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

2018· article· en· W3176903333 sur OpenAlex

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Notice bibliographique

RevueThe FASEB Journal · 2018
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueInnovative Teaching Methods
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésSet (abstract data type)Task (project management)Mathematics educationControl (management)PsychologyPoint (geometry)Course (navigation)Medical educationComputer scienceMedicineMathematicsArtificial intelligenceEngineering

Résumé

récupéré en direct d'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 .

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,011
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,800
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0110,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,112
Tête enseignante GPT0,419
Écart entre enseignants0,307 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle