Difference between revisions of "Computational Thinking"
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Computational Practices (from http://cases.umd.edu/) | Computational Practices (from http://cases.umd.edu/) | ||
| − | # Data Practices: The nature of how data are collected, created, analyzed, and shared is rapidly changing primarily due to advancements in computational technologies. | + | # <b>Data Practices</b>: The nature of how data are collected, created, analyzed, and shared is rapidly changing primarily due to advancements in computational technologies. |
## Collecting Data: Data are collected through observation and measurement. Computational tools play a key role in gathering and recording a variety of data across many different archival endeavors. Computational tools can be useful in different phases of data collection, including the design of the collection protocol, recording, and storage. | ## Collecting Data: Data are collected through observation and measurement. Computational tools play a key role in gathering and recording a variety of data across many different archival endeavors. Computational tools can be useful in different phases of data collection, including the design of the collection protocol, recording, and storage. | ||
| − | ## Creating Data | + | ## Creating Data: The increasingly computational nature of working with archival data underscores the importance of developing computational thinking practices in the classroom. Part of the challenge is teaching students that answers are drawn from the data available. In many cases archivists use computational tools to generate data… at scales that would otherwise be impossible. |
## Manipulating Data | ## Manipulating Data | ||
## Analyzing Data | ## Analyzing Data | ||
## Visualizing Data | ## Visualizing Data | ||
# Modeling and Simulation Practices | # Modeling and Simulation Practices | ||
Revision as of 20:41, 13 January 2021
Computational Practices (from http://cases.umd.edu/)
- Data Practices: The nature of how data are collected, created, analyzed, and shared is rapidly changing primarily due to advancements in computational technologies.
- Collecting Data: Data are collected through observation and measurement. Computational tools play a key role in gathering and recording a variety of data across many different archival endeavors. Computational tools can be useful in different phases of data collection, including the design of the collection protocol, recording, and storage.
- Creating Data: The increasingly computational nature of working with archival data underscores the importance of developing computational thinking practices in the classroom. Part of the challenge is teaching students that answers are drawn from the data available. In many cases archivists use computational tools to generate data… at scales that would otherwise be impossible.
- Manipulating Data
- Analyzing Data
- Visualizing Data
- Modeling and Simulation Practices
