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Record W2973573962 · doi:10.18438/eblip29587

Libraries May Teach Some Skills through Mobile Application Games

2019· article· en· W2973573962 on OpenAlexvenueno aff
R Eric Miller

Bibliographic record

VenueEvidence Based Library and Information Practice · 2019
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningTest (biology)AdventureComputer scienceTask (project management)Mathematics educationMultimediaControl (management)PsychologyArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.500
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.006
GPT teacher head0.253
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreMethods

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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