本文選題：大數(shù)據(jù) + 在線視頻　； 參考：《現(xiàn)代遠(yuǎn)距離教育》2017年04期

【摘要】：隨著數(shù)據(jù)科學(xué)和學(xué)習(xí)分析的興起,教育領(lǐng)域內(nèi)數(shù)據(jù)集驅(qū)動(dòng)的研究進(jìn)入學(xué)界視野。視頻成為在線學(xué)習(xí)的主要資源,有必要從微觀層面對(duì)點(diǎn)擊流數(shù)據(jù)可視化分析,研究學(xué)習(xí)者在線學(xué)習(xí)行為,進(jìn)而優(yōu)化教學(xué)策略、提高學(xué)習(xí)效率。運(yùn)用數(shù)據(jù)思維方式,引入香港科技大學(xué)Vis MOOC項(xiàng)目,通過從不同角度對(duì)視頻點(diǎn)擊流行為信息進(jìn)行可視化分析,解讀其功能與實(shí)現(xiàn)方式,探索點(diǎn)擊流數(shù)據(jù)產(chǎn)生的學(xué)習(xí)規(guī)律。最后,在數(shù)據(jù)思維的MOOC學(xué)習(xí)行為研究、基于大數(shù)據(jù)的MOOC學(xué)習(xí)交互分析、學(xué)習(xí)分析視角下MOOC個(gè)性化學(xué)習(xí)、基于點(diǎn)擊流數(shù)據(jù)公開化的MOOC學(xué)習(xí)預(yù)測(cè)、大數(shù)據(jù)時(shí)代MOOC視頻注釋分析五個(gè)方面,提出對(duì)MOOC研究實(shí)踐的思考,解決當(dāng)前MOOC中存在的高輟學(xué)率、交互不足等問題,并利用數(shù)據(jù)挖掘和學(xué)習(xí)分析技術(shù)分析預(yù)測(cè)學(xué)習(xí)者學(xué)習(xí)行為,為其提供個(gè)性化服務(wù)支持。
[Abstract]:With the rise of data science and learning analysis, the research of data set driven in the field of education has entered the field of vision. Video has become the main resource of online learning. It is necessary to face the visual analysis of click stream data from the micro level, study the online learning behavior of the learners, and then optimize the teaching strategy and improve the learning efficiency. By introducing the Vis MOOC project of Hong Kong University Science & Technology to visualize the information of video clicks from different angles, interpret its function and realization, explore the learning rules produced by clicking stream data. Finally, the study of MOOC learning behavior in data thinking is based on the MOOC learning interaction analysis of large data, and the learning analysis perspective. The next MOOC personalized learning is based on the MOOC learning prediction of clicking flow data and the five aspects of MOOC video annotation analysis in the big data age. It puts forward thinking about the practice of MOOC research, solves the problems of high dropout rate and lack of interaction in current MOOC, and uses data mining and learning analysis techniques to analyze and predict the learning behavior of the learners. Provide personalized service support for it.
【作者單位】： 東北師范大學(xué);湖南第一師范學(xué)院;
【基金】：教育部人文社科規(guī)劃項(xiàng)目“基于知識(shí)圖譜的開放學(xué)習(xí)資源自主聚合研究”(編號(hào):14YJA880103) 吉林省高等教育教學(xué)改革立項(xiàng)課題“精品慕課課程質(zhì)量建設(shè)規(guī)范、路徑及教學(xué)實(shí)踐評(píng)價(jià)研究”(編號(hào):吉教高字[2015]32號(hào));基礎(chǔ)教育信息化技術(shù)湖南省重點(diǎn)實(shí)驗(yàn)室(編號(hào):2015TP1017) 湖南省哲學(xué)社會(huì)科學(xué)基金項(xiàng)目“‘互聯(lián)網(wǎng)+’促進(jìn)城鄉(xiāng)基礎(chǔ)教育均衡發(fā)展的創(chuàng)新機(jī)制與路徑研究”(編號(hào):16YBA094)
【分類號(hào)】：G434

【相似文獻(xiàn)】

相關(guān)期刊論文 前5條

1 盧強(qiáng);;近十年國(guó)內(nèi)教師專業(yè)發(fā)展研究的熱點(diǎn)和演進(jìn)——基于知識(shí)圖譜的可視化分析[J];上海教育科研;2013年04期

2 趙慧臣;王s，

本文編號(hào)：2118484