment, challenging assumptions about strong/weak ties being defined by contact frequency.
This document discusses various input methods for computers beyond keyboards and mice. It describes pen and touchscreen input systems used on tablets and handheld devices. It also outlines optical input technologies like bar code readers and image scanners that allow computers to interpret light-based inputs. Microphones and webcams are mentioned as audio-visual input devices that enable voice recognition, video chatting, and recording of sound and images.
Mainframe computers are extremely large and powerful machines that can process large amounts of data quickly. They contain multiple fast processors that can either work together on shared tasks or separately on individual tasks. Mainframe computers have large memory capacities of several terabytes and use hard disk packs and tape backups for data storage. Users connect to mainframes through dumb terminals with no local processing or memory.
The document describes the restructuring of a company's corporate structure from an old structure divided by product lines into a new structure organized by business units. The old structure separated the company's products and customers into different divisions for pro audio, lighting, musical instruments, and custom installation. The restructuring process consolidated these divisions by combining related customer groups, sales representatives, and product lines. This led to the creation of three new business units for professional audio/video, musical instruments, and custom installation, with each unit comprising related customers, representatives, products and business activities under one unified division.
This chapter discusses common computer input devices like the keyboard and mouse. It describes the standard QWERTY keyboard layout and the purpose of different types of keys. Mice can be mechanical or optical, and variants include trackballs and touchpads. Proper ergonomics are important to prevent repetitive strain injuries when using input devices extensively. The keyboard and mouse allow users to interact with computers by entering text, numbers, commands and selecting options.
This document provides an overview of DSL technology. It introduces different types of DSL including ADSL, VDSL, SDSL, and others. ADSL is described as the most common type for homes and small businesses, providing downstream speeds up to 1.5-1.59 Mbps and upstream up to 1.5 Mbps within an 18,000 foot range. DSL utilizes existing phone lines but offers higher speeds through digital technology. Advantages include always-on broadband access, faster speeds than dial-up, and fixed monthly billing. References for DSL standards are also provided.
This document discusses computer networks and networking concepts. It defines what a computer network is and different types of networks like LAN, MAN, and WAN. It then covers network topologies, describing physical topologies like bus, star, ring, mesh, tree and hybrid topologies. It also discusses common networking devices like routers, switches, hubs, bridges and others. Finally, it covers some common networking cables used like coaxial cable, Ethernet cable and optical fiber cable.
This document discusses three types of computer networks: local area networks (LANs), metropolitan area networks (MANs), and wide area networks (WANs). LANs connect computers within a limited local area like a home or single building. MANs operate within a larger area like a city using technologies like fiber cables and satellites. WANs cover the largest geographic areas, even spanning countries or borders, using long-distance transmission media.
Communication is considered as a core part of an organization, dynamically differentiated as internal and external communications. Read on to understand the various ways of structuring communication for different hierarchies and networks.
For more such innovative content on management studies, join WeSchool PGDM-DLP Program: http://bit.ly/ZEcPAc
Computers are used across many areas including education, banking, entertainment, transportation, offices, hospitals, defense, and design. They help with tasks like teaching students, keeping financial records, playing games, making reservations, sending emails, storing patient information, launching missiles, and designing publications and structures. Computers have become essential tools that are utilized in various sectors to increase efficiency, automation, and access to information.
There are four main types of computers: microcomputers, minicomputers, mainframes, and supercomputers. Microcomputers are small, portable, and low-cost devices used in homes and offices. Minicomputers are medium-sized machines that support multiple users at lower costs than mainframes. Mainframes are large, room-sized computers that serve many distributed users and servers. Supercomputers are the fastest and most expensive computers, used for advanced research applications that require parallel processing.
Asynchronous Transfer Mode (ATM) is a cell-based switching and multiplexing technology that was designed in the early 1990s to expedite the transmission of voice, video, and data over digital networks. ATM uses fixed-length cells of 53 bytes to carry traffic. It establishes virtual connections between endpoints to guarantee quality of service. ATM works by segmenting data into fixed-size cells at the source, transporting cells through a switch network via virtual circuits, and reassembling them at the destination. It provides benefits like high performance, integration of multiple data types, and adaptability to different network speeds.
Seminario eMadrid/SHEILA sobre "Analítica del Aprendizaje". ¿Cómo llegamos al...eMadrid network
Seminario eMadrid/SHEILA sobre "Analítica del Aprendizaje". ¿Cómo llegamos allí? Pasos hacia la adopción sistémica de la analítica de aprendizaje. Dragan Gasevic. Universidad de Edimburgo. 21/10/2016.
Social network analysis and academic performanceDragan Gasevic
This presentation is prepared for DALMOOC and talks about the use of social network analysis for understanding and prediction of academic performance. The presentation is based on
Gašević, D., Zouaq, A., Jenzen, R. (2013) 'Choose your Classmates, your GPA is at Stake!' The Association of Cross-Class Social Ties and Academic Performance. American Behavioral Scientist, 57(10), 1459-1478, http://www.sfu.ca/~dgasevic/papers_shared/abs2013.pdf
The document proposes a visualization tool called SFViz to explore and recommend friends in social networks by considering both social connections and user interests. SFViz extracts user interest information from tags, constructs tag networks, calculates similarities between users based on tag networks and social networks, and generates a compound graph for visualization. SFViz uses a radial, space-filling technique to visualize the tag hierarchy and a circular layout with edge bundling to show the social network. It was tested on a Last.fm music community dataset and allowed tag-based and friend recommendation exploration.
Nurturing the Connections: The Role of Quantitative Ethnography in Learning A...Dragan Gasevic
The document discusses the role of quantitative ethnography in learning analytics. It describes how quantitative ethnography can be used as a systematic approach to advance learning analytics by enhancing understanding and quality. Some key challenges discussed include limitations in adoption, validity, and measurement of learning analytics. The document advocates for the use of quantitative ethnographic methods and techniques to address these challenges and move the field forward.
Ranking Universities Using Linked Open DataRouzbehM
Ranking of universities represents a complex endeavor which involves gathering, weighting, and analyzing diverse data. Emerging semantic technologies enable the Web of Data, a giant graph of interconnected information resources, also known as Linked Data. A recent community effort, Linking Open Data project, offers the possibility of accessing a large number of semantically described and linked concepts in various domains. In this paper, we propose a novel approach to take advantage of this structured data in the domain of universities to develop proxy measures of their relative standing for ranking purposes. Derived from information theory, our approach of computing the Information Content for universities and ranking them based on these scores achieved results comparable to the international ranking systems such as Shanghai Jiao Tong University, Times Higher Education, and QS. The metric we developed can also be used for innovative semantic applications in a range of domains for entity ranking, information filtering, and multi-faceted browsing.
See: http://sydney.edu.au/engineering/it/~rouzbeh/university-rankings/
What's all the data about? - Linking and Profiling of Linked DatasetsStefan Dietze
This document discusses profiling and interlinking web datasets. It covers recent work on exploring, discovering, and searching linked data through entity and dataset interlinking recommendations and dataset profiling. It also discusses research areas like web science, information retrieval, and semantic web technologies. Some specific projects are mentioned for dataset profiling, entity linking, and generating structured topic profiles for datasets. Challenges around semantics, schemas, data consistency, and disambiguating entities are also outlined.
Our tool SFViz allows users to explore and find friends interactively based on interests. It uses a novel visualization that combines social networks and tags. SFViz extracts user interests from tags, constructs tag networks, and calculates similarities between users based on tags and social connections. It then generates a compound graph matching these networks and enables exploring recommendations through an interactive radial tag tree and circular social network layout. A case study on music data from Last.fm demonstrates tag-based and friend recommendation exploration in SFViz.
This document outlines an e-learning webinar for instructors on facilitating online courses. It discusses moving from a teaching role to one of facilitating and coaching. It also covers the skills needed like communication skills and software knowledge. Engaging learners can be done through interaction, open-ended tasks, collaboration and reflection. Evaluation should include students, instructors, the course and technology. Instructors benefit from convenience, pre-packaged materials and reduced costs. Challenges include communicating without nonverbal cues, managing time/assignments, technology issues and building community.
Learning with me Mate: Analytics of Social Networks in Higher EducationDragan Gasevic
Effects of social interactions are reported in research on higher education to lead to positive outcomes such as higher levels of internalization, sense of community, academic achievement, metacognition, and student retention. The role of social networks has especially been emphasized in research due to the availability of theoretical foundations and analytic methods to investigate their effects in higher education. The increased use of technologies in education allows for the collection of large and rich datasets about social networks which call for the use of novel analytics methods. This talk will first give a brief overview of the existing work on and lessons learned from some well-known studies on social networks in higher education in diverse situations from face-to-face to massive open online courses. The talk will then identify critical challenges that require immediate attention in order for the study of social networks to make a sustainable impact on learning and teaching. The most important take away from the talk will be that
- computational aspects of the study of social networks need to be integrated deeply with theory, research and practice,
- novel methods for the study of critical dimensions (discourse, structure and dynamics) that shape network formation and network effects are necessary, and
- innovative instructional approaches are essential to address the changing conditions created by contemporary educational and technological contexts.
Web Science Synergies: Exploring Web Knowledge through the Semantic WebStefan Dietze
The document discusses exploring web data and knowledge through the semantic web. It describes how the semantic web adds meaning to data through shared vocabularies and schemas. It also discusses challenges with the large number and diversity of linked open datasets, including issues with accessibility, heterogeneity of schemas, and data quality. It proposes approaches to address these challenges, such as dataset profiling, metadata catalogs, and infrastructure for federated querying.
Turning Data into Knowledge (KESW2014 Keynote)Stefan Dietze
The document discusses turning data into knowledge through profiling and interlinking web datasets. It covers recent work on linked data exploration, discovery, and search including entity and dataset interlinking recommendations and dataset profiling. It also discusses ensuring data consistency and resolving conflicts. The document then examines challenges with reusing and interlinking the long tail of linked datasets and issues regarding structure, semantics, interlinking, and persistence of linked data on the web.
This document discusses how social network analysis can be used to identify employees who are likely to be high performers and receive promotions. It explains that traditional factors like education, experience, and personality are not the only predictors of success - how central an employee is in the organization's social network is also important. The document outlines different centrality measures that can be analyzed, such as betweenness and closeness. It also notes that data on relationships both within and outside of work should be collected and analyzed to map employees' social networks. The results of this analysis could help identify top performers and predict their career trajectories.
This study examined college students' online research behaviors through a survey of 282 students. The survey asked about students' internet usage patterns, how they find study information online, and how they evaluate credibility of sources. The results showed that students primarily use search engines like Google to find information for studying due to convenience. They prefer using the library database for academic projects because they perceive the information to be more vetted. However, students value efficiency over credibility and expertise when conducting research. The study recommends improving information literacy training for students to help them better evaluate sources and use library databases.
Methods for Intrinsic Evaluation of Links in the Web of DataCristina Sarasua
The current Web of Data contains a large amount of interlinked data. However, there is still a limited understanding about the quality of the links connecting entities of different and distributed data sets. Our goal is to provide a collection of indicators that help assess existing interlinking. In this paper, we present a framework for the intrinsic evaluation of RDF links, based on core principles of Web data integration and foundations of Information Retrieval. We measure the extent to which links facilitate the discovery of an extended description of entities, and the discovery of other entities in other data sets. We also measure the use of different vocabularies. We analysed links extracted from a set of data sets from the Linked Data Crawl 2014 using these measures.
The field of learning analytics emerged in the early 2010s as researchers and practitioners sought to make use of the large amounts of digital trace data being produced by students in online and technology-enhanced learning environments. Early work in learning analytics focused on using basic LMS and student information system data to identify patterns between student behaviors and outcomes. However, critics argued this work was limited and did not address deeper learning challenges. By the mid-2010s, the field was establishing itself as a distinct "tribe" with its own conferences and journals, though it drew scholars from various disciplines and lacked a unified theoretical framework. Most recent reviews find the field is moving from predictive modeling toward a deeper understanding of the student experience but that evidence of impact on
Learning analytics are more than a technologyDragan Gasevic
Learning analytics aim to optimize learning through measurement, collection, analysis and reporting of student data. While interest is growing, few institutions have fully adopted analytics. Challenges include a lack of data-informed culture, focusing on solutions over research, and privacy concerns. Fully realizing analytics potential requires multidisciplinary teams, addressing complex educational systems, and developing an analytics-focused culture.
Tianpei Xie's research focuses on robust machine learning from multiple data sources. He has developed algorithms for robust classification in the presence of noisy or corrupted training data, including GEM-MED which jointly performs classification and anomaly detection. He has also developed methods for multi-view learning on statistical manifolds, including CMV-MED which co-regularizes multiple models using a robust consensus measure based on information divergence between probability density functions. Current work involves predicting node attributes in networks by combining network topology and node distributions. He has published papers in major machine learning conferences and journals and maintains websites with details of his research activities.
Tianpei Xie's research focuses on robust machine learning from multiple data sources. He has developed algorithms for robust classification in the presence of noisy or corrupted training data, including GEM-MED which jointly performs classification and anomaly detection. He has also developed methods for multi-view learning on statistical manifolds, including CMV-MED which co-regularizes multiple models using a robust consensus measure based on information divergence between probability density functions. Current work involves predicting node attributes in networks by combining network topology and node distributions. He has published papers in major machine learning conferences and journals and maintains websites with details of his research activities.
Working with Social Media Data: Ethics & good practice around collecting, usi...Nicola Osborne
Slides from a workshop delivered for the University of Edinburgh Digital Scholarship programme, on 18th October 2017. For further information on the programme see: http://www.digital.cahss.ed.ac.uk/ or #DigScholEd. If you are interested in hosting a similar workshop, or adapting these slides please contact me: nicola.osborne@ed.ac.uk.
Can learning analytics offer meaningful assessment? Dragan Gasevic
The emergence of learning analytics afforded for the analysis of digital traces of user interaction with technology. This analysis offers many opportunities to advance understanding and enhance learning and the environments in which learning occurs. Existing research has shown how learning analytics can provide contributions to different areas of education such as prediction of student success, uncovering learning strategies, understanding affective states, and unpacking the role social networks in learning. While these results have shown much promise, one critical challenge remains unclear – how learning analytics can help track learning progression and inform assessment especially from the perspective of the 21st century skills. This talk will explore opportunities and challenges for the integration of methods commonly used in learning analytics to analyze different digital traces with methods commonly used in assessment. The talk particularly focuses on open learning environments where analytics-based assessment is rather underexplored in contrast to assessment in specialized (intelligent tutoring) systems where the combined use of data mining and assessment has been established for some time now.
Towards Strengthening Links between Learning Analytics and AssessmentDragan Gasevic
. The emergence of learning analytics afforded for the analysis of digital traces of user interaction with technology. This analysis offers many opportunities to advance understanding and enhance learning and the environments in which learning occurs. Existing research has shown how learning analytics can provide contributions to different areas of education such as prediction of student success, uncovering learning strategies, understanding affective states, and unpacking the role social networks in learning. While these results have shown much promise, one critical challenge remains unclear – how learning analytics can help track learning progression and inform assessment especially from the perspective of the 21st century skills. This talk will explore opportunities and challenges for the integration of methods commonly used in learning analytics to analyze different digital traces with methods commonly used in assessment and psychometric research. The paper particularly focuses on open learning environments where analytics-based assessment is rather underexplored in contrast to assessment in specialized (intelligent tutoring) systems where the combined use of data mining and psychometric techniques has been established for some time now.
Let’s get there! Towards policy for adoption of learning analyticsDragan Gasevic
1) The document discusses challenges in adopting learning analytics and proposes a policy framework to guide the process.
2) Key adoption challenges include developing leadership, engaging stakeholders, providing training in data literacy, and establishing policies.
3) The framework suggests mapping the political context, identifying stakeholders, desired behavior changes, and developing an engagement strategy. It also involves analyzing capacity and establishing monitoring frameworks.
4) The goal is to provide an inclusive adoption process that embraces the complexity of educational systems and promotes innovation.
State and Directions of Learning Analytics Adoption (Second edition)Dragan Gasevic
The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new field learning analytics and mobilized the education sector to embrace the use of data for decision-making. This talk will first introduce the field of learning analytics and touch on lessons learned from some well-known case studies. The talk will then identify critical challenges that require immediate attention in order for learning analytics to make a sustainable impact on learning, teaching, and decision making. The talk will conclude by discussing a set of milestones selected as critical for the maturation of the field of learning analytics. The most important take away from the talk will be that
- systemic approaches to the development and adoption of learning analytics are critical,
- multidisciplinary teams are necessary to unlock a full potential of learning analytics, and
- capacity development at institutional levels through the inclusion of diverse stakeholders is essential for full learning analytics adoption.
This is the second edition of the talk that previously gave under the same title on several occasions. The second edition reflects many developments happened in the field of learning analytics, especially those in the following two projects - http://he-analytics.com and http://sheilaproject.eu.
Wearable technologies should promote adaptive learnersDragan Gasevic
1) The document discusses the potential for wearable technologies to support and study adaptive learners.
2) While wearables could collect more learner data than ever before, their use must focus on promoting adaptive learning skills over just adaptive algorithms.
3) Both the opportunities and challenges of using wearables to understand learning processes are explored, emphasizing the need for theory-driven research and interdisciplinary teams to ensure technologies actually improve learning outcomes.
Technologies to support self-directed learning through social interactionDragan Gasevic
This talk will describe underlying principles, design, and experience gained with ProSolo, a platform that supports personalized, competency-based learning through social interaction. Traditional educational models are primarily focused on classroom education and training typically associated with the notion of credit hours as the (only) route towards formal credentials. This limits opportunities for creating personalized learning pathways in the changing educational context. ProSolo provide users with the ability to unbundle education programs, courses, and units into discrete yet inter-related competencies, allowing learners to construct their education pathway in a manner that better reflects their interests and future career motivations and requirements. ProSolo is developed with the intention of providing learners with opportunities to customize, modify, and personalize their self-directed learning journey. ProSolo supports the development of skills for self-directed learning by allowing learners to control the planning, learning, and presentation of outcomes associated with their learning. To support learners with different levels of prior knowledge, study skills, and cultural backgrounds, ProSolo offers features for supporting self-directed learning through three types of scaffolds, including instructional, social, and technological. Learning in ProSolo occurs within a socially rich environment that aggregates learners’ information created and shared in their existing online spaces. ProSolo makes use of learning analytics to empower learners and instructors in this new model of education. ProSolo was used in the Data, Learning, and Analytics MOOC and is currently being piloted at several university sites.
Learning analytics: An opportunity for higher education?Dragan Gasevic
Slides used in my keynote at the Annual Conference of the European Association of Distance Teaching Universities - The open, online, flexible higher education conference - #OOFHEC2015
Keynote delivered by George Siemens (@gsiemens), Dragan Gasevic (@dgasevic), and Ryan Baker (@BakerEDMLab) at the 8th International Educational Data Mining Conference (EDM 2015) in Madrid, Spain on June 27, 2015
Educational data mining and learning analytics have to date largely focused on specific research questions that provide insight into granular interactions. These insights have bee abstracted to include the development of predictive models, intelligent tutors, and adaptive learning. While there are several domains where holistic or systems models have provided additional explanatory power, work around learning has not created holistic models with the level of concreteness or richness required. The need for both granular and integrated high-level view of learning is further influenced by distributed, life long, multi-spaced learning that today defines education. Drawing on social and knowledge graph theory, we propose the development of a Personal Learning Graph (PLeG) - an open and learner-owned profile that addresses cognitive, affective, and related elements that reflect what a learner knows, is able to do, and processes through which she learns best. This talk will introduce PLeG, detail required technical infrastructure, and articulate how it would interact with established learning software.
Learning analytics are more than measurementDragan Gasevic
Slides used for the keynote
Learning analytics are more than measurement
at
Policies for Educational Data Mining and Learning Analytics Briefing
organized by http://www.laceproject.eu/
Learning analytics and MOOCs: What have we learned so far and where to go?Dragan Gasevic
This document discusses learning analytics and MOOCs. It summarizes that feedback loops between students and instructors are weak in MOOCs. Learning analytics can be used to predict attrition and performance. Research has identified learner subpopulations in MOOCs. Better data collection and more robust methods are still needed to understand the process of learning beyond just counting and coding behaviors. Privacy and ethical considerations must also be addressed.
Social network analysis and understanding of massive open online coursesDragan Gasevic
This document discusses using social network analysis to understand the flow of information in connectivist MOOCs (cMOOCs). It analyzes data from Twitter and a 2011 cMOOC to identify influential nodes, network centers, brokers, and communities. Key findings include that the #cck11 hashtag was the most influential node in weeks 1 and 12, and that network modularity detected 19 communities in the course with 26% of participants in the largest community during week 1 and 12% in the largest by week 12.
Social network analysis and social presenceDragan Gasevic
This presentation is prepared for DALMOOC and talks about the use of social network analysis for the development of social capital based on social presence in communities of inquiry The presentation is based on
Kovanović, V., Joksimović, S., Gašević, D., Hatala, M., “What is the source of social capital? The association between social network position and social presence in communities of inquiry,” In Proceedings of 7th International Conference on Educational Data Mining – Workshops, London, UK, 2014, http://ceur-ws.org/Vol-1183/gedm_paper03.pdf
Social network analysis and learning designDragan Gasevic
This presentation is prepared for DALMOOC and talks about the use of social network analysis for improvement of learning design. The presentation is based on
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439-1459, doi:10.1177/0002764213479367
Social network analysis and creative potentialDragan Gasevic
This presentation is prepared for DALMOOC and talks about the use of social network analysis for understanding of creative potential. The presentation is based on
Dawson, S., Tan, J. P. L., & McWilliam, E. (2011). Measuring creative potential: Using social network analysis to monitor a learners' creative capacity. Australasian Journal of Educational Technology, 27(6), 924-942.
Network modularity and community identificationDragan Gasevic
The presentation describes the notion of network modularity as a method used for identification of communities in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
This document discusses challenges and opportunities in learning analytics. It addresses what should be measured, issues around instrumentation, capturing interventions and social networks. It also discusses challenges around scaling qualitative analysis, temporal processes, longitudinal studies, privacy, ethics and data sharing. Overall, the document advocates that learning analytics should go beyond just measuring outcomes and page access counts, and should seek to understand learning processes, contexts and the effects of students' own decisions.
Tools and Methods to Enhance Information Seeking, Sensemaking and LearningDragan Gasevic
Opportunities to facilitate learning on the Internet are widely recognized across subject matters, levels of education and situations ranging from extending one’s hobbies to life-long learning relating to workers’ changing roles in the workplace. However, information available in the Internet, even in formal academic courses, is rarely presented using empirically proven findings from the learning sciences. Often, learners are left “on their own” to figure out which tactics work best for them in seeking and understanding information, and studying to learn it. Given that most learners have weak skills in these areas and in self-regulating learning, this sets a stage for major failures in sensemaking and learning that can have dire societal consequences. On the other hand, there are open issues with the existing (a) tools that are typically designed for a hypothetical but factually non-existent “average” user; and (b) methods that are too often based on self-reports (e.g., questionnaires) that are insufficient to advance research on sensemaking and complex learning processes that involve dynamic feedback loops.
This talk (i) discusses results of several studies, in which we have addressed the above challenges, and (ii) outlines promising research topics that spans across the three main research cornerstones – computational, socio-cognitive, and user-centered design.
12. Data sources
http://www.snappvis.org/
Dawson, S., Bakharia, A., & Heathcote, E. (2010). SNAPP: Realising the affordances of real-time SNA within networked
learning environments. In Proceedings of the 7th International Conference on Networked Learning (pp. 125-133).
13. Data sources
Class enrollments
-cross-class networks-
Gašević, D., Zouaq, A., Jenzen, R. (2013). Choose your Classmates, your GPA is at Stake!' The Association of Cross-
Class Social Ties and Academic Performance. American Behavioral Scientist, 57(10), 1459-1478. doi:
10.1177/0002764213479362