本文關鍵詞： MOOC LTSA 資源評估 學習行為評估 遺傳算法 個性化學習　出處：《西安科技大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著互聯(lián)網(wǎng)技術的迅速發(fā)展,網(wǎng)絡課程在國內(nèi)外已經(jīng)取得了令人驕傲的成果,先后出現(xiàn)了許多優(yōu)秀的網(wǎng)絡課程資源,此外,MOOC作為網(wǎng)絡課程的一種新型表現(xiàn)形式,它在教育領域引起了的巨大變革,但是在實際的運行中,網(wǎng)絡課程的實施仍面臨著很大的問題。資源更新率低,資源管理不當,學習者被動,課程輟學率高,個性化應用服務缺失等問題成為當前絕大數(shù)網(wǎng)絡課程可持續(xù)發(fā)展的瓶頸,導致很多課程出現(xiàn)了只有教師的“教”,嚴重缺少學生的“學”的現(xiàn)象。本文以數(shù)據(jù)結構課程為背景,針對上述問題,設計并實現(xiàn)數(shù)據(jù)結構MOOC系統(tǒng)。首先借鑒MOOC課程的運行模式,分析并設計數(shù)據(jù)結構MOOC系統(tǒng)運行模式,在學生學習過程和教師教學過程中,教師和學生以及學生和學生之間的互動是系統(tǒng)運行的內(nèi)在動力,資源是二者互動的基礎,因此學習者的學習和資源的構建息息相關。在充分分析了課程運行模式之后,結合傳統(tǒng)經(jīng)典的LTSA框架,對其進行改進,提出數(shù)據(jù)結構MOOC學習管理框架,主要增加了學習者資源代理元件,學習者成績代理元件,學習者資源數(shù)據(jù)庫和三級代理模型。設計資源評估模型實現(xiàn)資源代理,設計學習行為評估模型實現(xiàn)學習者成績代理,設計三級代模式提高資源管理和學習行為評估的時效性和可操作性。最后,在對學生學習效果評估的數(shù)據(jù)基礎上,運用遺傳算法設計了智能組卷的組卷策略,并通過實驗驗證了遺傳算法的收斂性和試卷的相關性,達到個性化學習的目的。總之,數(shù)據(jù)結構MOOC系統(tǒng)通過對學習管理系統(tǒng)框架的改進,以“學習者”為中心,實現(xiàn)了資源的動態(tài)構建,建立了學生學習行為評估模型,設計的智能組卷滿足個性化應用的需求,對未來網(wǎng)絡課的發(fā)展具有重大影響。
[Abstract]:With the rapid development of Internet technology, online courses have made proud achievements at home and abroad. As a new manifestation of online courses, MOOC has caused great changes in the field of education. However, in the actual operation, the implementation of online courses is still faced with a lot of problems, and the rate of resource upgrading is low. Improper management of resources, passive learners, high dropout rate of courses, lack of personalized application services and other problems have become the bottleneck of the sustainable development of most online courses, leading to the emergence of "teaching" only by teachers in many courses. There is a serious lack of students'"learning" phenomenon. In this paper, the data structure MOOC system is designed and implemented against the background of the data structure course. Firstly, the operating mode of the MOOC course is used for reference. This paper analyzes and designs the operation mode of data structure MOOC system. In the process of students' learning and teaching, the interaction between teachers and students, as well as between students and students, is the internal motive force of the system. Resources are the basis of interaction between the two, so learners' learning is closely related to the construction of resources. After fully analyzing the course operation mode, combining with the traditional classical LTSA framework, we improve it. A data structure MOOC learning management framework is proposed, which mainly adds learner resource agent component and learner achievement agent component. Learner resource database and three-level agent model. Design resource evaluation model to implement resource agent, design learning behavior evaluation model to realize learner achievement agent. Third generation model is designed to improve the timeliness and maneuverability of resource management and learning behavior evaluation. Finally, on the basis of the data of students' learning effect evaluation, genetic algorithm is used to design an intelligent test paper generation strategy. The convergence of genetic algorithm and the relevance of test paper are verified by experiments. In a word, the data structure MOOC system improves the framework of learning management system by improving the framework of learning management system. Taking "learner" as the center, the dynamic construction of resources is realized, and the evaluation model of students' learning behavior is established. The intelligent test paper is designed to meet the needs of individualized application, which has a great influence on the development of network course in the future.
【學位授予單位】：西安科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：G434;TP311.12-4

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