Teaching and assessing students of information literacy in a single session—The case of the University of the West Indies Mona library
Bibliographic record
Abstract
Assessing the performance of information literacy (IL) students can be a daunting task for librarians globally. Most IL sessions are taught in 1 to 2 hours where any meaningful assessments are difficult to achieve. This research demonstrated how this feat was achieved in an active learning environment through the use of Google Forms. This mixed method study shows how this was effectively achieved to test both lower and higher order skills in a 2 hour session to one hundred and seventy-two foundation writing course students.The research tested a rarely examined feature of Google Forms which is the tool’s effectiveness in enabling comprehensive assessment, facilitating active learning, and identifying instructional errors in an IL instruction session. The findings show that Google Forms can be used to teach and administer a quiz using both multiple-choice as well as open-ended questions to assess both low and higher order learning skills in IL. Students were able to actively respond to questions while they were being taught, the data gathered and analyzed and used to inform future library instruction. It also showed that Google Forms are useful not simply to administer multiple-choice quizzes at the end of teaching but can be used in executing real-time assessment and support active learning. Because Google Forms support the easy creation of charts and downloading/exporting of statistics, results of assessments can be shared among librarians, faculty, and students to motivate and encourage digital pedagogy. It allows for greater collaboration with faculty in the cooperative teaching of students in single sessions where there is usually difficulty in having dialogue with faculty once a session ends. This case study is based on a limited number of students; thus, the findings of this research may not be generalized but the methodology and some skills in teaching the concepts encountered by librarians may be replicated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".