跟讀練習: Data vs. Findings vs. Insights - 通過影片學習英語口語
選擇練習模式
每種模式都會開啟全屏練習室。
29 句
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- Data findings and insights.
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They may feel and are related, but they're three different things.
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I'll explain those three different things in this video.
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They communicate different levels of research analysis completion.
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Insights represent the most complete level of research analysis.
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Findings are slightly weaker and lack context, and data is completely devoid of any information or analysis.
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Let's walk through these, starting with data and build up.
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Data is an unanalyzed collection of individual observations about users that may include transcripts, notes, metrics, or survey output.
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At this level, no analysis has been completed yet, and without analysis, we don't yet have information.
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Take for instance, a usability study where we collected transcripts and a log of the things that users clicked on.
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These are our data, but they don't tell us anything about what we learned collectively or what to do as a result.
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This is why we don't wanna make decisions based on raw data alone.
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However, when we take our data points and start to identify patterns using qualitative or quantitative analysis methods, we call those patterns findings.
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Findings describe patterns in collected data, which still lack consideration of background, past research, and organizational factors.
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For example, we might say that most users clicked on a contact support button on our website, but this finding still doesn't give the full story of what this looked like in the past and why we observed it.
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It lacks context.
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We need something with clear context for good design decision-making.
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This is where insights come into play.
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Insights do have this rich and necessary context, and this is why insights are the gold standard for making user experience design decisions based on research.
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Insights are focused explanations of opportunities based on user research and business context.
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So, if we knew most users clicked on the contact support button on our website, we might know it's because they were not able to find the information they were looking for, which drives up support volume costs.
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In this scenario, the insight would be that there is an opportunity to improve the discoverability of important information to drive down support volume costs.
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Context is key.
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On teams, it is important to clearly distinguish whether you are referring to data, findings, or insights because this will communicate whether you have more analysis to do or not.
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Once we have insights, we're ready to make good decisions.
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- Thanks for watching.
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If you wanna see more of our UX videos, take a look at these over here and consider subscribing to our channel.
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On our website, nngroup.com, you can access our free library of over 2000 articles.
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You can also register for one of our UX courses that offer live hands-on UX training.
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本課概述
在本課中,您將學習有關資料、發現和洞察之間的區別。這三者在研究分析中代表不同的完成程度。在這個過程中,您將提高理解複雜內容的能力,並能夠更好地使用這些概念進行決策。通過對這段影片的分析,您將能夠更自信地討論這些術語,從而在您的英語對話中使表達更加精準。
關鍵詞彙與短語
- 資料 (Data)
- 發現 (Findings)
- 洞察 (Insights)
- 模式 (Patterns)
- 使用者研究 (User Research)
- 上下文 (Context)
- 決策 (Decision-making)
- 可用性研究 (Usability Study)
練習技巧
為了有效提高您的英語發音,建議您使用 shadowing site 來反覆練習這段影片中的內容。您可以通過模仿講者的語調和語速來鞏固發音技巧。影片中的講述較為清晰,適合使用 看YouTube學英語 的方法來進行跟讀。
在練習時,注意講者的停頓與重音,這可以幫助您更自然地掌握英語句子的節奏。儘量在合適的時機插入 shadowspeaks 的技巧來記錄您的進步。您可以每次觀看影片後進行回放,嘗試總結您所學習到的 資料、發現 與 洞察 的具體應用。
通過反覆的語音模仿,您將能提高 提高英語發音 的流暢度,增強口頭表達的自信。封閉式的問題,或者是對內容的簡短總結也是很好的練習方式。最後,記得將您的 shadow speech 錄音與原音進行對比,這是檢驗您進步的一種有效方法。
什麼是跟讀法?
跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧,最初開發用於專業口譯員的培訓,並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大:您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同,跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性,語調,節奏,連讀,聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。


























