【摘要】：由于國家對精品課程建設的大力推廣,作為精品課程核心內容之一的精品課程網(wǎng)絡教學平臺也已經(jīng)得到普及。然而現(xiàn)在的精品課程網(wǎng)絡教學平臺普遍存在個性化學習推薦功能較弱、互動性較差等情況,所以用戶利用網(wǎng)絡教學平臺進行學習的體驗并不優(yōu)秀,這也成為了導致網(wǎng)絡教學平臺利用率不高的因素之一。本文基于關聯(lián)規(guī)則挖掘技術和AJAX技術,以軟件工程技術為指導,設計并實現(xiàn)了具有個性化學習內容推薦功能和較強互動功能的精品課程網(wǎng)絡教學平臺。個性化學習內容推薦功能包含兩個核心子模塊：一是實現(xiàn)關聯(lián)規(guī)則挖掘子模塊,二是利用關聯(lián)規(guī)則實現(xiàn)學習內容推薦子模塊。在關聯(lián)規(guī)則挖掘模塊中采用了Apriori算法對用戶的訪問日志進行關聯(lián)規(guī)則挖掘,實現(xiàn)用戶訪問系統(tǒng)時的學習內容推薦；同時,基于用戶訪問的內容數(shù)據(jù)具有層次性的特點,本文也研究了利用ML-SH挖掘算法對同層數(shù)據(jù)進行關聯(lián)規(guī)則挖掘,從而實現(xiàn)了板塊之間的訪問推薦效果。在平臺實現(xiàn)的基礎上,本文對平臺的關聯(lián)規(guī)則模塊進行了測試,并對測試過程中關聯(lián)規(guī)則模塊可能存在的問題進行了分析。同時,為了獲知用戶對平臺推薦的學習內容的滿意程度,即系統(tǒng)推薦的效果,本文提出了利用統(tǒng)計用戶訪問系統(tǒng)推薦的內容數(shù)量占其當次訪問的內容數(shù)量的比值,作為評判用戶滿意度的方式,并以該方式對系統(tǒng)的推薦效果進行了實驗測試,實驗結果證明用戶對推薦的內容是感到滿意的。
[Abstract]:Due to the national promotion of the construction of high-quality courses, as one of the core contents of high-quality courses, the network teaching platform of high-quality courses has also been popularized. However, the current excellent course network teaching platform generally has the situation that the personalized learning recommendation function is weak, the interaction is poor and so on, so the user's experience of using the network teaching platform to carry on the study is not excellent. This has also become one of the factors that lead to the low utilization rate of network teaching platform. Based on association rule mining technology and AJAX technology, this paper designs and implements a network teaching platform for excellent courses with personalized learning content recommendation function and strong interaction function under the guidance of software engineering technology. The personalized learning content recommendation function consists of two core sub-modules: one is to realize the association rule mining sub-module, the other is to use the association rules to realize the learning content recommendation sub-module. In the association rule mining module, Apriori algorithm is used to mine the user's access log, and the learning content recommendation is realized when the user accesses the system. At the same time, based on the hierarchical characteristics of the content data accessed by users, this paper also studies the use of ML-SH mining algorithm to mine association rules for the same layer of data, so as to achieve the effect of access recommendation between plates. On the basis of the implementation of the platform, this paper tests the association rules module of the platform, and analyzes the possible problems of the association rules module in the testing process. At the same time, in order to know the satisfaction of users with the learning content recommended by the platform, that is, the effect of system recommendation, this paper proposes to use the ratio of the number of recommended content to the number of content that users visit the system. As a way to judge user satisfaction, the recommendation effect of the system is tested in this way. The experimental results show that the user is satisfied with the recommended content.
【學位授予單位】：廣西大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP311.13;TP393.092

【參考文獻】

相關期刊論文 前1條

1 陳以海;;高校精品課程網(wǎng)站建設探索[J];中國教育信息化;2008年01期

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本文編號：2483689