Gamification of learning in an introductory cell biology class
Notice bibliographique
Résumé
Students taking introductory cell biology need to master a large amount of new vocabulary and course content before they can achieve a deeper understanding of fundamental concepts. Games are notoriously good at engaging their target audience and keeping players on task, and if carefully designed, games have the potential to be tremendous pedagogical tools. In order to facilitate learning for our students, we are developing an online game based on cognitive science, which is designed to encourage students to engage with the class material. A pilot trivia game ( https://www.biolingo.ca/ ) designed to test interest in such a game in our students was released in March 2017, with a bank of over 250 questions covering the class content (Bloom levels 1 and 2), which students were at liberty to use as a study tool. Students were able to choose between subject categories and a 15 questions quiz would be randomly generated from the question bank. Students were given access to the game 3 weeks before the final exam, playing time was not limited and quizzes were completed on a voluntary basis. On the final exam, students were asked to self‐report how much time they spent on the game and all 491 students answered. Students were grouped according to two categories: their use of the game (>3h were considered high users, 1–3h mid‐users, <1h low users and 0h are non users) and their exam result (A>80%, B = 70–79%, C= 60–69%, D= 50–59%, E= 40–49%, F<40%). The majority of students (67.3% of all A students, 54.2% of all B students, 54% of all C students, 58.2% of all D students, 56,8% of all E students and 33.3% of F students) accessed the game at least once. Interestingly, the proportion of mid‐users and high‐users was the greatest in F students (23.3% and 13.3% of all F students respectively) and comparatively low in A students (10% and 0.3% respectively). Clearly, students were curious about the online game and were eager to try it. It is also interesting to see that struggling students seemed to be eager to use a tools that could help them practice and improve their learning. Further studies using a modified version of the pilot game, designed to stimulate distributed learning and repeat testing throughout the term, as well as tying the students' game performance to their course mark, will help us research the potential for increased learning through the carefully designed gamification of content. Support or Funding Information Teaching and learning support services (TLSS) University of Ottawa and eCampusOntario 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.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».