Bibliographic record
Abstract
A Review of: Kaneko, K., Saito, Y., Nohara, Y., Kudo, E., & Yamada, M. (2018). Does physical activity enhance learning performance? Learning effectiveness of game-based experiential learning for university library instruction. Journal of Academic Librarianship, 44(5), 569-581. https://doi.org/10.1016/j.acalib.2018.06.002 Abstract Objective – To understand the impact of a mobile application game for library knowledge acquisition, task performance, and the process of learning. Design – The main experiment included a pretest, learning experience, post-test, and a questionnaire. One month later, a post-experiment was conducted, including a test of “declarative knowledge” and a behavioural test. Setting – Kyushu University in Fukuoka, Japan Subjects – 36 first-year undergraduate students, of which 25 were female and 11 were male. Students were divided into experimental and control groups. 32 students completed the study. Methods – In the main experiment, students responded to the same 20 question pre-test on library use, and then both groups participated in learning experiences designed to convey knowledge about using the library. The control group’s learning setting was a web-based tutorial about the library. The experimental group’s learning setting was “Library Adventures: Unveil the Hidden Mysteries!” a “game-based learning environment” developed by the researchers (Kaneko, Saito, Nohara, Kudo, & Yamada, 2015, p. 404), which required students to complete activities by physically moving through the library. For both groups, learning content related to local library procedures, like hours, arrangement of collections, and methods for locating books and articles. The game collected data that the authors analyzed using statistical methods in an attempt to validate quizzes that were embedded in the game. After finishing the learning experience, all students completed the 20-question post-test, and then responded to the Instructional Materials Motivation Survey (IMMS), a questionnaire designed to gauge learning motivation using the Attention, Relevance, Confidence, and Satisfaction (ARCS) model. One month following the main experiment, all students took a test of declarative knowledge and completed a skills test. Main Results – Experimental and control group students gained about the same level of declarative knowledge. All students lost some knowledge in the one-month gap between the main and post-experiment. Students who had learned through Library Adventure were able to borrow a journal and locate a newspaper article more effectively than the control group. In contrast, tutorial users made study room reservations more quickly than the experimental group. More significantly, the IMMS instrument demonstrated that game-based learners scored higher in attention, relevance, and satisfaction than tutorial-based learners. Experimental and control group participants demonstrated the same level of confidence. Conclusion – While inconclusive about the effectiveness of games versus tutorials for acquisition and retention of knowledge, the authors concluded that game-based instructional content may foster greater learner engagement, aiding some students in understanding how to use the library in a manner superior to web-based tutorials. Librarians and instructional designers developing game-based learning experiences for novice library users may find this research informative.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.017 |
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".