CLUSTERING ANALYSIS SYSTEM BASED ON STUDENTS' MOTIVATION AND LEARNING BEHAVIOR IN MOOC

基於學生學習動機與行為之磨課師分群分析系統

曾建維(Jian-Wei Tzeng) ； 黃能富(Nen-Fu Huang) ； 李加安(Chia-An Lee) ； 陳羿先(Yi-Hsien Chen) ； 莊安琦(An-Chi Chuang)

《國立臺灣科技大學人文社會學報》 17卷2期 (2021/06) Pp. 145-170

摘要

With an increase in the number of massive open online course (MOOC), the amount of learning educational big data has also increased. Artificial intelligence technology is now applicable to big data analysis. This study implements a clustering system based on learning educational big data. This study uses the MOOC course offered at a university in the north of Taiwan as experimental data. Meanwhile, observing the ratio of the number of students who watched videos to that of students who finished practice exercises, we clustered students into different groups by using a K-means clustering module. Then, we use the deep learning prediction module to determine whether or not the students will change their clustering result next week. This system aims to provide teachers and students clustering results for the next week to recommend suitable learning strategies, which can facilitate the most appropriate guidance and more adaptive counseling.

關鍵字

cluster analysis ； deep learning ； K-means ； learning educational Big Data ； MOOC

並列摘要

隨著大規模開放線上課程（磨課師）的推展，應用教育大數據與人工智慧分析學習行為的模式也更加盛行，本研究開發一個基於大數據分析學習行為的分群系統，並以2016年開設之「計算機網路概論」磨課師課程做為數據來源。系統將基於K-means分群模組，分析學生觀看影片的比例與完成練習題的比例並將學生分群。另一方面，透過基於深度學習的預測模組，可以預測該學生於隔一週的學習行為是否改變。本系統希望透過預測模組提供老師與學生分群結果，並針對分群結果提供適合個人的學習策略推薦，達到引導學習與提供適性化學習之效果。

並列關鍵字

磨課師 ； 教育大數據 ； K-means分群 ； 深度學習 ； 適性化學習

參考文獻

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