Digital Education Outlook 2021（Ⅰ） 譯題：2021年數(shù)字教育發(fā)展觀（一）

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

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

Executive Summary

Digitalisation opens up new possibilities for education. While education has always been rich in data such as grades or administrative information on students’ absenteeism， the use of data to help students learn better and teachers to teach better， and to inform decision-making in educational administrations is recent. Education stakeholders have had a difficult relationship with technology， alternating between strong enthusiasm and scepticism. Might digital technology， and， notably， smart， technologies based on artificial intelligence， learning analytics， robotics， and others， transform education in the same way they are transforming the rest of society？ The book focuses on how smart technologies can change education in the classroom and support the management of education organisations and systems.

Smart technologies in the classroom

Adaptive learning technology such as intelligent tutoring systems enable the personalisation of students' learning using similar approaches： they detect the knowledge （or knowledge gaps） of students; they diagnose the next appropriate steps for students’ learning; they act by providing new exercises， new curriculum units， some form of instruction， or just notifying the teacher. This approach is now being expanded beyond mere knowledge acquisition and factoring in behavioural dimensions such as learning self-regulation or style.

As keeping students engaged and motivated is key to learning effectiveness， a new domain of technology development focuses on measuring engagement and interventions to keep students engaged， both in digital and physical learning environments. Measuring engagement is difficult but a host of new automated approaches have been developed， from eye trackers to the monitoring and analysis of other facial features. Improving engagement typically takes two routes： proactive approaches try to stimulate engagement with incentives， gamification， etc.; reactive approaches do it in a more sophisticated way by continually monitoring engagement， detecting when engagement is waning， and adapting instruction to address periods of disengagement.

While smart technologies focusing on personalising learning for individuals are probably the most pervasive， another approach is to consider the classroom or rather what happens in the classroom as the subject of the learning analytics. The objective is to support teachers in orchestrating the learning in their classroom and to propose rich and effective learning scenarios to their students. Some classroom analytics techniques provide teachers with real-time feedback to help manage transitions from one task to the next as their students work individually， in small groups or collectively， for example. They also give feedback to teachers on their classroom behaviour so they can reflect on and learn from their practice.

Social robots are also being increasingly developed for learning uses. Usually powered by the personalisation systems mentioned above， they support teachers in different ways： as instructors or tutors for individuals or small groups， but also as peer learners allowing students to “teach” them. Telepresence robots also allow teachers or students to teach or study remotely and offer new possibilities for students who are ill and cannot physically attend class. They can also mobilise a remotely located teaching workforce， for example teachers from another country to teach foreign languages.

Technology also enables students with special needs to participate in education and to make inclusive education a reality. With well-known applications such as speech-to-text， text-to-speech， and auto-captioning， etc.， AI allows blind， visually impaired， deaf and hard-of-hearing students to participate in traditional educational settings and practices. Some smart technologies facilitate the diagnosis and remediation of some special needs （e.g. dysgraphia） and support the socio-emotional learning of students with autism so they can more easily participate in mainstream education.

Those smart technologies usually assume and require a human-in-the-loop： a teacher. The level of automation of actions and decisions should be conceived of as a continuum between actions that are fully automated at one end and， at the other end， actions over which humans have full control. As of today， AI systems remain hybrid and request human intervention at a certain point in the process.

譯文

摘要

數(shù)字化為教育開辟了新的可能性。雖然教育一直有豐富的數(shù)據(jù)，例如學生的缺勤記錄或成績，但最近才開始利用數(shù)據(jù)來幫助學生更好地學習、幫助教師更好地教學并為教育管理部門的決策提供參考。教育利益相關(guān)者與技術(shù)的關(guān)系處境艱難，一直在強烈的熱情和懷疑之間徘徊。數(shù)字技術(shù)，尤其是基于人工智能、學習分析、機器人技術(shù)和其他技術(shù)的智能技術(shù)，是否會像改變社會其他領(lǐng)域一樣改變教育？本報告重點討論了智能技術(shù)如何改變課堂教育并支持教育組織和系統(tǒng)的管理。

課堂中的智能技術(shù)

智能輔導(dǎo)系統(tǒng)等自適應(yīng)學習技術(shù)可以使用類似的方法實現(xiàn)學生學習的個性化：檢測學生的知識（或知識缺口）;診斷學生學習的下一個合適的步驟;提供新的練習、新的課程單元、某種形式的教學指導(dǎo)或只是通知教師采取行動。目前，這種方法已超越單純的知識獲取，并擴展到諸如學習自我調(diào)節(jié)或?qū)W習風格等行為維度。

由于保持學生的參與度和積極性是提高學習效率的關(guān)鍵，因此，技術(shù)發(fā)展的一個新領(lǐng)域是測量參與度，以保持學生在數(shù)字和物理學習環(huán)境中的深度參與。測量參與度并非易事，但已有一系列新的自動化方法被開發(fā)出來，包括眼球追蹤器以及其他面部特征的監(jiān)測和分析。提高學生參與度通常有兩種方法：一種是主動的方法，即嘗試通過獎勵和游戲化等方式刺激用戶黏性;一種是更為復(fù)雜的反應(yīng)性方法，即通過不間斷的監(jiān)測來捕捉參與度下降的信息，并及時給予調(diào)整和指導(dǎo)。

除了最普遍的專注個性化學習的智能技術(shù)，另一種方式是將教室或教室中發(fā)生的事情作為學習分析的主題。后者旨在幫助教師在課堂上協(xié)調(diào)學習，并為學生提供豐富有效的學習場景。例如，一些課堂分析技術(shù)為教師提供實時反饋，以幫助教師在學生單獨學習、小組學習或集體學習時完成不同任務(wù)之間管理的過渡。此外，學生的課堂行為將被反饋給教師，供教師反思并從實踐中學習。

社交機器人也越來越多地被開發(fā)用于學習。它們通常由上述個性化系統(tǒng)提供支持，以不同的方式支持教師：作為個人或小組的導(dǎo)師，也作為同伴學習者，允許學生“教”它們。除此之外，遠程機器人還允許教師或?qū)W生遠程教學或?qū)W習，為生病或無法親自上課的學生提供新的可能性。它們甚至還可以動員遠程教學人員，例如來自另一個國家的教師來教授外語。

同時，智能技術(shù)還使有特殊需要的學生能夠參與教育，使全納教育成為現(xiàn)實。通過語音轉(zhuǎn)文本、文本轉(zhuǎn)語音以及自動字幕等知名應(yīng)用程序，人工智能已經(jīng)能夠幫助盲人、視力受損、失聰和聽力障礙的學生參與到傳統(tǒng)的教育實踐中去。其中一些智能技術(shù)還有助于某些特殊需要（如書寫困難）的診斷和補救，并幫助自閉癥學生的社會情緒學習，使他們更容易參與主流教育。

這些智能技術(shù)通常假設(shè)并需要一個人的操作——教師。行動和決策的自動化水平應(yīng)該被視為一個連續(xù)體，一端是完全自動化的行動，另一端則是完全由人類控制的行動。到目前為止，人工智能系統(tǒng)仍然是混合型的，在過程中的某一點上還是需要人工干預(yù)的。