CH10 HCL

Question 1

What is one benefit of combining sensor data with self-reports?

Answer 1

It provides both objective and subjective insights.

Question 2

What did the StudentLife study reveal about student stress?

Answer 2

Stress increased near deadlines and social activity declined.

Question 3

What can provoke more honest responses from people in studies?

Answer 3

Using disruptive or unexpected design probes.

Question 4

Why might standard interviews not reveal real feelings?

Answer 4

People may hide or underreport their concerns.

Question 5

What does visualizing energy usage help achieve?

Answer 5

Encourage behavioral change to save energy.

Question 6

What is data fusion in the context of Big Data?

Answer 6

Combining multiple data types/sources for richer analysis.

Question 7

What ethical risk is associated with location-based services?

Answer 7

Potential to track and identify individuals.

Question 8

What kind of data is extracted through scraping?

Answer 8

Publicly available data, often without direct user consent.

Question 9

What does the term “quantified self” refer to?

Answer 9

People using tech to track and analyze their own data.

Question 10

What does the term “massive scale” imply in data collection?

Answer 10

Involves data from millions or even billions of sources.

Question 11

Why is storytelling important in data visualization?

Answer 11

It helps contextualize the data for better understanding.

Question 12

What’s a common use of social network maps?

Answer 12

Identifying influencers or isolated individuals in groups.

Question 13

What is one risk of overvisualization?

Answer 13

Confusing or misleading interpretations.

Question 14

What are the primary emotions detected in sentiment analysis?

Answer 14

Happiness, sadness, anger, fear, enthusiasm.

Question 15

What does “zoom and filter” refer to in visualization?

Answer 15

Navigating between detailed and summary views of data.

Question 16

Why is scale important in data visualization?

Answer 16

To ensure accurate interpretation and comparison.

Question 17

What does crowdsourcing rely on?

Answer 17

Voluntary participation from the public.

Question 18

Why are tools like Tableau popular?

Answer 18

User-friendly interfaces for non-technical users.

Question 19

What does “data granularity” refer to?

Answer 19

The level of detail in a dataset.

Question 20

What makes a probe “provocative”?

Answer 20

It disrupts norms to uncover hidden attitudes.

Question 21

What kind of data is used in face-tracking AI?

Answer 21

Visual data, especially from cameras.

Question 22

What is the goal of integrating AI into visualization tools?

Answer 22

Automate analysis and improve usability.

Question 23

What is “natural language interface” in data tools?

Answer 23

Lets users ask questions using everyday language.

Question 24

How can AI improve business analytics?

Answer 24

Automating insights and predictions.

Question 25

Why must AI decisions be explainable?

Answer 25

To build trust and understanding among users.

Question 26

What is the role of dashboards in organizations?

Answer 26

Provide real-time performance and trend monitoring.

Question 27

What privacy issue arises with sensor networks in buildings?

Answer 27

Potential to deduce individual identities from patterns.

Question 28

What’s a benefit of citizen science projects?

Answer 28

Engages the public in scientific data collection.

Question 29

What is the purpose of visualizing bat calls for the public?

Answer 29

Raise awareness and interest in research.

Question 30

How do scientists use spectrograms?

Answer 30

To analyze specific audio features.

Question 31

What is a challenge with massive data volumes?

Answer 31

Managing, processing, and interpreting them effectively.

Question 32

How is trust built in AI systems?

Answer 32

Through transparency, explainability, and fairness.

Question 33

What does it mean for data to be “socially acceptable”?

Answer 33

Collected and used in ways society agrees is fair and respectful.

Question 34

What is a risk of combining data from multiple sources?

Answer 34

Re-identification of anonymous individuals.

Question 35

How can feedback from data change behavior?

Answer 35

Visual cues can encourage healthier or more sustainable habits.

Question 36

What are anomaly detections in big data?

Answer 36

Identifying outliers or unexpected patterns.

Question 37

What’s one danger of automated data collection?

Answer 37

Lack of consent and awareness from those tracked.

Question 38

Why is human-centered design critical in data systems?

Answer 38

Ensures systems align with human values and needs.

Question 39

How does data visualization lead to discovery?

Answer 39

By revealing hidden relationships and trends.

Question 40

What ethical question must always be asked when using data at scale?

Answer 40

Are we respecting users’ rights and privacy?

Question 41

What is another term for "data at scale"?

Answer 41

Big Data.

Question 42

Name three types of data included in Big Data.

Answer 42

Numbers, images, videos.

Question 43

What is the potential of Big Data?

Answer 43

Solving problems and benefiting society and individuals.

Question 44

What are the key challenges in using data at scale?

Answer 44

Collecting, analyzing, and communicating findings.

Question 45

Why is privacy a concern in Big Data?

Answer 45

Large-scale data collection may expose personal information.

Question 46

What is an ethical concern of tracking people at airports like Heathrow?

Answer 46

It may violate their privacy.

Question 47

What are two indirect methods of collecting personal data?

Answer 47

Device tracking and sentiment analysis.

Question 48

What is crowdsourcing?

Answer 48

Collecting data by getting input from many people, usually online.

Question 49

What does Google Trends analyze?

Answer 49

The motivations behind search queries.

Question 50

What is needed to get new insights from Big Data?

Answer 50

Asking the right questions.

Question 51

Example of a provocative probe in research?

Answer 51

The Quantified Toilets project.

Question 52

What does sentiment analysis measure?

Answer 52

Emotions in text (positive, negative, or neutral).

Question 53

What is a sentiment score of +10 interpreted as?

Answer 53

Extremely positive.

Question 54

What does Social Network Analysis (SNA) examine?

Answer 54

Relationships between people or groups.

Question 55

What kind of visualization is used in SNA?

Answer 55

Connection maps.

Question 56

What is the goal of data visualization tools?

Answer 56

Amplify human cognition.

Question 57

What is Ben Shneiderman’s visualization mantra?

Answer 57

Overview first, zoom and filter, then details on demand.

Question 58

What does the StudentLife study measure?

Answer 58

Students’ stress and activity over a semester.

Question 59

What’s one benefit of combining sensing and subjective reporting?

Answer 59

A comprehensive view of behavior.

Question 60

What do visualizations help detect in big datasets?

Answer 60

Patterns, trends, and anomalies.

Question 61

Name one tool that helps visualize financial data.

Answer 61

FINVIZ S&P 500 market map.

Question 62

What is Smart Citizen?

Answer 62

A community-based environmental data toolkit.

Question 63

What does Zoho Analytics visualize?

Answer 63

Sales data.

Question 64

What type of data can be visualized using sound graphs?

Answer 64

Bird and insect sounds.

Question 65

What do bat call data visualizations show?

Answer 65

Sound patterns for public and scientific audiences.

Question 66

What does Tableau’s Ask Data tool allow?

Answer 66

Asking questions in natural language.

Question 67

How does AI help with data analysis tools?

Answer 67

Automates analytic tasks.

Question 68

What’s an example of telling stories with data?

Answer 68

Explaining trends in student health during exams.

Question 69

Name one method to analyze social media posts.

Answer 69

Sentiment analysis.

Question 70

What kind of visual data can show weather changes?

Answer 70

Weather Underground’s Wundermap.

Question 71

What does the term “data scraping” mean?

Answer 71

Extracting data from websites or platforms.

Question 72

What’s an example of a citizen science platform?

Answer 72

iNaturalist.org.

Question 73

What kind of probes challenge social norms?

Answer 73

Provocative probes.

Question 74

What is the value of combining data sources?

Answer 74

Richer, multifaceted insights.

Question 75

What does a saliency map do in AI?

Answer 75

Shows what parts of an image influenced AI decisions.

Question 76

What tool uses face-tracking in AI?

Answer 76

DeepCam software.

Question 77

Why is visualization important in Big Data?

Answer 77

It helps uncover hidden relationships and insights.

Question 78

What is one danger of highly granular data?

Answer 78

It may allow individual identification.

Question 79

What are dashboards used for?

Answer 79

Monitoring real-time data trends.

Question 80

What challenge exists in making powerful tools accessible?

Answer 80

Simplifying interfaces for non-experts.

Question 81

What is a key privacy concern with energy feedback?

Answer 81

Identifying who is in specific rooms.

Question 82

What makes a design “fair”?

Answer 82

Equal treatment without bias or discrimination.

Question 83

What is accountability in data ethics?

Answer 83

Ensuring data is accurate and correct.

Question 84

What does transparency mean in AI?

Answer 84

Making system decisions visible.

Question 85

Define explainability in AI.

Answer 85

Ability to explain system decisions in simple terms.

Question 86

How is privacy defined in data ethics?

Answer 86

Respecting socially acceptable personal data use.

Question 87

What is human-centered design in HCI?

Answer 87

Designing systems with users' needs and ethics in mind.

Question 88

How can AI make amoral decisions?

Answer 88

By lacking human judgment and ethical consideration.

Question 89

What is the role of HCI in Big Data systems?

Answer 89

To ensure ethical, user-centered designs.

Question 90

What are five ethical principles of data design?

Answer 90

Fairness, accountability, transparency, explainability, privacy.