Better together: combining research and writing support for student scholars
Bibliographic record
Abstract
Originally presented at the Medical Library Association 2016 Annual Meeting and Exhibition, Toronto, ON, May 16, 2016. Objectives: Libraries offer workshops on finding evidence; writing centers offer assistance with synthesizing evidence into effective literature reviews. The University of Maryland (UMD) Libraries and the UMD Graduate School Writing Center investigated the effectiveness of combining these traditionally separate efforts into a suite of instructional programming, which presented research and writing as linked and iterative components of a process rather than distinct stages of scholarship. Methods: The UMD Libraries sought to increase its impact on student scholarship by creating information literacy workshops that better met the needs of students. To determine student needs, library staff queried liaison librarians, who noted that graduate and upper-level undergraduate students struggle with entering into their discipline’s academic discourse. To demonstrate to students the iterative process of finding and using evidence, library and writing center staff developed programming on how to write a literature review for a thesis or dissertation. Staff from both departments designed instructional content on how to find, access, store, and use evidence effectively when writing. These sessions also introduced students to the concept of scholarship as a conversation, how to apply the stasis theory of composition, and explained the rhetorical purpose of conducting a literature review. Attendance and session evaluations were used to measure effectiveness. Results: A traditional graduate student workshop series held in the library, which presented research and writing as separate processes, was attended by 22 students in 2013 and 35 students in 2014. The literature review workshops, which offered integrated research and writing instruction, were attended by 119 students in 2014 and 96 students in 2015. Conclusions: This successful collaboration highlights the benefits of forming strategic partnerships with groups on campus who have shared values with the library, as these sessions developed in concert with the Graduate School Writing Center substantially improved student attendance at library workshops. Subsequently, discipline specific breakout sessions have been developed, which utilize the expertise of liaison librarians to offer focused training on specific resources and writing techniques within subject disciplines. This collaboration also has led to the development of additional experimental programming with the Graduate School Writing Center. Some of these initiatives include librarian office hours in the writing center, and workshops on transforming a traditional research paper into a compelling oral presentation.
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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 | 0.094 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.006 | 0.047 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".