EMBEDDING LIBRARIANS IN ENGINEERING PROGRAMS: THREE CASE STUDIES WITH ENGINEERING STUDENTS
Bibliographic record
Abstract
As the information landscape becomes increasingly complex, librarians must adapt accordingly. With information so readily available, students overestimate their research skills and lack awareness of how the library can help. However, librarians’ academic training makes them ideal resources to support students’ complex information needs - whether students know it or not. In this paper, we argue that embedded librarianship is the solution to this disconnect between librarian and user. Specifically, this paper provides case studies at two Canadian universities of librarians approaching embedded librarianship from different directions. At the University of Waterloo, two engineering librarians worked toward an embedded model of librarianship where this was not yet an established model in the Faculty of Engineering. At the University of Saskatchewan, a librarian was hired with the intention of the new position being embedded, without a formal structure or precedent for this within the College of Engineering. The term “embedded librarian” describes a service model where an academic librarian participates in an academic course or program on a continuing basis in order to understand the learning objectives and determine which resources best support them. In order to “do this, the librarian has to be familiar with the work and understand the domain and goals. Doing this, the librarian becomes an invaluable member of the team” [1]. The variables associated with embeddedness include location, funding, management and supervision, and participation [1]. To this end, the authors explore how each of these variables contribute to the success of moving towards this embedded model: how moving out of the library influences overall connection, how they acquired funding to grow a new collection, how management supports the overall goal, and how sustained participation in the program grows new opportunities. At both universities, librarians have seen most success embedding in programs with a strong emphasis on integrated STEM education where the focus is on providing real-world context with the aim of graduating well-rounded engineers [2]. The authors will discuss how programmatic learning outcomes and trends in integrated and interdisciplinary education have allowed them to stretch beyond the traditional boundaries of academic librarianship to demonstrate value to the Engineering departments in new ways. This paper reports on the experiences, advantages, and lessons learned in moving toward this model, and provides concrete examples for adapting these concepts to programs at other institutions. Through an intrinsic case study [3] the authors aim to understand how librarians’ embeddedness can adapt and change to support student learning in different contexts. This session is targeted towards practicing engineering librarians and engineering faculty members and educators. Attendees will leave the session with ideas on how to stimulate new partnerships between their library and Engineering programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".