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Enregistrement W2992653110

Educating All for the Betterment of Agriculture

2015· article· en· W2992653110 sur OpenAlexaboutno aff
Sherry A. Heishman

Notice bibliographique

Revue˜The œAgricultural education magazine · 2015
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueDiverse Educational Innovations Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMindsetAgricultural educationQuarter (Canadian coin)PsychologyMeaning (existential)SociologyPedagogyMathematics educationManagementAgricultureHistory
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Editor's Note: Ms. Heishman asked for a list of questions to guide the development of her article. I included the questions to provide flow for the article.1. Briefly describe the teaching environment when started. How has that changed over the past 30 years?I began teaching in 1985. Although there were women teaching before me, there were none in my county. Agricultural educators were overwhelmingly a male majority. Many of them were much older than me having taught 20 years or more, living in the old world that women were not necessarily ag teachers. Although that was the mindset I never met one that was not supportive and willing to help the rookie. That mindset of old was not only among the male teachers but also the principals and superintendents. I interviewed for a position in a rival school in my home county during the time I was student teaching. Virginia Tech was on the quarter system and I would finish my student teaching in March and could start the position at that time. There were not many openings for ag teachers during that time close to home. Ag teachers stayed in the career for life. The second question that came from the school principal during that interview was Do think can handle a man's job? However true and honest in the meaning that question may have been; my response for the time was you will have to hire me now and find out. The superintendent tried to smooth over the question but had no choice but to say we will give a try. That principal told me before he retired from the high school three years later: bad question I have ever had the opportunity to ask. That question provided the push and drive to prove I could do the job.As young teenager I learned to artificially inseminate cows at the age of 15. Again that was a time when many farmers were not that comfortable with a teenage, let alone a girl breeding their cows. Again this made me want to do the best job possible and earn my spot.2. Relate some success stories - either personal and/or students.I have been very lucky to have (as other have too) many wonderful individuals I have watched mature into hard working and successful adults. We all know the three circle model and how important each area is to our program but more importantly to the development of the student.With the push for standardized tests, there is a need for data and to explain how it measures what my students learn. I was told that the knowledge of the student was always reflected by data. I had a conversation with my principals and asked them to explain how to put this success in terms of data.We worked with local food pantry to cut up deer as a part of the Hunters for Hungry program. I had a student who during her freshmen year would not enter the shop, her 10th grade year she peeked out the shop door, the junior year she cut up the deer, and her senior year she actually went through the whole process including skinning the deer.She gained skills that will be with her for life! There was no standardized test to measure what she had learned; so I she really didn't gain anything. The principal looked at me and said; guess got me on that one.One of the joys of teaching is watching a student that is quiet and withdrawn in other classes come alive when making a presentation or talking with a group of younger students about agriculture. Individual student's successes might not seem huge to many but if they learn to be useful citizens then we have helped a little.My FFA officer team was asked to facilitate an opening ice breaker and team building activity for the faculty at the beginning of the school year. The students were great kids but many shy and soft spoken. They developed an entire STEM lesson with a hook, outlined the day, planned an activity, developed a rubric for grading and conducted a debriefing of what the activity should have brought forth in the teachers minds. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,013
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,034
Score d'incertitude au seuil0,115

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,013
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0050,003
Science ouverte0,0020,001
Intégrité de la recherche0,0080,009
Charge utile insuffisante (le modèle a refusé de juger)0,0340,020

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.

Tête enseignante Opus0,058
Tête enseignante GPT0,286
Écart entre enseignants0,228 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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