Adaptive Micro Learning - Using Fragmented Time to Learn
暫譯: 適應性微學習 - 利用碎片時間進行學習

Geng Sun , Jun Shen , Jiayin Lin

  • 出版商: World Scientific Pub
  • 出版日期: 2020-03-09
  • 售價: $3,270
  • 貴賓價: 9.5$3,107
  • 語言: 英文
  • 頁數: 210
  • 裝訂: Hardcover
  • ISBN: 9811207453
  • ISBN-13: 9789811207457
  • 海外代購書籍(需單獨結帳)

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商品描述

This compendium introduces an artificial intelligence-supported solution to realize adaptive micro learning over open education resource (OER). The advantages of cloud computing and big data are leveraged to promote the categorization and customization of OERs micro learning context. For a micro-learning service, OERs are tailored into fragmented pieces to be consumed within shorter time frames.Firstly, the current status of mobile-learning, micro-learning, and OERs are described. Then, the significances and challenges of Micro Learning as a Service (MLaaS) are discussed. A framework of a service-oriented system is provided, which adopts both online and offline computation domain to work in conjunction to improve the performance of learning resource adaptation.In addition, a comprehensive learner model and a knowledge base is prepared to semantically profile the learners and learning resource. The novel delivery and access mode of OERs suffers from the cold start problem because of the shortage of already-known learner information versus the continuously released new micro OERs. This unique volume provides an excellent feasible algorithmic solution to overcome the cold start problem.

商品描述(中文翻譯)

這本彙編介紹了一種人工智慧支持的解決方案,以實現基於開放教育資源(OER)的自適應微學習。利用雲計算和大數據的優勢,促進OER微學習情境的分類和定制。對於微學習服務,OER被切割成碎片,以便在更短的時間內進行消耗。首先,描述了移動學習、微學習和OER的現狀。接著,討論了作為服務的微學習(Micro Learning as a Service, MLaaS)的重要性和挑戰。提供了一個服務導向系統的框架,該框架採用在線和離線計算領域協同工作,以改善學習資源適應的性能。此外,準備了一個全面的學習者模型和知識庫,以語義化地描述學習者和學習資源。OER的新型交付和訪問模式因為已知學習者信息的短缺而遭遇冷啟動問題,這與不斷釋放的新微OER形成對比。這本獨特的著作提供了一個出色的可行算法解決方案,以克服冷啟動問題。