Evidence Based Practice Using Formative Assessment in Library Research Support
Bibliographic record
Abstract
Abstract
 
 Objective – The purpose of this study was to develop and review the effectiveness of a new evidence-based approach for teaching library research support.
 
 Methods – Formative assessment, through two variations of the One Minute Paper model, is used to poll the experiences of university researchers in library research support sessions. Prior to a session, Polling One Minute Papers (POMPs) assess what researchers know about topics that will be covered in the session. After a session, Reflective One Minute Papers (ROMPs) review whether university researchers achieved the intended learning outcomes of the session. POMPs were used for 16 sessions and ROMPs were used for a subset of 11 of these sessions. Examples of responses from the POMPs and ROMPs were presented to describe and analyse the effectiveness of this approach for library support of research.
 
 Results – POMP and ROMP responses were remarkably informative given their simplicity and the little effort required on the part of the instructing librarian or researchers. The completion rate of POMPs was 72.7%. They gave researchers the opportunity to self-assess their current level of knowledge or skills about the topic to be covered in the upcoming session. The librarian could then tailor the session content to this level of knowledge. POMP responses were shared as part of the session content, enabling researchers to benchmark themselves against their peers. Completion rate of ROMPs was 20.9%, with the level of reflection in the individual researchers’ responses varying from shallow to insightful. Deeper responses stated how the researchers would use what they learned or pose new questions which emerged from their learning.
 
 Conclusion – Polling One Minute Papers (POMPs) and Reflective One Minute Papers (ROMPs) are an effective and efficient approach for guiding the learning of researchers and closing the feedback loop for librarians. These tools extend the opportunity for librarians to engage with researchers and, through tailoring of session content, assist to maximise the benefit of library research support sessions for both librarians and researchers. Sharing of POMP and ROMP responses can assist librarians to coordinate the teaching of the researchers that they support. At an institutional level, evidence in POMPs and ROMPs can be used to demonstrate the value that the library has contributed to improving awareness and performance of its researchers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.554 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".