Digital EducationOutlook 2021（Ⅱ）譯題：2021年數字教育發展觀（二）

Product by OECD （Organization for Economic Co-operation and Development） 經濟合作與發展組織

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

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

組織和系統管理層面的智能技術

以人工智能和學習分析為動力的智能技術也可以用來管理教育組織機構。它們可用于多種用途，例如，根據對學生學習和學習路徑的分析優化改進機構的課程設置。盡管這仍然是一個新生趨勢，但在整個教育組織中采用學習分析可以改變教育機構的文化。

早期預警系統可以很好地利用教育系統和組織所收集的行政微觀數據，從中識別出有高中輟學風險的學生。雖然確定一套完備的早期預警指標尚且存在困難，但一些系統已經顯示出了高水平的準確性，并豐富了針對學生退學原因的思考。為了避免出現學生分析出錯的風險，公開和透明的算法顯得很重要。

基于游戲的標準化評估也建立在智能技術和智能數據分析技術的基礎之上，將評估擴展到傳統測試（紙筆或基于計算機）無法輕易衡量的技能。這些技能包括更高層次的技能（例如創造力）或情感以及行為技能（例如協作、行為策略）。基于游戲的測試可以分析眼球追蹤數據和音頻記錄，并處理自然語言和信息，例如任務時間或使用模擬。

最后，作為一種“驗證基礎設施”，區塊鏈技術為教育和培訓領域的認證開辟了新途徑。區塊鏈技術使得對個人或機構聲明（包括其特征和資質）的確認成為可能，不僅可以立即得出結論，而且具有高度確定性。這有助于消除文憑（和其他記錄）造假，促進學習者和工作者在機構和地區之間的流動，并通過對自己數據的控制來提高個人的能力。目前，區塊鏈項目正在世界各地開展，這可能會改變教育和終身學習系統管理學位和資格證書的方式。

政策指南

我們有充分的理由相信，智能技術可以促進教育系統的有效性、公平性和成本效益。同時，要獲得這些好處，需要牢記智能技術的幾個重要方面。

智能技術是人與人工智能的混合系統。讓最終用戶參與到系統設計中，將重要決策的控制權交給人類，并以公開透明的方式與社會保持協商，是使智能技術既發揮作用又為社會所接受的關鍵。

智能技術雖然能夠以許多不同的方式支持人類，但并不完美。透徹地了解它們在測量、診斷或行動方面的準確程度是一項重要要求。然而，它們的極限應該與人類執行類似任務的極限進行比較。

更多關于智能技術在課堂內外的有效教學應用以及它們用于系統管理目的的證據應該得到資助，而不應該僅僅關注該技術。還可以制定出快速生成這些證據的標準。

智能技術的采用依賴基于風險評估的、健全的數據保護和隱私監管，但在沒有監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越受到人們關注，它可以通過第三方驗證的“開放算法”進行驗證。

智能技術是有成本的，成本效益分析應該指導它們的應用，承認它們提供的好處已經超越了金錢。在許多情況下，數據模式的確定有助于更好地設計政策和干預措施，從而更有可能提高公平性或有效性。除此之外，政策制定者還應鼓勵開發那些由于開放標準和互操作性而可以負擔得起并且可持續的技術。