Provocation to Learn - A Study in the Use of Personal Response Systems in Information Literacy Instruction
Bibliographic record
Abstract
The appearance of Personal Response Systems (PRS) or “clickers” in university classrooms has opened an avenue for new forms of communication between instructors and students in large-enrolment classes. Because it allows instructors to pose questions and receive tabulated responses from students in real-time, proponents of this technology herald it as an innovative means for encouraging higher levels of participation, fostering student engagement, and streamlining the assessment process. Having already been experimentally deployed across disciplines ranging from business to the arts and sciences, it is also beginning to be used in the context of information literacy instruction. In this project we employed the technology not to transfer actual skills, but to advertise the existence of online library guides, promote the use of the library within the context of the course itself, and “provoke” students to adopt a more active approach to research as a recursive process. Our findings suggest that students adapt easily to the use of this technology and feel democratically empowered to respond to their instructors in a variety of ways that include anonymous clicker responses as well as more traditional means such as the raising of hands and posing questions verbally. The particular value of this study was to show that these broader findings seem equally applicable to pedagogical settings in which learning objectives are built around and integrated with the principles of information literacy.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".