Online Educa Berlin conference: Big Data in Education - theory and practiceMike Moore
Online Educa Berlin Conference Presentation
Big Data in Education - Theory and Practice
Presented December 6, 2013 by
Mike Moore, Sr. Advisory Consultant - Analytics
Desire2Learn, Inc.
Opening/Framing Comments: John Behrens, Vice President, Center for Digital Data, Analytics, & Adaptive Learning Pearson
Discussion of how the field of educational measurement is changing; how long held assumptions may no longer be taken for granted and that new terminology and language are coming into the.
Panel 1: Beyond the Construct: New Forms of Measurement
This panel presents new views of what assessment can be and new species of big data that push our understanding for what can be used in evidentiary arguments.
Marcia Linn, Lydia Liu from UC Berkeley and ETS discuss continuous assessment of science and new kinds of constructs that relate to collaboration and student reasoning.
John Byrnes from SRI International discusses text and other semi-structured data sources and different methods of analysis.
Kristin Dicerbo from Pearson discusses hidden assessments and the different student interactions and events that can be used in inferential processes.
Panel 2: The Test is Just the Beginning: Assessments Meet Systems Context
This panel looks at how assessments are not the end game, but often the first step in larger big-data practices at districts/state/national levels.
Gerald Tindal from the University of Oregon discusses State data systems and special education, including curriculum-based measurement across geographic settings.
Jack Buckley Commissioner of the National Center for Educational Statistics discussing national datasets where tests and other data connect.
Lindsay Page, Will Marinell from the Strategic Data Project at Harvard discussing state and district datasets used for evaluating teachers, colleges of education, and student progress.
Panel 3: Connecting the Dots: Research Agendas to Integrate Different Worlds
This panel will look at how research organizations are viewing the connections between the perspectives presented in Panels 1 and 2; what is known, what is still yet to be discovered in order to achieve the promised of big connected data in education.
Andrea Conklin Bueschel Program Director at the Spencer Foundation
Ed Dieterle Senior Program Officer at the Bill and Melinda Gates Foundation
Edith Gummer Program Manager at National Science Foundation
Implementing analytics - Rob Wyn Jones, Shri Footring and Rebecca DaviesJisc
Led by Rob Wyn Jones, consultant and Shri Footring, senior co-design manager - enterprise, both Jisc.
With contribution from Rebecca Davies, pro vice-chancellor and chief operating officer, Aberystwyth University.
Connect more in Wales, 7 July 2016
A presentation at Networkshop47 by Andrew Davis, infrastructure and critical services manager, Jisc and Nicole Stewart, cyber security analysis apprentice, Jisc.
Are you really ready to roll out learning analytics across your entire instit...Jisc
Speaker: Steve Hoole, senior analytics consultant.
This workshop will enable delegates to consider a process to assist them finding suitable solution for learning analytics implementation. It will discuss how to move from small pilots to institutional wider implementation for learning analytics considering issues such as legal and ethical requirements such as GDPR compliance, planning for intervention management and ensuring staff and student engagement and support.
Implementing analytics - Paul Bailey, Tessa Rogowski and Roy CurrieJisc
Led by Paul Bailey, senior co-design manager, Jisc.
With contributions from:
Tessa Rogowski, Assistant director - IT services, University of Essex
Roy Currie, director of information and learning technologies, Bedford College
Connect more in Nottingham, Tuesday 12 July 2016.
Learning Analytics Primer: Getting Started with Learning and Performance Anal...Watershed
Navigating the scope of disruptive analytics solutions to deliver maximum impact. Learn more about the importance of scalable learning in organizations that want to embrace an environment of continuous improvement. Mike Rustici provides a workshop on the five steps to get started with learning and performance analytics. Ranging from gathering your data using methods like experience API, to setting metrics and evaluating impact of learning programs.
Data Analytics Ethics Issues and Questions
Presented at the University of Chicago Booth Big Data & Analytics Roundtable, April 2018
Presenter:
Arnie Aronoff, Ph.D.
Instructor, MScA in Data Analytics
Instructor, School of Social Services Administration
The University of Chicago
Group Concept OD
Organizational Development and Training
(312) 259-4544
aaronoff33@gmail.com
Presented by
Online Educa Berlin conference: Big Data in Education - theory and practiceMike Moore
Online Educa Berlin Conference Presentation
Big Data in Education - Theory and Practice
Presented December 6, 2013 by
Mike Moore, Sr. Advisory Consultant - Analytics
Desire2Learn, Inc.
Opening/Framing Comments: John Behrens, Vice President, Center for Digital Data, Analytics, & Adaptive Learning Pearson
Discussion of how the field of educational measurement is changing; how long held assumptions may no longer be taken for granted and that new terminology and language are coming into the.
Panel 1: Beyond the Construct: New Forms of Measurement
This panel presents new views of what assessment can be and new species of big data that push our understanding for what can be used in evidentiary arguments.
Marcia Linn, Lydia Liu from UC Berkeley and ETS discuss continuous assessment of science and new kinds of constructs that relate to collaboration and student reasoning.
John Byrnes from SRI International discusses text and other semi-structured data sources and different methods of analysis.
Kristin Dicerbo from Pearson discusses hidden assessments and the different student interactions and events that can be used in inferential processes.
Panel 2: The Test is Just the Beginning: Assessments Meet Systems Context
This panel looks at how assessments are not the end game, but often the first step in larger big-data practices at districts/state/national levels.
Gerald Tindal from the University of Oregon discusses State data systems and special education, including curriculum-based measurement across geographic settings.
Jack Buckley Commissioner of the National Center for Educational Statistics discussing national datasets where tests and other data connect.
Lindsay Page, Will Marinell from the Strategic Data Project at Harvard discussing state and district datasets used for evaluating teachers, colleges of education, and student progress.
Panel 3: Connecting the Dots: Research Agendas to Integrate Different Worlds
This panel will look at how research organizations are viewing the connections between the perspectives presented in Panels 1 and 2; what is known, what is still yet to be discovered in order to achieve the promised of big connected data in education.
Andrea Conklin Bueschel Program Director at the Spencer Foundation
Ed Dieterle Senior Program Officer at the Bill and Melinda Gates Foundation
Edith Gummer Program Manager at National Science Foundation
Implementing analytics - Rob Wyn Jones, Shri Footring and Rebecca DaviesJisc
Led by Rob Wyn Jones, consultant and Shri Footring, senior co-design manager - enterprise, both Jisc.
With contribution from Rebecca Davies, pro vice-chancellor and chief operating officer, Aberystwyth University.
Connect more in Wales, 7 July 2016
A presentation at Networkshop47 by Andrew Davis, infrastructure and critical services manager, Jisc and Nicole Stewart, cyber security analysis apprentice, Jisc.
Are you really ready to roll out learning analytics across your entire instit...Jisc
Speaker: Steve Hoole, senior analytics consultant.
This workshop will enable delegates to consider a process to assist them finding suitable solution for learning analytics implementation. It will discuss how to move from small pilots to institutional wider implementation for learning analytics considering issues such as legal and ethical requirements such as GDPR compliance, planning for intervention management and ensuring staff and student engagement and support.
Implementing analytics - Paul Bailey, Tessa Rogowski and Roy CurrieJisc
Led by Paul Bailey, senior co-design manager, Jisc.
With contributions from:
Tessa Rogowski, Assistant director - IT services, University of Essex
Roy Currie, director of information and learning technologies, Bedford College
Connect more in Nottingham, Tuesday 12 July 2016.
Learning Analytics Primer: Getting Started with Learning and Performance Anal...Watershed
Navigating the scope of disruptive analytics solutions to deliver maximum impact. Learn more about the importance of scalable learning in organizations that want to embrace an environment of continuous improvement. Mike Rustici provides a workshop on the five steps to get started with learning and performance analytics. Ranging from gathering your data using methods like experience API, to setting metrics and evaluating impact of learning programs.
Data Analytics Ethics Issues and Questions
Presented at the University of Chicago Booth Big Data & Analytics Roundtable, April 2018
Presenter:
Arnie Aronoff, Ph.D.
Instructor, MScA in Data Analytics
Instructor, School of Social Services Administration
The University of Chicago
Group Concept OD
Organizational Development and Training
(312) 259-4544
aaronoff33@gmail.com
Presented by
An examination of the ethical considerations involved in data analyticsUncodemy
Data analytics can be used for various purposes, including marketing, product development, and customer service. One of the primary benefits of data analytics is that it can help you identify patterns in your data that you might not have been able to see with other methods.
The Role of Community-Driven Data Curation for EnterprisesEdward Curry
With increased utilization of data within their operational and strategic processes, enterprises need to ensure data quality and accuracy. Data curation is a process that can ensure the quality of data and its fitness for use. Traditional approaches to curation are struggling with increased data volumes, and near real-time demands for curated data. In response, curation teams have turned to community crowd-sourcing and semi-automatedmetadata tools for assistance. This chapter provides an overview of data curation, discusses the business motivations for curating data and investigates the role of community-based data curation, focusing on internal communities and pre-competitive data collaborations. The chapter is supported by case studies from Wikipedia, The New York Times, Thomson Reuters, Protein Data Bank and ChemSpider upon which best practices for both social and technical aspects of community-driven data curation are described.
E. Curry, A. Freitas, and S. O’Riáin, “The Role of Community-Driven Data Curation for Enterprises,” in Linking Enterprise Data, D. Wood, Ed. Boston, MA: Springer US, 2010, pp. 25-47.
Follow our presentation to learn about the role of statistical analysis in fraud detection. From data mining to clustering, learn the techniques necessary to quickly anticipate and detect health care fraud, waste, and abuse.
Building Digital Trust: The role of data ethics in the digital ageAccenture Technology
Data is the biggest risk that is unaccounted for by businesses today. In the past, the scope for digital risk was limited to cybersecurity threats but leading organizations must now also recognize risks from lackluster ethical data practices. Mitigating these internal threats is critical for every player in the digital economy, and cannot be addressed with strong cybersecurity alone.
In this webinar, Dale Sanders will provide a pragmatic, step-by-step, and measurable roadmap for the adoption of analytics in healthcare-- a roadmap that organizations can use to plot their strategy and evaluate vendors; and that vendors can use to develop their products. Attendees will have a chance to learn about:
1) The details of his eight-level model, 2) A brief introduction to the HIMSS/IIA DELTA Model, 3) The importance of permanent organizational teams to sustain improvements from analytic investments, 4) The process of curating and maturing data governance, and 5) The coordination of a data acquisition strategy with payment and reimbursement strategies
Ethical Considerations in Data Analyticsarchijain931
The age of data analytics has ushered in a wealth of opportunities for organizations and individuals to derive valuable insights from data. However, with great power comes great responsibility. Ethical considerations in data analytics have become increasingly important as the potential for misuse and privacy breaches has grown. In this article, we will explore the ethical challenges and principles that guide responsible data analytics, emphasizing the need for transparency, fairness, and accountability.
Enabling Data Governance - Data Trust, Data Ethics, Data QualityEryk Budi Pratama
Presented on PHPID Online Learning 35.
Komunitas PHP Indonesia
Title: Enabling Data Governance - The Journey through Data Trust, Ethics, and Quality
Eryk B. Pratama
Global IT & Cybersecurity Advisor
The Why And How Of Machine Learning And AI: An Implementation Guide For Healt...Health Catalyst
Join Kenneth Kleinberg, Health IT Strategist, and Eric Just, Senior Vice President, Health Catalyst, as they discuss the What, Why, and How of Machine Learning and AI for healthcare leaders.
Attendees will learn:
Practical steps, timeframes and skills as well as real-time data and moving targets associated with the Implementation of ML and AI
How to deal with challenges inherent in ML and AI implementation
What the future holds for ML and AI
Sdal air education workforce analytics workshop jan. 7 , 2014.pptxkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
Similar to Wellbeing analytics code of practice (20)
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This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
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3. Comparison with Learning Analytics
Same…
•Might consider data for
• Self-awareness/benchmarking (apps, dashboards…)
• Single data entry (counsellor knows your workload)
• Triage (what’s the most appropriate source of help)
• Alerts (intervention may be needed)
But different…
•Not a closed system: many more sources
• To identify stresses (“environment”)
• To suggest response (“behaviour”)
•Even higher risk
• Sensitive data/inferences
• Consequences of false positives
• Consequences of false negatives
• Self-fulfilling prophecies
•Provide tools to support those?
3 Wellbeing Analytics Code of Practice
4. Learning Analytics CoP (ethics-based) a good start
4
What does law (GDPR+DPA18) add?
•NOT “is this lawful?”
• Many routes, many answers to that
•Take safeguards from all relevant sections
Wellbeing Analytics Code of Practice
https://www.jisc.ac.uk/guides/code-of-practice-for-learning-analytics
6. Key differences from Learning Analytics
•Must be overseen by health professionals
• As learning analytics is overseen by learning professionals
• But can be operated by appropriately trained tutors & support staff
•Testing & Validation even more important
• Potentially even more serious consequences of error
• Best to treat these as separate processes to avoid data leakage
•Must have sufficient resources to use the data/flags raised
• Otherwise processing merely increases risk
•High risk: likely to require a formal Data Protection Impact Assessment…
•Wider range of sources: need to consider Purpose Compatibility…
6 Wellbeing Analytics Code of Practice
7. Tools: DPIA template
Assessing risk to individuals
•Likely legal requirement for wellbeing projects
• Confidence-building measure anyway
•Based on ICO approach
•Jisc fills in some likely risks
• Institutions add/remove/assess severity
•Jisc fills in some likely controls
• Institutions add/remove/assess residual risk
https://www.jisc.ac.uk/guides/code-of-practice-for-wellbeing-and-mental-health-analytics Annex
A
7 Wellbeing Analytics Code of Practice
8. Tools: Purpose compatibility matrix
Assessing scariness to individuals
•Helps select among (existing) data sources
•Purpose similarity
• Data collected/used for similar purposes
•E.g. individual tutorial support
• Data collected/used for different purposes
•E.g. statistics
•How hard to communicate the change?
•Distance => Risk
• Of causing alarm/hurt/resistance
• Of destructive reaction
https://www.jisc.ac.uk/guides/code-of-practice-for-wellbeing-and-mental-health-analytics Annex B
8 Wellbeing Analytics Code of Practice
9. References
• Code of Practice
• https://www.jisc.ac.uk/guides/code-of-practice-for-wellbeing-and-mental-health-analytics
• Information Commissioner on Data Protection Impact Assessments
• https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-
protection-regulation-gdpr/accountability-and-governance/data-protection-impact-
assessments/
• Information Commissioner on Purpose Compatibility
• https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-
protection-regulation-gdpr/principles/purpose-limitation/
• Information Commissioner Sandbox report
• https://ico.org.uk/media/for-organisations/documents/2618023/jisc-regulatory-sandbox-final-
report.pdf
• Background and Updates
• https://regulatorydevelopments.jiscinvolve.org/wp/tag/wellbeing/
9 Wellbeing Analytics Code of Practice
10. Discussion Points:
• How to make data protection your friend?
• How important are wellbeing analytics to your
institution?
• How would you go about implementing wellbeing
analytics?
and
• Anything else that you think is important?
Wellbeing Analytics Code of Practice10