DO Get Technical! Using Technology in Library Instruction
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.030 |
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".