應用大數據分析提供與試卷相關之教學影片推薦服務;Applying Big Data Analytics to Provide a Test Related to Digital Teaching Videos Recommendation Service

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Title:	應用大數據分析提供與試卷相關之教學影片推薦服務;Applying Big Data Analytics to Provide a Test Related to Digital Teaching Videos Recommendation Service
Authors:	李佳軒;Lee, Jia-Xuan
Contributors:	資訊工程學系
Keywords:	開放教育資源;推薦系統;關聯規則
Date:	2017-07-19
Issue Date:	2017-10-27 14:35:32 (UTC+8)
Publisher:	國立中央大學
Abstract:	由於科技的普及與開放教育資源 (Open Educational Resources, OER)平臺的建立，教學者可任意使用OER平臺內的資源製作課程或是學習者可任意選擇所需的教學資源作使用，OER平臺除了提供教學資源外，許多OER平臺也提供線上測驗的服務，根據文獻，學習者利用測驗結果作學習，當學習者自行搜尋則會面臨OER平臺常有的資訊過載的問題。本研究提出利用關聯規則建立與試卷相關之教學影片服務，透過分析試卷被使用的時機結合關聯規則建立知識架構內概念特徵集，將試卷與概念特徵集相似度比對後得到分類結果，與教學影片產生關聯性，將推薦結果與高成就學習者觀看影片清單結合，給予學習者最佳化後的教學影片推薦清單，來減少資訊過載的問題並讓學習者學習高成就學生學習模式來提升學習者學習成效。 ;Due to the establishment of the Open Educational Resources (OER) platform, the instructor can use the educational resources in the OER platform or the learner can arbitrarily select the required teaching resources for use. In addition to providing the OER platform educational resources, many OER platforms also provide online quiz services, according to the literature, learners use the results of the test for learning. When the learners has finished the test, they will face overloading information problem which always happens in the OER platform. This paper proposes of the use of the association rules to establish test related to digital teaching videos recommendation service. By analyzing the timing of the use of the test, the conceptual feature set of the knowledge structure is established, and the similarity between the tests and the conceptual feature sets is compared with the classfiaction results. The results of the presentation are combined with the list of highly viewed learners′ videos, giving learners an optimized list of recommendation to reduce the problem of overloading information and allowing learners to learn high-level student learning patterns to improve learning effect
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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