Academic Excellent Educators: A Student Election or an Administrator Selection?
Notice bibliographique
Résumé
Dear Editor in chief Each year, Iranian university authorities, as a good tradition on Teacher’s Day, choose and introduce their top educators to their academic community. Undoubtedly, this effort is a good action, generally leading to effective promotion of educational system. However, criteria for these selections are always a matter of big debate. In overview, an academic excellent educator is supposed to be at a superior level of educational competency in comparison with his/her colleagues. University administrators and students are two individual references may rate to criteria. However, many surveys have revealed that students distinctively highlight different criteria for teacher excellence (1, 2). From academic authorities’ perspective, faculty members must be excelled in a combination of abilities and activities in three fields: Ability of teaching, quantity of teaching, and scholar engagement (3). In contrast, students commonly believe that an excellent educator is distinct from his/her colleagues by the quality of his/her course delivery and type of his/her educational interactions with learners. (4). In essence, from learners’ point of view, quantity of teaching is not generally a privilege for a teacher. Moreover, learners are not concerned about scholarship engagement of their educators. Consequently, neither quantity of teaching activities, nor scholar engagement of educators does affect on learners’ assessment of their educational competency. What learners notice sharply and consider it well in their teacher evaluation is quality of teaching and type of educational communication, behavior and characteristics of an educator. Despite some disagreement, many educational researchers believe that learner assessment provides a legitimate measurement tool with multi criteria to rate excellent educators. The same researchers have also shown that professional ability of faculty members in course delivery, their personality and type of their communications with learners both inside and outside classrooms are of utmost plausible criteria in learner assessments (3-7). In a student-centered academic system and especially in terms of customer approval, student satisfaction is one of major evidence of a successful teaching. If learners accept their teacher professionally and emotionally, then they are ready to hear, understand, communicate with, and especially learn from him/her. We all have heard this Persian old statement: “If an educator teaches with passion and love, then he/ she can make an uninterested learner to take part in classroom even on weekends willingly”. The above statement clearly indicates that from old days, learner’s viewpoint has had a key value in enhancement of educational communication of learners as well as their enthusiasm to learn. Therefore, it seems that in a student-centered educational system, learner assessment make significant contribution to rate educators and allocates their level of excellence. The attitude of students towards teachers is highly important in American and Canadian universities. There are many local and national websites where students can vote for choosing best academic teachers. For example, site www.ratemyprofessors.com works daily to record university students’ assessments for more than 3.1 million educators. In some modern universities, learner assessments weigh up to 30-40% of a comprehensive assessment (360-Degree Assessment) aiming educator excellence (3، 7 (. This means that less weight of learner assessment, more lose of teaching quality - a criterion considered as focal point for annual teaching excellence and educator promotion. In addition, in a 360-Degree Assessment of educators, any decline weight of learner assessment, leads to wider gap between students’ selections and university administrators’ elections. This may consequently distrust students to academic educational evaluation system.
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 | 0,006 | 0,007 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 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 ».