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Games as active learning strategies: a faculty development workshop

2006· article· en· W2028700992 on OpenAlexaffabout
Kalyani Premkumar, Deirdre Bonnycastle

Bibliographic record

VenueMedical Education · 2006
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsInteractivityContext (archaeology)Variety (cybernetics)Medical educationActive learning (machine learning)TeamworkReading (process)Computer sciencePsychologyMultimediaMathematics educationMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Context and setting‘Games as Active Learning Strategies’ was 1 of 7 active learning workshops offered to faculty at the College of Medicine, University of Saskatchewan, Saskatoon, Canada. The workshop was interactive and conducted in a computer laboratory. Why the idea was necessary There is evidence to show that games, defined by some as ‘fun with a purpose’, foster active learning, allow for interactivity, promote collaboration, peer-learning and teamwork, and increase motivation. Despite their potential to enhance learning, there is very little use of games in the teaching and learning of medicine. We therefore designed and developed a workshop that would enable participants to create a game using self- generated questions. What was done A literature review was completed to identify: the rationale for using games; different types of games available; and the use and examples of games in medicine. We then designed and developed the 2.5-hour interactive workshop. The objectives were to: discuss the rationale for using games; provide examples of a variety of games used effectively in medical education, and create game(s) using PowerPoint. (PowerPoint was used because most faculty use it in day-to-day teaching and free templates are available.) We were excited by the response to the workshop. Participants included representatives from physical therapy, continuing professional learning, pathology, nursing, family medicine and the Lung Association. Participants were given a pre-reading package with review articles on games and were asked to bring 10 multiple-choice questions. During the workshop, different types of games were viewed and the participants then discussed theory behind their use, how each type can be used in its specific setting and cautions to bear in mind while using the games. Next, following a demonstration on how to create a game using Powerpoint templates, the participants created their own games. Despite varying levels of familiarity with the software, all participants were able to create a game. At the end of the workshop, each participant received a CD containing numerous Powerpoint templates and links to examples and articles describing the use of games. Evaluation of results and impact We administered a workshop evaluation survey consisting of 13 open-ended and 10-point Likert scale questions. All participants strongly agreed (9–10/10) that the workshop was well organised and facilitated, that they learnt a lot from other participants and that their expectations were met. Participants indicated that they planned to use this information in their teaching. On follow-up we learnt that these games were being used in reviewing 2 pathology courses, during a microbiology lecture, and obstetrics course in continuing education, and for learning new information by community-based faculty. There have been requests for more such workshops at the medical college and university teaching and learning centre, a national conference and even an international venue. Whilst we are heartened by the outcome, we are aware and have reiterated to the participants the danger of the medium becoming more memorable than the message. The ultimate worth of using games is in the learning that emanates and the value of the information for practice.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.023
GPT teacher head0.385
Teacher spread0.362 · 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
GenreEmpirical

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".

Quick stats

Citations15
Published2006
Admission routes2
Has abstractyes

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