應用巨量資料技術探討MOOCs學習者之課程影片關聯性分析與適性化教材推薦;Subproject 1: Applying Big Data Technique to Moocs Learners’ Course Video Analytics and Adaptive Course Material Recommendation

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/78695

Title:	應用巨量資料技術探討MOOCs學習者之課程影片關聯性分析與適性化教材推薦;Subproject 1: Applying Big Data Technique to Moocs Learners’ Course Video Analytics and Adaptive Course Material Recommendation
Authors:	楊鎮華;陳雅玲
Contributors:	國立中央大學資訊工程系
Date:	2018-12-19
Issue Date:	2018-12-20 13:43:31 (UTC+8)
Publisher:	科技部
Abstract:	MOOCs能為學習者留下他們的所參與的學習活動之歷程，並應用巨量資料進行資料萃取分析，進而從學習者的學習歷程中預測或是解釋學生的學習狀況與行為之變化，並進一步建立模型 (Model)找出隱性的學習模式，以便瞭解學習者在學習過程中是否遇到學習困難或需要額外的學習幫助，而巨量資料技術就是本研究計晝達到上述目的主要方式。由於MOOCs的教材通常是教學者於課前預先錄製的教材影片，學習者對於所上的課程可能有先備知識不足，以及面對不易理解的内容時缺乏資源輔助及解惑。當學習者針對影片不易理解時， MOOCs系統沒有即時回饋，學習者也不知從何下手。同樣的，教學者在課程教學上也會因為缺乏學習者給予回饋，而不知道從何幫助學習者。因此，子計晝一針對上述現況，提供課程影片關聯性分析與適性化教材推薦模式，此模式會根據總計晝之最佳化學習路徑並配合課程學習内容推薦，結合新聞時事，充實適性化教材，讓學習者除了課堂内的專業知識外，還可以了解課堂外與專業知識相關的脈動；子計晝一也規劃課程影片導覽，引導學習者了解課程影片，將其内化成自身的專業學科知識。也會根據總計晝之最佳學習路徑提供之教材，配合影片歷程分析與學習行為分析來推薦學習者合適的教材内容。 ;MOOCs can keep students’ learning records of the leaning activities they join. By big data analysis, students’ learning conditions and learning behaviors can be predicted from their learning records. Furthermore, the model can be built and the hidden learning mode can be find out so that the instructor can know whether students face learning difficulties or they need extra help. The main method to achieve the aforementioned goals is the big data technology. Owing that the teaching materials of MOOCs are usually the learning films that are recorded in advanced, students may face the problem of not having enough background knowledge of the course they take and not having enough assistance while they get into difficulties. Once students do not understand the course film, MOOCs system does not provide them with instant feedback mechanism. Students may not know how to overcome their problems. Likewise, the instructor may not know how to help students because MOOCs system does not have student’ feedback. To solve the aforementioned problems, sub-project 1 provides correlation analysis of course films and the recommendation model of adaptive teaching materials. This model will recommend teaching materials according to the best learning path provided by the main project and the course contents. In addition, combining with news and events, the recommendation model enriches the adaptive teaching materials. In addition to the in-class knowledge, Students can learn professional knowledge outside the classroom by the recommendation model. Sub-project 1 also provides course film navigation to help students realize the film and further absorb the knowledge. Moreover, sub-project 1 will recommend students with adaptive materials according to the materials suggested by the best learning path in the main project, the learning process analysis and the learning behavior analysis.
Relation:	財團法人國家實驗研究院科技政策研究與資訊中心
Appears in Collections:	[Department of Computer Science and information Engineering] Research Project

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