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2024-11-25

文章どころか動画理解できない人たちについて教えてください

AIちゃんに聞いて見た

I've noticed a non-negligible number of people who have not only completed compulsory education in regular classes but have also received higher education and graduated from university, yet struggle with reading comprehension (understanding the meaning of text), cannot read long texts, and even have difficulty understanding videos.

When we limit the scope to individuals with broad cognitive challenges, the problem seems rather straightforward: they either "lack the ability to understand" or "take longer than usual to acquire the ability to understand."

Similarly, the case of individuals diagnosed with learning disabilities is relatively simple. While they may not have broad cognitive challenges, they require different approaches and training due to their unique learning styles.

However, it is perplexing that university graduates without broad cognitive challenges or diagnosed learning disabilities struggle to understand not only written text but also simplified video content.

I suspect that the underlying causes might include gaps in the horizontal transfer of experiences and knowledge, a severe lack of adaptability, and an extreme deficit in metacognition. Has there been any research on these issues? Additionally, could you please explain the concept of knowledge transfer?

 

(正規の学級で義務教育を修了し、さら高等教育を受けて大学卒業したにもかかわらず、読解力(文章意味理解する能力)に苦しみ、長文を読むことができず、さらには動画理解にも困難を感じる人が少なくないことに気づきました。

 

認知能力に広範な課題を抱える個人範囲限定すると、問題比較的単純です。「理解する能力がない」か、「理解する能力を獲得するのに通常よりも時間がかかる」かのいずれかです。

 

同様に、学習障害と診断された個人場合比較的単純です。彼らは広範な認知能力課題を抱えていないかもしれませんが、独自学習スタイルのために異なるアプローチトレーニング必要となります

 

しかし、広範な認知能力課題学習障害を持たない大学卒者が、書かれたテキストだけでなく、簡略化されたビデオコンテンツ理解にも苦労していることは不可解です。

 

私は、その根本的な原因として、経験知識の水平的転移の欠如、メタ認知の極端な欠如などが考えられるのではないかと疑っています。これらの問題に関する研究はありますか?また、知識転移概念について説明していただけますか?)

 

 

Chat GPTちゃんの回答

Yes, research and analysis exist around the issues you described, particularly in the domains of education, psychology, and cognitive science. Let’s break down your observations and questions step by step:

 

Key Issues with Reading and Comprehension

 

1. Reading Comprehension Among Educated Individuals

• This phenomenon—educated individuals struggling with comprehensionis indeed perplexing, especially given their presumed exposure to a variety of learning materials. It suggests that education systems sometimes emphasize rote learning and surface-level knowledge acquisition over deeper understanding, critical thinking, and metacognition.

2. Possible Underlying Causes

• Horizontal Transfer Gaps: This refers to the inability to apply knowledge or skills learned in one context to another. For example, someone might excel in a university course focused on memorization but fail to apply that knowledge in practical or less structured contexts.

• Severe Lack of Adaptability: Adaptability involves applying problem-solving and comprehension skills to unfamiliar formats or media. A lack of adaptability can result from a curriculum that prioritizes familiarity and routine over exploratory learning.

• Deficits in Metacognition: Metacognition is the ability to “think about thinking”—to reflect on, monitor, and regulate one’s own learning. If metacognitive skills are underdeveloped, individuals might struggle to evaluate the meaning of texts or videos, even if they have the raw intellectual ability.

 

Research Insights

 

1. Knowledge Transfer

The concept of knowledge transfer involves applying skills, knowledge, or strategies learned in one context to another. It is often classified into:

• Horizontal Transfer: Applying knowledge across similar contexts (e.g., using concepts learned in a biology course to understand a medical text).

• Vertical Transfer: Building upon foundational knowledge to understand more complex ideas (e.g., using basic algebra to solve advanced calculus problems).

Studies suggest that transfer is more likely to occur when:

• Learners deeply understand underlying principles, not just surface details.

Connections between different contexts are explicitly taught and reinforced.

2. Factors Limiting Transfer

• Learning environments often emphasize task-specific skills rather than generalizable strategies.

• Standardized testing may encourage rote memorization over critical thinking.

• Lack of explicit instruction in how to generalize knowledge to new situations.

3. Metacognitive Deficits

Research highlights the importance of metacognition in learning. Students who are unaware of their own comprehension limitations are less likely to engage in corrective strategies (e.g., rereading, seeking clarification). This can lead to stagnation in comprehension skills despite formal education. Effective interventions include:

• Teaching self-monitoring techniques.

• Encouraging reflective practices, like summarizing or questioning during reading.

4. Cognitive Overload in Multimedia

Even videos, if not designed properly, can overwhelm learners. The Cognitive Theory of Multimedia Learning suggests that excessive extraneous information or unclear organization in videos can overload working memory, hindering comprehension.

 

Strategies and Research-Based Solutions

 

1. Improving Knowledge Transfer

Active Learning: Techniques such as problem-based learning or case studies can improve transfer by encouraging the application of knowledge to new situations.

• Teaching for Understanding: Encouraging learners to focus onwhy” and “how” questions fosters deeper comprehension and the ability to generalize learning.

2. Fostering Adaptability

• Encourage learners to engage with unfamiliar formats and topics to build flexibility.

• Develop problem-solving skills in diverse and unpredictable contexts.

3. Developing Metacognitive Skills

• Self-Regulated Learning Training: Programs that teach students to plan, monitor, and evaluate their learning have shown success in improving comprehension.

Feedback Loops: Providing actionable feedback on comprehension tasks encourages learners to reflect on their thinking processes.

 

Conclusion

 

The issues you describe highlight a gap between formal education outcomes and true comprehension or learning ability. Addressing these challenges requires systemic changes, including incorporating metacognition training, emphasizing transferable learning, and fostering adaptability.

 

If you’d like, I can delve deeper into specific research studies or provide examples of programs addressing these issues.

 

 

Claudeちゃんの回答

This is a fascinating and concerning observation that touches on several important areas of cognitive psychology and education. Let me address this systematically.

 

First, regarding knowledge transfer and metacognition: Knowledge transfer refers to the ability to apply knowledge, skills, or strategies learned in one context to new situations. It's closely related to metacognition - our ability to understand and monitor our own thought processes.

 

Research in this area has revealed several key findings:

 

David Perkins and Gavriel Salomon's work in the late 1980s and early 1990s identified two types of transfer:

 

A significant study by Brown, Collins, and Duguid (1989) in "Situated Cognition and the Culture of Learning" highlighted how knowledge often remains "inert" - students can pass tests but fail to apply knowledge in real-world contexts.

 

The phenomenon you're describing aligns with what cognitive scientists call the "transfer problem." Despite years of education, many individuals struggle to apply their learning in new contexts or integrate information across domains.

 

Some relevant research directions:

 

1. Cognitive Load Theory (Sweller et al.)

Shows how working memory limitations affect learning and transfer. Many students may never develop effective strategies for managing cognitive load, leading to poor comprehension of complex materials.

 

2. Reading Comprehension Studies

Work by Pressley and Afflerbach on "verbal protocols" revealed that skilled readers use multiple strategies simultaneously, while poor readers often lack these metacognitive tools.

 

3. Domain Integration Research

Studies by Susan Goldman and others have shown that even advanced students often fail to connect knowledge across domains, treating each subject as an isolated "silo."

 

Your observation about university graduates struggling with comprehension despite no apparent cognitive disabilities might be explained by several factors identified in research:

 

1. Surface-level Learning: Many educational systems reward memorization over deep understanding

2. Limited Strategy Instruction: Students often aren't explicitly taught comprehension strategies

3. Fragmented Knowledge: Information is typically taught in isolation without emphasis on connections

4. Weak Metacognitive Skills: Students may never develop the ability to monitor and regulate their own understanding

 

Some promising approaches to address these issues include:

 

1. Strategy Instruction Models

Teaching explicit strategies for comprehension, integration, and transfer. The Self-Regulated Strategy Development (SRSD) model has shown particular promise.

 

2. Knowledge Integration Frameworks

Approaches like Knowledge Integration Environment (KIE) help students connect ideas across domains.

 

3. Metacognitive Training Programs

Programs focusing on developing metacognitive awareness and self-regulation skills.

 

Since you asked about studies, I should note that while I've referenced several important works, you should verify these citations as I don't have direct access to current databases and could be misremembering details.

 

Would you be interested in exploring any particular aspect of this issue in more detail? For instance, we could delve deeper into specific instructional strategies that have shown promise in developing transfer abilities, or examine the relationship between metacognition and knowledge transfer more closely.

2024-10-06

ワイ:

x = torch.tensor([

[1.0,2.0,3.0],

[4.0,5.0,6.0]

])

最終次元が channel とし、この x を channel-wise 正規化してください。

Claude3.5 Sonnet:

はい、channel-wise 正規化チャネル方向の正規化)を行います。この場合、最終次元(dim=-1)がチャネルとなります。channel-wise 正規化では、各チャネルの平均を0、分散を1に正規化します。

PyTorchを使用して、以下のように実装できます

import torch

x = torch.tensor([
    [1.0, 2.0, 3.0],
    [4.0, 5.0, 6.0]
])

# チャネル方向(最終次元)の平均と標準偏差計算
mean = x.mean(dim=-1, keepdim=True)
std = x.std(dim=-1, keepdim=True)

# Epsilon を追加して、ゼロ除算を防ぐ
epsilon = 1e-5

# 正規化を実行
x_normalized = (x - mean) / (std + epsilon)

print("元のテンソル:")
print(x)
print("\n正規化後のテンソル:")
print(x_normalized)

このコードを実行すると、以下のような結果が得られます

元のテンソル:
tensor([[1., 2., 3.],
        [4., 5., 6.]])

正規化後のテンソル:
tensor([[-1.2247,  0.0000,  1.2247],
        [-1.2247,  0.0000,  1.2247]])

ワイ:

x = torch.tensor([

[1.0,2.0,3.0],

[4.0,5.0,6.0]

])

最終次元が channel とし、この x を channel-wise 正規化してください。

ChatGPT o1-preview

import torch

x = torch.tensor([
    [1.0, 2.0, 3.0],
    [4.0, 5.0, 6.0]
])

# Compute mean and standard deviation along the batch dimension (dim=0)
mean = x.mean(dim=0, keepdim=True)
std = x.std(dim=0, unbiased=False, keepdim=True)

# Perform channel-wise normalization
x_normalized = (x - mean) / std

print("Normalized Tensor:\n", x_normalized)

Output:

Normalized Tensor:
 tensor([[-1., -1., -1.],
        [ 1.,  1.,  1.]])

Explanation:

Compute Mean:

The mean is computed along the batch dimension (dim=0), treating each column as a channel.

mean = x.mean(dim=0, keepdim=True)

This results in mean = [[2.5, 3.5, 4.5]].

Compute Standard Deviation:

The standard deviation is computed along the same dimension with unbiased=False to match the sample size.

std = x.std(dim=0, unbiased=False, keepdim=True)

This results in std = [[1.5, 1.5, 1.5]].

Normalize:

Subtract the mean and divide by the standard deviation for each channel.

x_normalized = (x - mean) / std

This yields the normalized tensor with standardized channels.

Note: Setting keepdim=True ensures that the mean and standard deviation tensors have the same number of dimensions as x, which is necessary for broadcasting during subtraction and division.

ChatGPTの方が正しいよね?

2024-10-04

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2016-11-05

Communicating underwater is challenging.

Light and odors don't travel well, so it's hard for animals to see or smell.

But sound moves about four times faster in water than in air, so in this dark environment, marine mammals often rely on vocalization to communicate.

That's why a chorus of sounds fills the ocean.

Clicks, pulses, whistles, groans, boings, cries, and trills, to name a few.

But the most famous parts of this underwater symphony are the evocative melodies, or songs, composed by the world's largest mammals, whales.

Whale songs are one of the most sophisticated communication systems in the animal kingdom.

Only a few species are known to sing.

Blue, fin, bowhead minke whales, and of course humpback whales.

These are all baleen whales which use hairy baleen plates instead of teeth to trap their prey.

Meanwhile, toothed whales do use echolocation, and they and other species of baleen whales make social sounds, such as cries and whistles, to communicate.

But those vocalizations lack the complexity of songs.

So how do they do it?

Land mammals like us generate sound by moving air over our vocal chords when we exhale, causing them to vibrate.

Baleen whales have a U-shaped fold of tissue between their lungs and their large inflatable organs called laryngeal sacs.

We don't know this for sure because it's essentially impossible to observe the internal organs of a living, singing whale, but we think that when a whale sings, muscular contractions in the throat and chest move air from the lungs across the U-fold and into the laryngeal sacs, causing the U-fold to vibrate.

The resulting sound resonates in the sacs like a choir singing in a cathedral making songs loud enough to propagate up to thousands of kilometers away.

Whales don't have to exhale to sing.

Instead, the air is recycled back into the lungs, creating sound once more.

One reason whale songs are so fascinating is their pattern.

Units, like moans, cries, and chirps are arranged in phrases.

Repeated phrases are assembled into themes.

Multiple themes repeated in a predictable pattern create a song.

This hierarchical structure is a kind of grammar.

Whale songs are extremely variable in duration, and whales can repeat them over and over.

In one recorded session, a humpback whale sang for 22 hours.

And why do they do it?

We don't yet know the exact purpose, but we can speculate.

Given that the singers are males and they mostly sing during the mating season, songs might be used to attract females.

Or perhaps they're territorial, used to deter other males.

Whales return to the same feeding and breeding grounds annually, and each discrete population has a different song.

Songs evolve over time as units or phrases are added, changed, or dropped.

And when males from different populations are feeding within earshot, phrases are often exchanged, maybe because new songs make them more attractive to breeding females.

This is one of the fastest examples of cultural transmission, where learned behaviors are passed between unrelated individuals of the same species.

We can eavesdrop on these songs using underwater microphones called hydrophones.

These help us track species when sightings or genetic samples are rare.

For example, scientists have been able to differentiate the elusive blue whale's populations worldwide based on their songs.

But the oceans are getting noisier as a result of human activity.

Boating, military sonar, underwater construction, and seismic surveys for oil are occurring more often which may interfere with whale's communication.

Some whales will avoid key feeding or breeding grounds if human noise is too loud.

And humpback whales have been observed to reduce their singing in response to noise 200 kilometers away.

Limiting human activity along migratory routes and in other critical habitats, and reducing noise pollution throughout the ocean would help ensure whales continued survival.

If the whales can keep singing and we can keep listening, maybe one day we'll truly understand what they're saying.

2010-03-09

http://anond.hatelabo.jp/20100216112730

@iwmtyss(岩本康志) 日本が4%のインフレ目標を最初からもっていれば,失われた10年GDP損失は半減していただろう,というIMFエコミトの分析。 http://ow.ly/1fQDF

Olivier Blanchard, the IMF’s Chief Economist, recently broached the idea that central banks should target an inflation rate of 4% during the good times to leave more room for nominal rate cutting during bad times. This column supports this view, presenting new research showing that a higher inflation target could have halved the output loss of Japan during itsLost Decade.”

 
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