Teaming up to Teach Teamwork in an LIS Master’s Degree Program
Bibliographic record
Abstract
Abstract Objective – Collaboration and working in teams are key aspects of all types of librarianship, but library and information studies (LIS) students often perceive teamwork and group work negatively. LIS schools have a responsibility to prepare graduates with the skills and experiences to be successful working in teams in the field. Through a grant from the university office of assessment, the assessment committee at the University of Rhode Island Graduate School of Library and Information Studies explored their department’s programmatic approach to teaching teamwork in the MLIS curriculum. Methods – This research followed a multi-method design including content analysis of syllabi, secondary analysis of student evaluation of teaching (SET) data, and interviews with alumni. Syllabi were analyzed for all semesters from fall 2010 to spring 2016 (n = 210), with 81 syllabi further analyzed for details about their team assignments. Some data was missing from the dataset of SETs purchased from the vendor, resulting in a dataset of 39 courses with SET data available. Interviews were conducted with a convenience sample of alumni about their experiences with teamwork in the LIS program and their view of how well the LIS curriculum prepared them for teamwork in their careers (n = 22). Results – Findings indicate that, although alumni remembered teamwork happening too often, it was required in just over one-third of courses in the sample period (fall 2010 to spring 2016), and teamwork accounted for about one-fifth of assignments in each of these courses. Alumni reported mostly positive experiences with teamwork, reflecting that teamwork assignments are necessary for the MLIS program because teamwork is a critical skill for librarianship. Three themes emerged from the findings: alumni perceived teamwork to be important for librarians and therefore for the MLIS program, despite this perception there is also a perception that the program has teamwork in too many courses, and questions remain about whether faculty perceive teaching teamwork as important and how to teach teamwork skills in the MLIS curriculum. Conclusions – Librarians need to be able to collaborate internally and externally, but assigning team projects does not guarantee students will develop the teamwork skills they need. An LIS program should be proactive in teaching skills in scheduling, time management, personal accountability, and peer evaluation to prepare students to be effective collaborators in their careers.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".