Computational Thinking

Computational Practices (from http://cases.umd.edu/)

1. Data Practices: The nature of how data are collected, created, analyzed, and shared is rapidly changing primarily due to advancements in computational technologies.
   1. 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.
   2. 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.
   3. Manipulating Data: Computational tools make it possible to efficiently and reliably manipulate large and complex archival holdings. Data manipulation includes sorting, filtering, cleaning, normalizing, and joining disparate datasets.
   4. Analyzing Data: There are many strategies that can be employed when analyzing data for use in an archival context, including looking for patterns or anomalies, defining rules to categorize data, and identifying trends and correlations.
   5. Visualizing Data: Communicating results is an essential component of understanding archival data and computational tools can greatly facilitate that process. Tools include both conventional visualizations such as graphs and charts, as well as dynamic, interactive displays.
2. Modeling and Simulation Practices: The ability to create, refine, and use models of archival phenomena is a central practice… Models can include flowcharts and diagrams.
   1. Using Computational Models to Understand a Concept: Computational models that demonstrate specific ideas or phenomena can serve as powerful learning tools. Students can use computational models to deepen their understanding of archival science.
   2. Using Computational Models to Find and Test a Solution: Computational models can be used to test hypotheses and discover solutions to problems. They make it possible to test many different solutions quickly, easily, and inexpensively before committing to a specific approach.
   3. Assessing Computational Models: Students who have mastered this practice will be able to articulate the similarities and differences between a computational model and the phenomenon that it is modeling.
   4. Designing Computational Models: Part of taking advantage of computational power… is designing new models that can be run on a computational device. Students… will be able to define the components of the model, describe how they interact, decide what data will be produced by the model.
   5. Constructing Computational Models: An important practice… is the ability to create new or extend existing computational models. This requires being able to encode the model features in a way that a computer can interpret.