Multimedia Approaches to Learning the Foundations of Library and Information Science
Bibliographic record
Abstract
This paper presents a case study of two types of multimedia resources that were integrated as supplementary learning materials into the design and delivery of two different graduate courses on the historical foundations of library and information science (LIS): video and audio lectures from an online course on the history of information (integrated into a doctoral seminar), and a curated playlist of a weekly public radio broadcast on the history of ideas (integrated into a master’s course). It also considers some of the limitations of compiling LIS-related audiovisual materials from disparate online sources, with references to examples. By analyzing and critiquing these three applications of multimedia resources in LIS graduate courses, this paper attempts to answer the following research question: Beyond traditional pedagogical strategies such as lectures and text-based readings and assignments, how might students, practitioners, and the general public gain a sweeping understanding of our field? The paper aims to help LIS educators to diversify their pedagogical strategies and reach people outside their classrooms. By incorporating these kinds of multimedia resources into course designs, educators may help to empower students to actively and creatively apply what they learn in class to the analysis of historical events, biographies, and social movements, develop technical skills that will benefit their professional development, and produce deliverables that can be shared on public platforms to reach a wider audience beyond LIS classrooms.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".