Questões Militares
Foram encontradas 143.000 questões
Resolva questões gratuitamente!
Junte-se a mais de 4 milhões de concurseiros!
Leia o texto para responder à questão.
AI tech products at schools and universities
Every few years, an emerging technology shows up at the doorstep of schools and universities promising to transform education. The most recent? Technologies powered by generative artificial intelligence, also known as GenAI. These technologies are sold on the potential they hold for education. As optimistic as these visions of the future may be, the realities of educational technology over the past few decades have not lived up to their promises, as shown by rigorous investigations of technology after technology – from mechanical machines to computers, from mobile devices to massive open online courses.
Yet, educational technology evangelists forget, remain unaware or simply do not care. Or they may be overly optimistic that the next new technology will be different than before.
Here are four questions I believe should be answered before school officials purchase any technology that relies on AI.
1. Is there evidence that a product works?
Compelling evidence of the effect of GenAI products on educational outcomes does not yet exist. Therefore, and unfortunately, it is the consumer who carries the onus of appraising products. My recommendation is: use multiple means for assessing product effectiveness.
2. [...]
Oftentimes, there is a divide between what entrepreneurs build and educators need. For example, one shortcoming of the One Laptop Per Child program – an ambitious program that sought to put small, cheap but sturdy laptops in the hands of children from families of lesser means – is that the laptops were designed for idealized younger versions of the developers themselves, not so much the children who were actually using them.
Initiatives have been implemented in which entrepreneurs and educators work together to improve educational technology products. Some products are developed with input from students and educators. Questions to ask vendors might be: In what ways were educators and learners included? How did their input influence the final product?
3. What educational beliefs shape this product?
Educational technology is rarely neutral. It is designed by people, and people have beliefs, experiences, ideologies and biases that shape the technologies they develop.
It is important for educational technology products to rely on what educators have experienced as relevant to the students they meet in their real-life classes. Questions to ask include: What pedagogical principles guide this product? What particular learning does it support or discourage?
4. Does the product level the playing field?
Finally, people ought to ask how a product addresses educational inequities. Is this technology going to help reduce the learning gaps between different groups of learners? Or is it one that aids some learners – often those who are already successful or privileged – but not others? Is it adopting an asset-based or a deficit-based approach to addressing inequities?
Educational technology vendors and startups may not have answers to all of these questions. But they should still be asked and considered. Answers could lead to improved products.
(George Veletsianos. https://theconversation.com, 15.04.24. Adaptado)
Observe a palavra destacada em negrito nas duas frases a seguir:
I. “Yet, educational technologist evangelists forget, remain unaware or simply do not care.” (parágrafo 2)
II. “Compelling evidence of the effect of GenAI products on educational outcomes does not yet exist” (parágrafo 5).
O uso da palavra yet está corretamente explicado na alternativa:
Leia o texto para responder à questão.
AI tech products at schools and universities
Every few years, an emerging technology shows up at the doorstep of schools and universities promising to transform education. The most recent? Technologies powered by generative artificial intelligence, also known as GenAI. These technologies are sold on the potential they hold for education. As optimistic as these visions of the future may be, the realities of educational technology over the past few decades have not lived up to their promises, as shown by rigorous investigations of technology after technology – from mechanical machines to computers, from mobile devices to massive open online courses.
Yet, educational technology evangelists forget, remain unaware or simply do not care. Or they may be overly optimistic that the next new technology will be different than before.
Here are four questions I believe should be answered before school officials purchase any technology that relies on AI.
1. Is there evidence that a product works?
Compelling evidence of the effect of GenAI products on educational outcomes does not yet exist. Therefore, and unfortunately, it is the consumer who carries the onus of appraising products. My recommendation is: use multiple means for assessing product effectiveness.
2. [...]
Oftentimes, there is a divide between what entrepreneurs build and educators need. For example, one shortcoming of the One Laptop Per Child program – an ambitious program that sought to put small, cheap but sturdy laptops in the hands of children from families of lesser means – is that the laptops were designed for idealized younger versions of the developers themselves, not so much the children who were actually using them.
Initiatives have been implemented in which entrepreneurs and educators work together to improve educational technology products. Some products are developed with input from students and educators. Questions to ask vendors might be: In what ways were educators and learners included? How did their input influence the final product?
3. What educational beliefs shape this product?
Educational technology is rarely neutral. It is designed by people, and people have beliefs, experiences, ideologies and biases that shape the technologies they develop.
It is important for educational technology products to rely on what educators have experienced as relevant to the students they meet in their real-life classes. Questions to ask include: What pedagogical principles guide this product? What particular learning does it support or discourage?
4. Does the product level the playing field?
Finally, people ought to ask how a product addresses educational inequities. Is this technology going to help reduce the learning gaps between different groups of learners? Or is it one that aids some learners – often those who are already successful or privileged – but not others? Is it adopting an asset-based or a deficit-based approach to addressing inequities?
Educational technology vendors and startups may not have answers to all of these questions. But they should still be asked and considered. Answers could lead to improved products.
(George Veletsianos. https://theconversation.com, 15.04.24. Adaptado)
Leia o texto para responder à questão.
AI tech products at schools and universities
Every few years, an emerging technology shows up at the doorstep of schools and universities promising to transform education. The most recent? Technologies powered by generative artificial intelligence, also known as GenAI. These technologies are sold on the potential they hold for education. As optimistic as these visions of the future may be, the realities of educational technology over the past few decades have not lived up to their promises, as shown by rigorous investigations of technology after technology – from mechanical machines to computers, from mobile devices to massive open online courses.
Yet, educational technology evangelists forget, remain unaware or simply do not care. Or they may be overly optimistic that the next new technology will be different than before.
Here are four questions I believe should be answered before school officials purchase any technology that relies on AI.
1. Is there evidence that a product works?
Compelling evidence of the effect of GenAI products on educational outcomes does not yet exist. Therefore, and unfortunately, it is the consumer who carries the onus of appraising products. My recommendation is: use multiple means for assessing product effectiveness.
2. [...]
Oftentimes, there is a divide between what entrepreneurs build and educators need. For example, one shortcoming of the One Laptop Per Child program – an ambitious program that sought to put small, cheap but sturdy laptops in the hands of children from families of lesser means – is that the laptops were designed for idealized younger versions of the developers themselves, not so much the children who were actually using them.
Initiatives have been implemented in which entrepreneurs and educators work together to improve educational technology products. Some products are developed with input from students and educators. Questions to ask vendors might be: In what ways were educators and learners included? How did their input influence the final product?
3. What educational beliefs shape this product?
Educational technology is rarely neutral. It is designed by people, and people have beliefs, experiences, ideologies and biases that shape the technologies they develop.
It is important for educational technology products to rely on what educators have experienced as relevant to the students they meet in their real-life classes. Questions to ask include: What pedagogical principles guide this product? What particular learning does it support or discourage?
4. Does the product level the playing field?
Finally, people ought to ask how a product addresses educational inequities. Is this technology going to help reduce the learning gaps between different groups of learners? Or is it one that aids some learners – often those who are already successful or privileged – but not others? Is it adopting an asset-based or a deficit-based approach to addressing inequities?
Educational technology vendors and startups may not have answers to all of these questions. But they should still be asked and considered. Answers could lead to improved products.
(George Veletsianos. https://theconversation.com, 15.04.24. Adaptado)
Another very relevant reading ability to be developed in students is that of recognizing the gist of a text, or of a self-contained part of a text. A teacher’s instruction to help develop this ability would include asking the students to reread subitem 2 and provide a subtitle that both shows their understanding of the excerpt and corresponds to the way the text has been structured.
One correct subtitle would be:
Leia o texto para responder à questão.
AI tech products at schools and universities
Every few years, an emerging technology shows up at the doorstep of schools and universities promising to transform education. The most recent? Technologies powered by generative artificial intelligence, also known as GenAI. These technologies are sold on the potential they hold for education. As optimistic as these visions of the future may be, the realities of educational technology over the past few decades have not lived up to their promises, as shown by rigorous investigations of technology after technology – from mechanical machines to computers, from mobile devices to massive open online courses.
Yet, educational technology evangelists forget, remain unaware or simply do not care. Or they may be overly optimistic that the next new technology will be different than before.
Here are four questions I believe should be answered before school officials purchase any technology that relies on AI.
1. Is there evidence that a product works?
Compelling evidence of the effect of GenAI products on educational outcomes does not yet exist. Therefore, and unfortunately, it is the consumer who carries the onus of appraising products. My recommendation is: use multiple means for assessing product effectiveness.
2. [...]
Oftentimes, there is a divide between what entrepreneurs build and educators need. For example, one shortcoming of the One Laptop Per Child program – an ambitious program that sought to put small, cheap but sturdy laptops in the hands of children from families of lesser means – is that the laptops were designed for idealized younger versions of the developers themselves, not so much the children who were actually using them.
Initiatives have been implemented in which entrepreneurs and educators work together to improve educational technology products. Some products are developed with input from students and educators. Questions to ask vendors might be: In what ways were educators and learners included? How did their input influence the final product?
3. What educational beliefs shape this product?
Educational technology is rarely neutral. It is designed by people, and people have beliefs, experiences, ideologies and biases that shape the technologies they develop.
It is important for educational technology products to rely on what educators have experienced as relevant to the students they meet in their real-life classes. Questions to ask include: What pedagogical principles guide this product? What particular learning does it support or discourage?
4. Does the product level the playing field?
Finally, people ought to ask how a product addresses educational inequities. Is this technology going to help reduce the learning gaps between different groups of learners? Or is it one that aids some learners – often those who are already successful or privileged – but not others? Is it adopting an asset-based or a deficit-based approach to addressing inequities?
Educational technology vendors and startups may not have answers to all of these questions. But they should still be asked and considered. Answers could lead to improved products.
(George Veletsianos. https://theconversation.com, 15.04.24. Adaptado)
Uma peculiaridade da Carta de 1824 foi incluir um artigo reproduzindo quase palavra por palavra a Declaração dos Direitos do Homem emitida na França em 1789. Comparado ao original havia, no entanto, algumas omissões bastante significativas e curiosas. Não foi incluído na Carta outorgada o artigo que, na versão original francesa, dizia: “O princípio de toda soberania reside essencialmente na nação. Nenhum corpo nem indivíduo podem exercer autoridade que não emane expressamente dela”. Também faltava o artigo VI: “A lei é expressão da vontade geral”. Finalmente, o artigo II: “O objetivo de toda associação política é a preservação dos direitos naturais e inalienáveis do homem. Estes direitos são a liberdade, a propriedade, a segurança e a resistência perante a opressão” foi reproduzido omitindo-se as seis últimas palavras.
(Emília Viotti da Costa, Da monarquia à república:
momentos decisivos, p. 141-142. Adaptado)
Para Emília Viotti da Costa, tais omissões podem revelar
Considerando um nível de significância de 10%, o valor aproximado calculado do teste (utilize para os cálculos os valores inteiros aproximados das frequências esperadas), juntamente com a sua conclusão, são, respectivamente:
Diante disso, o gráfico de valores preditos
versus resíduos padronizados (ei), que indica uma especificação do
modelo inadequada para a situação em estudo, é: PASSO 1: Escolha a variável que fornece a maior soma de quadrados da regressão em regressão linear simples com Y ou, de maneira equivalente, que forneça o maior valor de R2. Chamaremos essa variável inicial de X1.
PASSO 2: Escolha a variável que, quando inserida no modelo, fornece o maior aumento em R2, na presença de X1, sobre o valor de R2 encontrado no passo 1, isto é, a variável Xj para a qual:
R(βj |β1) = R(β1, βj) – R(β1)
é maior. Vamos chamá-la de variável X2. O modelo de regressão com X1 e X2 é, então, ajustado e R2 é observado.
PASSO 3: Escolha a variável Xj que fornece o maior valor de:
R(βj |β1, β2) = R(β1, β2, βj) – R(β1, β2),
resultando novamente em um aumento em R2 sobre aquele dado no PASSO 2. Ao chamar essa variável de X3, agora temos um modelo de regressão que envolve X1, X2 e X3. Esse processo é continuado até que a variável inserida mais recentemente falhe ao produzir um aumento significativo na regressão explicada. Tal aumento pode ser determinado em cada passo, devendo-se usar o teste F (ou t) apropriado.
Por exemplo, no PASSO 2, o valor:
pode ser determinado para testar a adequação de X2
no modelo. De maneira similar, no PASSO 3 a razão: 
testa a adequação de X3
no modelo. Se f < f(1, n-3; α) no PASSO 2, para um nível de significância preestabelecido, X2 não é incluído e o processo é encerrado, resultando em uma equação linear simples que relaciona Y e X1.
Contudo, se f >f(1, n-3; α) deve-se seguir para o PASSO 3. Novamente, se f < f(1, n-4; α) no PASSO 3, X3 não é incluído e o processo é encerrado com a equação de regressão apropriada que contém as variáveis X1 e X2.
Notações utilizadas:
R2 é o coeficiente de determinação do modelo de regressão;
R(.) é a soma dos quadrados do modelo de regressão em questão;
βj é o coeficiente do modelo de regressão que acompanha a variável Xj;
A notação ‘|’ indica a probabilidade condicional;
é o quadrado do erro médio para o modelo que
contém as variáveis X1 e X2; 
é o quadrado do erro médio para o modelo que
contém as variáveis X1, X2 e X3. Essa descrição se refere ao método de seleção de variáveis:
da verdadeira proporção de crianças com
o índice de massa corpórea (IMC) maior que 30, p, é correto afirmar que: • Experimento I: anota-se a face superior do lançamento de três moedas.
• Experimento II: anota-se a face superior do lançamento de dois dados.
• Experimento III: anota-se a face superior do lançamento de duas moedas e três dados.
Considere que todos os dados utilizados nesses experimentos têm seis faces. O número de elementos do espaço amostral de cada experimento é respectivamente:
A probabilidade de pegar, ao acaso, uma roupa ou um item que precise de conserto é igual a:
Sobre essa distribuição, é correto afirmar que
Conheça nossos planos