本文關(guān)鍵詞： MOOC LTSA 資源評(píng)估 學(xué)習(xí)行為評(píng)估 遺傳算法 個(gè)性化學(xué)習(xí)　出處：《西安科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著互聯(lián)網(wǎng)技術(shù)的迅速發(fā)展,網(wǎng)絡(luò)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(luò)課程資源,此外,MOOC作為網(wǎng)絡(luò)課程的一種新型表現(xiàn)形式,它在教育領(lǐng)域引起了的巨大變革,但是在實(shí)際的運(yùn)行中,網(wǎng)絡(luò)課程的實(shí)施仍面臨著很大的問題。資源更新率低,資源管理不當(dāng),學(xué)習(xí)者被動(dòng),課程輟學(xué)率高,個(gè)性化應(yīng)用服務(wù)缺失等問題成為當(dāng)前絕大數(shù)網(wǎng)絡(luò)課程可持續(xù)發(fā)展的瓶頸,導(dǎo)致很多課程出現(xiàn)了只有教師的“教”,嚴(yán)重缺少學(xué)生的“學(xué)”的現(xiàn)象。本文以數(shù)據(jù)結(jié)構(gòu)課程為背景,針對(duì)上述問題,設(shè)計(jì)并實(shí)現(xiàn)數(shù)據(jù)結(jié)構(gòu)MOOC系統(tǒng)。首先借鑒MOOC課程的運(yùn)行模式,分析并設(shè)計(jì)數(shù)據(jù)結(jié)構(gòu)MOOC系統(tǒng)運(yùn)行模式,在學(xué)生學(xué)習(xí)過程和教師教學(xué)過程中,教師和學(xué)生以及學(xué)生和學(xué)生之間的互動(dòng)是系統(tǒng)運(yùn)行的內(nèi)在動(dòng)力,資源是二者互動(dòng)的基礎(chǔ),因此學(xué)習(xí)者的學(xué)習(xí)和資源的構(gòu)建息息相關(guān)。在充分分析了課程運(yùn)行模式之后,結(jié)合傳統(tǒng)經(jīng)典的LTSA框架,對(duì)其進(jìn)行改進(jìn),提出數(shù)據(jù)結(jié)構(gòu)MOOC學(xué)習(xí)管理框架,主要增加了學(xué)習(xí)者資源代理元件,學(xué)習(xí)者成績代理元件,學(xué)習(xí)者資源數(shù)據(jù)庫和三級(jí)代理模型。設(shè)計(jì)資源評(píng)估模型實(shí)現(xiàn)資源代理,設(shè)計(jì)學(xué)習(xí)行為評(píng)估模型實(shí)現(xiàn)學(xué)習(xí)者成績代理,設(shè)計(jì)三級(jí)代模式提高資源管理和學(xué)習(xí)行為評(píng)估的時(shí)效性和可操作性。最后,在對(duì)學(xué)生學(xué)習(xí)效果評(píng)估的數(shù)據(jù)基礎(chǔ)上,運(yùn)用遺傳算法設(shè)計(jì)了智能組卷的組卷策略,并通過實(shí)驗(yàn)驗(yàn)證了遺傳算法的收斂性和試卷的相關(guān)性,達(dá)到個(gè)性化學(xué)習(xí)的目的�？傊�,數(shù)據(jù)結(jié)構(gòu)MOOC系統(tǒng)通過對(duì)學(xué)習(xí)管理系統(tǒng)框架的改進(jìn),以“學(xué)習(xí)者”為中心,實(shí)現(xiàn)了資源的動(dòng)態(tài)構(gòu)建,建立了學(xué)生學(xué)習(xí)行為評(píng)估模型,設(shè)計(jì)的智能組卷滿足個(gè)性化應(yīng)用的需求,對(duì)未來網(wǎng)絡(luò)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(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：G434;TP311.12-4

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