On Thursday, February 14 from 9:30 a.m. to 12:00 p.m. the Office of Academic Innovation hosted our first Data Showcase - an event for all University of Michigan (U-M) community members to come take a tour through the data that power our work.
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
The K-State Online Canvas LMS Data Portal and Five Years of Activated Third-P...Shalin Hai-Jew
The presenter will introduce the K-State LMS data portal and introduce some available insights from there and focus on one particular facet of this big data--the third-party apps that K-State faculty, admin, and staff have activated and what that says about how we're using Canvas.
Canvas LMS data portal for the Kansas State University instance
A data dictionary: Version 1.16.2 (https://portal.inshosteddata.com/docs)
Data extraction and processing
What it can tell us: (un)available data and information
Activated third-party tools in K-State Online Canvas LMS instance
Some caveats
What this says about what K-Staters (early adopters) are using
Practical applications of this third-party app activation data
Adding value to LMS data portal data
Across System Learning Environment and Dashboard Design for K12 Teachers and ...Jessie Chuang
Implementing xAPI in several educational technologies:
Flipped learning platform - 1Know
IRS and assessment - HDTEDU
eBook readers - Delta
Google Apps for Education - MiTAC
Mobile Learning Apps - Claro
Goals:
Helping Chinese CoP members implement xAPI, and showcasing Taiwan vendors and the integrated services
Across System Learning Environment and Dashboard Design for 350,000+ K12 teachers and students
Building the foundation for extensible learning data analytics, functionalities, and services developed/offered by 3rd parties
X api chinese cop monthly meeting feb.2016Jessie Chuang
Topics
XAPI Vocabulary spec. From ADL
Linked Data / Semantic web. / Web 3.0
Linked Data in education and content recommender
Semantic search and Google Knowledge Graph
APIs eat software (connect with partners and services)
How should we exploit data and build intelligence layer?
Case Study (Hong Ding Educational Technology)
Monetize your data and add value (intelligence)
Personal Learning Environments for Humanitarian Learning and DevelopmentDon Presant
Case study in progress of an initiative designed to balance the needs of learner and organization. Powered by Open Badges. A project of Médecins sans frontières presented at the ePortfolio and Identity Conference 2015.
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
The K-State Online Canvas LMS Data Portal and Five Years of Activated Third-P...Shalin Hai-Jew
The presenter will introduce the K-State LMS data portal and introduce some available insights from there and focus on one particular facet of this big data--the third-party apps that K-State faculty, admin, and staff have activated and what that says about how we're using Canvas.
Canvas LMS data portal for the Kansas State University instance
A data dictionary: Version 1.16.2 (https://portal.inshosteddata.com/docs)
Data extraction and processing
What it can tell us: (un)available data and information
Activated third-party tools in K-State Online Canvas LMS instance
Some caveats
What this says about what K-Staters (early adopters) are using
Practical applications of this third-party app activation data
Adding value to LMS data portal data
Across System Learning Environment and Dashboard Design for K12 Teachers and ...Jessie Chuang
Implementing xAPI in several educational technologies:
Flipped learning platform - 1Know
IRS and assessment - HDTEDU
eBook readers - Delta
Google Apps for Education - MiTAC
Mobile Learning Apps - Claro
Goals:
Helping Chinese CoP members implement xAPI, and showcasing Taiwan vendors and the integrated services
Across System Learning Environment and Dashboard Design for 350,000+ K12 teachers and students
Building the foundation for extensible learning data analytics, functionalities, and services developed/offered by 3rd parties
X api chinese cop monthly meeting feb.2016Jessie Chuang
Topics
XAPI Vocabulary spec. From ADL
Linked Data / Semantic web. / Web 3.0
Linked Data in education and content recommender
Semantic search and Google Knowledge Graph
APIs eat software (connect with partners and services)
How should we exploit data and build intelligence layer?
Case Study (Hong Ding Educational Technology)
Monetize your data and add value (intelligence)
Personal Learning Environments for Humanitarian Learning and DevelopmentDon Presant
Case study in progress of an initiative designed to balance the needs of learner and organization. Powered by Open Badges. A project of Médecins sans frontières presented at the ePortfolio and Identity Conference 2015.
This slide was introduced ISO/IEC JTC1 SC36 (Information Technology for Learning, Education and Training, ITLET) Prague meeting. Thanks to this brief contribution. SC36 established Ad-hoc Group (AHG) on environments and resources for AR and VR in June 25 2016.
Acronyms:
LET: Learning, Education and Training
AR: Augmented Reality
VR: Virtual Reality
In the US, over half the districts and charter schools have fewer than 1,000 students. 85% have fewer than 10,000 students. Do these schools have the resources and scale to afford modern data analysis systems, or will "big data" leave these small schools behind? Across the US, almost half the students are served by a district or charter school with under 10,000 students. Schools this size, and even many larger ones, rarely have the financial means to implement modern data analysis systems, while many larger schools have spent millions on advanced technology to drive academic achievement and operational efficiency. In fact, many small schools struggle with simple operational and accountability reporting. Is it acceptable for big data to leave the small schools behind? What can be done?
In this talk we will explore these challenges and get feedback from the audience on current challenges and potential solutions, including: federal and state initiatives such as State Longitudinal Data Systems (SLDS), notably the Texas Student Data System; services provided through Regional Education Agencies / Service Centers; and the impact of emerging free or low cost data standards and software tools.
Introduction to KERIS Issue Report about prospects for educational purposes of Virtual Reality and Mixed Reality pertaining to Augmented Reality. This material was used at the JTC1/SC24 WG9 Seoul meeting.
KERIS 이슈리포트: 가상현실 및 혼합현실 활용 가능성 및 전망을 소개하는 슬라이드입니다. JTC1/SC24 WG9 서울 회의에서 소개된 자료입니다.
Data Analytics: Opportunities and Challenges for Business SchoolsErika Fille Legara
On 23 April 2018, my co-presentor Prof. Balaji Padmanabhan of USF MUMA and I shared to business academics and administrators at the 2018 AACSB ICAM our data analytics and data science journeys in our respective institutions -- Asian Institute of Management and Muma College of Business, University of South Florida.
Transforming Education through Disruptive TechnologiesAspire Systems
IT budget cuts post-recession have forced education CIO’s to increase dependence on emerging cost-effective technologies like collaboration platforms, web based applications and the now buzzed Cloud Computing. However, the technology invasion in education is still nascent and various revolutionary concepts, like augmented reality and semantic web, are on the verge of becoming mainstream.
To penetrate beyond the inevitable hype and disruption, this webinar will be looking at the following:
- The best emerging technologies that education software providers should invest in
- Technologies recommended for classroom adoption among educational institutions
- Effects of adopting such disruptive technologies
- Obtaining the best out of established technologies
Education must capitalize on the trend within technology toward big data. New types of data are becoming available. From evidence approaches to xAPI and the whole Training and Learning Architecture(TLA) big data is the foundation of all.
The future of data analytics education is marked by diverse trends and innovations. Online learning, micro-credentials, and interdisciplinary approaches are democratizing access and specialization. Technology integration, such as AI and cloud-based labs, enhances learning experiences, while project-based and personalized learning foster practical skills and adaptability. Ethical considerations and industry collaboration are integrated, and interactive tools, gamification, and VR/AR provide engaging education. Challenges include content updates, equitable access, data privacy, and quality assurance. Overall, data analytics education is evolving to meet the demands of a data-driven world, emphasizing adaptability, inclusivity, and ethical practices.
How Universities Use Big Data to Transform EducationHortonworks
Student performance data is increasingly being captured as part of software-based and online classroom exercises and testing. This data can be augmented with behavioral data captured from sources such as social media, student-professor meeting notes, blogs, student surveys, and so forth to discover new insights to improve student learning. The results transcend traditional IT departments to focus on issues like retention, research, and the delivery of content and courses through new modalities.
Hortonworks is partnering with Microsoft to show you how the Hortonworks Data Platform (HDP) running on the Microsoft stack enables you to develop a “single view of a student”.
This slide was introduced ISO/IEC JTC1 SC36 (Information Technology for Learning, Education and Training, ITLET) Prague meeting. Thanks to this brief contribution. SC36 established Ad-hoc Group (AHG) on environments and resources for AR and VR in June 25 2016.
Acronyms:
LET: Learning, Education and Training
AR: Augmented Reality
VR: Virtual Reality
In the US, over half the districts and charter schools have fewer than 1,000 students. 85% have fewer than 10,000 students. Do these schools have the resources and scale to afford modern data analysis systems, or will "big data" leave these small schools behind? Across the US, almost half the students are served by a district or charter school with under 10,000 students. Schools this size, and even many larger ones, rarely have the financial means to implement modern data analysis systems, while many larger schools have spent millions on advanced technology to drive academic achievement and operational efficiency. In fact, many small schools struggle with simple operational and accountability reporting. Is it acceptable for big data to leave the small schools behind? What can be done?
In this talk we will explore these challenges and get feedback from the audience on current challenges and potential solutions, including: federal and state initiatives such as State Longitudinal Data Systems (SLDS), notably the Texas Student Data System; services provided through Regional Education Agencies / Service Centers; and the impact of emerging free or low cost data standards and software tools.
Introduction to KERIS Issue Report about prospects for educational purposes of Virtual Reality and Mixed Reality pertaining to Augmented Reality. This material was used at the JTC1/SC24 WG9 Seoul meeting.
KERIS 이슈리포트: 가상현실 및 혼합현실 활용 가능성 및 전망을 소개하는 슬라이드입니다. JTC1/SC24 WG9 서울 회의에서 소개된 자료입니다.
Data Analytics: Opportunities and Challenges for Business SchoolsErika Fille Legara
On 23 April 2018, my co-presentor Prof. Balaji Padmanabhan of USF MUMA and I shared to business academics and administrators at the 2018 AACSB ICAM our data analytics and data science journeys in our respective institutions -- Asian Institute of Management and Muma College of Business, University of South Florida.
Transforming Education through Disruptive TechnologiesAspire Systems
IT budget cuts post-recession have forced education CIO’s to increase dependence on emerging cost-effective technologies like collaboration platforms, web based applications and the now buzzed Cloud Computing. However, the technology invasion in education is still nascent and various revolutionary concepts, like augmented reality and semantic web, are on the verge of becoming mainstream.
To penetrate beyond the inevitable hype and disruption, this webinar will be looking at the following:
- The best emerging technologies that education software providers should invest in
- Technologies recommended for classroom adoption among educational institutions
- Effects of adopting such disruptive technologies
- Obtaining the best out of established technologies
Education must capitalize on the trend within technology toward big data. New types of data are becoming available. From evidence approaches to xAPI and the whole Training and Learning Architecture(TLA) big data is the foundation of all.
The future of data analytics education is marked by diverse trends and innovations. Online learning, micro-credentials, and interdisciplinary approaches are democratizing access and specialization. Technology integration, such as AI and cloud-based labs, enhances learning experiences, while project-based and personalized learning foster practical skills and adaptability. Ethical considerations and industry collaboration are integrated, and interactive tools, gamification, and VR/AR provide engaging education. Challenges include content updates, equitable access, data privacy, and quality assurance. Overall, data analytics education is evolving to meet the demands of a data-driven world, emphasizing adaptability, inclusivity, and ethical practices.
How Universities Use Big Data to Transform EducationHortonworks
Student performance data is increasingly being captured as part of software-based and online classroom exercises and testing. This data can be augmented with behavioral data captured from sources such as social media, student-professor meeting notes, blogs, student surveys, and so forth to discover new insights to improve student learning. The results transcend traditional IT departments to focus on issues like retention, research, and the delivery of content and courses through new modalities.
Hortonworks is partnering with Microsoft to show you how the Hortonworks Data Platform (HDP) running on the Microsoft stack enables you to develop a “single view of a student”.
The Future of Data Analytics Education_ Trends and Innovations (2).pdfUncodemy
The future of data analytics education, particularly the Data Analytics Course in Dehradun with Uncodemy, embodies dynamic innovation, adaptability, and an unwavering commitment to preparing individuals for the data-driven world. In an evolving industry, it's imperative to keep education aligned with shifting demands. This entails staying updated with swiftly evolving technologies, addressing concerns about equitable access, navigating the intricacies of data privacy and ethics, and ensuring high quality and consistency in online and micro-credential courses. To fully unlock the potential of data analytics education, it is of utmost importance to invest dedicated efforts, champion inclusivity, and uphold ethical standards. By doing so, we can empower individuals to embark on a journey of learning and professional growth in the field of data analytics, thereby fostering innovation and progress in our data-centric society. Explore the Data Analytics Course in Dehradun with Uncodemy and seize valuable opportunities in this dynamic field.
How DeepSphere.AI Transformed Fresh Graduates Into Data Scientists At Databri...HemaMaliniP5
DeepSphere.AI transformed students who could now become Data Scientists (At Databricks).
Click here ➡️ https://lnkd.in/gzfwdMev For extensive details about this benchmark study.
LET'S START WITH A PROVEN AND VERIFIABLE ACCOMPLISHMENT:
BACKGROUND:
To date, we have not seen a benchmark study on the effectiveness of a data science program.
In this article, we provide enough details based on our teaching and some of the large-scale data science industry projects we are working on with Google and the AWS team. Many data science programs are offered in the market, from big brand names to small educational institutions. Still, we are unsure which one to enroll in and which may be the best choice to achieve my goals and objectives.
WHAT DO WE SEE IN THE MARKET:
We have several conceptual data science programs taught by academically well-qualified professionals without industry implementation exposure. We may also see another extreme, a data science program filled with python programs and taught by technical experts.
We need a balanced curriculum where the learner can learn both concepts and technology, which should be guided by someone who has successfully implemented one or two data science projects for real industry clients at a production scale. The modern data science curriculum should teach beyond use case development and Python syntax.
A BENCHMARK STUDY FOR HIGHLY PRODUCTIVE DATA SCIENCE LEARNING:
We conducted this benchmark study with 500+ students studying data science in the bachelor's program and 243+ teachers teaching data science.
500+ DATA SCIENTISTS: Our six semesters bachelor's in data science program is offered at SRM university. The study focused on transforming students into employable data scientists with industry skills. Around 500+ students are studying across all campuses and semesters.
243+ DATA SCIENCE TEACHERS: HODs, professors, assistant professors, research scholars, and management staff from 110 universities and colleges provided feedback both in quantitative and qualitative formats. Approximately 243 teachers participated in this benchmark and shared their views on our data science program advancement. Here is the participant's profile.
PhDs: 63
Professors: 09
Assistant Professors: 55
Associate Professors: 34
Research Scholars: 11
WHAT DO YOU NEED TO LOOK FOR IN AN EFFECTIVE AND PRODUCTIVE DATA SCIENCE LEARNING PROGRAM:
An effective data science program should balance some or all of the following, an Interdisciplinary learning approach, industrial curriculum, data science advancement, and enabling technologies such as Databricks, Google Cloud, AWS, SAP (Business Process), and other relevant technologies.
I am sharing our benchmarked and proven interdisciplinary data science curriculum, which is currently taught to 500+ data scientists and transforms learners into employable resources at the end.
Data Science Course: A Gateway to the World of Insights and Opportunities Uncodemy
Data science, a multidisciplinary field, has emerged as a powerful tool for extracting meaningful insights from vast and complex datasets. As the demand for data-driven solutions grows, so does the requirement for skilled data scientists who can unlock the potential of data.
Uncovering the Potential_ Examining Bhopal's Data Science Courses.pptxtech23250
To bridge the gap between theory and practice, many data scientist courses in Bhopal emphasize hands-on learning experiences. Participants engage in practical exercises, projects, and case studies, working with real-world datasets to apply theoretical concepts in practical scenarios. Through hands-on experimentation and exploration, students develop proficiency in data analysis, modeling, and interpretation.
IWMW 2002: How I Learned To Stop Worrying And Love The E-StrategyIWMW
Workshop session at IWMW 2002 on " How I Learned To Stop Worrying And Love The E-Strategy" facilitated by Tracey Stanley
http://www.ukoln.ac.uk/web-focus/events/workshops/webmaster-2002/materials/stanley/
Confirming PagesLess managing. More teaching. Greater AlleneMcclendon878
Confirming Pages
Less managing. More teaching. Greater learning.
INSTRUCTORS GET:
• Interactive Applications – book-specific interactive
assignments that require students to APPLY what
they’ve learned.
• Simple assignment management, allowing you to
spend more time teaching.
• Auto-graded assignments, quizzes, and tests.
• Detailed Visual Reporting where student and
section results can be viewed and analyzed.
• Sophisticated online testing capability.
• A filtering and reporting function
that allows you to easily assign and
report on materials that are correlated
to accreditation standards, learning
outcomes, and Bloom’s taxonomy.
• An easy-to-use lecture capture tool.
Would you like your students to show up for class more prepared? (Let’s face it, class
is much more fun if everyone is engaged and prepared…)
Want ready-made application-level interactive assignments, student progress
reporting, and auto-assignment grading? (Less time grading means more time teaching…)
Want an instant view of student or class performance relative to learning
objectives? (No more wondering if students understand…)
Need to collect data and generate reports required for administration or
accreditation? (Say goodbye to manually tracking student learning outcomes…)
Want to record and post your lectures for students to view online?
INSTRUCTORS...
With McGraw-Hill's Connect® MIS,
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Want an online, searchable version of your textbook?
Wish you could reference your textbook online while you’re doing
your assignments?
Want to get more value from your textbook purchase?
Think learning MIS should be a bit more interesting?
Connect® Plus MIS eBook
If you choose to use Connect™ Plus MIS, you have an affordable and
searchable online version of your book integrated with your other
online tools.
Connect® Plus MIS eBook offers features like:
• Topic search
• Direct links from assignments
• Adjustable text size
• Jump to page number
• Print by section
Check out the STUDENT RESOURCES
section under the Connect® Library tab.
Here you’ll find a wealth of resources designed to help you
achieve your goals in the course. You’ll find things like quizzes,
PowerPoints, and Internet activities to help you study.
Every student has different needs, so explore the STUDENT
RESOURCES to find the materials best suited to you.
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Management Information Systems
FOR THE INFORMATION AGE
NINTH EDITION
Stephen Haag
DANIELS COLLEGE OF BUSINESS
UNIVERSITY OF DENVER
Maeve Cummings
KELCE COLLEGE OF BUSINESS
PITTSBURG STATE UNIVERSITY
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MANAGEMENT INFORMATION SYSTEMS FOR THE INF ...
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
3. My job, Year 1: Wrangle the data
Coursera
edX
ART 2.0
ECoach
Problem
Roulette
GradeCraft Tandem
Sage
ViewPoint
Michigan Online Revenue
Events
Process &
Bandwidth
Collaborators
Vendor Data Homegrown Tool
Data
Data about AI
4. My job, Year 2:
Create an excellent environment to
support interdisciplinary research
Coursera
Online Learning
Data Warehouse
(OLDW)
edX
Student Data
Warehouse
Collaboration between IQ & AI
● Build community awareness of
Academic Innovation datasets
● Identify blockers to research and
address
● Establish ongoing research
partnerships to ensure we’re fulfilling
the promise of these innovations
7. ● Data and information have overtaken knowledge and truth in English-language usage.
● Data and information are more synonymous than either are with knowledge or truth.
8.
9. Universities are largely responsible for designing and enabling the IoT
c.f., This month’s Academic Innovation offering
But Universities have been reluctant to apply an IoT
approach to people. Why?
● IoP ≠ IoT because people ≠ things.
● “Business of Learning” ill-defined; what exactly are
we optimizing?
● Who wants to look like Facebook? Data
ownership + rights are evolving and often
unclear. (Who “owns” grades?)
10. My IoP projects with Academic Innovation...
Academic Report
Tools
(ART 2.0)
Mission:
● promote deeper knowledge of the University of Michigan’s
curricular history within the campus community, and, in so
doing,
● support exploration, discovery, and decision making by
U-M students, faculty and staff.
Mission:
● provide equal opportunity for all students to acquire
competency through practice testing and distributed
practice.
Common themes: Access, Transparency
Common approach: Iterative development with community input
14. Community input: ART 2.0 Steering Team 2015-17
● 18 members across
○ 5 colleges
○ Student Life
○ Registrar
○ Center for Research on Learning and Teaching
○ Central Student Government
○ Central IT
● Bi-weekly, one-hour meetings during Fall, Winter terms
● Team members guide development and serve as communicators with their constituencies
Academic
Reporting
Tools 2.0
15. Simple design:
multiple decks of
cards, each with
relevant descriptive
statistics for every
● course
● instructor
● major
● student
...
https://legendsplayingcards.com/
Academic
Reporting
Tools 2.0
20. Majority of students on campus have used ART 2.0
Opportunity: Understand impact on student choices and outcomes.
Academic
Reporting
Tools 2.0
21. Opportunities we’re engaged with
● Cornell implementation
● Connect majors to career outcomes (data sources...)
● Personalization (student cards)
– support exploration for intellectual breadth, disciplinary depth
– simplify (ONE CLICK!!) registration process
– proximity to credentials tool
– support new forms of Official Transcript
● Magnify functionality for faculty, staff, administrators
– advisory group: LSA, CoE, Ross, Ford, Stamps, SEAS, +
– challenge: multiple players in this space
● Institutionalizing the service
– shared ITS-RO-AI
– design & implement effective, sustainable governance
Academic
Reporting
Tools 2.0
26. Review of learning techniques in the educational psychology
literature finds practice testing and distributed practice
(learning partitioned into multiple sessions) as the only two
techniques having high utility.
Problem Roulette
supports both
practice testing and
distributed practice
modalities.
32. Opportunity for ITS + AI + Colleges
Embrace their roles as centralizing forces for academic
information and services by nurturing alliances and
supporting communities of practice within and without U-M.
Partnerships and long-term governance models for
robust services are key.
33. Building the future:
Infrastructure for Innovation
Ben Hayward
Associate Director for Software Development & User Experience
Office of Academic Innovation
hayward@umich.edu
34. Academic Innovation | Definitions & Translation
Application programming interface (API )
● A set of functions and procedures allowing the creation of applications that access the features or data of an operating system,
application, or other service.
Data Warehouse (DW)
● In computing, a data warehouse is a system used for reporting and data analysis, and is considered a core component of
business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current
and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise.
Data Integration
● Data integration involves combining data residing in different sources and providing users with a unified view of them. This
process becomes significant in a variety of situations, which include both commercial and scientific domains.
Abstraction
● In software engineering and computer science, abstraction is the process for constructing generalized concept-objects which
are created by keeping common features or attributes to various concrete objects or systems of study
35. Four-month iterative cycles
Faculty Partners
& Innovators
Academic
Innovation
Academic Innovation | A Model For Problem Solving
OUR PROCESS
Faculty
Partners
AI Developers, UX Designers,
Behavioral Scientists, and
Data Scientists
Outside
Partnerships
Product development & iteration
Application Data Research
Seek outside
interest to
grow product
36. Personalized Learning
at Scale
Technology for
Innovative Pedagogy
Tool for
Online Learning
Academic Innovation | Applications Transforming Education
ECoach
Personalized messaging to
students
ART
Academic data to help make
choices
Sage
Resources and reflection for
student mental health
Problem Roulette
Practice Problems for Exam
Preparation
ViewPoint
Role-playing simulations
GradeCraft
Gameful pedagogy for learning
Tandem
Supporting productive and
equitable group work
Michigan Online
Making our elite public
research university’s learning
experiences accessible at scale
Online Learning Tools
Expanding the capabilities of
online learning
37. Academic Innovation | Where is the data?
Student
Information
System
edX Coursera Canvas
3rd Party
● Information about our students’ backgrounds, performance, course load, field of study, etc…
● Information about our courses’ instructors, enrollment, assignment structure, grades
● Information about our degrees’ populations, sequencing, pathways
● Information about our students’ study habits, interests
38. Academic Innovation | Data Storage to Data Service
Transforming and mapping data into actionable services for research and
applications.
● UM Institutional Data Service
○ Data Source: Academic Innovation Data Warehouse
● Online Learning Data Service
○ Data Source: Online Learning Data Warehouse
● Unizin Data Service
○ Data Source: Unizin Data Warehouse
● API Network
○ Data Source: Canvas, Qualtrics, Google, Problem Roulette, EECS Autograder, Moodle, etc...
39. Data Services
API Network
Student
Record
Data Origins
Student
Record
3rd Party
AI DW
edX Coursera
Online
Learning DW
Canvas
Unizin DW
Academic Innovation | Data Storage to Data Service
40. Data
Services
API Network
Unizin DWAI DW
Data
Integrators Institutional
Data
Online
Learning
Data
Grade
Data
Academic Innovation | Data Service to Data Integrators
Online
Learning
DW
Grade
Data
Behavior
Data
41. Academic Innovation | Data Integration
Don’t assume the source, create the format.
● Grade Data Integrator: A structure for Gradebooks, Grading Schemes,
Assignments, Assignment Categories, Submissions, etc...
○ Services: Unizin DW, Moodle API, Canvas API
● Behavior Data Integrator: A structure for behavioral categories, instances
and affiliated user actions
○ Services: Problem Roulette API, EECS Autograder API, Course.Work API,
Canvas API
● Institutional Data Integrator: A structure for representing terms, degrees,
majors, courses and students
○ Services for UM , Cornell
45. Applications
Data
Origins
Student
Information
System
Infrastructure for Innovation: The Data Ecosystem
3rd Party
Data
Services
AI DW
API Network
Data
Integrators
Institutional
Data
Behavior
Data
Grade
Data
Abstracted
Technologies
edX Coursera
Online
Learning DW
Online
Learning Data
Michigan Tailoring
System (MTS)
Event Tracking data.ai
Canvas
Unizin DW
Grade
Data
Randomization
Engine
Personalized Learning at Scale Technology for Innovative Pedagogy Tools for Online Learning
46. Using data to visualize MOOC
design and pedagogy
Dr. Rebecca M. Quintana
Learning Experience Design Lead
Office of Academic Innovation
Yuanru Tan Noni Korf
48. MOOCs
One issue for learning
design teams is grasping
the overall course
structure without a
mediational tool of aid
Quintana, Tan, Gabriele, & Korf, 2018
49. Beads!
We used beads to
represent the structure
of Massive Open Online
Courses (MOOCs) as a
mediational tool with a
MOOC design team.
Quintana, Tan, Gabriele, & Korf, 2018
50. A: Section heading
B: 10-minute lecture video
C: 10-minute interview video
D: Textual guide
E: Reflection activity
F: Course reading
G: External resources
H: Sub-heading
I: Lecture > 10 mins
J: Interview > 10 mins
K: Visual guide
L: Discussion forum
M: Team-work activity
N: Quiz
51. We wanted to provide opportunities for course designers to
examine a familiar phenomenon through an uncommon
medium, provoking curiosity and exploration
Focus group
● Beaded representations of 5 MOOCs
● School of Education MicroMasters courses
● Professor, course designers, managers, builders
How can beaded representations of online course
structure lead to insights that could impact learner
experience?
What might be the value of eliciting insight among
design team members?
52.
53. CCDs
AKA “course composition
diagrams” are interactive
digital representations
that depict the structure
of a MOOC (i.e., content
types, sequence of
elements).
Quintana, Tan, & Korf, 2018 (best
paper award, OTL SIG, AERA)
Seaton, 2016
55. We wanted to create opportunities for reflection by course
design team members, to offer a better understanding of the
impact of design choices
Online open-ended survey
● CCDs of 10 MOOCs launched in previous year
● Professor, course designers, managers, builders
● Inductive, qualitative analysis
What do course composition diagrams reveal/obscure
about the design of a MOOC?
How, if at all, to course composition diagrams allow
course design teams to reflect on the impact of their
design choices?
56. What do course composition diagrams reveal
about the design of a MOOC?
● Bird’s eye view
● Quantitative aspects
● Relational aspects of course elements
Analysis also revealed semantic connections to visual
language of design (e.g., balance, variety, repetition,
pattern, rhythm, emphasis, and movement)
● Differences among course elements
Easily
understood
What do course composition diagrams obscure
about the design of a MOOC?
So simple, it
ceases to be
useful
Reflection on Design
● Opportunities for comparison
● Congruence with perception
● Confirmation of design choices
● Questioning design choices
57. Characterizing MOOC
Pedagogies
Visual methods are now
part of our set of
tools, which allow us to
understand and
characterize the
underlying pedagogies of
MOOCs
Quintana & Tan, 2019
Epistemology Objectivist 1 2 3 4 5 Constructivist
Role of teacher Teacher-center
ed
1 2 3 4 5 Student-centered
Focus of activities Convergent 1 2 3 4 5 Divergent
Structure Less structure 1 2 3 4 5 More structure
Approach to content Concrete 1 2 3 4 5 Abstract
Feedback Infrequent,
unclear
1 2 3 4 5 Frequent,
constructive
Cooperative Learning Unsupported 1 2 3 4 5 Integral
Accomodation of Individual
Difference
Unsupported 1 2 3 4 5 Multi-faceted
Activities/assessments Artificial 1 2 3 4 5 Authentic
User role Passive 1 2 3 4 5 Generative
Swan et al.’s Assessing MOOC Pedagogies framework
Course Composition Diagrams
58. Cluster 1: Applied Data Science with Python 1, 3, 4, 5
Cluster 2: Mindware, Model Thinking, Internet History, Intro to Thermodynamics
Cluster 3: Sampling People, AIDS, Cataract Surgery
Cluster 4: Instructional Methods, Graduate Study, Learning for Equity
Cluster 5: Act on Climate, Applied Data Science with Python 2
Cluster 6: Clinical Skills, Successful Negotiation
Cluster 7: Science of Success, Digital Democracy
Characterizing MOOC
Pedagogies
Visual methods are now
part of our set of
tools, which allow us to
understand and
characterize the
underlying pedagogies of
MOOCs
Quintana & Tan, 2019
59. Student mental health at Michigan:
what we know, what we don't know,
and what we can do
Dr. Meghan Duffy
Professor of Ecology and Evolutionary Biology, LS&A
Faculty Innovator in Residence, Office of Academic Innovation
60.
61. Student mental health: what we know
● Many Michigan students have MH diagnoses:
○ Depression (25%), Generalized Anxiety (18%), Social Anxiety (8%),
ADHD (7%), OCD (3%)
● 44% of undergrads & 41% of grad students reported that
mental or emotional difficulties affected their academic
performance in the past 4 weeks
Sources: CAPS College Student Mental Health Survey; Eisenberg et al. 2007
62. Student mental health: what we know
● In Intro STEM courses:
○ 23% of students reported a previous diagnosis of a depressive disorder
and 25% reported a previous diagnosis of an anxiety disorder.
○ First generation and LGBTQ+ students had significantly higher scores on
the PHQ-8 (depression) and GAD-7 (anxiety) screeners.
○ Most students were aware of at least some on campus mental health
resources.
Source: Morgan Rondinelli Honors Thesis
63. Student mental health: what we know
● Recent survey of US economics grad students:
○ 18% of currently experience moderate to severe symptoms of anxiety
○ 25% have a mental health diagnosis
○ 11% reported suicidal thoughts on at least several days in the past two
weeks
● MH influences performance & increases likelihood of
leaving
Source: Barreira et al. working paper, Healthy Minds Study
64. Student mental health: what we don’t know
● What data are we already collecting that could give us
insights into student mental health and well-being?
65. Student mental health: what we don’t know
● What is the phenology of student well-being? (4Q Project)
Wikipedia: J.hagelüken
66. Student mental health: what we don’t know
● What are some easy changes that could improve
well-being?
UMich College Sleep Disorders Clinic
Dr. Shelley
Hershner
67. Student mental health: what we can do
● Wellness playbook: wellness coaching at scale
○ Model: ECoach’s Exam Playbook
○ Goal: encourage students to:
■ reflect on why wellness is important to them
■ plan for how to improve well-being,
■ connect with resources
69. Partnership opportunities
● Phenology/4Q Project needs:
○ courses/student populations to run in
○ to link with existing data (e.g., Canvas usage), would need data
scientist/analyst
● Small changes: sleep
○ Need instructors!
● Wellness playbook
○ in development, open to input!
● Grad student mental health
○ in planning phase
Interested? Contact: duffymeg@umich.edu
70. Understanding global learners
through billions of lines of
clickstream data
Dr. Christopher Brooks
Research Assistant Professor, School of Information
Director of Learning Analytics & Research
Office of Academic Innovation
brooksch@umich.edu @cab938
71. Motivation
My research is in learning analytics and educational data science
I’m specifically interested in understanding scaled learning experiences, like Massive Open
Online Courses, and global learning populations through a mixture of observational,
experimental, and computational methods
My lab, the educational technology collective (etc.),
is made up of students, postdocs, and
collaborators from a breadth of disciplinary and
scholarly backgrounds
72. Part 1: Scaled Learning
How has the MOOC population had changed since the
early days of the phenomena (2012).
Strong implications for researchers as well as
instructional designers and educational technologists
Used a quantitative approach looking at how discourse
and language are changing in forums
Nia Dowell
(UM Postdoc)
Dowell, N. M., Brooks, C., Kovanović, V., Joksimović, S., & Gašević, D. (2017, April).
The Changing Patterns of MOOC Discourse. In Proceedings of the Fourth (2017)
ACM Conference on Learning@ Scale (pp. 283-286). ACM.
76. Peer Review and Written Feedback
How do peers review short written works from students of different
socioeconomic groups? Previous work has explored bias in evaluation, we
are interested in bias in qualities of responses.
Heeryung Choi
(PhD Student)
77. Predicting Student Success
An explosion in the interest in predicting student success over the last decade, both in MOOCs
and in on-campus higher education. Now a core part of Learning Analytics (LAK) and
Educational Data Mining (EDM) conferences
Both computationally and educationally interesting!
Lots of different reasons to predict success:
- understanding the determinants of success
- changing outcomes for all/some students
- administratively practical (it scales)
Craig Thompson
(PhD Student, usask)
78. C. Brooks, C. Thompson, S. Teasley. (2015) A Time
Series Interaction Analysis Method for
Building Predictive Models of Learners using
Log Data. 5th International Conference on
Learning Analytics and Knowledge 2015 (LAK'15)
C. Brooks, C. Thompson, S. Teasley. (2015) Who
You Are or What You Do: Comparing the
Predictive Power of Demographics vs. Activity
Patterns in Massive Open Online Courses
(MOOCs). The second annual conference on
Learning At Scale 2015 (L@S2015), Works in
Progress track.
79. Frustrations
There are dozens of predictive modeling in MOOC papers, and each uses different:
a. Feature engineering methods
b. Training methods
c. Modeling methods and
hyperparameters
d. Training and evaluation data
e. Predictive outcomes
Comparison of features/models/parameters
is impossible. Replication of results is impossible.
Josh Gardner
(Washington)
80. W. Li, C. Brooks, F. Schaub (2019). The Impact of Student Opt-Out on
Educational Predictive Models. 9th International Conference on Learning
Analytics and Knowledge (LAK19). March, 2019. Tempe, AZ.
Educational Predictive Model Biases
Where does bias come from?
- Data collection practices and social inequalities
- Population changes over time
- Opt outs, right to be forgotten, FERPA, PIPEDA, GDPR
Warren Li
(PhD Student,
Michigan)
Florian Schaub
(Faculty,
Michigan)
81. W. Li, C. Brooks, F. Schaub (2019). The Impact of Student Opt-Out on
Educational Predictive Models. 9th International Conference on Learning
Analytics and Knowledge (LAK19). March, 2019. Tempe, AZ.
Educational Predictive Model Biases
Where does bias come from?
- Data collection practices and social inequalities
- Population changes over time
- Opt outs, right to be forgotten, FERPA, PIPEDA, GDPR
Warren Li
(PhD Student,
Michigan)
Florian Schaub
(Faculty,
Michigan)
82. Ryan Baker
(Faculty, Penn)
Josh Gardner
(PhD Student,
Washington)
J. Gardner, C.
Brooks, R. Baker
(2019). Evaluating
the Fairness of
Predictive Student
Models Through
Slicing Analysis.
9th International
Conference on
Learning Analytics
and Knowledge
(LAK19). March,
2019. Tempe, AZ.
83. Personalization and Inclusion
There are several reasons inclusion is interesting to study in MOOCS:
1. The population isn’t as WEIRD (western, educated, industrialized, rich, democratic)
2. Multiple motivations for learning; interest, edutainment, jobs skills, social integration
3. There is learning beyond the immediate (e.g. higher ed): lifelong learning in a
semi structured environment
4. A/B testing is baked into the platform
Rene Kizilcec
(Cornell)
Kizilcec, R. and Brooks, C. (2017). Diverse Big Data and Randomized Field Experiments in Massive Open
Online Courses. In Lang, C., Siemens, G., Wise, A. F., and Gaevic, D., editors, The Handbook of Learning
Analytics, pages 211–222. Society for Learning Analytics Research (SoLAR) 1st edition.
84. Situational Video Cues and Activity
Based in part on Cheryan et al. (2009) looking at interest in pursuing computer science by female
students.
Pre-registered a set of hypothesis at OSF:
1. Primary: Retention in the female condition will be higher for women, but retention in the
female condition will be no different for men (between conditions)
2. Secondary: (a) completion (b) achievement (c) forum participation and (d) certificate
participation of women will be higher in the female condition
86. Results
No difference in achievement or drop out for the two populations (women and men; n~23k each)
when compared across conditions within population.
But, a difference in discourse amount (though not prevalence of discourse)?
(Similar results found for quantity of interaction (clickstreams))
C. Brooks, J. Gardner, Kaifeng Chen (2018)
How Gender Cues in Educational Video
Impact Participation and Retention.
Festival of Learning, June, 2018. London
UK. Full Crossover Paper.
87. In my research group we’ve looked specifically at MOOC trends broadly, predictive models for student
success, and inclusion and personalization.
The data the University of Michigan has on MOOC learners, and the flexibility of our platforms, have
made this a fertile area for understanding global learners
Quick Conclusions
Christopher Brooks, School of Information, University of Michigan
brooksch@umich.edu http://edtech.labs.si.umich.edu
88. What do students value about
learning online, and how can this
impact program design?
Sarah Dysart
Director of Online & Hybrid Degrees
Office of Academic Innovation
sdysart@umich.edu @SarahDysart
89. What learners are we trying to reach?
Underrepresented learners
Career changers/advancers
Non-traditional learners
● Students who delay enrollment by a year or more
● Having dependents other than a spouse
● Being a single parent
● Working full time while enrolled
● Being financially independent
● Attending part time
91. … but what about ...
Synchronous class sessions
Synchronous office hours
On-campus orientations
On-campus engagements/residencies
Field placement requirements
95. Important Factors that Drive Enrollment Decisions
What are the most important factors in your decision about which school to
enroll for an online program? [Selected top three]
All
Students
Tuition & Fees 34%
Reputation of the Program 13%
Reputation of the School 11%
Home Location of the School 11%
Quality of Faculty 6%
The School Offers Multiple Study Formats 6%
The School Reflects my Values 6%
Alumni Achievements 3%
Magda, A. J., & Aslanian, C. B. (2018). Online college students 2018: Comprehensive data on demands and preferences. Louisville, KY: The Learning House, Inc.
96. We need to better
understand what
online students value.
(and how that differs across groups, and why)
97. Where do we start?(um, where do we get the data, Sarah?)
99. Expectancy-Value Theory
Expectancy for Success
Subjective Task Value
Achievement Related
Choices, Engagement,
Persistence
Wigfield, A., & Eccles, J. S. (2000). Expectancy–Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25(1), 68–81.
https://doi.org/10.1006/ceps.1999.1015
100. Specifically:
Values Enrollment Choices
Schunk, D. H., Pintrich, P. R., & Meece, J. L. (2014). Motivation in education: theory, research, and applications (4th ed.).
Upper Saddle River, N.J: Pearson/Merrill Prentice Hall.
101. Subjective Task Value
Interest-Enjoyment Value
Attainment Value
Utility Value
Relative Cost
$$$$$
Task Effort Cost
Outside Effort Costs
Loss of Valued Alternatives
Emotional Cost
Flake, J. K., Barron, K. E., Hulleman, C., McCoach, B. D., & Welsh, M. E. (2015). Measuring cost: The forgotten component of expectancy-value theory.
Contemporary Educational Psychology, 41, 232–244. https://doi.org/10.1016/j.cedpsych.2015.03.002
102. Learner Populations
Our enrolled students are those for whom certain costs are less of an issue
As we begin to develop program portfolios, we can turn to our learner communities
in the open environment to measure value components associated with various
program characteristics (i.e. cost of program, synchronous requirements,
on-campus commitments, etc.)
Leveraging our relationship with peers whose program characteristics differ from
ours
103. In short…
We don’t have this data yet, but I think we can get there.
The data can give us a starting point for understanding why motivation to enroll in
programs may differ across demographic groups and subject areas
104. Thank you!
The Team at Academic Innovation
academicinnovation@umich.edu @UMichiganAI