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Enregistrement W599060947

PROBLEM-BASED LEARNING IN A LARGE CLASSROOM SETTING: METHODOLOGY, STUDENT PERCEPTION AND PROBLEM-SOLVING SKILLS

2011· article· en· W599060947 sur OpenAlex

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affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevueEDULEARN proceedings · 2011
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueProblem and Project Based Learning
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésProblem-based learningFacilitatorLifelong learningMathematics educationCurriculumActive learning (machine learning)Experiential learningProcess (computing)Small group learningPerceptionPsychologyComputer sciencePedagogyMedical educationMedicineArtificial intelligence
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Problem-based learning (PBL) can be described as a learning environment where the problem drives the learning. Students are given a problem that is posed such that they realize the need to gain up to date, evidence-based knowledge before they can solve the problem. This drives the students to investigate and discuss identified learning issues in groups with the instructor as facilitator and coach. The following immediate benefits to students have been identified: increased retention of information; an integrated (rather than discipline-bound) knowledge base; development of lifelong learning skills; exposure to real-life experience at an earlier stage in the curriculum; increased student-faculty liaison; and an increase in overall motivation (Greening, 1998). These advantages of PBL could stem from the fact that this process is based on several modern insights on learning, including constructive, selfdirected, collaborative and contextual learning. It will be demonstrated how a PBL approach has been used in the University of British Columbia Okanagan 3rd and 4th year undergraduate biology and biochemistry classes of 50 85 students, although this instructional methodology is not limited to life sciences and can be used in other disciplines. Problems are presented and solved through group discussion and independent study without the need for additional tutors. This technique was introduced to enhance the learning experience and effectiveness by supplementing standard lecture material with a novel, interactive course delivery technique. It is becoming evident that PBL in a small group setting has a robust positive effect on student learning and skills. PBL studies develop student research and independent problem-solving skills. They also challenge students, show them the relevance of the material they are studying, and emphasize the benefits and importance of teamwork and effective communication. However, very little research has been done on the educational benefits of PBL in a large classroom setting. Furthermore, several studies have suggested that PBL may not be superior to conventional educational approaches in all aspects of learning. Therefore, it cannot be assumed that introducing the PBL technique to a large undergraduate class setting will lead to enhanced student learning as well as satisfaction. The superiority, or at least the non-inferiority, of PBL over the standard course delivery techniques must be proven for each individual PBL delivery method. We are therefore exploring various approaches that could be used to compare student learning during PBL exercises and standard didactic lectures, and to assess student perception of this process. We have performed a study that shows that student problem-solving skills are improved after they are exposed to PBL exercises in a large classroom setting. By using student surveys and other techniques, we have also identified a number of parameters that show increased student engagement and satisfaction during the PBL exercises compared to standard didactic lectures. Future studies aimed at assessing student learning during the large class PBL exercises will also be discussed. This research is needed to justify further implementation of PBL techniques in courses that are delivered to large undergraduate classes.

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)0,009
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,143
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0090,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,038
Tête enseignante GPT0,329
Écart entre enseignants0,292 · 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