Chapter 8 The Promise and Peril of Data and Analytics

Question 1

What are significant issues in higher education related to data and analytics?

Answer 1

Inequity and bias

Access to colleges and modalities, gaps in retention, persistence, and completion, and lack of diversity in curricula.

Question 2

What tools have shown promise in addressing inequities in higher education?

Answer 2

Predictive analytics, machine learning, artificial intelligence, deep learning analytics

These tools can help understand and organize data to eliminate inequities.

Question 3

True or False: Advanced analytics are guaranteed to decrease inequities in higher education.

Answer 3

False

Advanced analytics can reinforce systemic obstacles and biases if misused.

Question 4

What is the role of predictive analytics and machine learning in admissions?

Answer 4

They help predict which applicants are most likely to enroll, persist, and graduate

This can highlight inequality and obstacles to upward mobility.

Question 5

What type of data are colleges using to profile students?

Answer 5

Biometric data, personal financial information, social media information

These data sources can be used to estimate student enrollment likelihood.

Question 6

Fill in the blank: Students want colleges to use their data in _______ ways.

Answer 6

open and transparent

This increases the likelihood that students will earn a degree.

Question 7

What was the impact of machine learning on course failure rates at Ivy Tech Community College?

Answer 7

Reduced by 3.3 percentage points

Equivalent to 3,100 more students passing their classes.

Question 8

What is the Central Florida Education Ecosystem Database (CFEED)?

Answer 8

A data repository for multiple educational institutions

It includes Orange County Public Schools, Osceola County Schools, UCF, and Valencia College.

Question 9

What approach has the University of Kentucky implemented for student success?

Answer 9

Values-focused approach to analytics

It uses key indicators like academic success, financial stability, and engagement.

Question 10

What unethical practice did Simon Newman propose at Mount St. Mary’s University?

Answer 10

Cull the class of first-year students to boost retention rates

This led to widespread calls for his resignation.

Question 11

What are some invisible risks associated with advanced analytics?

Answer 11

Bias in data, discriminatory results, unfair labeling of students

These can perpetuate systemic inequities.

Question 12

How can bias in algorithms originate?

Answer 12

From unrepresentative training data, reliance on flawed information

Can lead to decisions with disparate impacts on certain groups.

Question 13

What is an example of bias in analytics affecting job search ads?

Answer 13

Ads tend to privilege men over women

This demonstrates gender bias in algorithmic decision-making.

Question 14

What are some forms of bias that can affect data analytics?

Answer 14

• Sample bias
• Confirmation bias
• Chronological bias
• Measurement bias
• Simpson’s paradox
• Overfitting or underfitting
• Correlation bias
• Stereotyping bias
• Modeling bias

These biases can occur at different phases of a project.

Question 15

What is ascertainment bias?

Answer 15

When certain members of a sample are less likely to be included in the results

This can skew findings in studies.

Question 16

What is a potential consequence of biased predictive models?

Answer 16

They may inaccurately predict outcomes for underrepresented populations

This can disadvantage students of color and low-income students.

Question 17

What challenges do college leaders face regarding data and analytics?

Answer 17

Convergence of financial model erosion, changing student body composition

These challenges complicate the implementation of equitable analytics.

Question 18

What is a significant source of challenges for college leaders in higher education?

Answer 18

The convergence of multiple major developments, including the erosion of the traditional financial model and changing composition of student bodies.

Question 19

What event accelerated challenges in higher education after 2008?

Answer 19

The Great Recession.

Question 20

What has increased the demand for data in colleges and universities?

Answer 20

Renewed calls for racial and social justice and the need to better serve diverse student populations.

Question 21

What complicates the development and scaling of advanced analytics in colleges?

Answer 21

Collision of resource constraints and expectations about resource distribution.

Question 22

True or False: Senior campus leaders should prioritize developing a culture of evidence and advanced analytics capabilities.

Answer 22

True.

Question 23

What can happen if a college lacks a strategy for advanced analytics?

Answer 23

It may not be able to measure progress effectively.

Question 24

What is a common response from faculty when resources are reallocated towards advanced analytics?

Answer 24

Calls for hiring more full-time faculty instead of relying on adjuncts.

Question 25

What may lead colleges to outsource analytics to external vendors?

Answer 25

Lack of a strategy and resource constraints combined with pressure from stakeholders.

Question 26

What is a significant risk when relying on external vendors for analytics?

Answer 26

The potential for biased solutions that do not align with the institution's needs.

Question 27

How can leadership transitions impact data analytics efforts in colleges?

Answer 27

They can halt momentum and lead to regression in analytics capabilities.

Question 28

What is essential for sustaining a data analytics system regardless of leadership changes?

Answer 28

A well-maintained and well-monitored data governance system.

Question 29

What is the first step toward creating an analytics-powered institution?

Answer 29

Create a plan for an equity-driven approach and deputize a leader.

Question 30

What role is increasingly being created in colleges to oversee data analytics?

Answer 30

Chief Strategy Officer or Chief Data Officer.

Question 31

Fill in the blank: The process of data discovery includes identifying where data _______.

Answer 31

[data reside].

Question 32

What must be communicated clearly across all levels of institutional leadership?

Answer 32

The importance and value of advanced analytics.

Question 33

What can help colleges overcome resource constraints when investing in analytics?

Answer 33

Creating innovation and reinvestment funds from efficiencies and cost savings.

Question 34

What is a hidden cost associated with better data integration?

Answer 34

Cultural and political capital.

Question 35

What is a key step in the development of a data governance system?

Answer 35

Creating and empowering a data governance committee.

Question 36

Why might department heads resist the integration of data analytics?

Answer 36

They may view data as their property and resist new priorities.

Question 37

What should campus leaders do to demonstrate their commitment to analytics?

Answer 37

Practice data-informed decision making.

Question 38

What framework should be developed to guide decision making in analytics?

Answer 38

A decision-making framework that privileges analytics.

Question 39

What is the ultimate goal of using advanced analytics in higher education?

Answer 39

To improve student outcomes, promote academic excellence, and ensure financial sustainability.

Question 40

What must leaders consider when implementing advanced analytics?

Answer 40

The promise and peril of analytics, including minimizing risks to underserved students.

Question 41

Fill in the blank: Successful execution of analytics initiatives often depends on how _______ is managed.

Answer 41

[change].