Orienting Student Using a Case-Based Instructional Approach: A Case Study
Notice bibliographique
Résumé
Purpose: Orientation facilitates the transition to a new academic program to meet the need of new students to know programmatic items such as academic expectations, courses, policies and procedures. We used a problem based learning (PBL) approach to introduce our students to the expectations of our intensive 10-week Medical College Admission Test (MCAT) Preparation Program. Method: There were 17 participants in this study. A PBL case about five disadvantaged and underrepresented students preparing to retake the MCAT was used to deepen understanding of the medical school application process, the structure of the MCAT to motivate and help participants develop appropriate strategies for preparing and taking the exam. The participants were divided into 3 groups that worked through the 3-session case during a one-week orientation. After the orientation, a survey instrument consisting of ten 5-point Likert items and eight openended items was administered to the participants. Student comments regarding the case, faculty observation of process and group dynamics were also collected. Results: Overall class mean of 4.50 on a 5-point Likert Scale and the overwhelmingly positive student comments indicate that participants perceived the PBL experience to be highly valuable. The faculty observed an increased confidence and enthusiasm in the students as they progressed through the PBL case. Conclusion: A group of disadvantaged and underrepresented premedical students found that confronting a PBL case for preparing this group for the MCAT was valuable. It is likely that PBL could be adapted and used to promote the preparation for other standardized tests, and for other populations. ********** Orientation is a critical time for students in transition (Knott, 1978). Formal orientation sessions employ a variety of instructional approaches to acquaint students with the expectations of an academic program (Howe & Perry; Smith & Zimmer, 1978). This paper describes the use of problem-based learning as an orientation tool in a program designed to help students from disadvantaged and underrepresented groups prepare to retake the Medical College Admission Test (MCAT). The MCAT is a major hurdle for students seeking admission to medical school. In common with most standardized admissions tests, MCAT scores disproportionately screen out applicants from disadvantaged and underrepresented groups, relative to their demographic importance. It is now generally recognized that, increasing the diversity of the physician workforce will increase the numbers of physicians interested in working in medically underserved communities, with corresponding benefits to public health (Nickens, Ready & Petersdorf, 1994; Institute of Medicine, 2002). Selectively helping to boost MCAT performance is a strategy to strengthen admission credentials of students from disadvantaged and underrepresented backgrounds. Premedical postbaccalaureate programs have addressed this issue, with varying degrees of success, and have served to expand the pool of successful medical school applicants (Whitten, 1999). Since 1995, Indiana University School of Medicine has operated an internally funded premedical postbaccalaureate program which enrolls applicants from disadvantaged and underrepresented backgrounds. Since inception, our program has focused on cooperative learning strategies within small groups following the example of successful academic programs for students from underrepresented backgrounds Fullilove and Treisman (1990). The educational goals are to encourage students to become self-directed learners, confident in their abilities to analyze problems and find information. These are the same goals that are embodied in our MCAT preparation program. PBL in medical eduation originated at the McMaster University School of Medicine in Hamilton, Ontario with the goal of supporting adult and women learners through a rigorous medical education process (Barrows, 1983). …
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 | 0,007 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 ».