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Huom. Materiaaleja päivitetään sivulle

Kollaboratiivinen matkakertomus ja muistiinpanot EDUCAUSE-verkoston 2018 vuosikokouksesta.

Aiempia muistiinpanoja:

EDUCAUSE 2017, Philadelphia

EDUCAUSE 2015, Indianapolis


EDUCAUSE is a nonprofit association whose mission is to advance higher education through the use of information technology.

EDUCAUSE Annual Conference 30.10.–2.11.2018 Denver, Colorado

EDUCAUSE 2019 14.-17.10. Chicago


Key themes and findings from EDUCAUSE conference 2018

These notes ja thoughts are gathered from various sessions

Transformation of education IT

Digital transformation of education

  • Some examples of challenges of transforming IT
    • aging system, silos, technical debt, inaccessible data, closed platforms, complexity
  • #EDU2030 opportunities: "Technologies such as blockchain, artificial intelligence, and virtual and mixed reality allow us to solve problems that we could not solve before. How might we leverage these emerging technologies to improve individual learning experiences? More broadly, how might open standards on credentials, competencies, and assessments be used to bridge the education and workforce ecosystems? As increasing amounts of data about individuals are generated through this technology-rich learning environment, how might we allow people to own and securely manage their own digital identities, electing when and how that information is shared?"


Data-enabled institutions

Univeristy as a data business
  • Drivers (Session: The Future of Higher Education: Our Response to Disruption)
    • Shifting skills: Create a strategic investment fund and build a workforce development center, create continuous learning and improvement culture, actively manage human resource debt
    • Digital transformation: Build adaptability, create a futurology program for scenario planning, manage the health of organization
    • Income challenges: Create more graceful entry and exit points, leverage digital technologies to make sure students have a best most efficient experience, drive down administrative costs through automation, look for partnerships
    • HE financial crises: Automate everything to drive down costs, look for merger opportunities, find alternate funding sources, collapse department
  • Action (Session: The Future of Higher Education: Our Response to Disruption)
    • Measure automatisation – constantly adjust performance
    • Measure expectations – constantly align to new needs
    • Measure outcomes – high light the best, fix the worst 
The need for data analytics 

(Session: Putting 4 of the Top 10 IT Issues into Practice: Focus on Analytics)

  • Unified data management strategy to answer questions
  • There is a need to break organizational silos with regard to touchpoints with a student's life
  • Need to understand why certain students persist, not just those who left (very small numbers, hard to analyze)
  • Tying together data from different systems encourages discussion about how data can be useful downstream
    • Data is not a departmental asset but an institutional asset
  • Need to be able to interrogate data to answer questions on the spot in management meetings
  • Analytics need change all the time, need to establish an environment to do analytics for anything
  • Student care model (intrusive advising) needs to be balance with security and privacy

Session: Putting 4 of the Top 10 IT Issues into Practice: Focus on Analytics

https://benchmarks.it.unt.edu/educause-research-top-10-it-issues

Session: Putting 4 of the Top 10 IT Issues into Practice: Focus on Analytics

Flexible learning

  • #EDU2030: New models of learning require new, extended convening spaces - MOOC's are not enough, students need experiences that engage


Student Success

Learning analytics to support student success
  • Improving student success by better use of data and analytics is set as a clear goal in many HEI's 
  • Student data should be used in various ways to find solutions on how to improve one's success. Faculties & teachers need to be better engaged into using analytics. 
  • Using data to identify students who might be needing more support in their studies or are falling behind and to provide them with solutions to help the situation e.g. tutoring
  • Gathering evidence of student action
    • Direct learning evidence: exams, clicker data, portfolio data, adaptive learning data, rubrics data, minute papers, capstone data
    • Indirect learning evidence: student surveys, grades, retention & persistence data, graduation rates, click-stream data, card swipe engagement data, exit interview data
  • In many cases we are still in the process of understanding what data is needed and why that data is relevant. In some cases too little data is still collected. 
  • How to collect data from the classroom? How to analyse learning? 
  • Recommendations on how to use analytics to support student success (NASPA)
    • Recommendation 1: Identify and expand institutionally appropriate roles for IR, IT, and student affairs 

    • Recommendation 2: Transcend or remove certain organizational silos to improve communication across all position levels. 
    • Recommendation 3: Prioritize measuring student outcomes. 
    • Recommendation 4: Increase the use of qualitative data, especially from students.
    • https://www.naspa.org/rpi/reports/data-and-analytics-for-student-success
Enhancing the student life cycle
  • Emphasis on personal engagement and communication 
  • Communication with the learner continues from admission until alumni 
  • There is a clear need to standardize data in HEI's as at the moment a students might need to provide their personal data over and over again in different stages of their studies (e.g. applying to exchange studies or internship or to an other school's courses)
    • Data from across the students lifecycle should be better integrated to provide students with better service
  • #EDU2030 opportunities: Curating lifelong leraning pathways - ensuring that people have learning opportunities through out their working life and that this opportunities matches their needs
Analytics to Support Student Success: Results from a National Study
  • 970 responses
  • Focus on four core areas
    • Types of student success data projects
      • First-year students and first-generation students are the leading focus of student success studies
      • Student pipeline
      • Academic progress
      • Efficiency of degree completion
      • Career pathways and post-graduation outcomes
      • Student ability to afford higher education
    • Structures in Place
      • Data governance: a team sport
      • Most institutions need development
    • Level of Coordination
      • IR, Study Affairs, IT shares responsibilities
    • Programs, interventions and outcomes
      • Data privacy
      • Inherent bias
      • Communication
      • Outcomes: progress toward goals measured mostly, ROI measured considerably less, more than half of the projects did not measure costs-> How do they know if program is valuable?

Student-centric approach to SIS development in John Hopkins Univerity (Session: Beyond Technology: Creating the Next-Generation Student Experience)











Analytics

Learning analytics dashboards
  • The audience of dashboards has expanded from administrators to teachers and learners
  • Course-level data as an entry point to engage with instructors, build familiarity and design a prototype (University of Wisconsin-Madison)
  • Dashboards support students to visualise if they are keeping up with the pace of their studies, students can better monitor their progress, success and performance against that of their peers
Information Management
  • Data for the faculties to better support the students life cycle - make better decision and provide better learning experiences 
AI and Predictive Analysis (PA) - Session: Demystifing AI and Presictive Analytics in Higher Ed
  • Current state of AI and PA in HE
    • Most promising applications are the ones were students are provided more agency. 
    • Predictive models have been built for instructors and advisors but now there's more focus on course level models and more demand from learners themselves 
    • If we are trying to influence student behavior timely, we need more real-time data from learning systems to be analyzed in a stream. Can also be new kind of data, e.g. sound spaces of class rooms indicate what teaching model is used.
    • Need to model more complex behavior and constructs: "why does this student succeed/fail, not just what is happening".
  • How to succeed in use of AI & PA
    • Vision and plan before investing in systems
    • PA strategies should be approached systematically as a on-going process and not a tool (e.g. policies need to be changed),
    • Intentional building of meaningful models to answer pedagogical questions so as not to be led astray by the data
    • Transparency with the data and models used
  • Key ethical concerns with AI & PA
    • No code of ethics as a field (compared to research code, medical ethics etc.). 
    • Visceral negative impact of students need to be mitigated as best we can
    • People implementing the algorithms have bias
      • Analogue with open science: analyze open datasets to see what they are good for and if they can be reproduced


Open resources

Open Educational Materials (OER)
  • Course books are still expensive and out of reach for many students and OER are seen as a possible solution to ease the problem
  • The goal in many cases is to provide every student access to course materials through digitalization and OER. 


Diversity

Education for all 
  • Keynote Alexis Ohanian: "Education is core to our society's survival",  "We need the ideas of as many people as possible" 


EDUCAUSE TOP 10 IT Issues for 2019

1. Information Security Strategy
Developing a risk-based security strategy that effectively detects, responds to, and prevents security threats and challenges

2. Student Success
Serving as a trusted partner with other campus units to drive and achieve student success initiatives

3. Privacy
Safeguarding institutional constituents’ privacy rights and maintaining accountability for protecting all types of restricted data

4. Student-Centered Institution
Understanding and advancing technology’s role in optimizing the student experience (from applicants to alumni)

5. Digital integrations 
Ensuring system interoperability, scalability, and extensibility, as well as data integrity, security, standards, and governance, across multiple applications and platforms

6. Data-Enabled Institution
Taking a service-based approach to data and analytics to reskill, retool, and reshape a culture to be adept at data-enabled decision-making

7. Sustainable Funding
Developing funding models that can maintain quality and accommodate both new needs and the growing use of IT services in an era of increasing budget constraints

8. Data Management and Governance
Implementing effective institutional data-governance practices and organizational structures

9. Integrative CIO
Repositioning or reinforcing the role of IT leadership as an integral strategic partner of institutional leadership in achieving institutional missions

10. Higher Education Affordability
Aligning IT’s priorities and resources with institutional priorities and resources to achieve a sustainable future


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