Delivering Information Literacy via Facebook: Here Comes the Spinach!
Bibliographic record
Abstract
Abstract Objective – Information literacy (IL) skills are critical to undergraduate student success and yet not all students receive equal amounts of curriculum-integrated IL instruction. This study investigated whether Facebook could be employed by libraries as an additional method of delivering IL content to students. To test whether students would engage with IL content provided via a library Facebook page, this study compared the engagement (measured by Facebook’s reach and engagement metrics) with IL content to the library’s normal marketing content. Methods – We ran a two-part intervention using the University of Canterbury Library’s Facebook page. We created content to help students find, interpret, and reference resources, and measured their reception using Facebook’s metrics. Our first intervention focused on specific courses and mentioned courses by name through hashtagging, while our second intervention targeted peak assessment times during the semester. Statistics on each post’s reach and engagement were collected from Facebook’s analytics. Results – Students chose to engage with posts on the library Facebook page that contain IL content more than the normal library marketing-related content. Including course-specific identifiers (hashtags) and tagging student clubs and societies in the post further increased engagement. Reach was increased when student clubs and societies shared our content with their followers. Conclusion – This intervention found that students engaged more with IL content than with general library posts on Facebook. Course-targeted interventions were more successful in engaging students than generic IL content, with timeliness, specificity, and community being important factors in building student engagement. This demonstrates that academic libraries can use Facebook for more than just promotional purposes and offers a potential new channel for delivering IL content.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".