本文選題：MOOC + 蟻群聚類算法��； 參考：《成都理工大學(xué)》2017年碩士論文

【摘要】：MOOC(massive open online courses),即大型開放式網(wǎng)絡(luò)課程,MOOC平臺一般都是面向全球或者某個(gè)特定范圍群體,通過MOOC網(wǎng)絡(luò)平臺向?qū)W生傳授知識正如火如荼、在全球范圍內(nèi)迅猛發(fā)展。具有優(yōu)質(zhì)的師資、優(yōu)質(zhì)的課程內(nèi)容的結(jié)構(gòu)體系是其主要特征。但MOOC平臺也存在許多不可忽視的問題,比如學(xué)生資源的差異,學(xué)生的來源、學(xué)習(xí)工作背景、學(xué)習(xí)環(huán)境的差異等等,這些差異會直接導(dǎo)致學(xué)生在MOOC平臺學(xué)習(xí)效果的差異。為促進(jìn)MOOC環(huán)境下學(xué)生的學(xué)習(xí)質(zhì)量,提升總體教學(xué)質(zhì)量,實(shí)行學(xué)生間的作業(yè)互評是一項(xiàng)有效的措施。通過學(xué)生間的作業(yè)互評不僅可以有效的加深學(xué)生對課程內(nèi)容的理解,對促進(jìn)學(xué)生學(xué)習(xí)效果的共同提高具有良好的效果;并對解決MOOC環(huán)境下教師不可能對學(xué)生作業(yè)逐一批改的困境給出了一種有效的解決方法。學(xué)生間的作業(yè)互評如何分組?學(xué)生間如何進(jìn)行互評?一種方式是隨機(jī)的指定兩名學(xué)生進(jìn)行互評,但這種方式由于網(wǎng)絡(luò)環(huán)境下學(xué)生彼此間的差異太大,可能導(dǎo)致出現(xiàn)不夠理想的互評結(jié)果,學(xué)生互評成績與實(shí)際成績相差很大。本文提出一種基于學(xué)生背景大致相同情況下的作業(yè)互評模式,即進(jìn)行互評學(xué)生的學(xué)習(xí)成績、所在地域、學(xué)習(xí)環(huán)境等大致相同。解決問題的基本思路是將所有學(xué)生首先進(jìn)行分組,也就是將背景大致相同的學(xué)生分成一組,然后在組內(nèi)進(jìn)行學(xué)生互評。這種作業(yè)互評分組模式雖然存在缺陷,但仍然不失為一種實(shí)際應(yīng)用中可供參考的作業(yè)互評分組模式。在一定范圍內(nèi),這種作業(yè)互評分配模式會使得學(xué)生所得成績盡可能的接近學(xué)生的真實(shí)成績。本文首先簡要介紹了研究背景,和所運(yùn)用方法的必要性。接著介紹了MOOC平臺與蟻群聚類算法的相關(guān)基本理論知識及數(shù)學(xué)模型,第4章是本文重點(diǎn),主要闡述了具體使用蟻群聚類算法來對學(xué)生進(jìn)行分組;其中包括學(xué)生分組的問題,以及蟻群聚類算法的可行性,和具體分組實(shí)現(xiàn)等。第5章以某高校為例,給出了一個(gè)輕型MOOC平臺的設(shè)計(jì)框架,以及學(xué)生作業(yè)互評信息系統(tǒng)的設(shè)計(jì)思路。最后給出了結(jié)論與建議。本文對MOOC平臺下的學(xué)生作業(yè)分組研究具有實(shí)際應(yīng)用價(jià)值;此外,在聚類過程中所給出的多值離散型屬性的距離計(jì)算方法,使得離散屬性量化后距離具有較高的均衡性,也是具有一定的實(shí)際應(yīng)用意義。
[Abstract]:The MOOC(massive open online course platform is generally oriented to the global or a specific group. It is in full swing to impart knowledge to the students through the MOOC network platform, and it is developing rapidly in the global scope. With high-quality teachers, the structure of high-quality curriculum content is its main characteristics. However, there are many problems in MOOC platform, such as the difference of student resources, the source of students, the background of learning work, the difference of learning environment, and so on. These differences will directly lead to the difference of students' learning effect in MOOC platform. In order to promote the students' learning quality and improve the overall teaching quality under the environment of MOOC, it is an effective measure to carry out the homework evaluation among students. Through the students' homework evaluation, we can not only deepen the students' understanding of the course content, but also improve the students' learning effect. This paper also gives an effective solution to the problem that teachers can not correct students' homework one by one under MOOC environment. How to group the students' homework reviews? How to conduct mutual assessment among students? One way is to assign two students randomly to evaluate each other. However, due to the great differences between students in the network environment, there may be insufficient mutual evaluation results, and the difference between the students' mutual evaluation results and the actual results is very big. In this paper, a kind of homework evaluation model based on the same background is put forward, that is to say, the students' achievement, location and learning environment are roughly the same. The basic idea of solving the problem is to group all the students first, that is, to divide the students with roughly the same background into a group, and then to evaluate each other in the group. Although there are some defects in this model, it is still a reference mode in practical application. To a certain extent, this assignment model will make the students' scores as close as possible to the students' real scores. This paper first briefly introduces the research background, and the necessity of the methods used. Then introduced the MOOC platform and ant colony clustering algorithm related basic theoretical knowledge and mathematical model, the fourth chapter is the focus of this paper, mainly describes the specific use of ant colony clustering algorithm to group students, including the problem of student grouping, And the feasibility of ant colony clustering algorithm, and the implementation of specific groups. In chapter 5, taking a university as an example, the design framework of a lightweight MOOC platform and the design idea of student assignment evaluation information system are given. Finally, the conclusions and suggestions are given. This paper has practical application value to the study of student assignment grouping on MOOC platform, in addition, the distance calculation method of multi-valued discrete attributes given in the clustering process makes the distance after quantization of discrete attributes have a higher equalization. Also has certain practical application significance.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：G434;TP18

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