What would justify this claim? · 什么论据能支持这一主张？
| English | 中文 | Pinyin · 拼音 |
|---|---|---|
| claim/kleɪm/ | 主张 | zhǔ zhāng |
| evidence/ˈevɪdəns/ | 证据 | zhèng jù |
| sample/ˈsæmpl/ | 样本 | yàng běn |
| correlation/ˌkɒrɪˈleɪʃn/ | 相关关系 | xiāng guān guān xì |
A claim in your feed
- A post says, “Students learn better when every lesson uses a video.” This claim 主张 goes beyond the one classroom shown in the post.
- Before agreeing, ask what “learn better” means. A higher test score, a better explanation and stronger interest are different outcomes.
信息流中的主张
- 某帖子称：“当每节课都使用视频时，学生学得更好。”这一 主张 超出了该帖子所展示的一间教室的范围。
- 在表示赞同之前，应询问“学得更好”的具体含义。更高的考试分数、更清晰的解释以及更强的兴趣属于不同的结果。
Evidence has a job
- Evidence 证据 should support the exact claim. A survey of student preferences can show what students like; it does not by itself show what they learn.
- Compare classes using a relevant learning measure. Check whether the groups studied the same content and had similar starting points.
证据有其特定功能
- 证据 应当支持确切的主张。一项关于学生偏好的调查可以表明学生喜欢什么，但仅凭此并不能直接证明他们学到了什么。
- 使用相关的学习指标比较不同班级。检查各组是否学习了相同的内容且具有相似的起始水平。
Which measure best tests the claim that videos improve understanding of forces? · 哪项测量指标最能检验“视频能提高对力的理解”这一主张？
An explanation task measures the stated learning outcome more directly than popularity. · 相较于受欢迎程度，解释任务更直接地衡量了所述的学习成果。
Who was included?
- A sample 样本 drawn only from a video club may favour videos. Ask who was included, who was left out and whether that matters.
- A large sample does not remove a weak measurement or an unfair comparison. Size and quality answer different questions.
谁被纳入了样本？
- 仅从视频社团抽取的 样本 可能偏向视频。应询问哪些人被纳入、哪些人被排除，以及这是否有影响。
- 大样本并不能消除测量方法的缺陷或比较的不公平性。规模与质量回答的是不同的问题。
A large sample automatically removes selection bias. · 大样本量并不能自动消除选择偏差。
Selection bias concerns how the sample was chosen; a larger biased sample can remain biased. · 选择偏差涉及样本的选取方式；即使是有偏差的大样本依然可能存在偏差。
Consider another explanation
- The video class may also have had a different teacher or more revision time. A correlation 相关关系 between videos and scores does not establish cause.
- Look for a comparison that changes the use of videos while keeping other important conditions similar. Explain the limits that remain.
考虑其他解释
- 使用视频的班级可能还配备了不同的教师或更多的复习时间。相关关系 并不等同于因果关系。
- 寻找一个在改变视频使用的同时保持其他重要条件相似的比较案例，并说明仍存在的局限性。
Video users score higher, but they also revise for longer. What is the most careful conclusion? · 观看视频的学生分数较高，但他们复习时间也更长。最严谨的结论是什么？
The observed groups differ in another factor that could affect scores. · 观察到的组别在其他可能影响分数的因素上存在差异。
Make a careful conclusion
- Replace “Videos always improve learning” with a conclusion whose scope fits the evidence. State the learners, task and measure that were studied.
- In TOK, connect the example to how knowledge is justified. Naming “bias” is only a start; explain how it changes what the evidence can support.
得出审慎的结论
- 避免使用“视频总能提高学习效果”这类表述，而应根据证据的范围得出结论。明确指出所研究的对象、任务和测量方式。
- 在知识论中，将例子与知识的正当化过程联系起来。仅仅指出“偏见”只是第一步；还需解释它如何限制了证据所能支持的范围。
Which conclusion best fits evidence from one class? · 哪一个结论最符合单一班级的证据？
The conclusion states the setting and outcome instead of extending beyond the evidence. · 该结论陈述了研究情境和结果，而非超出证据范围进行推断。
Match each term to its role in this lesson. · 将每个术语与其在本课中的作用相匹配。
The distinctions help you explain your decisions clearly. · 这些区分有助于你清晰地阐述自己的决策依据。
Which checks help evaluate the video-learning claim? · 哪些检验有助于评估“视频学习”的主张？
Choose actions supported by the evidence and the purpose of the task; reject shortcuts that hide limitations or invent information. · 选择有证据支持且符合任务目标的行为；拒绝掩盖局限性或编造信息的捷径。
A preference survey finds that 80% of students like videos. This supports a claim about preference, not a claim that videos cause better understanding.
一项偏好调查显示，80% 的学生喜欢视频。这只能支持关于偏好的主张，不能证明视频能带来更好的理解。
A precise percentage can still describe a biased sample or the wrong outcome.
精确的百分比仍可能描述的是一个有偏差的样本或错误的结果。
Compare a claim with the exact evidence offered for it. Check the measurement, sample and competing explanations before drawing a conclusion.
将主张与其提供的确切证据进行比较。在得出结论之前，先检查测量方法、样本和竞争性解释。