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Record W4392670174 · doi:10.18438/eb1ip29750

Graduate Assistants Trained in Reference May Not Consistently Apply Reference Interview and Instructional Strategies in Reference Interactions

2020· article· en· W4392670174 on OpenAlexvenueaboutno aff
Sarah Schroeder

Bibliographic record

VenueEvidence Based Library and Information Practice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceInformation retrievalMedical educationPsychologyLibrary scienceMathematics educationMedicine

Abstract

fetched live from OpenAlex

A Review of: Canuel, R., Hervieux, S., Bergsten, V., Brault, A., & Burke, R. (2019). Developing and assessing a graduate student reference service. Reference Services Review, 47(4), 527–543. https://doi.org/10.1108/RSR-06-2019-0041 Abstract Objective – To evaluate the effectiveness of a reference training program for graduate student employees that seeks to encourage use of reference interview and instruction techniques in virtual and in-person reference interactions. Design – Naturalistic observation with qualitative content analysis. Setting – A large, public research university in Montreal, Canada. Subjects – Three graduate students in Library and Information Science employed by the university library to provide virtual and in-person reference services. Methods – After completing a training program, the three participants provided virtual and in-person reference training for two consecutive semesters. They self-recorded their desk interactions in a Google form. These self-reports, along with their online chat transcripts from QuestionPoint, were the subject of this study’s analysis. Focusing on the QuestionPoint data, the authors coded the transcripts from these participants’ online reference interactions to reflect the presence or absence of a reference interview and various instructional techniques in their responses to patrons. Also, all in-person and virtual questions were examined and categorized as being either transactional or reference questions. Reference questions were further categorized as basic, intermediate, or advanced questions. Main Results – Of the chat transcripts analyzed, 49% were classified as containing reference questions rather than transactional questions. At the desk, 21.9% of interactions were coded as reference questions. Taking the two semesters together, 232 of 282 virtual reference questions were considered basic, while 41 were labelled intermediate, and 9 classified as advanced. Similarly, of 136 desk reference questions, 120 were classified as basic, 14 as intermediate, and 2 as advanced. In their coding of chat transcripts, researchers indicated whether the interaction contained no reference interview, a partial reference interview, or a complete reference interview. Virtual chat transcripts from both fall and winter semesters showed that no reference interview took place in 77.3% of interactions. Authors noted evidence of partial reference interviews in 19.3% of fall transcripts and 21.5% of winter transcripts. Complete reference interviews took place in 3.4% of fall and 1.2% of winter transcripts. Additionally, authors found that 65.5% of chat transcripts contained elements of instruction, with Modelling and Resource Suggestion being the most prevalent forms. Conclusion – Because the graduate students used complete or partial reference interviews in a small number of their virtual reference questions, the authors of this study determined that more emphasis ought to be placed on reference interviews, particularly virtual reference interactions, in future training programs. Graduate students employed instructional strategies in observed virtual reference interactions, a promising trend.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.120
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.226
GPT teacher head0.401
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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