Chapter 2 Demystifying Data and Analytics Flashcards
What has contributed to the hype surrounding big data and advanced analytics in higher education?
The massive growth of transaction data and new data sources, such as smartphones and social media.
What are the initial dimensions of big data?
- Volume
- Variety
- Velocity
What additional dimensions have emerged in the concept of big data?
- Veracity
- Variability
- Value
Define structured data.
Data in a predetermined format with standardized fields and storage.
What percentage of data today is estimated to be unstructured?
Approximately 95%.
What is data literacy?
The ability to comprehend data, including understanding data sources, analysis techniques, and effective communication.
How is analytics defined in higher education?
The use of data, statistical analysis, and models to gain insight and act on complex issues.
What are the five kinds of analytics identified by IBM?
- Planning analytics
- Descriptive analytics
- Diagnostic analytics
- Predictive analytics
- Prescriptive analytics
What is the focus of planning analytics?
Analyzing historical performance, identifying patterns and trends, and predicting outcomes.
What does descriptive analytics examine?
What has already happened.
What is the purpose of diagnostic analytics?
To understand why something happened by analyzing data for outliers.
What does advanced analytics involve?
Using advanced techniques like machine learning and data mining to reveal patterns and make predictions.
Define predictive analytics.
Uses techniques like regression and forecasting to determine the likelihood of future events.
What is the goal of prescriptive analytics?
To make recommendations about actions that can influence future outcomes.
What does learning analytics focus on?
The collection and analysis of student data to improve learning outcomes.
How does Oracle define artificial intelligence (AI)?
Systems or machines that mimic human intelligence and can iteratively improve based on collected information.
What are the three types of machine learning?
- Supervised
- Unsupervised
- Reinforced
What do deep learning systems rely on?
Neural networks that enable computers to learn from observational data.
What characterizes a culture of evidence in higher education?
Data and analytics are used to inform decision-making broadly, consistently, and collaboratively.
What challenge do colleges face in developing a culture of evidence?
The rapid acceleration of data creation and analytics capabilities.
What can lead to discomfort among campus leaders regarding analytics?
The amorphous nature of the concept and variations in its definition.
What does a culture of evidence mean?
Making decisions based on data rather than anecdotal evidence
According to the American Association of State Colleges and Universities’ Center for Student Success.
What are the pillars that support a strong culture of evidence?
- Leadership
- Governance
- Infrastructure and politics
- Established goals and accountability
- Support and strategic use
Identified by the American Association of State Colleges and Universities.
True or False: In a culture of evidence, data is viewed negatively.
False
In a culture of evidence, using data is viewed positively.
Fill in the blank: A culture of evidence positions a college or university to strengthen the quality of the _______ experience.
[student]
What types of data are included in a culture of evidence?
- Traditional quantitative data
- Data from focus groups
- Data from town halls
- Data from social media
Synthesizing these various data types helps identify institutional opportunities and challenges.
What is often a barrier to understanding data in colleges and universities?
Stakeholders often have reasons for not sharing data
This prevents a comprehensive understanding of the intersection of scale, impact, quality, and sustainability.
What must campus leaders articulate to promote a culture of evidence?
A clear focus on student success and equity
Without this, stakeholders may continue with traditional methods.
What is a key factor in the effective use of data in colleges?
A well-articulated rationale for using data and training for faculty and staff
This is important for those who do not know how to use data effectively.
What should colleges assess to jump-start efforts to build a culture of evidence?
- Leadership commitment to using data
- Understanding of data governance and utility
- Inventory of data integration across roles
- Support for scaled-out use of evidence
- Use of data for aspirational and operational pursuits
This assessment helps identify the current state versus desired state in data usage.
What is a challenge related to transfer policies in higher education?
Standard approaches to evaluating and applying credits can lead to rejection of hard-earned credits
This is due to students taking unique pathways to a degree.
True or False: Career advising should not be a part of preparing students for their future careers.
False
Career advising is essential for helping students translate their skills into a value proposition for employers.
What does a culture of evidence require from leadership?
Commitment to effective use of data and clear articulation of institutional aspirations
This includes empowering faculty and staff and designing a framework for governance.
Fill in the blank: The emergence of _______ presents an opportunity for a paradigm shift in how colleges use data.
[big data and advanced analytics]
What has led to the mobilization of federal and state legislatures to improve degree attainment?
The revelation that the rate of postsecondary degree attainment in the U.S. had slipped below the top ten rates among OECD countries
This was part of the completion agenda that included demands for accountability.
What is required to reduce barriers to student success related to data analysis?
Focus on data governance, privacy, and security
This helps address implicit bias within data and analyses.
