Emerging Technologies 2019 In Higher Education Administration - - PowerPoint PPT Presentation

Emerging Technologies 2019

• In Higher Education Administration

Corinne Picataggi and Jason Pully

Trending Topics in HigherEd IT

• Blockchain
• Chatbots
• Machine Learning / Artificial Intelligence

– W&M experimentation and application

• descriptions, potential use cases, considerations

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What is Blockchain?

• A distributed ledger that validates components

by consensus of participants

– The chain is an expandable list of securely connected records which significantly reduces the

• pportunity for unauthorized access or

manipulation. – Decentralized management of data – Data ownership transformation

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Blockchain Flow

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custom diagram here

Blockchain: Use Cases and Benefits

• Stackable certificates
• Digital diplomas that are verifiable by potential

employers and other institutions

• Owner autonomy over their records
• Globalizing education across multiple providers
• Sharing research

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Blockchain Flow – Degree Completion

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custom diagram here

Blockchain: Considerations and Risks

• Identifying appropriate use cases
• Protecting endpoints: input and output
• Ensuring the security required to write to the

chain is sufficient (managing encrypted keys)

• Developing solid test cases
• Standards and regulation

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Blockchain in Use: MIT Digital Diploma

• 2017 Pilot – 111 graduates

received digital diplomas

• Students can share

diploma immediately with whomever they want

Citation: Digital Diploma Debuts At Mit Elizabeth Durant-Alison Trachy- | Office of Undergraduate Education - http://news.mit.edu/2017/mit-debuts-secure-digital-diploma-using- bitcoin-blockchain-technology-1017

• No enrollment verification process to

validate diploma

What is a Chatbot?

• An application that conducts a

conversation verbally or through text

– Evolution: From Question & Answer to Reasoning and Contextual Decision Making

• Old: Siri, Alexa, Google Assitant, Cortana
• New: Microsoft's Xiaoice, Retail Markets

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Chatbot: Use Cases and Benefits

• Build student engagement by

extending service and

• utreach
• Improve employee service

centers

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Citation: Five Reasons Why Chatbots Are the Future Of Customer Service Aakrit Vaish - https://www.entrepreneur.com/article/325830

• Support prospective students and visitors to website

by answering questions at their convenience

Chatbot: Considerations and Risks

• Outsourcing – delegating the message
• Exposing the platform – malicious threats
• Protecting sensitive information
• Language considerations when supporting a

global community

• Personalization versus Socialization

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What is Machine Learning / AI?

• Relying on a computer system to complete

a specific task using patterns and inference, without providing explicit instructions.

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All About Data

• Different than Analytics

– Defined outcomes

• Machine Learning vs. Traditional

Statistical Modeling

– Statistical modeling operates on generally known relationships while machine learning can help discover relationships.

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ML/AI: Use Cases and Benefits

• Predict who would buy meal plans
• Student success
• Financial Aid needs
• Tuition forecasting
• Donor data
• Aggregating datasets based on previous collection
• Event attendance and success
• Space planning and management

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Machine Learning / AI: Risks

• Explaining

– I don’t know ~ the computer did it!

• Errors

– Identifying errors and preventing recurrence

• Ethics

– How should data be used? – What data is appropriate to use? – Relationships between admission and success

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Can we get by without it ML?

• Society, and particularly social networking, is

creating tailor-made experiences for people.

– This is becoming an expectation in society that we are seeing translate into student expectations.

• Managing Student Expectations

– 97% of students say technologies that support them

• utside of class are just as important

– 87% of students said the tech “savvy” of schools is important when applying

16 Citation:

• - Data Sources: Wakefield Study with Ellucian (2017);

DJS Research

Data Sources

• W&M runs 70+ systems, many of which

create unique data that can be used in analysis

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How will it affect decision making?

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Where is William & Mary?

• Descriptive and diagnostic analytics
• IT is experimenting with predictive

analytics

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What Has W&M IT Done?

• Work Request Team Assignments
• Work Request Estimated Hours

– About 3000 tickets a year assigned

• Goals from Performance Management

– Over 8000 goals from supervisors – Create concise goal library

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Also Applied at W&M

• Machine Learning to match incoming

students with a Faculty Advisor

– Student collaboration project, IT facilitated

• Because of our experimentation with machine

learning, we were positioned to take what these students had done and operationalize

• it. We are now using it to assign advisors.

21 Citation: https://www.wm.edu/news/stories/2018/math- undergrads-deploy-algorithm-to-revamp-student- advising-system.php

Where would you start?

• Data quality and literacy
• Start small with something you know and

understand.

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QUESTIONS?

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