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Increasing Student Engagement with the Use of Competition in a Large Human Physiology Course

2018· article· en· W3176903333 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsWestern University
Fundersnot available
KeywordsSet (abstract data type)Task (project management)Mathematics educationControl (management)PsychologyPoint (geometry)Course (navigation)Medical educationComputer scienceMedicineMathematicsArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

We know students tend to participate and engage more with the material we teach when grades are assigned. However, we wanted to determine if using a game to increase student motivation without assigning grades was effective in our large introductory human physiology course. One component of the lecture course are mandatory tutorials. Our seven tutorial groups were made into teams and these teams were awarded points throughout the year based on scoring parameters set out by the game. Two years of the course served as a control with the same assignments and evaluations, and the alternating subsequent years served as the experimental condition where the game was incorporated. It was hypothesized that the use of a game would increase time on task and student engagement. We predicted that in a completion assignment, students would do more productive work if there was a point based game that did not provide them with extra marks. For each year of the study, there were 4 assignments assigned using the website PeerWise. Here, students derived an online repository of multiple choice questions. For each assignment, they created and posted 2 multiple choice questions. To complete their assignment, they then had to answer 5 questions and provide feedback on those questions authored by their peers. For completion of all components of the assignment on time, they were awarded 1% for their final grade, for a total of 4% for these assignments. Overall, students had to write 8 questions, then answer and comment on 20 questions to earn full grades for the assignments. Within the site, students also earned badges for certain types of performances. For example, badges were awarded for authoring a question, or for answering 10 questions in a row correctly. These were not recognized for the completion grade, but were used as a component of the scoring point system in the second experimental year. In control year 1 (N=518) and control year 2 (N=566), students posted the same total number of questions, with a mean of 7.6 for the year, which was below the threshold for full marks. However, in each year of the competition (N = 519 and N= 567), the total number of questions posted by students was significantly higher with a mean of 8.2 and 8.4, p < 0.05 (one‐way ANOVA, Tukey's post‐hoc). Interestingly, students in all years answered more questions than required by the assignments, but was significantly higher in both competition years (75.99 questions) compared to the years when there was no competition (58.45 questions), p <0.05 (unpaired t‐test). When badges were recognized for points, students earned significantly more badges on PeerWise compared to the control year of study, p <0.05 (unpaired t‐test) with a mean of 20.1 in the control year and 31.2 in the competition. Another interesting finding was that students had better attendance in tutorial for the years of the course when we incorporated a competition. These data suggest that students may have developed camaraderie while earning points together with the goal of winning a year‐end tutorial pizza party. Our data suggest that students can be encouraged to do more productive work in a course when they are playing a game as a part of their course work. We hope to further this analysis to determine if other aspects of student's work were improved due to competition, including quality of questions that they created and overall performance in the course. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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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)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.112
GPT teacher head0.419
Teacher spread0.307 · 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