Competências representam uma combinação de conhecimentos,
habilidades e atitudes, que envolvem capacidades e domínios
distintos, como as descritas na tabela a seguir.
Considerando essas informações, a correta associação dos
elementos de competências com as respectivas capacidades e
domínios é:
A avaliação de programas educacionais é um desafio para grande parte do setor empresarial. Pesquisas apontam que ainda predominam nas empresas as avaliações de reação, que focalizam a análise do desempenho do instrutor, do planejamento e da utilidade do curso. Contudo, para se adequarem aos modelos mais modernos de avaliação de ações educacionais corporativas, as empresas precisam incluir em sua avaliação de programas educacionais outros níveis de resultados dessas ações, tais como:
A Educação a Distância tem sido fortemente impulsionada pelo uso das novas Tecnologias da Informação e da Comunicação – TICs. Alguns modelos de desenho instrucional estão sendo construídos especificamente para projetos instrucionais na web, sendo importante que tais projetos atendam ao critério da consistência, que no contexto das novas TICs, pode ser definido como:
A organização da atividade de aprendizagem demanda dos educadores a preparação do processo de desenvolvimento concreto das ações ou etapas que a compõem, retratado no(a):
Texto – A eficácia das palavras certas
Havia um cego sentado numa calçada em Paris. A seus pés, um
boné e um cartaz em madeira escrito com giz branco gritava:
"Por favor, ajude-me. Sou cego". Um publicitário da área de
criação, que passava em frente a ele, parou e viu umas poucas
moedas no boné. Sem pedir licença, pegou o cartaz e com o giz
escreveu outro conceito. Colocou o pedaço de madeira aos pés
do cego e foi embora.
Ao cair da tarde, o publicitário voltou a passar em frente ao cego
que pedia esmola. Seu boné, agora, estava cheio de notas e
moedas. O cego reconheceu as pegadas do publicitário e
perguntou se havia sido ele quem reescrevera o cartaz,
sobretudo querendo saber o que ele havia escrito.
O publicitário respondeu: "Nada que não esteja de acordo com o
conceito original, mas com outras palavras". E, sorrindo,
continuou o seu caminho. O cego nunca soube o que estava
escrito, mas seu novo cartaz dizia: "Hoje é primavera em Paris e
eu não posso vê-la". (Produção de Texto, Maria Luíza M. Abaurre
e Maria Bernadete M. Abaurre)
A frase abaixo em que a substituição de uma oração reduzida por uma desenvolvida equivalente é inadequada é:
READ TEXT I AND ANSWER QUESTIONS 16 TO 20
TEXT I
Will computers ever truly understand what we're saying?
Date: January 11, 2016
Source University of California - Berkeley
Summary:
If you think computers are quickly approaching true human
communication, think again. Computers like Siri often get
confused because they judge meaning by looking at a word's
statistical regularity. This is unlike humans, for whom context is
more important than the word or signal, according to a
researcher who invented a communication game allowing only
nonverbal cues, and used it to pinpoint regions of the brain where
mutual understanding takes place.
From Apple's Siri to Honda's robot Asimo, machines seem to be
getting better and better at communicating with humans. But
some neuroscientists caution that today's computers will never
truly understand what we're saying because they do not take into
account the context of a conversation the way people do.
Specifically, say University of California, Berkeley, postdoctoral
fellow Arjen Stolk and his Dutch colleagues, machines don't
develop a shared understanding of the people, place and
situation - often including a long social history - that is key to
human communication. Without such common ground, a
computer cannot help but be confused.
“People tend to think of communication as an exchange of
linguistic signs or gestures, forgetting that much of
communication is about the social context, about who you are
communicating with," Stolk said.
The word “bank," for example, would be interpreted one way if
you're holding a credit card but a different way if you're holding a
fishing pole. Without context, making a “V" with two fingers
could mean victory, the number two, or “these are the two
fingers I broke."
“All these subtleties are quite crucial to understanding one
another," Stolk said, perhaps more so than the words and signals
that computers and many neuroscientists focus on as the key to
communication. “In fact, we can understand one another without
language, without words and signs that already have a shared
meaning."
(Adapted from http://www.sciencedaily.com/releases/2016/01/1
60111135231.htm)
According to the researchers from the University of California, Berkeley:
READ TEXT II AND ANSWER QUESTIONS 21 TO 25:
TEXT II
The backlash against big data
[…]
Big data refers to the idea that society can do things with a large
body of data that weren't possible when working with smaller
amounts. The term was originally applied a decade ago to
massive datasets from astrophysics, genomics and internet
search engines, and to machine-learning systems (for voicerecognition
and translation, for example) that work
well only when given lots of data to chew on. Now it refers to the
application of data-analysis and statistics in new areas, from
retailing to human resources. The backlash began in mid-March,
prompted by an article in Science by David Lazer and others at
Harvard and Northeastern University. It showed that a big-data
poster-child—Google Flu Trends, a 2009 project which identified
flu outbreaks from search queries alone—had overestimated the
number of cases for four years running, compared with reported
data from the Centres for Disease Control (CDC). This led to a
wider attack on the idea of big data.
The criticisms fall into three areas that are not intrinsic to big
data per se, but endemic to data analysis, and have some merit.
First, there are biases inherent to data that must not be ignored.
That is undeniably the case. Second, some proponents of big data
have claimed that theory (ie, generalisable models about how the
world works) is obsolete. In fact, subject-area knowledge remains
necessary even when dealing with large data sets. Third, the risk
of spurious correlations—associations that are statistically robust
but happen only by chance—increases with more data. Although
there are new statistical techniques to identify and banish
spurious correlations, such as running many tests against subsets
of the data, this will always be a problem.
There is some merit to the naysayers' case, in other words. But
these criticisms do not mean that big-data analysis has no merit
whatsoever. Even the Harvard researchers who decried big data
"hubris" admitted in Science that melding Google Flu Trends
analysis with CDC's data improved the overall forecast—showing
that big data can in fact be a useful tool. And research published
in PLOS Computational Biology on April 17th shows it is possible
to estimate the prevalence of the flu based on visits to Wikipedia
articles related to the illness. Behind the big data backlash is the
classic hype cycle, in which a technology's early proponents make
overly grandiose claims, people sling arrows when those
promises fall flat, but the technology eventually transforms the
world, though not necessarily in ways the pundits expected. It
happened with the web, and television, radio, motion pictures
and the telegraph before it. Now it is simply big data's turn to
face the grumblers.
(From http://www.economist.com/blogs/economist explains/201
4/04/economist-explains-10)
The base form, past tense and past participle of the verb “fall” in “The criticisms fall into three areas” are, respectively:
READ TEXT II AND ANSWER QUESTIONS 21 TO 25:
TEXT II
The backlash against big data
[…]
Big data refers to the idea that society can do things with a large
body of data that weren't possible when working with smaller
amounts. The term was originally applied a decade ago to
massive datasets from astrophysics, genomics and internet
search engines, and to machine-learning systems (for voicerecognition
and translation, for example) that work
well only when given lots of data to chew on. Now it refers to the
application of data-analysis and statistics in new areas, from
retailing to human resources. The backlash began in mid-March,
prompted by an article in Science by David Lazer and others at
Harvard and Northeastern University. It showed that a big-data
poster-child—Google Flu Trends, a 2009 project which identified
flu outbreaks from search queries alone—had overestimated the
number of cases for four years running, compared with reported
data from the Centres for Disease Control (CDC). This led to a
wider attack on the idea of big data.
The criticisms fall into three areas that are not intrinsic to big
data per se, but endemic to data analysis, and have some merit.
First, there are biases inherent to data that must not be ignored.
That is undeniably the case. Second, some proponents of big data
have claimed that theory (ie, generalisable models about how the
world works) is obsolete. In fact, subject-area knowledge remains
necessary even when dealing with large data sets. Third, the risk
of spurious correlations—associations that are statistically robust
but happen only by chance—increases with more data. Although
there are new statistical techniques to identify and banish
spurious correlations, such as running many tests against subsets
of the data, this will always be a problem.
There is some merit to the naysayers' case, in other words. But
these criticisms do not mean that big-data analysis has no merit
whatsoever. Even the Harvard researchers who decried big data
"hubris" admitted in Science that melding Google Flu Trends
analysis with CDC's data improved the overall forecast—showing
that big data can in fact be a useful tool. And research published
in PLOS Computational Biology on April 17th shows it is possible
to estimate the prevalence of the flu based on visits to Wikipedia
articles related to the illness. Behind the big data backlash is the
classic hype cycle, in which a technology's early proponents make
overly grandiose claims, people sling arrows when those
promises fall flat, but the technology eventually transforms the
world, though not necessarily in ways the pundits expected. It
happened with the web, and television, radio, motion pictures
and the telegraph before it. Now it is simply big data's turn to
face the grumblers.
(From http://www.economist.com/blogs/economist explains/201
4/04/economist-explains-10)
The phrase “lots of data to chew on” in Text II makes use of figurative language and shares some common characteristics with:
Texto – A eficácia das palavras certas
Havia um cego sentado numa calçada em Paris. A seus pés, um
boné e um cartaz em madeira escrito com giz branco gritava:
"Por favor, ajude-me. Sou cego". Um publicitário da área de
criação, que passava em frente a ele, parou e viu umas poucas
moedas no boné. Sem pedir licença, pegou o cartaz e com o giz
escreveu outro conceito. Colocou o pedaço de madeira aos pés
do cego e foi embora.
Ao cair da tarde, o publicitário voltou a passar em frente ao cego
que pedia esmola. Seu boné, agora, estava cheio de notas e
moedas. O cego reconheceu as pegadas do publicitário e
perguntou se havia sido ele quem reescrevera o cartaz,
sobretudo querendo saber o que ele havia escrito.
O publicitário respondeu: "Nada que não esteja de acordo com o
conceito original, mas com outras palavras". E, sorrindo,
continuou o seu caminho. O cego nunca soube o que estava
escrito, mas seu novo cartaz dizia: "Hoje é primavera em Paris e
eu não posso vê-la". (Produção de Texto, Maria Luíza M. Abaurre
e Maria Bernadete M. Abaurre)
A frase abaixo em que o emprego do demonstrativo sublinhado está inadequado é:
Texto – A eficácia das palavras certas
Havia um cego sentado numa calçada em Paris. A seus pés, um
boné e um cartaz em madeira escrito com giz branco gritava:
"Por favor, ajude-me. Sou cego". Um publicitário da área de
criação, que passava em frente a ele, parou e viu umas poucas
moedas no boné. Sem pedir licença, pegou o cartaz e com o giz
escreveu outro conceito. Colocou o pedaço de madeira aos pés
do cego e foi embora.
Ao cair da tarde, o publicitário voltou a passar em frente ao cego
que pedia esmola. Seu boné, agora, estava cheio de notas e
moedas. O cego reconheceu as pegadas do publicitário e
perguntou se havia sido ele quem reescrevera o cartaz,
sobretudo querendo saber o que ele havia escrito.
O publicitário respondeu: "Nada que não esteja de acordo com o
conceito original, mas com outras palavras". E, sorrindo,
continuou o seu caminho. O cego nunca soube o que estava
escrito, mas seu novo cartaz dizia: "Hoje é primavera em Paris e
eu não posso vê-la". (Produção de Texto, Maria Luíza M. Abaurre
e Maria Bernadete M. Abaurre)
“Havia um cego sentado numa calçada em Paris. A seus pés, um boné e um cartaz em madeira escrito com giz branco gritava: “Por favor, ajude-me. Sou cego”. Um publicitário da área de criação, que passava em frente a ele, parou e viu umas poucas moedas no boné. Sem pedir licença, pegou o cartaz e com o giz escreveu outro conceito. Colocou o pedaço de madeira aos pés do cego e foi embora”. O texto pertence ao modo narrativo de organização discursiva, caracterizado pela evolução cronológica das ações. O segmento que comprova essa evolução é:
Texto – A eficácia das palavras certas
Havia um cego sentado numa calçada em Paris. A seus pés, um
boné e um cartaz em madeira escrito com giz branco gritava:
"Por favor, ajude-me. Sou cego". Um publicitário da área de
criação, que passava em frente a ele, parou e viu umas poucas
moedas no boné. Sem pedir licença, pegou o cartaz e com o giz
escreveu outro conceito. Colocou o pedaço de madeira aos pés
do cego e foi embora.
Ao cair da tarde, o publicitário voltou a passar em frente ao cego
que pedia esmola. Seu boné, agora, estava cheio de notas e
moedas. O cego reconheceu as pegadas do publicitário e
perguntou se havia sido ele quem reescrevera o cartaz,
sobretudo querendo saber o que ele havia escrito.
O publicitário respondeu: "Nada que não esteja de acordo com o
conceito original, mas com outras palavras". E, sorrindo,
continuou o seu caminho. O cego nunca soube o que estava
escrito, mas seu novo cartaz dizia: "Hoje é primavera em Paris e
eu não posso vê-la". (Produção de Texto, Maria Luíza M. Abaurre
e Maria Bernadete M. Abaurre)
A frase abaixo, de Millôr Fernandes, que exemplifica o emprego da vírgula por inserção de um segmento entre sujeito e verbo é:
READ TEXT I AND ANSWER QUESTIONS 16 TO 20
TEXT I
Will computers ever truly understand what we're saying?
Date: January 11, 2016
Source University of California - Berkeley
Summary:
If you think computers are quickly approaching true human
communication, think again. Computers like Siri often get
confused because they judge meaning by looking at a word's
statistical regularity. This is unlike humans, for whom context is
more important than the word or signal, according to a
researcher who invented a communication game allowing only
nonverbal cues, and used it to pinpoint regions of the brain where
mutual understanding takes place.
From Apple's Siri to Honda's robot Asimo, machines seem to be
getting better and better at communicating with humans. But
some neuroscientists caution that today's computers will never
truly understand what we're saying because they do not take into
account the context of a conversation the way people do.
Specifically, say University of California, Berkeley, postdoctoral
fellow Arjen Stolk and his Dutch colleagues, machines don't
develop a shared understanding of the people, place and
situation - often including a long social history - that is key to
human communication. Without such common ground, a
computer cannot help but be confused.
“People tend to think of communication as an exchange of
linguistic signs or gestures, forgetting that much of
communication is about the social context, about who you are
communicating with," Stolk said.
The word “bank," for example, would be interpreted one way if
you're holding a credit card but a different way if you're holding a
fishing pole. Without context, making a “V" with two fingers
could mean victory, the number two, or “these are the two
fingers I broke."
“All these subtleties are quite crucial to understanding one
another," Stolk said, perhaps more so than the words and signals
that computers and many neuroscientists focus on as the key to
communication. “In fact, we can understand one another without
language, without words and signs that already have a shared
meaning."
(Adapted from http://www.sciencedaily.com/releases/2016/01/1
60111135231.htm)
Based on the summary provided for Text I, mark the statements below as TRUE (T ) or FALSE (F ). ( ) Contextual clues are still not accounted for by computers. ( ) Computers are unreliable because they focus on language patterns. ( ) A game has been invented based on the words people use. The statements are, respectively:
READ TEXT II AND ANSWER QUESTIONS 21 TO 25:
TEXT II
The backlash against big data
[…]
Big data refers to the idea that society can do things with a large
body of data that weren't possible when working with smaller
amounts. The term was originally applied a decade ago to
massive datasets from astrophysics, genomics and internet
search engines, and to machine-learning systems (for voicerecognition
and translation, for example) that work
well only when given lots of data to chew on. Now it refers to the
application of data-analysis and statistics in new areas, from
retailing to human resources. The backlash began in mid-March,
prompted by an article in Science by David Lazer and others at
Harvard and Northeastern University. It showed that a big-data
poster-child—Google Flu Trends, a 2009 project which identified
flu outbreaks from search queries alone—had overestimated the
number of cases for four years running, compared with reported
data from the Centres for Disease Control (CDC). This led to a
wider attack on the idea of big data.
The criticisms fall into three areas that are not intrinsic to big
data per se, but endemic to data analysis, and have some merit.
First, there are biases inherent to data that must not be ignored.
That is undeniably the case. Second, some proponents of big data
have claimed that theory (ie, generalisable models about how the
world works) is obsolete. In fact, subject-area knowledge remains
necessary even when dealing with large data sets. Third, the risk
of spurious correlations—associations that are statistically robust
but happen only by chance—increases with more data. Although
there are new statistical techniques to identify and banish
spurious correlations, such as running many tests against subsets
of the data, this will always be a problem.
There is some merit to the naysayers' case, in other words. But
these criticisms do not mean that big-data analysis has no merit
whatsoever. Even the Harvard researchers who decried big data
"hubris" admitted in Science that melding Google Flu Trends
analysis with CDC's data improved the overall forecast—showing
that big data can in fact be a useful tool. And research published
in PLOS Computational Biology on April 17th shows it is possible
to estimate the prevalence of the flu based on visits to Wikipedia
articles related to the illness. Behind the big data backlash is the
classic hype cycle, in which a technology's early proponents make
overly grandiose claims, people sling arrows when those
promises fall flat, but the technology eventually transforms the
world, though not necessarily in ways the pundits expected. It
happened with the web, and television, radio, motion pictures
and the telegraph before it. Now it is simply big data's turn to
face the grumblers.
(From http://www.economist.com/blogs/economist explains/201
4/04/economist-explains-10)
The three main arguments against big data raised by Text II in the second paragraph are:
Regina foi nomeada como Design Instrucional e, ao assumir seu cargo, uma das primeiras tarefas que lhe deram foi a redação dos objetivos instrucionais de um curso que seria realizado em breve no IBGE. Regina era uma profissional bastante preparada e por isso buscou redigir os objetivos instrucionais especificando:
Em diferentes ambientes de trabalho tem-se observado uma substituição do trabalho humano por máquinas. Se, por um lado, esse fenômeno reduz postos de trabalho; por outro, o trabalho humano torna-se menos operacional. Nesse novo contexto, há maior demanda por educação e por profissionais qualificados em grande parte dos postos de trabalho em função:
