| dc.description.abstract | With the outbreak of the Covid-19 in 2019, home quarantine or remote work has become a daily life for many people.
Traditional teaching methods have also been affected by the epidemic.
At this time, Massive Open Online Courses (MOOCs) have highlights the importance of distance teaching and digital learning.
With the development of artificial intelligence-related technologies and the birth of various novel data analysis methods, recommendation systems and performance prediction have become an important research
direction.
Massive Open Online Courses (MOOCs) are digital learning methods that have been expanding in recent years. Courses are sent to learners through the Internet.
This kind of online learning is highly autonomous and independent of time and location, and this is an excellent learning resource for those who are motivated to learn.
It′s a very good learning resource for people with learning
motivation.
This research uses the BookRoll online e-book learning system developed by Kyoto University in Japan and the review and answer system developed by National Center University to analyze learning strategies based on the action logs of students using the Bookroll, and study the effects between learning behavior and learning
effectiveness.
This study explores the learning behavior logs of students to understand the activities or behaviors of students with better learning effectiveness, and provides teachers as a reference for counseling, thereby improving students′ learning effectiveness.
We process the learning process data collected on the Bookroll platform and each practice system including Cloze, Short-Ans, OJ, Viscode and Assessment, and analyze the learning logs of students, summarize the learning strategies of students.
This research hopes to find out the correlation between learning actions and learning effectiveness, and provide teachers with references for tutoring students.
We use the learning logs of the Python programming course in the second semester of the 109 academic year of National Central University in the mixed teaching scenario to analyze the learning strategy using learning logs left by the students on the Bookroll can
be summarized by the clustering algorithm Its learning strategy.
The study found that all learning strategies summarized by students using BookRoll and other practice systems logs can using unsupervised learning clustering are significantly related to learning effectiveness, and can be used in learning early warning or recommendation mechanisms to achieve precise teaching interventions in the future. | en_US |