DO Get Technical! Using Technology in Library Instruction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Today’s post-secondary students are digital natives. Much has been said and written about how to reach this generation, and the consensus seems to be that we need to meet them on their turf. In this session presented at WILU 2011 in Regina, SK, two librarians from the University of Lethbridge shared their experiences with using technology to engage students in library instruction. The hands-on session introduced some simple tools librarians can learn quickly and apply to spice up their instruction with technology. These include creating online animated videos using Xtranormal, a low-cost tool way to create polished and humourous videos to introduce or summarize key information literacy concepts; and adding interactive polling to PowerPoint presentations using a tool called Poll Everywhere, which is an effective way to instantly engage students in instruction using the web or web-enabled devices. Interactive polling eliminates many of the challenges of using clickers which are prevalent in many post-secondary library instruction environments. The presenters also discussed how they have experimented with wikis to encourage active learning and student collaboration in a series of library instruction sessions. Wikis allow for free and paperless student participation in knowledge creation in an online forum. Finally, they demonstrated how they have used Skype to deliver library instruction at a distance, including the use of the screen sharing feature. The presenters stressed the ease of use of these free or low-cost tools to improve classroom engagement and add interest to sessions.
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.
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 | 0.001 | 0.000 |
| 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 | 0.084 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it