Integrating computational methods, digital tools, and archival reasoning into modern archival workflows.
This course introduces students to computational thinking in archival science, emphasizing how abstraction, decomposition, algorithm design, systems thinking, NLP, machine learning, digital preservation, and user access tools can support archival work.
Course rationale: Digital transformation has changed archival science. Archivists increasingly need to manage complex digital records, big data, metadata verification, automation, AI-assisted workflows, and scalable systems for access and preservation.
Computational Thinking in Archival Practice introduces the fundamental principles of computational thinking within archival science. As digital tools and methods play an increasingly significant role in archival work, future archivists need to understand how computational methods can support appraisal, arrangement, description, access, records management, and preservation.
Students participate in hands-on exercises that replicate real-world archival challenges. The course emphasizes practical problem-solving, ethical reflection, and the integration of computational tools into traditional archival workflows.
Students learn how abstraction, decomposition, algorithm design, and systems thinking can improve archival workflows.
Students explore software, scripting, NLP, machine learning concepts, and digital tools for processing and analyzing archival collections.
Students design practical workflows that integrate computational tools into archival description, preservation, and records management.
Students evaluate issues such as privacy, algorithmic bias, access, transparency, and accountability in computational archival practice.
Core framing: computational thinking is not about replacing archival judgment. It is about giving archivists structured methods for solving complex information problems at scale.
Computational thinking, abstraction, decomposition, algorithm design, and systems thinking.
Students identify real-world applications of computational tools in archival science and write a short analysis of benefits and challenges.
Digital records, preservation challenges, format stability, and computational analysis.
Students compare digital and physical records and summarize three computational tools used for digital record analysis.
NLP, machine learning, batch file processing, OCR, and archival text extraction.
Students rename a batch of PDF files and apply OCR to generate text for metadata extraction and analysis.
Metadata extraction, NER, topic modeling, and automated description support.
Students use basic topic modeling to identify themes in OCR-generated text and evaluate how useful the results are for archival description.
Digital preservation challenges, preservation packaging, integrity, and computational workflows.
Students create preservation packages and discuss how computational methods support integrity, storage, and long-term access.
Search interfaces, retrieval models, semantic search, and user-centered archival access.
Students design a simple search interface for a digital archive and evaluate how it improves user access.
IDEF0 models, electronic records management, metadata verification, and AI-assisted workflows.
Students develop a computational records management plan using workflow modeling techniques.
Final project, GitHub documentation, automation, scalability, and workflow design.
Students design, document, and present an archival workflow that integrates computational tools to address a real-world archival challenge.
| Component | Purpose | Approximate Weight |
|---|---|---|
| Participation | Engagement in discussion, collaboration, and hands-on activities. | 10% |
| Weekly Assignments and Activities | Applied exercises demonstrating computational thinking and archival problem solving. | 60% |
| Final Group Project | Collaborative design of an archival workflow integrating computational tools. | 30% |
Traditional archival techniques remain essential, but they are no longer sufficient by themselves for managing born-digital records, large-scale digitized collections, complex metadata environments, and computational discovery systems. This course helps students bridge foundational archival knowledge with emerging technical skills.
By integrating computational thinking into archival education, students become better prepared to lead in environments shaped by digital records, automation, AI-assisted metadata, preservation challenges, and user-centered access systems.
This course complements HARC short courses and seminars by providing a deeper framework for computational archival science, digital preservation, records management, and scalable archival workflow design.
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