Notice bibliographique
Résumé
As we wrap up summer, this issue of JCBE spans the globe and the process of CBE. The first two articles highlight factors in and indicators of student success. What a great way to welcome the fall issue with a focus on student success! In their manuscript entitled Factors that Contribute to Student Success and Satisfaction at Brandman University, Dr. Gilmer-Echols and her colleagues identify factors that are most influential in student success for Brandman's model of Competency-Based Education (CBE). Defining success as program longevity, the authors explored the results of student satisfaction surveys of various student services, such as academic coaching and the writing/math center. The discussion section offers a number of opportunities for further study that may be of interest to CBE researchers. In their article, Learning and Individual Differences in Skilled Competency-Based Performance: Using a Course Planning and Learning Tool as an Indicator for Student Success, Dr. Sean Gyll and his colleague at Western Governors University used a course planning and learning tool (CPLT) to identify factors in student success, such as the use of resources or the summative assessment. They defined student success as measured by planning and learning (using the course planning tool), knowledge, and confidence/experience (using a course preassessment). Implications for supporting student success include the importance of assessing the learner profile by using the CPLT. The third article, The Teacher Education Curriculum and its Competency-Based Education Attributes, comes to JCBE from Tanzania where Dr. Tarmo and his colleague at the University of Dar Es Salaam provide an analysis of the Diploma of Secondary Education to establish its integration of CBE attributes. Using a qualitative content analysis of documents and arguing that implementation of the CBE approach now mandated in K-12 schools is dependent upon teacher preparation, the author(s) found contradictory attributes in the Diploma that constrain CBE implementation in K-12 classrooms. The fourth article opens up the conversation about how to support widespread adoption of CBE, Implementing Competency-Based Education in Multiple Programs: A Workshop to Structure and Monitor Programs' Priorities Using ADDIE. Dr. LaFleur and his colleagues from Laval University Faculty of Medicine in Canada report on their support of the transition of 29 medical residency programs to CBE. They describe nine workshops they offered and how the ADDIE instructional model was used to assess and monitor levels of CBE implementation. Implications of their work extend to training faculty for CBE adoption. The fifth and final manuscript explores a new approach to developing CBE models, Developing competency frameworks using natural language processing: An exploratory study. Dr. Garman from Rush University and colleagues from Oak Park, Illinois, as well as Idaho State and DePaul universities, generate a set of leadership competency domains using a language processing approach called Latent Dirichlet Allocation (LDA). The LDA-generated competency domains are compared to a set of human-generated domains and assessed for coherence. The findings are intriguing and suggest opportunities for refinement of an LDA approach in establishing and evaluating domains of competency.
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 distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».