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Qiuzhe Ping

Bio: Qiuzhe Ping is an academic researcher. The author has contributed to research in topics: Machine learning & Artificial intelligence. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

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TL;DR: In this paper , five machine learning models are employed to predict academic performance of an engineering mechanics course, taking online learning behaviors, comprehensive performance as input and final exam scores (FESs) as output.
Abstract: With long-term impact of COVID-19 on education, online interactive live courses have been an effective method to keep learning and teaching from being interrupted, attracting more and more attention due to their synchronous and real-time interaction. However, there is no suitable method for predicting academic performance for students participating in online class. Five machine learning models are employed to predict academic performance of an engineering mechanics course, taking online learning behaviors, comprehensive performance as input and final exam scores (FESs) as output. The analysis shows the gradient boosting regression model achieves the best performance with the highest correlation coefficient (0.7558), and the lowest RMSE (9.3595). Intellectual education score (IES) is the most important factor of comprehensive performance while the number of completed assignment (NOCA), the live viewing rate (LVR) and the replay viewing rate (RVR) of online learning behaviors are the most important factors influencing FESs. Students with higher IES are more likely to achieve better academic performance, and students with lower IES but higher NOCA tend to perform better. Our study can provide effective evidences for teachers to adjust teaching strategies and provide precise assistance for students at risk of academic failure in advance. [ FROM AUTHOR]

1 citations


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TL;DR: In this paper , the authors presented an 18-week online English course for 43 senior high school students in Taiwan, which aimed to cultivate autonomous EFL learners, who learned to read and write with the support of three AI-powered tools.
Abstract: During the Covid-19 pandemic, global teachers gained extensive experiences with teaching online courses. To design quality online courses in the post-pandemic era, the impact of the latest technology, such as artificial intelligence (AI), must be considered. Investigating how teachers incorporate AI-powered tools and how students perceive learning experiences with the tools is crucial for informing effective online course design. In this case study, we presented an 18-week online English course for 43 senior high school students in Taiwan. Designed according to learning theories, this course aimed to cultivate autonomous EFL learners, who learned to read and write with the support of three AI-powered tools (i.e. Linggle Write, Linggle Read, and Linggle Search). Data were collected from the instructors and students in both qualitative and quantitative formats. Results showed that a learning loop was created to connect online learning with offline practice; students had better optimal experience in student-centered presentations than in teacher-centered lectures; and language proficiency predicted semester grade and assignment quantity. This study has both theoretical and practical value; it serves as an example of how to design a quality synchronous online course on AI tools for EFL reading and writing based on theoretical approaches.