Training to Teach Graduate Information Literacy Sessions Using a Team-based Mentorship Approach: Report on a Pilot Project at the OISE Library
Bibliographic record
Abstract
Staffing changes in the summer of 2015 at the University of Toronto’s Ontario Institute for Studies in Education (OISE) Library and an increased student enrolment in one of OISE’s core programs meant that the OISE Library would not be able to meet the demand for information literacy (IL) instruction in the fall. A new librarian and two Library and Information Science (LIS) graduate students were hired in early September and had only a short time to learn about OISE’s students, programs, and IL instruction more broadly before designing, delivering, and evaluating IL sessions for graduate students in OISE’s Master of Teaching (MT) program. Recognizing and drawing on each team member’s unique skills and experiences, the OISE Library piloted a team-based mentorship approach to training which enabled the team to collaboratively develop their instructional skills while meeting the goal of a better IL session for its MT students. In turn, this approach provided the LIS student instructors the unique opportunity to gain instructional experience within the library, teaching fellow graduate-level students. This paper outlines the rationale for piloting a team-based mentorship approach, describes the various implementation stages of the pilot project, and explores the ways in which the instruction team incorporated MT student feedback to improve its teaching. LIS students on the instruction team share their perspectives on participating in an instruction team. Des changements de personnel au cours de l’été 2015 à la Bibliothèque de la Ontario Institute for Studies in Education (OISE) de l’Univeristé de Toronto ainsi qu’une augmentation des inscriptions à l’un des programmes d’études de base signifiaient que la Bibliothèque OISE ne serait pas en mesure de répondre à la demande de formation documentaire à l’automne. Un nouveau bibliothécaire et deux étudiants diplômés en science de l’information ont été embauchés au début septembre et n’ont eu que peu de temps pour en apprendre davantage sur les étudiants et les programmes d’OISE ainsi que sur la formation documentaire au sens large avant de devoir conceptualiser, offrir et évaluer des ateliers pour les étudiants diplômés du programme de maîtrise en enseignement. En reconnaissant et en tirant parti des habiletés et expériences uniques de chaque membre de l’équipe, la Bibliothèque OISE a mis sur pied une approche de mentorat collective pour la formation qui a permis à l’équipe de développer de façon collaborative ses techniques d’enseignement dans le but d’offrir une meilleure session aux étudiants inscrits au programme de maîtrise en enseignement. Cette approche a donné aux formateurs étudiants une occasion unique de parfaire leur expérience d’enseignement au sein de la bibliothèque tout en formant d’autres étudiants diplômés. Cet article explique pourquoi cette approche de mentorat collective a été choisie, décrit les étapes de mise sur œuvre du projet et discute des façons dont l’équipe de formation a intégré les commentaires des étudiants diplômés afin d’améliorer son enseignement. Les étudiants en sciences de l’information faisant partie de l’équipe partagent leur perspective quant à leur participation au sein d’une équipe d’enseignement.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".