Abstract from the International Medical Education Conference 2007 (OS)
Notice bibliographique
Résumé
Introduction: Students approach their learning in at least two qualitatively different ways.In the surface approach, students see tasks as being imposed, for which they develop coping strategies focused on reproduction of essentials and memorizing information for assessment rather than for understanding.Surface learning is the tacit acceptance of information and memorization as isolated and unlinked facts.It leads to superficial retention of material for examinations and does not promote understanding or long-term retention of knowledge and information ( Evans et al., 2003).In the deep approach, students seek to understand ideas to allow them to relate and integrate knowledge from other parts of their study and thereby develop conceptual frameworks from which they can derive solutions to novel problems.Deep learning involves the critical analysis of new ideas, linking them to already known concepts and principles, and leads to understanding and long-term retention of concepts so that they can be used for problem solving in unfamiliar contexts.Deep learning promotes understanding and application for life (Gordon et al., 2002).Objectives: To assess the learning styles of medical students using the Biggs questionnaire.To assess the preferred teaching methods adopted by medical students in AIMST. Materials and Methods:Study design was cross-sectional study of medical students and dental students in AIMST.Setting: AIMST Medical school, Sungai Petani, Kedah.Participants: A total of 463 students (417 Medical students and 110 Dental students) participated in the study.Main outcome measures: Learning approach (surface and deep learning style), preferred study habits, academic achievement.M e t h o d s : A 20-item in Biggs's Revised Study Process Questionnaire (R-SPQ-2F) was employed to measure the students' learning methods/approaches (Kember et al., 2004).The questionnaire was also used to examine the preferred method of teaching (Kember et al., 2001).The students were asked to choose whether they preferred PBL or Lecture.Next they were asked to choose whether they preferred learning through simulation teaching in clinical skill lab or clinical bedside teaching in the hospital.The reasons why they liked or disliked a preferred method of teaching were elicited.The SPM and STPM grades of the students were also collected to be used as an indicator of achievement.Statistical Analysis: Descriptive analysis of the data was done using SPSS 13.0.Karl's Pearson Correlation was used to look for a relation between academic achievement and type of learner.Also it was used to look a correlation between method of teaching (Lectures, PBL, Simulator and Clinical Bedside Teaching) and type of learner (Superficial and deep approach).Results: 52.7% of dental and medical students liked lectures.47.1% liked the PBL sessions while 0.2% liked both equally.56.4% liked clinical bed side teaching, 41.7% liked simulator teaching in clinical skill lab while 1.9% liked both equally.Karl's Pearson Correlation revealed a significant positive correlation between high academic achievement and deep approach learners and a positive correlation between low academic achievement and surface learners.Karl's Pearson Correlation revealed a significant correlation between deep approach learning and PBL; surface approach learning and lectures; deep approach learning and clinical bed side teaching; Simulator teaching showed a negative correlation with deep learners and no correlation with superficial learners.The main reasons for students liking lecture method was that all topics were covered and for liking PBL was that it was interesting and more participation was possible as smaller groups were involved.The clinical bed side teaching was preferred as patients were real; those who preferred simulators said that practicing in a dummy was easier.Discussion: As the coverage of topics important for the exams were more extensive, a majority of the students preferred lectures to PBL.But the deep approach learners liked the PBL sessions as they were able to gain more knowledge through self directed learning as they faced new problems.Thus PBL suited students who have self discipline to take active responsibility for their own learning.Similarly clinical bed side teaching was preferred as it gave them real life experience with the patients.Some students agreed that simulator and bed side teaching were complementary.Deep approach learners were convinced that PBL and clinical bed side teaching helped them in building up communication skills, better participation, more involvement, interpersonal relationship and problem solving capacity. Conclusions:Deep approach learners supported problembased learning (PBL) and clinical bed side teaching as an effective method of learning and superficial learners supported lectures.The findings suggest that students with deep learning motives and approaches reap the most benefit from PBL and clinical bed side teaching.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,005 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,339 | 0,107 |
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 ».