本文選題：在線學(xué)習(xí) + 學(xué)習(xí)行為分析　； 參考：《內(nèi)蒙古師范大學(xué)》2017年碩士論文

【摘要】：近年來,隨著互聯(lián)網(wǎng)與通信技術(shù)的發(fā)展,基于網(wǎng)絡(luò)的在線學(xué)習(xí)以其靈活性、開放性的特點吸引著眾多教育研究者和學(xué)習(xí)者。盡管網(wǎng)絡(luò)學(xué)習(xí)以學(xué)習(xí)者為中心,打破時空限制,實現(xiàn)了學(xué)習(xí)者的自主學(xué)習(xí)和個性化學(xué)習(xí),但是在線學(xué)習(xí)的時空分離限制了師生信息交流的直接性和及時性,教師無法直接對學(xué)習(xí)者的學(xué)習(xí)行為特征進(jìn)行觀察,也不利于教師及時給學(xué)習(xí)者提供有針對性的指導(dǎo)和幫助。學(xué)習(xí)分析技術(shù)能夠提高網(wǎng)絡(luò)教學(xué)質(zhì)量和在線學(xué)習(xí)效果,為預(yù)測學(xué)生的在線學(xué)習(xí)效果、判定影響學(xué)習(xí)者成績的關(guān)鍵因素提供了可行的方法。隨著在線學(xué)習(xí)的發(fā)展,學(xué)習(xí)分析技術(shù)將成為未來教育技術(shù)研究的主要內(nèi)容。本文在行為科學(xué)理論的基礎(chǔ)上,結(jié)合Blackboard網(wǎng)絡(luò)課程,以《心理學(xué)概論》網(wǎng)絡(luò)課程的在線學(xué)習(xí)行為作為研究對象,根據(jù)相關(guān)網(wǎng)絡(luò)學(xué)習(xí)行為模型,把在線學(xué)習(xí)行為分為五類,并將可作為學(xué)習(xí)者行為特征的數(shù)據(jù)進(jìn)行歸類。對五類網(wǎng)絡(luò)學(xué)習(xí)行為分析,通過統(tǒng)計學(xué)的方法,確定影響成績的數(shù)據(jù)變量,構(gòu)建學(xué)習(xí)成績預(yù)測模型并驗證模型的可靠性。本論文共分為六個部分:第一部分是緒論部分,闡述了論文研究的背景、意義、國內(nèi)外研究現(xiàn)狀等內(nèi)容。第二部分是理論基礎(chǔ)及相關(guān)概念,介紹了行為科學(xué)理論,并對在線學(xué)習(xí)行為和學(xué)習(xí)分析技術(shù)進(jìn)行了概念界定。第三部為網(wǎng)絡(luò)課程簡介與數(shù)據(jù)的獲取,根據(jù)網(wǎng)絡(luò)學(xué)習(xí)行為模型,針對Blackboard網(wǎng)絡(luò)課程數(shù)據(jù),參考學(xué)習(xí)者規(guī)范和對象元數(shù)據(jù)規(guī)范選取行為特征的量化參數(shù)。第四部分是在線學(xué)習(xí)行為分析,根據(jù)選取的網(wǎng)絡(luò)行為特征的數(shù)據(jù)變量,對在線學(xué)習(xí)行為進(jìn)行分析,在分析學(xué)習(xí)者活動規(guī)律的同時初步判定了與學(xué)習(xí)效果相關(guān)的學(xué)習(xí)行為。第五部分是成績預(yù)測模型的建立與驗證,首先從統(tǒng)計學(xué)的角度探究與學(xué)習(xí)效果有關(guān)的學(xué)習(xí)行為,然后對學(xué)習(xí)者的期末成績建立回歸模型,最后選取60分為臨界值,使用二項邏輯回歸分析驗證成績預(yù)測模型的準(zhǔn)確率。第六部分為研究的結(jié)論與建議,研究發(fā)現(xiàn)與成績呈顯著相關(guān)的數(shù)據(jù)變量有13個,最終建立的模型解釋度為29%,自變量在線天數(shù)、測試總分、題庫點擊量、發(fā)布博客的數(shù)量顯著,二項邏輯回歸方程預(yù)測準(zhǔn)確率為78.1%,最后根據(jù)結(jié)論提出建議。本研究幫助教師及時了解學(xué)習(xí)者的在線學(xué)習(xí)情況提供了參考,對存在學(xué)習(xí)風(fēng)險的學(xué)習(xí)者及時進(jìn)行干預(yù)和幫助。幫助教師進(jìn)行課程和資源的設(shè)計、開發(fā),合理安排和組織網(wǎng)絡(luò)課堂教學(xué)活動,并且制定網(wǎng)絡(luò)課程考核標(biāo)準(zhǔn)。
[Abstract]:In recent years, with the development of Internet and communication technology, web-based online learning has attracted many educational researchers and learners for its flexibility and openness. Although online learning is learner-centered, breaks the limitation of time and space, and realizes learner's autonomous learning and individualized learning, the separation of online learning time and space limits the directness and timeliness of information exchange between teachers and students. Teachers can not directly observe the characteristics of learners' learning behavior, nor is it conducive for teachers to provide timely guidance and help to learners. Learning analysis technology can improve the quality of online teaching and the effect of online learning. It provides a feasible method for predicting students' online learning effect and judging the key factors that affect learners' achievement. With the development of online learning, learning analysis technology will become the main content of educational technology research in the future. On the basis of behavioral science theory and Blackboard network course, this paper takes the online learning behavior of the network course as the research object, according to the related network learning behavior model, divides the online learning behavior into five categories. The data can be classified as learner behavior characteristics. In the analysis of five kinds of online learning behaviors, the data variables that affect the achievement are determined by statistical method, and the prediction model of learning achievement is constructed and the reliability of the model is verified. This paper is divided into six parts: the first part is the introduction part, which describes the background, significance, research status at home and abroad. The second part is the theoretical basis and related concepts, introduces the behavioral science theory, and defines the online learning behavior and learning analysis technology. The third part is the introduction of online courses and the acquisition of data. According to the online learning behavior model, the quantitative parameters of behavior characteristics are selected according to the Blackboard online course data, referring to the learner specification and object metadata specification. The fourth part is the analysis of online learning behavior. According to the selected data variables of network behavior characteristics, the online learning behavior is analyzed, and the learning behaviors related to learning effect are preliminarily determined while analyzing the rules of learners' activities. The fifth part is the establishment and verification of the achievement prediction model. Firstly, it explores the learning behaviors related to the learning effect from the perspective of statistics, then establishes a regression model for the final grades of the learners, and finally selects 60 as the critical value. The accuracy of the performance prediction model was verified by binary logistic regression analysis. The sixth part is the conclusions and recommendations of the study. The study found that there are 13 data variables significantly related to the results, the final interpretation of the model is 29, independent variables online days, total test scores, number of hits to the question bank, the number of blog posts is significant. The prediction accuracy of binomial logistic regression equation is 78.1. Finally, some suggestions are put forward according to the conclusion. This study provides a reference for teachers to understand learners' online learning in time, and provides timely intervention and help for learners with learning risks. To help teachers design, develop, arrange and organize the online classroom teaching activities, and establish the assessment standard of online courses.
【學(xué)位授予單位】：內(nèi)蒙古師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：G434;B84-4

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