[教育時評]激發批判性思考
拿我們的【新聞英文-Marketing & Influencers 補充包】 https://bit.ly/2YJPZvb
在上一篇文章中,我們將批判性思考大致定義為——使我們能處理資訊並解決問題的能力。讓我們藉由下列問題,來進一步限縮定義。
1、哪些人是最佳的批判性思想家?
2、你為什麼認為這些人是批判性思想家?
3、他們擁有哪些技能?
你想到了誰?他們擁有哪些技能?
也許有些人會想到喬姆斯基(Noam Chomsky)、霍金(Stephen Hawking)或喬治・歐威爾(George Orwell)。他們所共有者包含了好奇心、客觀性與分析性思考等諸多核心特點。
至此,我們的定義已相當清晰。我們知道即便批判性思考是一種抽象的技能,但仍可想到一些批判性思想家,並對其加以分析。
以下是與批判性思考相關的六大核心技能列表,以及有助於開發核心技能的問題:
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1. Interpretation 詮釋
•What does this mean?
•What’s happening?
•How should we understand that (e.g., what he or she just said)?
•What is the best way to characterize/categorize/classify this?
•In this context, what was intended by saying/doing that?
•How can we make sense out of this (experience, feeling, or statement)?
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2. Analysis 分析
•Please tell us again your reasons for making that claim.
•What is your conclusion/What is it that you are claiming?
•Why do you think that?
•What are the arguments pro and con?
•What assumptions must we make to accept that conclusion?
•What is your basis for saying that?
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3. Inference 推論
•Given what we know so far, what conclusions can we draw?
•Given what we know so far, what can we rule out?
•What does this evidence imply?
•If we abandoned/accepted that assumption, how would things change?
•What additional information do we need to resolve this question?
•If we believed these things, what would they imply for us going forward?
•What are the consequences of doing things that way?
•What are some alternatives we haven’t yet explored?
•Let’s consider each option and see where it takes us.
•Are there any undesirable consequences that we can and should foresee?
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4. Evaluation 評估
•How credible is that claim?
•Why do we think we can trust what this person claims?
•How strong are those arguments?
•Do we have our facts right?
•How confident can we be in our conclusion, given what we now know?
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5. Explanation 解釋
•What were the specific findings/results of the investigation?
•Please tell us how you conducted that analysis.
•How did you come to that interpretation?
•Please take us through your reasoning one more time.
•Why do you think that (was the right answer/was the solution)?
•How would you explain why this particular decision was made?
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6. Self-regulation 自我調整
•Our position on this issue is still too vague; can we be more precise?
•How good was our methodology, and how well did we follow it?
•Is there a way we can reconcile these two apparently conflicting conclusions?
•How good is our evidence?
•OK, before we commit, what are we missing?
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請留意,這六大核心技能只是定義批判性思考的一種方式。若你想閱讀我們有關批判性思考的最後一篇文章,以及學習如何發展此一能力,歡迎按讚或分享貼文,並於留言區回覆「I want to develop my critical thinking」。
我們將於5月30日晚間8:00 ,從留言的粉絲中抽出3位幸運的得獎者。贈送獎品為——《Times》、《People》以及《Bloomberg Businessweek》雜誌各一本。我會透過臉書通知得獎的同學。
抽獎活動已圓滿結束!
感謝大家的參與! Stay tuned for our next critical thinking post!
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參考文獻:
Facione, P. A. (2011). Critical thinking: What it is and why it counts. Insight assessment, 2007(1), 1-23.
McPeck, J. E. (2016). Critical thinking and education. Routledge.
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Try asking yourself some of these questions every time you encounter new information.
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AI國際鏈結辦公室於11/22(四)舉辦一場AI專題演講!
演講詳細資訊如下,歡迎大家踴躍參加!
#題目: AI Chip Design Challenges at the Edge – from Deep Learning Model to Hardware.
#講者: Bike Xie, Director of engineering, Kneron Inc
#時間: 2018/11/22(四)10:00-11:20AM
#地點: 國立清華大學 台達館106室
#報名及詳細資訊: https://reurl.cc/yEv58
#演講摘要: Since the remarkable success of AlexNet on the 2012 ImageNet competition, deep learning models and especially CNN models have become the architecture of the choice for many computer vision tasks. However, inference of a CNN model can be highly computational expensive, especially for the end-user devices, such as the internet of thing (IoT) devices, which have a very limited computing capability with low-precision arithmetic operators. A typical CNN model might require billions of multiply-accumulate operations (MACs), load millions of weights, and draw several watts power for a single inference. Limited computing resources and storage become the major obstacle to run computation-hungry CNN on IoT devices.
Many design techniques in the area of model structure, compiler, and hardware architecture are making it possible to deploy CNN models on edge devices. This report discusses the design challenges for AI chip at the edge and briefly introduces these design techniques. A well-designed small size model might only require much less storage and computation resource. Therefore, model compression techniques including pruning, quantization, model distillation become substantial to deploy CNN models on edge devices. Compiling CNN models to hardware instructions is another critical step. operation fusion, partition, and ordering might significantly improve the memory efficiency and model inference speed. Finally, hardware architecture for AI chip is currently one of the hottest topics in circuit design. Dedicated AI accelerators provide an opportunity to optimize the data movement in order to minimize memory access and maximize MAC efficiency.
主辦:國立清華大學AI創新研究中心專案-國際鏈結計畫
聯絡資訊:
田小姐 03-5715131 分機34908
黃小姐 03-5715131 分機34905
draw inference 在 Brett 林熙老師 Facebook 的最佳解答
Critical SAT and ACT Advice:
Don't take your school brain to the test!
What in earth does that mean?
Basically, if you are in a typical western schooling environment (e.g. TAS, TES, KC, etc), you are taught to draw conclusions in a big way. Teachers like to push you to use your imagination and infer all kinds of things that are not in reading passages. In fact, for many school teachers, especially literature teachers, the more you infer the happier they are.
So, what's the problem?
SAT, ACT, TOEFL, IELTS, GRE, and GMAT are standardized tests, which means that there can be only one correct answer. The kind of inferring that is common in school leads to multiple possible answers, so it's unsuitable for standardized tests.
The basic rule is this: only make one tiny inference - the smaller the better. Let me illustrate what I mean.
If your friend looks like he's in pain, and he's massaging his leg, what can you reasonably infer?
Only one thing: his leg hurts.
What can't you reasonably infer?
He got injured playing basketball.
He had an accident.
He woke up with a sore leg.
His sister kicked him.
A dog bit him.
He's malnourished.
He's got a rash.
Etc.
Even though this is boring as an inference, it's the only thing you can REASONABLY infer. Anything else is evidence of an overactive imagination and will give you a LOWER SCORE!
Remember: in a standardized test like the SAT and ACT, make the smallest inference possible. In fact, if you see direct evidence of an answer choice, that's even better than a choice you have to draw an inference about.
Always use what I term "the point test" - if you can't point directly to the evidence for an answer choice, it's probably wrong.
Thinking about it mathematically:
No inference > small inference > large inference
Remember: there is no place for imagination in a standardized test - always find evidence in the passage.
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Draw inferences by analyzing dataIn this lesson you will learn to draw inferences by analyzing data.ADDITIONAL MATERIALSLesson slides ... ... <看更多>
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Draw inferences from the textIn this lesson you will learn how to draw inferences about a phrase by looking for supporting details. ... <看更多>