Technology Use in Designing Curriculum for Archivists: Utilizing Andragogical Approaches in Designing Digital Learning Environments for Archives Professional Development
Bibliographic record
Abstract
Abstract Technology has a significant impact in archival institutions. The creation and need to preserve digital records require archivists to have the necessary training, and ongoing professional development. In addition, technology is embedded in many archival processes, making knowledge of technology use vital for archivists. While technology may be a challenge for archivists in terms of archival management, it also presents a useful means to support training and professional development. This paper is based on the experimental research conducted by the researchers, as instructors, in developing curriculum based on theories of andragogy for the purposes of developing intentional curriculum for professional development of archivists in digital learning environments. In this article, we will focus on the application of technology for the purposes of training archives professionals. We have explored archives training through the application of andragogy theory in online training through Louisiana State University and Mohawk College. In addition, we will review the literature relating to the use of technology to support both outreach and marketing to educate clients of archival institutions. Social media tools offer a broad means to engage clients, as these platforms already function as “community hubs for activity, featuring many users, regular updates, and active forum discussions” (Terras). The literature suggests that there is have been significant inroads in developing intentional curriculum for digital learning environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".