Exploring the Impact of Individualized Research Consultations Using Pre and Posttesting in an Academic Library: A Mixed Methods Study
Bibliographic record
Abstract
Abstract Objective – Academic librarians consistently offer individualized help to students and researchers. Few studies have empirically examined the impact of individualized research consultations (IRCs). For many librarians, IRCs are an integral part of their teaching repertoire. However, without any evidence of an IRC’s effectiveness or value, one might ask if it’s worth investing so much time and effort. Our study explored the impact of IRCs on students' search techniques and self-perceived confidence levels. We attempted to answer the following questions: 1) Do IRCs improve students’ information searching techniques, including the proper use of keywords and/or subject headings, the accurate use of Boolean operators, and the appropriate selection of specialized resources/databases? 2) Do IRCs influence students’ confidence level in performing effective search strategies? Methods – Our study used a mixed-methods approach. Our participants were students from the Faculties of Health Sciences and Medicine at the University of Ottawa, completing an undergraduate or graduate degree, and undertaking a research or thesis project. Participants were invited to complete two questionnaires, one before and one after meeting with a librarian. The questionnaires consisted of open-ended and multiple choice questions, which assessed students' search techniques, their self-perceived search techniques proficiency and their confidence level. A rubric was used to score students' open-ended questions, and self-reflective questions were coded and analyzed for content using the software QSR NVivo. Results – Twenty-nine completed pre and posttests were gathered from February to September 2016. After coding the answers using the rubric, two paired-samples t-tests were conducted. The first t-test shows that students’ ability to use appropriate keywords was approaching statistical significance. The second t-test showed a statistically significant increase in students’ ability to use appropriate search strings from the pretest to the posttest. We performed a last paired-samples t-test to measure students’ confidence level before and after the appointment, and a statistically significant increase in confidence level was found. Conclusion – Out of three paired t-tests performed, two showed a statistically significant difference from the pretest to the posttest, with one t-test approaching statistical significance. The analysis of our qualitative results also supports the statement that IRCs have a positive real impact on students’ search techniques and their confidence levels. Future research may explore specific techniques to improve search strategies across various disciplines, tips to improve confidence levels, and exploring the viewpoint of librarians.
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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 | 0.039 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".