Notice bibliographique
Résumé
Editor's Note Elizabeth A. Jones According to Banta, Jones, and Black (2009), outcomes assessment can be sustained over time if there is sufficient planning and implementation that occurs in a culture that values data or information and uses that evidence to make crucial decisions. The authors in this issue of JGE: The Journal of General Education carefully analyze and report key assessments that they have completed in specific types of courses. In the first article, Marie-France Orillion investigates the relationship between interdisciplinary curriculum and the outcomes that students achieve. She conducted a formal study and analyzed student work including take-home essay exams at the middle and end of the quarter as well as online discussions. The results from these assessments revealed some issues that faculty decided should be addressed within the curriculum. This article is instructive because it shows an example of assessments that can be used to make informed changes. Assessing what students learn in the major is very important. Neva Sanders-Dewey and Stephanie Zaleski conducted an assessment study to determine if students who take psychology courses gain significant knowledge as gleaned through pre- and posttests. They found that the number of psychology courses a student completes and grade point averages were significant predictors of factual knowledge demonstrated on the posttest. This type of study indicates a strategy for assessing student learning over time by using locally developed tests to identify significant gains in critical knowledge areas. It is sometimes challenging to assess student participation in classroom discussions. Kristine Bruss designed a project to create and communicate criteria to assess students as well as provide opportunities for guided practice and feedback. A humanities team developed a rubric that identified key areas of student performance important for effective discussion participation and then described student achievement at various levels that aligned with the key performance areas. The faculty members through these assessments discovered [End Page vii] insightful information about student performance particularly regarding students' strengths and limitations. Finally, in "Introducing a Culture of Civility in First-Year College Classes," Robert Connelly reviews key definitions and descriptions of civility. He then builds a case for educating students about civility in the first-year classroom. He proposes a model where students learn what is acceptable behavior and the norms and values that become part of the content that is facilitated by instructors. I hope that you find these articles to be useful and meaningful as instructors and consider implementing or revising assessments and using the results to identify key changes. As you read these articles, you may consider sharing some of your own work with JGE. References Banta, T. W., Jones, E. A., & Black, K. (2009). Designing effective assessment: Principles and profiles of good practice. San Francisco: Jossey-Bass. [End Page viii] Google Scholar Copyright © 2009 The Pennsylvania State University
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 | 0,004 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,005 | 0,002 |
| Intégrité de la recherche | 0,013 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,069 | 0,041 |
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 ».