Digital EducationOutlook 2021（Ⅱ）譯題：2021年數(shù)字教育發(fā)展觀（二）

Product by OECD （Organization for Economic Co-operation and Development） 經(jīng)濟(jì)合作與發(fā)展組織

該報(bào)告向我們介紹了應(yīng)用于教育領(lǐng)域的前沿技術(shù)，包括人工智能、機(jī)器人和區(qū)塊鏈等，并重點(diǎn)討論了這些智能技術(shù)如何為遠(yuǎn)程教育帶來(lái)更豐富的教學(xué)模式和更系統(tǒng)的管理方法。

Smart technologies at the organisation and system levels

Smart technologies powered by AI and learning analytics also allow for the management of education organisations. They can be used for a variety of purposes; for example， to enhance an institution’s curriculum based on an analysis of students’ learning and study paths. While this is still a nascent trend， a whole-of-organisation adoption of learning analytics can transform educational institutions’ culture.

Early warning systems that identify students at risk of dropping out from high school are a good use of the administrative micro-data that are increasingly being collected by education systems and organisations. While identifying a good set of early warning indicators remains difficult， a few systems have shown a high level of accuracy and enriched thinking about the reasons students drop out. In order to avoid the risks of student profiling， open and transparent algorithms are important.

Game-based standardised assessments also build on smart technologies and smart data analysis techniques to expand assessment to skills that cannot be easily measured by traditional （paper-and-pencil or computer-based） tests. These include higher-order skills （e.g. creativity） or emotional and behavioural skills （e.g. collaboration， behavioural strategy）. Game-based tests may analyse eye-tracking data and audio recording， and process natural language and information such as time-on-task or use simulations.

Finally， as a “verification infrastructure”， blockchain technology opens new avenues for credentialing in education and training. Blockchain technology enables the validation of claims about an individual or institution， including their characteristics and qualifications， and to do this instantly and with a very high level of certainty. This helps eliminate diploma （and other records） fraud， facilitates the movement of learners and workers between institutions and geographies， and empowers individuals by giving them increased control over their own data. Many blockchain initiatives are underway across the world， which may transform how education and lifelong learning systems manage degrees and qualifications.

Policy pointers

There are good reasons to believe that smart technologies can contribute to the effectiveness， equity and cost-efficiency of education systems. At the same times， there are a few important aspects of smart technologies to keep in mind to reap those benefits：

·Smart technologies are human-AI hybrid systems. Involving end users in their design， giving control to humans for important decisions， and negotiating their usage with society in a transparent way is key to making them both useful and socially acceptable.

·Smart technologies support humans in many different ways without being perfect. Transparency about how accurate they are at measuring， diagnosing or acting is an important requirement. However， their limits should be compared to the limits of human beings performing similar tasks.

·More evidence about effective pedagogical uses of smart technologies in and outside of the classroom as well as their uses for system management purposes should be funded without focusing on the technology exclusively. Criteria for this evidence to be produced quickly could also be developed.

·The adoption of smart technologies relies on robust data protection and privacy regulation based on risk assessment but also ethical considerations where regulation does not exist. For example， there is mounting concern about the fairness of algorithms， which could be verified through “open algorithms” verified by third parties.

·Smart technologies have a cost， and cost-benefit analysis should guide their adoption， acknowledging that their benefits go beyond pecuniary ones. In many cases， the identification of data patterns allows for better policy design and interventions that are more likely to improve equity or effectiveness. Policy makers should also encourage the development of technologies that are affordable and sustainable thanks to open standards and interoperability.

譯文

組織和系統(tǒng)管理層面的智能技術(shù)

以人工智能和學(xué)習(xí)分析為動(dòng)力的智能技術(shù)也可以用來(lái)管理教育組織機(jī)構(gòu)。它們可用于多種用途，例如，根據(jù)對(duì)學(xué)生學(xué)習(xí)和學(xué)習(xí)路徑的分析優(yōu)化改進(jìn)機(jī)構(gòu)的課程設(shè)置。盡管這仍然是一個(gè)新生趨勢(shì)，但在整個(gè)教育組織中采用學(xué)習(xí)分析可以改變教育機(jī)構(gòu)的文化。

早期預(yù)警系統(tǒng)可以很好地利用教育系統(tǒng)和組織所收集的行政微觀數(shù)據(jù)，從中識(shí)別出有高中輟學(xué)風(fēng)險(xiǎn)的學(xué)生。雖然確定一套完備的早期預(yù)警指標(biāo)尚且存在困難，但一些系統(tǒng)已經(jīng)顯示出了高水平的準(zhǔn)確性，并豐富了針對(duì)學(xué)生退學(xué)原因的思考。為了避免出現(xiàn)學(xué)生分析出錯(cuò)的風(fēng)險(xiǎn)，公開(kāi)和透明的算法顯得很重要。

基于游戲的標(biāo)準(zhǔn)化評(píng)估也建立在智能技術(shù)和智能數(shù)據(jù)分析技術(shù)的基礎(chǔ)之上，將評(píng)估擴(kuò)展到傳統(tǒng)測(cè)試（紙筆或基于計(jì)算機(jī)）無(wú)法輕易衡量的技能。這些技能包括更高層次的技能（例如創(chuàng)造力）或情感以及行為技能（例如協(xié)作、行為策略）。基于游戲的測(cè)試可以分析眼球追蹤數(shù)據(jù)和音頻記錄，并處理自然語(yǔ)言和信息，例如任務(wù)時(shí)間或使用模擬。

最后，作為一種“驗(yàn)證基礎(chǔ)設(shè)施”，區(qū)塊鏈技術(shù)為教育和培訓(xùn)領(lǐng)域的認(rèn)證開(kāi)辟了新途徑。區(qū)塊鏈技術(shù)使得對(duì)個(gè)人或機(jī)構(gòu)聲明（包括其特征和資質(zhì)）的確認(rèn)成為可能，不僅可以立即得出結(jié)論，而且具有高度確定性。這有助于消除文憑（和其他記錄）造假，促進(jìn)學(xué)習(xí)者和工作者在機(jī)構(gòu)和地區(qū)之間的流動(dòng)，并通過(guò)對(duì)自己數(shù)據(jù)的控制來(lái)提高個(gè)人的能力。目前，區(qū)塊鏈項(xiàng)目正在世界各地開(kāi)展，這可能會(huì)改變教育和終身學(xué)習(xí)系統(tǒng)管理學(xué)位和資格證書的方式。

政策指南

我們有充分的理由相信，智能技術(shù)可以促進(jìn)教育系統(tǒng)的有效性、公平性和成本效益。同時(shí)，要獲得這些好處，需要牢記智能技術(shù)的幾個(gè)重要方面。

智能技術(shù)是人與人工智能的混合系統(tǒng)。讓最終用戶參與到系統(tǒng)設(shè)計(jì)中，將重要決策的控制權(quán)交給人類，并以公開(kāi)透明的方式與社會(huì)保持協(xié)商，是使智能技術(shù)既發(fā)揮作用又為社會(huì)所接受的關(guān)鍵。

智能技術(shù)雖然能夠以許多不同的方式支持人類，但并不完美。透徹地了解它們?cè)跍y(cè)量、診斷或行動(dòng)方面的準(zhǔn)確程度是一項(xiàng)重要要求。然而，它們的極限應(yīng)該與人類執(zhí)行類似任務(wù)的極限進(jìn)行比較。

更多關(guān)于智能技術(shù)在課堂內(nèi)外的有效教學(xué)應(yīng)用以及它們用于系統(tǒng)管理目的的證據(jù)應(yīng)該得到資助，而不應(yīng)該僅僅關(guān)注該技術(shù)。還可以制定出快速生成這些證據(jù)的標(biāo)準(zhǔn)。

智能技術(shù)的采用依賴基于風(fēng)險(xiǎn)評(píng)估的、健全的數(shù)據(jù)保護(hù)和隱私監(jiān)管，但在沒(méi)有監(jiān)管的情況下也需要考慮道德因素。例如，算法的公平性正越來(lái)越受到人們關(guān)注，它可以通過(guò)第三方驗(yàn)證的“開(kāi)放算法”進(jìn)行驗(yàn)證。

智能技術(shù)是有成本的，成本效益分析應(yīng)該指導(dǎo)它們的應(yīng)用，承認(rèn)它們提供的好處已經(jīng)超越了金錢。在許多情況下，數(shù)據(jù)模式的確定有助于更好地設(shè)計(jì)政策和干預(yù)措施，從而更有可能提高公平性或有效性。除此之外，政策制定者還應(yīng)鼓勵(lì)開(kāi)發(fā)那些由于開(kāi)放標(biāo)準(zhǔn)和互操作性而可以負(fù)擔(dān)得起并且可持續(xù)的技術(shù)。