This talk gives an introduction to the Center for Advancing Education and Research on Critical Infrastructure Resilience (CAESCIR), a new project at the Florida International University (FIU), sponsored by the Department of Homeland Security (DHS).
The San Jose State University (SJSU) School of Information (iSchool) hosts online and in-person open house events. Find out more about the iSchool's lifelong learning solutions in this presentation, originally given at the Santa Clara County Library District in Campbell, CA on September 29, 2015.
UCISA Learning Anaytics Pre-Conference WorkshopMike Moore
UCISA Learning Analytics Pre-Conference Workshop
Mike Moore - Sr. Advisory Consultant - Analytics
Desire2Learn, Inc.
UCISA Conference 2014, Brighton, UK
Presented Mar 26, 2014
Empowering the Instructor with Learning AnalyticsMichael Wilder
This document summarizes a presentation about leveraging data to improve online courses. It discusses using learning analytics to interpret student data in order to assess progress, predict performance, and identify issues. A case study examines data from a journalism course, including tracking reports from the learning management system and server logs. Surveys, journals, and other qualitative data provided insights. The analysis revealed opportunities to improve assignments, module organization, and support for different browsers/devices. Overall, learning analytics can help instructors understand student engagement and iteratively enhance online curriculum.
Presents an overview of the learning analytics field touching on the status of the technology, the challenges it faces, the arrival of predictive analytics to education and the best approach towards a successful implementation.
Laura Pasquini presented at the WNY Advising Technology Conference on connecting advising through technology. She discussed key trends in higher education like reduced funding and a changing student population that are impacting advising. Pasquini summarized research showing the importance of advising for student success and engagement. She outlined resources like degree audits, communication tools, and analytics that advisors can use to connect with students. Pasquini envisioned the future of advising being more integrated across departments and utilizing innovative technologies and online spaces to interact with students.
Analysing analytics, what is learning analytics?Moodlerooms
The document discusses learning analytics, which is defined as the measurement, collection, analysis and reporting of learner data to optimize learning. It describes how data from student profiles, activities, course content and results can be collected and analyzed descriptively, diagnostically, predictively and prescriptively. The document also addresses ethical concerns regarding data privacy, transparency and ensuring analytics are used to benefit students. It provides examples of how different stakeholders may use analytics and discusses the Open University's principles of applying analytics in an ethical manner that respects student consent and privacy.
The San Jose State University (SJSU) School of Information (iSchool) hosts online and in-person open house events. Find out more about the iSchool's lifelong learning solutions in this presentation, originally given at the Santa Clara County Library District in Campbell, CA on September 29, 2015.
UCISA Learning Anaytics Pre-Conference WorkshopMike Moore
UCISA Learning Analytics Pre-Conference Workshop
Mike Moore - Sr. Advisory Consultant - Analytics
Desire2Learn, Inc.
UCISA Conference 2014, Brighton, UK
Presented Mar 26, 2014
Empowering the Instructor with Learning AnalyticsMichael Wilder
This document summarizes a presentation about leveraging data to improve online courses. It discusses using learning analytics to interpret student data in order to assess progress, predict performance, and identify issues. A case study examines data from a journalism course, including tracking reports from the learning management system and server logs. Surveys, journals, and other qualitative data provided insights. The analysis revealed opportunities to improve assignments, module organization, and support for different browsers/devices. Overall, learning analytics can help instructors understand student engagement and iteratively enhance online curriculum.
Presents an overview of the learning analytics field touching on the status of the technology, the challenges it faces, the arrival of predictive analytics to education and the best approach towards a successful implementation.
Laura Pasquini presented at the WNY Advising Technology Conference on connecting advising through technology. She discussed key trends in higher education like reduced funding and a changing student population that are impacting advising. Pasquini summarized research showing the importance of advising for student success and engagement. She outlined resources like degree audits, communication tools, and analytics that advisors can use to connect with students. Pasquini envisioned the future of advising being more integrated across departments and utilizing innovative technologies and online spaces to interact with students.
Analysing analytics, what is learning analytics?Moodlerooms
The document discusses learning analytics, which is defined as the measurement, collection, analysis and reporting of learner data to optimize learning. It describes how data from student profiles, activities, course content and results can be collected and analyzed descriptively, diagnostically, predictively and prescriptively. The document also addresses ethical concerns regarding data privacy, transparency and ensuring analytics are used to benefit students. It provides examples of how different stakeholders may use analytics and discusses the Open University's principles of applying analytics in an ethical manner that respects student consent and privacy.
The document discusses emerging trends in librarianship and higher education. It notes that the abundance of online resources is challenging traditional roles of educators and libraries. Libraries must consider their unique value in providing sense-making and credibility assessment of information. Emerging technologies like MOOCs, learning analytics, and 3D printing will continue to impact higher education. Libraries need to focus on user needs, manage both physical and digital collections, and leverage technologies like the cloud to remain relevant gateways for managing information.
Learning Analytics: Seeking new insights from educational dataAndrew Deacon
1) Learning analytics seeks new insights from educational data by measuring, collecting, analyzing and reporting data about learners and learning environments to optimize learning.
2) There are three eras of social science research: collecting simple data on important questions; getting the most from little data; and today's "big data" deluge allowing new questions.
3) Educational data can be analyzed through psychometrics, educational data mining, and learning analytics, typically focusing on assessment, learning over time, and wider contexts respectively.
From Point A to Point B: Gaining Momentum through Transitions & New Types of...Rebecca Kate Miller
This document discusses helping students transition through various stages by connecting the dots between high school, college-level research, and adulthood. It notes challenges students face, including inadequate research skills, difficulty tying together information, and balancing multiple roles for adult students. The role of librarians is also discussed, including focusing on concepts rather than tools, embracing pedagogical expertise, and expanding responsibilities. Examples are provided of instruction programs that develop academic integrity tutorials and use communities of practice to strategically grow programming through reflection and partnerships. Overall, the document advocates connecting students to resources and supporting their development through transitions.
Learning analytics in higher education: Promising practices and lessons learnedBodong Chen
This document summarizes Bodong Chen's presentation on learning analytics in higher education. The presentation covered three parts: 1) A study of Australian universities' use of learning analytics, which identified different clusters of institutions based on their drivers and perceptions. 2) Initiatives at the University of Minnesota, including the Unizin consortium and pilots using learning management system data. 3) An experiment using learning analytics in one of Chen's own classes to promote student participation and awareness. Key cross-cutting considerations discussed were the interventionist nature of analytics and importance of cultural shifts, conversations, and building educator data literacy.
The document discusses requirements for learning analytics based on a lecture and workshop at East China Normal University. It begins with introductions and then outlines the day's plan to discuss definitions of analytics, actors in learning analytics, framework models, and requirements. It emphasizes starting with pedagogy and poses questions about what data is available and how to build trust. Ethical challenges are noted around data protection, privacy, transparency, and purpose. The goal is to use analytics to facilitate learning while avoiding instructivist approaches and stress for learners.
Instructional Technology and Local Institutional Cultures (VLC March 2015)UOInTRO
Sharing with our regional Virtual Learning Community--trends in comparator research as well as the results of a group survey about attitudes and perceptions at local institutions.
This document summarizes a workshop on developing information literacy skills for library and information studies students. The workshop aimed to gather practitioner views on:
1. The skills needed to deliver good information literacy instruction, such as knowledge of databases, teaching skills, and an awareness of learning styles.
2. How these skills are currently developed, through practical experience, training, and professional development opportunities.
3. Who supports skills development, like employers, professional associations, and colleagues on social media and mailing lists.
4. Whether library schools or workplaces are better for supporting ongoing skills growth, and recommendations that both are needed, with library schools teaching theory and providing practice opportunities.
Digital student experience: Online Learners updateJisc
This document discusses a study on online learners. It defines online learning broadly as including exclusively online courses, courses with online elements, and online study within mainly face-to-face courses. This broad definition means most post-compulsory learners will have some online component.
The study will involve a literature review, consultation with online learners and staff, and synthesis of findings. Preliminary findings from the literature identify factors influencing online learning outcomes, including learner characteristics, the digital environment, and course design. Dominant themes are self-regulated learning and affective issues. Successful online learners are characterized as motivated, organized, and digitally capable individuals who actively engage with course materials and interact with others. Provider support
HEAL 570: Selecting Technology for Higher EducationLaura Pasquini
The document discusses selecting technology to support advising in higher education. It notes key issues in higher ed around reduced funding and a need to focus on retention and completion. There is also a desire from advisors for integrated systems that allow holistic student support and communication across different tools and campus systems. The document provides examples of advisor wishes for technology, such as a single sign-on portal and tools to connect with students. It emphasizes the importance of selecting technologies that meet student and advisor needs, support learning outcomes, and are compatible with existing campus systems and resources.
Slides from Keynote presentation at the University of Southern California's 2015 Teaching with Technology annual conference.
"9:15 am – ANN Auditorium
Key Note: What Do We Mean by Learning Analytics?
Leah Macfadyen, Director for Evaluation and Learning Analytics, University of British Columbia
Executive Board, SoLAR (Society for Learning Analytics Research)
Leah Macfadyen will define and explore the emerging and interdisciplinary field of learning analytics in the context of quantified and personalized learning. Leah will use actual examples and case studies to illustrate the range of stakeholders learning analytics may serve, the diverse array of questions they may be used to address, and the potential impact of learning analytics in higher education."
This document summarizes a presentation about inspiring innovation in library instruction through the use of mobile devices and apps. The presentation discusses moving beyond an initial enthusiasm for technology ("technolust") to adopting mobile devices intentionally based on learner needs. It provides examples of how to integrate mobile apps and devices into instruction to meet objectives like organizing information, evaluating sources, and searching effectively. The presentation also covers strategies for instructional design, like chunking content and recognizing the cognitive limits of working memory. It aims to help libraries ask the right questions and apply best practices for meaningful mobile integration into their instruction programs.
Learning Analytics: New thinking supporting educational researchAndrew Deacon
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts to understand and optimize learning. There are three approaches to analyzing educational data: psychometrics, educational data mining, and learning analytics. Learning analytics is being used to ask new questions by analyzing data from MOOCs and social media at both the micro and macro levels. While analytics can inform educational research, concerns remain about how it may change definitions of knowledge and reduce context.
The document discusses integrating mobile devices and apps into teaching. It begins by noting the need for educators to innovate and experiment with mobile learning rather than imposing traditional pedagogical models. The goals are to help educators ask the right questions about integrating mobile devices, apply best practices, and be inspired to lead conversations around learning environments and technologies. The document then covers observing students' existing mobile behaviors, intentionally designing mobile integration, evaluating resources, and considering cognitive limitations to ensure effective instructional design.
This document discusses learning analytics and the differences between academic analytics and learning analytics. It provides:
- Definitions of academic analytics as focused on institutional decision making and management, while learning analytics focuses on supporting student learning and is aimed at learners and instructors.
- An overview of how learning analytics has evolved from traditional testing and assessment to incorporate larger datasets, models, personalization techniques, and insights from digital traces like online activity logs.
- Several examples of how learning analytics can provide insights at the individual student level, within groups, in the classroom, and across academic programs.
- Some of the challenges in implementing learning analytics including issues around ethics, data access, and developing institutional capacity like data science
A graduate employability lens for the Seven Pillars of Information LiteracyInformAll
This document discusses aligning information literacy with graduate employability. It begins with definitions of employability, noting it involves lifelong learning and developing skills beyond specific job requirements. A literature review found employers value soft skills like teamwork and communication over technical skills. The document explores how information literacy relates to desirable employability attributes like problem solving, working socially, and career management. It argues information literacy contributes to these attributes and workplace success through competencies like analyzing information to solve problems and tapping networks as knowledge sources. The document provides examples of integrating information literacy and employability frameworks in university programs and discusses libraries' roles in developing students' work-related skills.
This study evaluated the impact of an information literacy course on preservice teachers' skills. It compared a treatment group of 16 students who took the course to a comparison group of 10 students who did not. Both groups completed a questionnaire and thinking-aloud tasks involving finding articles, evaluating websites, and selecting Web 2.0 tools. Results showed the treatment group performed better on skills tests and demonstrated more familiarity with resources. However, neither group excellently completed all tasks. The study concluded that while the course helped, an enhanced long-term instruction program is needed to fully develop teachers' twenty-first century information literacy abilities.
Keynote Address, Expanding Horizons 2012, Macquarie University
http://staff.mq.edu.au/teaching/workshops_programs/expanding_horizons
"Learning Analytics": unprecedented data sets and live data streams about learners, with computational power to help make sense of it all, and new breeds of staff who can talk predictive models, pedagogy and ethics. This means rather different things to different people: unprecedented opportunity to study, benchmark and improve educational practice, at scales from countries and institutions, to departments, individual teachers and learners. "Benchmarking" may trigger dystopic visions of dumbed down proxies for 'real teaching and learning', but an emu response is no good. For educational institutions, our calling is to raise the quality of debate, shape external and internal policy, and engage with the companies and open communities developing the future infrastructure. How we deploy these new tools rests critically on assessment regimes, what can be logged and measured with integrity, and what we think it means to deliver education that equips citizens for a complex, uncertain world.
The document summarizes a meeting to discuss supporting staff to teach effectively online. It introduces Jisc's digital capability service and discovery tool, which includes a self-assessment quiz to evaluate digital skills. Feedback from the tool includes next steps and resources. A new question set on effective online teaching was developed through a review process. Key areas covered include knowledge acquisition, critical engagement, knowledge application, dialogue, collaboration, content creation, and supporting online learners. Challenges discussed include accessibility, non-institutional tools, assessing collaboration, specialist practices, and developing student online learning skills. Updates provided new case studies and information on digital capability events.
The document discusses learning analytics, which is defined as the measurement, collection, analysis and reporting of data about learners and their learning environments. It aims to understand and optimize learning. The document outlines the types of data that is collected on students, including profiles, activities, content accessed, and results. It also discusses the goals of improving student success, retention, and experience. Key topics covered include descriptive, diagnostic, predictive and prescriptive analytics. The document raises important ethical concerns around data access, ownership, transparency and privacy when applying learning analytics and discusses approaches taken by organizations like the Open University.
The document discusses emerging trends in librarianship and higher education. It notes that the abundance of online resources is challenging traditional roles of educators and libraries. Libraries must consider their unique value in providing sense-making and credibility assessment of information. Emerging technologies like MOOCs, learning analytics, and 3D printing will continue to impact higher education. Libraries need to focus on user needs, manage both physical and digital collections, and leverage technologies like the cloud to remain relevant gateways for managing information.
Learning Analytics: Seeking new insights from educational dataAndrew Deacon
1) Learning analytics seeks new insights from educational data by measuring, collecting, analyzing and reporting data about learners and learning environments to optimize learning.
2) There are three eras of social science research: collecting simple data on important questions; getting the most from little data; and today's "big data" deluge allowing new questions.
3) Educational data can be analyzed through psychometrics, educational data mining, and learning analytics, typically focusing on assessment, learning over time, and wider contexts respectively.
From Point A to Point B: Gaining Momentum through Transitions & New Types of...Rebecca Kate Miller
This document discusses helping students transition through various stages by connecting the dots between high school, college-level research, and adulthood. It notes challenges students face, including inadequate research skills, difficulty tying together information, and balancing multiple roles for adult students. The role of librarians is also discussed, including focusing on concepts rather than tools, embracing pedagogical expertise, and expanding responsibilities. Examples are provided of instruction programs that develop academic integrity tutorials and use communities of practice to strategically grow programming through reflection and partnerships. Overall, the document advocates connecting students to resources and supporting their development through transitions.
Learning analytics in higher education: Promising practices and lessons learnedBodong Chen
This document summarizes Bodong Chen's presentation on learning analytics in higher education. The presentation covered three parts: 1) A study of Australian universities' use of learning analytics, which identified different clusters of institutions based on their drivers and perceptions. 2) Initiatives at the University of Minnesota, including the Unizin consortium and pilots using learning management system data. 3) An experiment using learning analytics in one of Chen's own classes to promote student participation and awareness. Key cross-cutting considerations discussed were the interventionist nature of analytics and importance of cultural shifts, conversations, and building educator data literacy.
The document discusses requirements for learning analytics based on a lecture and workshop at East China Normal University. It begins with introductions and then outlines the day's plan to discuss definitions of analytics, actors in learning analytics, framework models, and requirements. It emphasizes starting with pedagogy and poses questions about what data is available and how to build trust. Ethical challenges are noted around data protection, privacy, transparency, and purpose. The goal is to use analytics to facilitate learning while avoiding instructivist approaches and stress for learners.
Instructional Technology and Local Institutional Cultures (VLC March 2015)UOInTRO
Sharing with our regional Virtual Learning Community--trends in comparator research as well as the results of a group survey about attitudes and perceptions at local institutions.
This document summarizes a workshop on developing information literacy skills for library and information studies students. The workshop aimed to gather practitioner views on:
1. The skills needed to deliver good information literacy instruction, such as knowledge of databases, teaching skills, and an awareness of learning styles.
2. How these skills are currently developed, through practical experience, training, and professional development opportunities.
3. Who supports skills development, like employers, professional associations, and colleagues on social media and mailing lists.
4. Whether library schools or workplaces are better for supporting ongoing skills growth, and recommendations that both are needed, with library schools teaching theory and providing practice opportunities.
Digital student experience: Online Learners updateJisc
This document discusses a study on online learners. It defines online learning broadly as including exclusively online courses, courses with online elements, and online study within mainly face-to-face courses. This broad definition means most post-compulsory learners will have some online component.
The study will involve a literature review, consultation with online learners and staff, and synthesis of findings. Preliminary findings from the literature identify factors influencing online learning outcomes, including learner characteristics, the digital environment, and course design. Dominant themes are self-regulated learning and affective issues. Successful online learners are characterized as motivated, organized, and digitally capable individuals who actively engage with course materials and interact with others. Provider support
HEAL 570: Selecting Technology for Higher EducationLaura Pasquini
The document discusses selecting technology to support advising in higher education. It notes key issues in higher ed around reduced funding and a need to focus on retention and completion. There is also a desire from advisors for integrated systems that allow holistic student support and communication across different tools and campus systems. The document provides examples of advisor wishes for technology, such as a single sign-on portal and tools to connect with students. It emphasizes the importance of selecting technologies that meet student and advisor needs, support learning outcomes, and are compatible with existing campus systems and resources.
Slides from Keynote presentation at the University of Southern California's 2015 Teaching with Technology annual conference.
"9:15 am – ANN Auditorium
Key Note: What Do We Mean by Learning Analytics?
Leah Macfadyen, Director for Evaluation and Learning Analytics, University of British Columbia
Executive Board, SoLAR (Society for Learning Analytics Research)
Leah Macfadyen will define and explore the emerging and interdisciplinary field of learning analytics in the context of quantified and personalized learning. Leah will use actual examples and case studies to illustrate the range of stakeholders learning analytics may serve, the diverse array of questions they may be used to address, and the potential impact of learning analytics in higher education."
This document summarizes a presentation about inspiring innovation in library instruction through the use of mobile devices and apps. The presentation discusses moving beyond an initial enthusiasm for technology ("technolust") to adopting mobile devices intentionally based on learner needs. It provides examples of how to integrate mobile apps and devices into instruction to meet objectives like organizing information, evaluating sources, and searching effectively. The presentation also covers strategies for instructional design, like chunking content and recognizing the cognitive limits of working memory. It aims to help libraries ask the right questions and apply best practices for meaningful mobile integration into their instruction programs.
Learning Analytics: New thinking supporting educational researchAndrew Deacon
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts to understand and optimize learning. There are three approaches to analyzing educational data: psychometrics, educational data mining, and learning analytics. Learning analytics is being used to ask new questions by analyzing data from MOOCs and social media at both the micro and macro levels. While analytics can inform educational research, concerns remain about how it may change definitions of knowledge and reduce context.
The document discusses integrating mobile devices and apps into teaching. It begins by noting the need for educators to innovate and experiment with mobile learning rather than imposing traditional pedagogical models. The goals are to help educators ask the right questions about integrating mobile devices, apply best practices, and be inspired to lead conversations around learning environments and technologies. The document then covers observing students' existing mobile behaviors, intentionally designing mobile integration, evaluating resources, and considering cognitive limitations to ensure effective instructional design.
This document discusses learning analytics and the differences between academic analytics and learning analytics. It provides:
- Definitions of academic analytics as focused on institutional decision making and management, while learning analytics focuses on supporting student learning and is aimed at learners and instructors.
- An overview of how learning analytics has evolved from traditional testing and assessment to incorporate larger datasets, models, personalization techniques, and insights from digital traces like online activity logs.
- Several examples of how learning analytics can provide insights at the individual student level, within groups, in the classroom, and across academic programs.
- Some of the challenges in implementing learning analytics including issues around ethics, data access, and developing institutional capacity like data science
A graduate employability lens for the Seven Pillars of Information LiteracyInformAll
This document discusses aligning information literacy with graduate employability. It begins with definitions of employability, noting it involves lifelong learning and developing skills beyond specific job requirements. A literature review found employers value soft skills like teamwork and communication over technical skills. The document explores how information literacy relates to desirable employability attributes like problem solving, working socially, and career management. It argues information literacy contributes to these attributes and workplace success through competencies like analyzing information to solve problems and tapping networks as knowledge sources. The document provides examples of integrating information literacy and employability frameworks in university programs and discusses libraries' roles in developing students' work-related skills.
This study evaluated the impact of an information literacy course on preservice teachers' skills. It compared a treatment group of 16 students who took the course to a comparison group of 10 students who did not. Both groups completed a questionnaire and thinking-aloud tasks involving finding articles, evaluating websites, and selecting Web 2.0 tools. Results showed the treatment group performed better on skills tests and demonstrated more familiarity with resources. However, neither group excellently completed all tasks. The study concluded that while the course helped, an enhanced long-term instruction program is needed to fully develop teachers' twenty-first century information literacy abilities.
Keynote Address, Expanding Horizons 2012, Macquarie University
http://staff.mq.edu.au/teaching/workshops_programs/expanding_horizons
"Learning Analytics": unprecedented data sets and live data streams about learners, with computational power to help make sense of it all, and new breeds of staff who can talk predictive models, pedagogy and ethics. This means rather different things to different people: unprecedented opportunity to study, benchmark and improve educational practice, at scales from countries and institutions, to departments, individual teachers and learners. "Benchmarking" may trigger dystopic visions of dumbed down proxies for 'real teaching and learning', but an emu response is no good. For educational institutions, our calling is to raise the quality of debate, shape external and internal policy, and engage with the companies and open communities developing the future infrastructure. How we deploy these new tools rests critically on assessment regimes, what can be logged and measured with integrity, and what we think it means to deliver education that equips citizens for a complex, uncertain world.
The document summarizes a meeting to discuss supporting staff to teach effectively online. It introduces Jisc's digital capability service and discovery tool, which includes a self-assessment quiz to evaluate digital skills. Feedback from the tool includes next steps and resources. A new question set on effective online teaching was developed through a review process. Key areas covered include knowledge acquisition, critical engagement, knowledge application, dialogue, collaboration, content creation, and supporting online learners. Challenges discussed include accessibility, non-institutional tools, assessing collaboration, specialist practices, and developing student online learning skills. Updates provided new case studies and information on digital capability events.
The document discusses learning analytics, which is defined as the measurement, collection, analysis and reporting of data about learners and their learning environments. It aims to understand and optimize learning. The document outlines the types of data that is collected on students, including profiles, activities, content accessed, and results. It also discusses the goals of improving student success, retention, and experience. Key topics covered include descriptive, diagnostic, predictive and prescriptive analytics. The document raises important ethical concerns around data access, ownership, transparency and privacy when applying learning analytics and discusses approaches taken by organizations like the Open University.
Cybersecurity strategy-brief-to-itc final-17_apr2015IT Strategy Group
This document provides a summary of Bob Turner's cybersecurity strategic plan briefing to the Information Technology Committee. The strategic plan aims to improve cybersecurity at UW-Madison through establishing a risk management framework, promoting cyber hygiene, facilitating incident response, and consolidating incident response capabilities. The plan aligns with UW-Madison's strategic priorities of education, research, community engagement, diversity, and resource stewardship. Key elements of the cybersecurity strategy include implementing data governance, establishing a risk management framework, improving user competence through training, consolidating security operations, enhancing threat intelligence, and establishing collaborative partnerships. The roadmap provided outlines the review and socialization process for the strategic plan.
The document summarizes the four phases of conducting a needs assessment: 1) Planning - defining the audience, data to be collected, and collection methods; 2) Collecting Data - determining sample size and collecting data via surveys; 3) Analyzing Data - identifying needs categories and prioritizing needs; 4) Compiling a Final Report - with the purpose, process, quantitative and qualitative results, and recommendations. It then provides details of each phase for a needs assessment conducted at San Jacinto College to transition classes online due to COVID-19, finding most staff, instructors, and students were willing and able to participate in the new online environment.
Analytics Goes to College: Better Schooling Through Information Technology wi...bisg
Higher education faces challenges in addressing economic and readiness problems for students from disadvantaged backgrounds. While free educational content helps, it does not solve complex readiness issues. For-profit models also struggle with serving students in remote, underserved areas. Analytics and technologies show promise in helping institutions address these "last mile" challenges through personalized, adaptive learning approaches. However, significant organizational changes and integration of disparate data sources will be required for institutions to fully leverage these tools. Open discussion is needed around ensuring insights into learning and student success remain available as public goods, not proprietary to any private vendor.
Adopting OER for Pathways, Certificates, & CoursesUna Daly
A panel of members from the Community College Consortium for Open Educational Resources (CCCOER) will share how they are adopting OER for Pathways, Certificates, and Courses at their colleges. CCCOER was founded in 2007 and now composes over 250 colleges in 22 states and provinces. Members collaborate online regularly and in-person at conferences on best practices for OER adoption. This cross-institutional sharing of open educational resources, open practices, open research, and open policies provides a powerful OER advocacy network for community colleges. New members have immediate access to a community of OER practitioners and experts who can help them launch their projects more efficiently and quickly. Meetups at regional and national conferences provide an opportunity to share and promote successful OER adoption strategies of our members with colleagues throughout higher education. Audience participation will be welcomed.
Our eLearning Panel will be moderated by Una Daly, CCCOER Director and our panelists include:
Cynthia Alexander, Distance Education Coordinator and Faculty at Cerritos College.
Cynthia leads the Online Teacher Certification program at Cerritos College and was an early adopter of OER in her teaching. The Business management department has also been using OER for over 5-years and OER has spread to many other departments through early efforts on the Kaleidoscope project.
Lorah Gough, Director, Distance Education at Houston Community College
Lorah works with faculty to find and adopt OER and is working to highlight OER in the new HCC strategic plan coming out next year. Two OER committees and the library are all strong partners in this effort.
Cheryl Knight, Instructional Designer at Cuyahoga Community College (Tri-C)
Cheryl leads the Save 100K project; focused on saving students money so they can concentrate on success. Started with a zero text cost math course and expanded to several disciplines and all 4 campuses in greater Cleveland are now participating.
Jake McBee, Instructional Designer, at North Central Texas College
Jake works on the Rural Information Technology Alliance (RITA) grant, shared by a four-college Texas consortium, building OER-based curriculum for certificates in high-demand information technology areas including networking, mobile apps, and cybersecurity.
Lisa Young, Tri-Chair Maricopa Millions Project;
Faculty Director, Teaching & Learning Center, Scottsdale Community College.
Lisa is tri-chair of the district-wide Maricopa Millions Project started in fall 2013 with the goal of saving $5 Million for students in five years. In two years, they are over 90% to achieving the goals. Maricopa Millions is now planning for zero-textbook pathways in multiple disciplines.
Our eLearning panel moderator will be Una Daly, director of CCCOER.
Student Success Plan Learner Relationship Management Tech Reviewshawngormley
The document describes the Student Success Plan (SSP), a software system and process designed to increase student retention, success, and graduation rates. SSP uses holistic counseling, web-based support tools, and interventions to identify and support at-risk students. It discusses how SSP was implemented at Sinclair Community College to increase retention rates, course success, GPA, and graduation rates. The document also provides an overview of the SSP technology, including its integration with other systems like SIS and how others can get involved or adopt SSP.
The presentation proposes a project to develop a comprehensive college access framework for metropolitan Atlanta. It introduces the team and their experience in college access and data analysis. The proposed work involves 4 phases: 1) project planning, 2) information gathering through literature review, mapping existing programs, and stakeholder interviews, 3) presenting key findings, and 4) developing an action plan and recommendations. The timeline spans from December 2013 through April 2014. The goal is to better serve underserved students through collaborative, data-driven strategies aligned across stakeholders.
[DSC Europe 22] Machine learning algorithms as tools for student success pred...DataScienceConferenc1
The goal of higher education institutions is to provide quality education to students. Predicting academic success and early intervention to help at-risk students is an important task for this purpose. This talk explores the possibilities of applying machine learning in developing predictive models of academic performance. What factors lead to success at university? Are there differences between students of different generations? Answers are given by applying machine learning algorithms to a data set of 400 students of three generations of IT studies. The results show differences between students with regard to student responsibility and regularity of class attendance and great potential of applying machine learning in developing predictive models.
This report summarizes the findings of a needs assessment conducted by the IT Resource Sharing Group regarding operational and reporting needs for student data at the University of Washington. The assessment found that while Schools share many common information needs, they also have unique needs. It also found a lack of awareness about existing central systems and a proliferation of "shadow systems" developed by individual units. The report concludes there is high frustration over access to and analysis of student data. It recommends acknowledging decentralized systems and creating processes to support secure and productive development across the university.
Maryland Global University focuses on developing professional development courses and academic programs to prepare students for today's world. It offers both on-campus and online programs with flexible scheduling and competitive tuition. The university is committed to excellence, diversity, integrity, and flexibility in education. It offers a Master's Certificate in Project Management and a Master's Certificate in Data Management. The Project Management program covers the principles and approaches of project management based on PMI standards. The Data Management program integrates foundations of information systems, database concepts, database design, and database development and administration.
Using Learning Analytics Data to Understand Project-Based LearningRebecca Reynolds
The document provides background information on the Globaloria program, which aims to develop students' contemporary learning abilities through game design. It discusses implementation of Globaloria in several US states, including West Virginia. Specifically, it examines data from a high school class that participated in Globaloria during the 2010-2011 school year, focusing on one team called the Carrot Wizards. The data sources provided allow analysis of factors at different levels (e.g. individual, team, classroom) that may have influenced student learning outcomes. Preliminary analysis of wiki log files shows patterns of wiki use over time varied across teams, with the Carrot Wizards team exhibiting more early and late activity than others.
1) The document discusses big data and learning analytics in education, including how it has been featured in the NMC Horizon Report from 2010-2013. It describes how big data can be used for educational research purposes such as modeling student knowledge, behavior, experiences, profiling student groups, and analyzing learning components and instructional principles.
2) Examples of learning analytics in practice are provided, including Purdue University's Signals project, Saddleback Community College's personalized learning system, and analytics tools used at other universities.
3) Potential applications of learning analytics discussed include using data to provide insights into student reading habits, facilitating anonymous peer feedback and grading in writing courses, and capturing data to engage students in interactive teaching situations.
This document discusses the use of analytics and data in education. It begins with definitions and an overview of how analytics can impact place, platform, people and practice in education. It then discusses how analytics works, using student data from various sources and applying algorithms to gain insights. Examples are given of universities using analytics to identify at-risk students and improve outcomes. The document also outlines challenges, such as privacy issues, and the future growth of analytics integration and learning data standards.
Speakers:
David Lewis, senior analytics consultant, Jisc
Martin Lynch, learning systems manager, University of South Wales
An opportunity to find out about how an institution has been implementing learning analytics to support the student journey with and opportunity to discuss issues and possibilities that the use of learning analytics may create.
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Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
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Key Takeaways:
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Charlie Greenberg, host
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Introducing FIU CAESCIR
1. Introducing FIU CAESCIR
PI: Jason Liu (Florida International University)
This material is based upon work supported by DHS under Grant Number 2017‐ST‐062‐000002
Center for Advancing Education and Studies on Critical Infrastructures Resilience
3. Florida International University (FIU)
• Founded in 1972, located in Miami, Florida
• One of the 12 Florida state universities
• Among top ten largest universities in US with ~55,000 students
• Minority-serving university (61% Hispanic, 13% African American)
• Contributes almost $9B each year to local economy in South Florida
• Carnegie R1 research university (with highest research activities)
• #1 for Hispanic and #5 for African American in number of undergraduates
awarded with engineering degree (ASEE)
• #2 in Florida “America’s Best Employers” (Forbes Magazine)
4. School of Computing and Information
Sciences (SCIS)
• 29 tenured or tenure-track faculty members
• Over 2000 students, including 200+ MS students, 90+ PhD students
• Offers BS, MS, PhD degrees in CS; MS in Telecommunication &
Networking, Cybersecurity, IT; and BS and BA in IT
• Computer Science ranked #39 for Total and Federally funded R&D
Research Expenditures for FYs 12-15 (NSF HERD Survey)
• Computer Science and Math ranked #1 in Research Expenditures
among high Hispanic enrollment institutions (NSF HERD Survey)
• Largest provider of Hispanic graduates at BS, MS and PhD levels in
computer science and engineering
5. SCIS National Awards
NSF CAREER Awardees (Li, Liu, Rangaswami)
Fellows (Iyengar, Chen)
Fellows (Iyengar, Weiss)
Fellow (Iyengar)
Fellow (Iyengar), Distinguished Scientists (Chen, Liu, Smith, Weiss)
Distinguished Educator (Weiss)
Faculty Awards (Iyengar, Li, Rangaswami, Rishe, Sadjadi)
Many FIU awards to faculty
Lifetime Achievement Award by the International Society of Agile Manufacturing (Iyengar)
6. Center for Advancing Education and Studies on
Critical Infrastructures Resilience (CAESCIR)
• Program: DHS Scientific Leadership Awards (SLA) for Minority
Serving Institutions (MSI)
• Project Period: 5 years (2017-2022)
• Program Officer: Stephanie Willett
• The Mission:
“To provide as an integrated research and education framework
with a specific focus on improving our nation’s critical
infrastructures security and resilience.”
7. Proposed Activities
1. Scholarships for students specialized in HS-STEM areas
2. Coordinated education of HS-STEM topics
3. Pursue research in HS-STEM areas
4. Engage early career faculty to pursue integrated HS-STEM
research and education activities
5. Partner with DHS Center of Excellence (Critical Infrastructure
Resilience Institute at University of Illinois, Urbana-Champaign)
8. 1. Scholarships for students specialized in
HS-STEM areas
• 7 undergraduate students
• 3 PhD students
• Student recruitment: website, brochure, information session
• Student performance tracking: full-time, GPA, participation of
center activities, productivity (esp. for graduate students)
• Academic advising: undergraduate students required to meet with
faculty mentor at least twice per semester
• Peer mentoring: undergraduates in research mentored at research
labs
• Social events: orientation, social events, symposium,
conferences, and possible COE research meetings
• Internship and career services: summer internship, career fair, …
9. 1. Scholarships for students specialized in
HS-STEM areas
• All students must be US citizens
• All students must maintain GPA ≥ 3.3/4
• All students must work for Homeland Security Enterprise
after graduation within one year (except going to grad
school, military service, or public health service)
• Student tracking, internship, and career services
• Undergraduate support: tuition + $18K/year stipend
• Graduate support: tuition + $31K/year stipend
• Including SCIS supplement to increase stipend +$6K/year
for undergrad and +$7K/year for grad
10. 2. Coordinated education of HS-STEM topics
Pilot new
curriculum
Assess outcomes
Adjust
Evaluate for
interest &
engagement
Disseminate
Modular
Curriculum to
the broader
computer
science
community
11. 2. Coordinated education of HS-STEM topics
Evaluation
•Evaluate existing curriculum for opportunities for integration
•Evaluate CISR business curriculum for opportunities for adaptation to computer science
Curriculum
Development
•Modular unit development for integration into existing computer science courses
Integration
•Identify courses and instructors for curricular integration
•Integrate modular units into existing computer science courses
Pilot
•Modular units will be integrated into two courses to pilot lesson plans and outcome
achievement
Data
Collection
•Data will be collected from CISR integrated courses
•Data includes: pre- and post-surveys, focus groups, and instructor and student interviews
12. 3. Pursue research in HS-STEM areas
• Eight projects on critical infrastructure resilience under three themes:
• Theme 1: Modeling resilient critical infrastructure systems:
• Modeling and simulation of interdependent critical infrastructures
• Modeling population-level psychological resilience to catastrophe
• Theme 2: Algorithmic foundations for infrastructure resilience:
• Robust opponent exploitation in imperfect-information games
• Emergency logistics operations using local computation algorithm
• Theme 3: Applications for infrastructure resilience
• Machine learning malware detection for infrastructure resilience
• Deep learning for social network fraud detection
• Real-time spam detection across online social networks at scale
• Distributed multi-robot patrolling strategies for critical infrastructure monitoring
13. • Phase I: Measuring & Modeling
• Data: Language Artifacts: News & Social Media
• Base Techniques: Natural Language Processing
• New Approaches: Event Timeline Analysis, Narrative
Extraction, Narrative Grouping
• Phase II: Big Data Collection & Analysis
• Historical Collection: Twitter Firehose, Lexis-Nexus, etc.
• Analysis: Machine Learning and Statistical Testing
• Questions:
• Which particular narratives of panic/resilience are most likely to
resonate with a population?
• Are particular narratives of resilience more effective than others?
• Can we use the data be used to design effective, just-in-time public
information campaigns?
Mandalay Bay
Massacre,
Oct 1, 2017
Hoaxes and
Fake News
Spread Panic
Sample Project: Mark Finlayson
Modeling Population-Level Psychological
Resilience to Catastrophe
14. Fraud Detection and Prevention
• FairPlay: Search Rank Fraud and
Malware Detection
• Use features extracted from user
behaviors
• FraudSys: Fraud Preemption System
• Detect fraud when created
• Impose Bitcoin-like computational
puzzles
• User action not posted until puzzle is
solved
Sample Project: Bogdan Carbunar
Upload App/Malware
Developer
Crowdsource
Search Rank Fraud
Workers
Fake
installs &
reviews
User
Install
Rate,
Review
1
2
3
Search Rank Fraud in Online Services
15. Robust Opponent Exploitation in Imperfect-
Information Games
• Many problems in security (both cybersecurity and national security) have
benefitted immensely in recent years from game-theoretic modeling
• Optimal thresholds against phishing attacks, randomized airport screening
• In many models, some information is private and
available to only some of the agents
• Defender may only know probability distribution
over attacker’s payoffs.
• Game-theoretic solution concepts fail to
take into account opponent behavior, but
pure opponent modeling can perform
poorly against strong/deceptive opponents
Sample Project: Sam Ganzfried
Full
opponent
exploitation
Game-theory
solution
concepts
e.g., Nash
equilibrium)
????
Exploitation
Exploitability
16. Adversarial Multi-Robot Patrolling
Sample Project: Leonardo Bobadilla
• Patrolling: Problem of repeatedly visiting a
sequence of regions in an environment with a
number of robots to prevent the intrusion
• Challenges:
• Adversaries always try to penetrate environment.
• Deterministic or frequency-based patrolling policies
can easily be exploitable.
• This problem in an adversarial setting also intractable
• Solution: Use the non-deterministic and distributed
patrolling policies Visibility-based Non-deterministic Patrolling
• Preliminary Ideas:
• The environment is considered as a graph.
• Find minimum size subset of regions that cove the
whole environment for each robot
• Each robot will patrol independently
• Develop a stochastic policy based on a Markov
chain that minimizes the average commute time
for that subset of regions.
17. 4. Engage early career faculty to pursue integrated
HS-STEM research and education activities
• PI Team: 10 faculty members, 6 are early career faculty
• More senior members take mentorship roles and day-to-day activities
Bobadilla Finlayson
Liu
Ganzfried Hu Ross Xie
Iyengar Carbunar Graham
18. Center Organization
• Steering Committee:
• Center Leadership Team: Jason Liu, Sitharama Iyengar
• Coordinator for Education and Workforce Development: Monique Ross
• Coordinator for Research Development: Bogdan Carbunar
• Recruitment and Dissemination Committee:
• Recruit students (especially undergraduate students)
• Administer CAESCIR scholarship, and student summer internships
• Administer student travel awards to attend DHS meetings, workshops, and conferences
• Outcome and Metrics Committee:
• track and measure Center’s outcome and reporting to the Steering Committee on a quarterly basis
• Metrics: student performance (academic and career development), faculty productivity
• External Advisory Board:
• DHS community, partner COE leaders, infrastructure providers and stakeholders, industry, other
governmental agencies, and other universities
• Review Center’s education and workforce development plans and research programs, make
recommendations
• Assess annually the progress of the Center in reports to the Center Director and DHS Manger
• Centers and Institutes Evaluation Committee (from university)
19. 5. Partner with DHS Center of Excellence
(CIRI at UIUC)
• Undergraduate summer internships
• Collaborative teaching with CIRI faculty who will give guest lectures
at relevant FIU courses and joint course development on critical
infrastructure resilience topics
• Participation of DHS workshops and meetings for faculty and
students from both institutions
• Collaboration on research projects for faculty and students with
mutual interests in critical infrastructure resilience
• Frequent interactions between the centers’ leaders to continuously
align the research and education activities of both institutions
20. Thank You!
This material is based upon work supported by DHS under Grant Number 2017‐ST‐062‐000002
Editor's Notes
Other rankings:
17th university for engagement and contributions to community (Washington Monthly)
Among 16 research universities with most Fulbright scholars (2015-16)
Google Play however has a problem: it hosts a suite of fraudulent behaviors, such as app developers that artificially boost the search rank of their apps (through fake reviews and installs), or even by malware developers that use the platform to promote and distribute malware. These behaviors are known as “search rank fraud”. The fraudulent developers hire specialized fraudsters who are experts at providing thousands of installs and hundreds of reviews for target apps
FairPlay, a system designed to detect fraud and malware targets in Google Play. Fair play has four modules; each module produces several features, that we used to train supervised learning algorithms, to pin point fraud and malware apps.
FraudSys, the first real time fraud preemption system. Unlike existing fraud detection solutions, FraudSys detects fraud at the time when it is created, then imposes Bitcoin-like computational penalties on the devices from which the fraud is posted.
Leadership team: Oversee all project activities, administer daily activities, maintain and expand relationships with DHS and partner DHS COEs; and Reporting
Education coordinator: Education program, and student training and work-force development activities
Research coordinator: Research program, early faculty mentoring, and collaborative research activities