The document provides Tony Retrosi's rules and advice for coaching bars. It outlines the essential skills and progressions for different age levels and ability levels from pre-team/level 3 up to levels 7-9. The key things needed for bars success are kip, cast handstand, clear hip circle, giant, and flyaway. It emphasizes teaching basics correctly from the start and conditioning to prepare for harder skills. Drills are outlined to build strength, body positions, and individual skills.
This document provides recommendations for sources to find job postings in journalism. It suggests checking the websites of large media organizations like the CBC and Globe and Mail. General job sites like Workopolis, Monster, and Indeed contain a wide range of postings. Journalism-specific sites such as j-source.ca and jeffgaulin.com also list jobs across Canada categorized by region and specialty. LinkedIn collects postings from across the internet and allows searching by profession or country. It also has a student job section for entry-level roles requiring less than six months of experience.
The document provides Tony Retrosi's rules and advice for coaching bars. It outlines the essential skills and progressions for different age levels and ability levels from pre-team/level 3 up to levels 7-9. The key things needed for bars success are kip, cast handstand, clear hip circle, giant, and flyaway. It emphasizes teaching basics correctly from the start and conditioning to prepare for harder skills. Drills are outlined to build strength, body positions, and individual skills.
This document provides recommendations for sources to find job postings in journalism. It suggests checking the websites of large media organizations like the CBC and Globe and Mail. General job sites like Workopolis, Monster, and Indeed contain a wide range of postings. Journalism-specific sites such as j-source.ca and jeffgaulin.com also list jobs across Canada categorized by region and specialty. LinkedIn collects postings from across the internet and allows searching by profession or country. It also has a student job section for entry-level roles requiring less than six months of experience.
This document provides guidance on teaching intermediate Yurchenko vaults. It outlines prerequisites, including roundoff entry vaults and double backs. It lists 10 commandments for teaching vaults, focusing on having a plan, running approach, body positions, and allowing athletes to play. Progressions are outlined from pike to layout vaults. Drills are recommended to work on body shape, power, height, and twisting skills on trampoline and tumble track to prepare athletes for more difficult vaults.
This document provides an outline for a workshop on getting money management skills improved for freelancers. The workshop covers identifying common money pitfalls, establishing personal and business budgets, tracking business income and expenses for tax purposes, and preparing tax returns. It provides tips on tracking both personal and business finances, expenses that can be written off, and resources for budgeting, bookkeeping, and doing taxes to help freelancers get their money management organized and "awesome."
This document lists questions that interviewers may ask candidates for journalism positions. It includes questions about the role of journalism in society and how technology is changing it, how the candidate stays informed on news and who they think covers news well, how the candidate will engage the next generation of readers, a story they would have liked to cover in the last year and why, how they handle mistakes and successes, how they deal with deadlines and view the company's credibility, and examples of stories they would like to cover along with their approach.
This document discusses how to build an effective team out of staff. It emphasizes the importance of making employees feel like valued members of a team. Some key points include:
- Employees are more motivated and productive when they feel recognized and appreciated for their work.
- Staff should be given real responsibilities that match their interests and talents. Coaches' education should be supported.
- Building personal relationships with staff through mentoring and problem-solving helps them feel like part of the team.
- Simple rewards, shared ownership of the program, and team-building activities can encourage team spirit among employees.
This document provides frequently asked questions and drills for teaching the Yurchenko vault. It recommends starting round off entry drills as early as gymnasts can perform a round off. Mastering the basic Yurchenko vault takes 12-18 months of training before advancing to more difficult entries. Skills like a laid out Yurchenko are required before working vaults with twists. Conditioning, hurdling, round off, back handspring, and flipping drills are outlined to build the skills needed to perform the vault successfully and safely.
This document provides tips and activities for effective warm-ups and wind-downs in gymnastics classes. It discusses the purpose of warm-ups and wind-downs in raising core temperature, getting students moving, and setting or ending the tone on a positive note. Various prop-based and game-based activities are suggested for warm-ups, such as relays, races, flashcards, and choreographed routines. For wind-downs, low-impact games and partner activities are recommended to leave students smiling and eager to return. The document emphasizes that warm-ups and wind-downs should be fun, safe, engaging, and encourage participation.
This document outlines Tony Retrosi's plan for daily basics warmups at his gymnastics training centers. The basics are intended to continue warming up the body, prepare athletes for events, and set the tone for workouts. The plan includes basics for tumbling, dance, beam, bars, and each event. Exercises focus on handstands, rolls, body positions, and movement in different directions and on different sides. The coach provides tips like keeping it simple, moving around to see all athletes, and ensuring good form. The daily basics session varies slightly in focus but always includes active warmups and fundamentals for each apparatus.
나만의 입맛지도를 친구들과 나누어보세요!
나만의 입맛지도 기록/ 공유 SNS 머스태드(mustadd)의 서울시 2030 챌린지 1000 6기 2차 면접 발표에 사용된 사업 소개 자료입니다.
본 자료는 사업소개서의 올바른 예시가 아닙니다.
좋은 사업소개서로써 공유하는 것이 아니라 앞으로 Start-up이나 소규모 창업을 할 때 첫 번째 사업 소개서 작성에 어려움을 겪으실 많은 분들에게 한 가지 예시로써 조금이나마 참고가 되었으면 하는 마음에 공유 드립니다.
This document provides guidance on teaching intermediate Yurchenko vaults. It outlines prerequisites, including roundoff entry vaults and double backs. It lists 10 commandments for teaching vaults, focusing on having a plan, running approach, body positions, and allowing athletes to play. Progressions are outlined from pike to layout vaults. Drills are recommended to work on body shape, power, height, and twisting skills on trampoline and tumble track to prepare athletes for more difficult vaults.
This document provides an outline for a workshop on getting money management skills improved for freelancers. The workshop covers identifying common money pitfalls, establishing personal and business budgets, tracking business income and expenses for tax purposes, and preparing tax returns. It provides tips on tracking both personal and business finances, expenses that can be written off, and resources for budgeting, bookkeeping, and doing taxes to help freelancers get their money management organized and "awesome."
This document lists questions that interviewers may ask candidates for journalism positions. It includes questions about the role of journalism in society and how technology is changing it, how the candidate stays informed on news and who they think covers news well, how the candidate will engage the next generation of readers, a story they would have liked to cover in the last year and why, how they handle mistakes and successes, how they deal with deadlines and view the company's credibility, and examples of stories they would like to cover along with their approach.
This document discusses how to build an effective team out of staff. It emphasizes the importance of making employees feel like valued members of a team. Some key points include:
- Employees are more motivated and productive when they feel recognized and appreciated for their work.
- Staff should be given real responsibilities that match their interests and talents. Coaches' education should be supported.
- Building personal relationships with staff through mentoring and problem-solving helps them feel like part of the team.
- Simple rewards, shared ownership of the program, and team-building activities can encourage team spirit among employees.
This document provides frequently asked questions and drills for teaching the Yurchenko vault. It recommends starting round off entry drills as early as gymnasts can perform a round off. Mastering the basic Yurchenko vault takes 12-18 months of training before advancing to more difficult entries. Skills like a laid out Yurchenko are required before working vaults with twists. Conditioning, hurdling, round off, back handspring, and flipping drills are outlined to build the skills needed to perform the vault successfully and safely.
This document provides tips and activities for effective warm-ups and wind-downs in gymnastics classes. It discusses the purpose of warm-ups and wind-downs in raising core temperature, getting students moving, and setting or ending the tone on a positive note. Various prop-based and game-based activities are suggested for warm-ups, such as relays, races, flashcards, and choreographed routines. For wind-downs, low-impact games and partner activities are recommended to leave students smiling and eager to return. The document emphasizes that warm-ups and wind-downs should be fun, safe, engaging, and encourage participation.
This document outlines Tony Retrosi's plan for daily basics warmups at his gymnastics training centers. The basics are intended to continue warming up the body, prepare athletes for events, and set the tone for workouts. The plan includes basics for tumbling, dance, beam, bars, and each event. Exercises focus on handstands, rolls, body positions, and movement in different directions and on different sides. The coach provides tips like keeping it simple, moving around to see all athletes, and ensuring good form. The daily basics session varies slightly in focus but always includes active warmups and fundamentals for each apparatus.
나만의 입맛지도를 친구들과 나누어보세요!
나만의 입맛지도 기록/ 공유 SNS 머스태드(mustadd)의 서울시 2030 챌린지 1000 6기 2차 면접 발표에 사용된 사업 소개 자료입니다.
본 자료는 사업소개서의 올바른 예시가 아닙니다.
좋은 사업소개서로써 공유하는 것이 아니라 앞으로 Start-up이나 소규모 창업을 할 때 첫 번째 사업 소개서 작성에 어려움을 겪으실 많은 분들에게 한 가지 예시로써 조금이나마 참고가 되었으면 하는 마음에 공유 드립니다.
Teaching and Learning Experience Design – der Ruf nach besserer Lehre: aber wie?Isa Jahnke
Der Ruf danach, dass es bessere Lehre geben muss oder das Lehre verbessert werden sollte, ist nicht neu. Es gibt auch schon seit längerer Zeit Rufe danach, dass Lehre der Forschung in Universitäten gleichgestellt werden soll. (Und in den letzten Jahren ist in Deutschland auch einiges an positiven Entwicklungen geschehen, z.B. durch die Aktivitäten des Stifterverbands). Wie kann die Verbesserung der Lehre weitergehen? Fehlt etwas in dieser Entwicklung? Ja, sagt dieser Beitrag, der zum Nachdenken und Diskutieren anregen soll. In diesem Beitrag wird ein forschungsbasierter Ansatz zur Diskussion gestellt. Es wird argumentiert, dass Lehre nur dann besser wird, wenn es mit den Prinzipen der Wissenschaft und Forschung angegangen wird (d.h. gestalten, Daten erheben, auswerten, verbessern). Es benötigt neue Verhaltensregeln oder -prinzipien bei der Gestaltung von Lehrveranstaltungen. Das bedeutet zum Beispiel das Prinzipien der Evidenzbasierung und wissenschaftliche Herangehensweisen im Lehr-Lerndesign als zentrales Fundament etabliert werden sollte. Evidenzbasierung hier meint, folgt man der Logik der Forschung, dass Lehrveranstaltungen als Intervention verstanden werden. Mit dieser Intervention werden Studierende befähigt, bestimmte vorab festgelegte Kompetenzen zu entwickeln. Und die Frage, die sich bei jeder Lehr-Lernveranstaltung dann stellt, ist, ob diese Objectives bzw. Learning Outcomes auch erreicht wurden. Klar ist, dass die subjektive Lehrevaluation der Studierenden oder auch die Notengebnung nicht ausreichen, um diese Frage zu beantworten. Hierfür gibt es eine Reihe von Methoden, die genutzt werden können, z.B. aus dem Bereich des User- / Learning Experience Design. Diese Methoden umfassen unter anderem Usability-Tests, Learner Experience Studies, Pre-/Post-Tests, und Follow-up Interviews. Diese können zur Gestaltung und Erfassung von effektiven, effizienten und ansprechenden digitalen Lerndesigns verwendet (Reigeluth 1983, Honebein & Reigeluth, 2022).
Der Beitrag will die Entwicklung zur Verbesserung von Lehre weiter pushen. Neue Ideen in die Bewegung bringen. Als Gründungsvizepräsidentin der UTN hab ich die Chance, hier ein neues Fundament für eine gesamte Uni zu legen. Wird das Gelingen? Ist dieser Ansatz, den ich hier vorstelle, eine erfolgsversprechende Option dafür? Hier können sich die TeilnehmerInnen an dieser Entwicklung beteiligen.
Mathematikunterricht in 1zu1 Ausstattungen.pptxFlippedMathe
Wie geht guter Mathematikunterricht? Und jetzt auch noch mit Tablet/Laptop? In dieser Fortbildung soll es genau darum gehen.
Sebastian Schmidt kennt vielleicht nicht Ihre persönliche Antwort auf guten (digitalen) Mathematikunterricht, aber er hat seit 2013 versucht, mit digitalen Hilfsmitteln seinen Unterricht kompetenzorientierter zu gestalten. Die Digitalisierung von Unterricht hat immer die Problematik, das Lernen der Schülerinnen und Schülern aus dem Fokus zu verlieren. Diese sollen digital mündig werden und gleichzeitig Mathematik besser verstehen.
In dieser eSession werden zahlreiche Methoden, Konzepte und auch Tools vorgestellt, die im Mathematikunterricht des Referenten erfolgreich eingesetzt werden konnten. Nicht alles kann am nächsten Tag im Unterricht eingesetzt werden, aber man erhält einen Überblick, was möglich ist. Sie entscheiden dann selbst, worauf Sie Ihren Fokus legen und wie Sie selbst in die 1:1-Ausstattung starten.
Lassen Sie sich überraschen und nehmen Sie mit, was für Sie sinnvoll erscheint. Auf der Homepage von Sebastian Schmidt gibt es neben Links und Materialien zur Fortbildungen auch Workshops fürs eigene Ausprobieren. https://www.flippedmathe.de/fortbildung/mathe-ws/
1. ################################################################################
###############################
### ANÁLISE ESPACIAL d
################################################################################
###############################
max(dist(d$coords))
# Fornece a maior distância da área considerando as coordenadas d$coords para
obter o cutoff de 50%
max(dist(d$coords)/2) # Calculo do cutoff de 50% da distancia maxima
min(dist(d$coords))
# Fornece a menor distância da área considerando as coordenadas d$coords para
obter o cutoff de 50%
cutoff=max(dist(d$coords))/2
cutoff/min(dist(d$coords))
# Construcao do Semivariograma Experimental ondimensional com 15 semivariancias
e com 30 pares
d.var <- variog(d,uvec=seq(0,750,l=15), estimator.type="classical",pairs.min=30)
# visualizacao da semivariograma experimental ondimensional
plot(d.var, main= 'Semivariograma')
# Informações do semivariograma experimental construido
distancia <- d.var$u
semivariancia <- d.var$v
pares <- d.var$n
tabela <- cbind(distancia,semivariancia,pares)
tabela
################################################################################
###################
# AJUSTE DE MODELOS UTILIZANDO O METODO DE ESTIMAÇÃO OLS VARIOFIT
################################################################################
###################
################################################################################
###################
####### AJUSTE DO MODELO ESFERICO SPH POR OLS
################################################################################
###################
plot(d.var,xlab='Distância',ylab='semivariância',main='Semivariograma Modelo
esférico por OLS')
desf.ols <- variofit(d.var,ini=c(0.25,500), nu=0.10, weights=
"equal",cov.model="sph")
desf.ols
# Mostra o gráfico das semivariâncias com o modelo ajusto e o rótulo.
lines(desf.ols,col="blue")
summary(desf.ols)
################################################################################
####################
###### AJUSTE DO MODELO EXPONENCIAL POR OLS
################################################################################
2. ####################
plot(d.var,xlab='Distancia',ylab='semivariância',main='Semivariograma Modelo
exponencial por OLS')
dexp.ols<-variofit(d.var,ini=c(0.25,500), nu=0.10, weights=
"equal",cov.model="exp")
dexp.ols
# Mostra o gráfico das semivariâncias com o modelo ajustado e o rótulo.
lines(dexp.ols,col="blue")
summary(dexp.ols)
################################################################################
###################
######## AJUSTE DO MODELO GAUSSIANO por OLS
################################################################################
###################
plot(d.var,xlab='Distância',ylab='semivariância',main='Semivariograma Modelo
Gaussiano por OLS')
dgaus.ols<-variofit(d.var,ini=c(0.25,500), nu=0.10,weights=
"equal",cov.model="gaus")
dgaus.ols
# Mostra o gráfico das semivariâncias com o modelo ajustado e os rótulos.
lines(dgaus.ols,col="blue")
summary(dgaus.ols)
################################################################################
##################
######### AJUSTE DO MODELO MATÉRN k=1 por OLS
################################################################################
##################
plot(d.var,xlab='Distância',ylab='semivariância',main='Semivariograma Modelo
Mátern K=1 por OLS')
dmatern1.ols<-variofit(d.var,ini=c(0.25,500), nu=0.10, weights=
"equal",cov.model= "matern", kappa = 1)
dmatern1.ols
# Mostra o gráfico das semivariâncias com o modelo ajustado e os rótulos.
lines(dmatern1.ols,col="blue")
summary(dmatern1.ols)
################################################################################
################
# AJUSTE DE MODELOS UTILIZANDO WLS1 VARIOFIT
################################################################################
###############
plot(d.var,xlab='Distância',ylab='semivariância',main= 'Semivariograma Modelo
Esférico por WLS1 ')
desf.wls<-variofit(d.var,ini=c(0.25,500), nu=0.10,cov.model="sph")
desf.wls
lines(desf.wls,col="blue")
summary(desf.wls)
################################################################################
#################
###### AJUSTE DO MODELO EXPONENCIAL POR WLS1
################################################################################
################
plot(d.var,xlab='Distância',ylab='Semivariância',main='Semivariograma Modelo
Exponencial por WLS1')
3. dexp.wls<-variofit(d.var,ini=c(0.25,500), nu=0.10,cov.model="exp")
dexp.wls
lines(dexp.wls,col="blue")
summary(dexp.wls)#Apresenta um resumo das estatísticas espaciais dos modelos
ajustados
################################################################################
################
###### AJUSTE DO MODELO GAUSSIANO POR WLS1
################################################################################
###############
plot(d.var,xlab='Distância',ylab='Semivariância',main='Semivariograma Modelo
Gaussiano por WLS1')
dgaus.wls<-variofit(d.var,ini=c(0.25,500), nu=0.10,cov.model="gaus")
dgaus.wls
lines(dgaus.wls,col="blue")
summary(dgaus.wls)
################################################################################
################
###### AJUSTE DO MODELO Matern k=1 POR WLS1
################################################################################
################
plot(d.var,xlab='Distância',ylab='Semivariância',main='Semivariograma Modelo
Matern k=1 por WLS1')
dmatern1.wls<-variofit(d.var,ini=c(0.25,500), nu=0.10,cov.model="matern",
kappa=1)
dmatern1.wls
lines(dmatern1.wls,col="blue")
summary(dmatern1.wls)
################################################################################
################
########### AJUSTE DE MODELOS UTILIZANDO MÁXIMA VEROSSIMILHANÇA LIKFIT
################################################################################
################
#################################################
####### AJUSTE DO MODELO ESFERICO POR MV
#################################################
plot(d.var,xlab='alcance',ylab='semivariância',main='Semivariograma Modelo
Esférico por MV')
desf.ml<-likfit(d,ini=c(0.25,500), nu=0.10,lambda=1, lik.method= "ML",
cov.model="sph")
desf.ml
lines(desf.ml,col="blue")
AIC(desf.ml)
summary(desf.ml)
##################################################
###### AJUSTE DO MODELO EXPONENCIAL POR MV
##################################################
plot(d.var,xlab='Distância',ylab='Semivariância',main='Semivariograma Modelo
Exponencial por MV')
dexp.ml<-likfit(d,ini=c(0.25,500), nu=0.10,lambda=1, lik.method= "ML",
cov.model="exp")
dexp.ml
lines(dexp.ml,col="blue")
summary(dexp.ml)
4. ####################################################
###### AJUSTE DO MODELO GAUSSIANO POR MV
####################################################
plot(d.var,xlab='Distância',ylab='Semivariância',main='Semivariograma Modelo
Gaussiano por MV')
dgaus.ml<-likfit(d,ini=c(0.25,500), nu=0.10, lambda=1, lik.method= "ML",
cov.model="gaus")
dgaus.ml
lines(dgaus.ml,col="blue")
summary(dgaus.ml)
######################################################
###### AJUSTE DO MODELO Matern k=1 POR MV
######################################################
plot(d.var,xlab='Distância',ylab='Semivariância',main='Semivariograma Modelo
Matern K=1 por MV')
dmatern1.ml<-likfit(d,ini=c(0.25,500), nu=0.10, lambda=1, lik.method= "ML",
cov.model="matern", kappa=1)
dmatern1.ml
lines(dmatern1.ml,col="blue")
summary(dmatern1.ml)
################################################################################
##########
# AJUSTE DE MODELOS UTILIZANDO MÁXIMA VEROSSIMILHANÇA RESTRITA MVR LIKFIT
################################################################################
##########
##############################################
####### AJUSTE DO MODELO ESFERICO POR MVR
##############################################
plot(d.var,xlab='Distância',ylab='Semivariância',main='Semivariograma Modelo
Esférico por MVR')
desf.mlr<-likfit(d,ini=c(0.25,500), nu=0.10, lik.method="RML", cov.model="sph")
desf.mlr
lines(desf.mlr,col="blue")
AIC(desf.mlr)
summary(desf.mlr)
##############################################
####### AJUSTE DO MODELO EXPONENCIAL POR MVR
##############################################
plot(d.var,xlab='Distância',ylab='Semivariância',main='Semivariograma Modelo
Exponencial por MVR')
dexp.mlr<-likfit(d,ini=c(0.25,500), nu=0.10, lik.method="RML", cov.model="exp")
dexp.mlr
lines(dexp.mlr,col="blue")
AIC(dexp.mlr)
summary(dexp.mlr)
##############################################
####### AJUSTE DO MODELO GAUSSIANO POR MVR
##############################################
#plot(d.var,xlab='Distância',ylab='Semivariância',main='Semivariograma Modelo
Gaussiano por MVR')
#dgaus.mlr<-likfit(d,ini=c(0.25,500), nu=0.10,lambda=1, lik.method= "RML",
cov.model="gaus")
#dgaus.mlr
#lines(dgaus.mlr,col="blue")
5. #summary(dgaus.mlr)
##############################################
####### AJUSTE DO MODELO MATERN POR MVR
##############################################
plot(d.var,xlab='Distância',ylab='Semivariância',main='Semivariograma Modelo
Matérn k=1 por MVR')
dmatern1.mlr<-likfit(d,ini=c(0.25,500), nu=0.10, lik.method="RML",
cov.model="matern", kappa=1)
dmatern1.mlr
lines(dmatern1.mlr,col="blue")
AIC(dmatern1.mlr)
summary(dmatern1.mlr)
6. #summary(dgaus.mlr)
##############################################
####### AJUSTE DO MODELO MATERN POR MVR
##############################################
plot(d.var,xlab='Distância',ylab='Semivariância',main='Semivariograma Modelo
Matérn k=1 por MVR')
dmatern1.mlr<-likfit(d,ini=c(0.25,500), nu=0.10, lik.method="RML",
cov.model="matern", kappa=1)
dmatern1.mlr
lines(dmatern1.mlr,col="blue")
AIC(dmatern1.mlr)
summary(dmatern1.mlr)