SA-FEM: Combined Feature Selection and Feature Fusion for Students' Performance Prediction.

Around the world, the COVID-19 pandemic has created significant obstacles for education, driving people to discover workarounds to maintain education. Because of the excellent benefit of cheap-cost information distribution brought about by the advent of the Internet, some offline instructional activity started to go online in an effort to stop the spread of the disease. How to guarantee the quality of teaching and promote the steady progress of education has become more and more important. Currently, one of the ways to guarantee the quality of online learning is to use independent online learning behavior data to build learning performance predictors, which can provide real-time monitoring and feedback during the learning process. This method, however, ignores the internal correlation between e-learning behaviors. In contrast, the e-learning behavior classification model (EBC model) can reflect the internal correlation between learning behaviors. Therefore, this study proposes an online learning performance prediction model, SA-FEM, based on adaptive feature fusion and feature selection. The proposed method utilizes the relationship among features and fuses features according to the category that achieved better performance. Through the analysis of experimental results, the feature space mined by the fine-grained differential evolution algorithm and the adaptive fusion of features combined with the differential evolution algorithm can better support online learning performance prediction, and it is also verified that the adaptive feature fusion strategy based on the EBC model proposed in this paper outperforms the benchmark method.

PILOT STUDY OF THE RELATIONSHIP BETWEEN VOCABULARY ABILITY AND LEARNING PERFORMANCE FOR UNFAMILIAR KNOWLEDGE IN ONLINE VIDEO CLASSES

The COVID-19 pandemic has led to the introduction of online courses in higher education worldwide, including online video courses. In a video course, it is difficult to predict which students cannot follow a course that provides unfamiliar knowledge. Therefore, this pilot study investigated the relationship between learning performance of unfamiliar knowledge and Japanese vocabulary ability. A total of 59 college students participated in this pilot study. After their Japanese proficiency levels were measured by their grammatical and vocabulary abilities, they took a video course about the “Structure of Brain” (as an unfamiliar topic). The students took a short pre- and post-lecture knowledge test. The results of this study revealed that students with a high level of Japanese vocabulary ability showed higher learning performance for unfamiliar knowledge delivered via the video lecture. This pilot study contributes to predicting students' online learning performance based on student properties. © 2021 Virtual Simulation Innovation Workshop, SIW 2021. All rights reserved.