Far from a Trivial Pursuit: Assessing the Effectiveness of Games in Information Literacy Instruction
Bibliographic record
Abstract
Abstract Objective – To determine whether playing library-related online games during information literacy instruction sessions improves student performance on questionnaires pertaining to selected research practices: identifying citation types and keyword and synonym development. Methods – 86 students in seven introductory English composition classes at a large urban university in the northeastern United States served as participants. Each class visited the library for library instruction twice during a given semester. In the experimental group students received information literacy instruction that incorporated two online games, and the control group received the same lesson plan with the exception of a lecture in place of playing games. A six-item pre- and posttest questionnaire was developed and administered at the outset and conclusion of the two-session classes. The 172 individual tests were coded, graded, and analyzed using SPSS. Results – A paired sample t-test comparing the control and experimental groups determined that that there was a statistically significant difference between scores on pre-tests and post-tests in the experimental group but not the control group. Conclusion – Students who played the online games improved significantly more from pre-test to post-test than students who received a lecture in lieu of playing online games, suggesting that participating in games related to the instruction they received resulted in an improved ability to select appropriate keywords and ascertain citation formats. These findings contribute to the evidence that online games concerning two frequently challenging research practices can be successfully applied to library instruction sessions to improve student comprehension of such skills.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".