Encouraging students' lifelong learning through graded information literacy assignments
Bibliographic record
Abstract
Purpose The paper seeks to argue that one of the ways librarians and library information literacy sessions can have a positive impact on students’ lifelong learning is to create and mark assignments. Design/methodology/approach If library information literacy sessions are to have a positive impact on students' lifelong learning, it is necessary to clearly define and delineate the term “lifelong learning” into its three components of cognition, behavior and information seeking skills. The three components are not linear, but intertwine. Multiple information literacy sessions must cognitively engage students to realize they have a learning need. Findings Information literacy instruction librarians are often overwhelmed with requests for 50‐minute one‐shot library classes which have questionable results in regards to student learning. Instead of having a marginal impact on thousands of students per year, information literacy librarians should use their time and resources by creating graded assignments with multiple IL classes and consider abandoning the 50‐minute one‐shot sessions. However, multiple IL sessions and marking assignments will take time. Originality/value By creating graded assignments, information literacy instruction librarians would have a close collaborative relationship with classroom faculty to reach perhaps fewer students but have a greater impact on students' information literacy and lifelong learning.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".