Student Performance Prediction Based on Blended Learning

• Published in: IEEE transactions on education Vol. 64; no. 1; pp. 66 - 73
• Main Authors: Xu, Zhuojia, Yuan, Hua, Liu, Qishan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2021; Institute of Electrical and Electronics Engineers, Inc; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Academic Achievement; Blended Learning; Classrooms; Computer networks; Distance learning; Education; Electronic Learning; Feature extraction; Flipped classroom; Individualized Instruction; individualized teaching; Learning Analytics; Linear regression; massive open online courses (MOOCs); Multiple regression models; Online Courses; Performance Factors; Performance prediction; Prediction; Predictive models; Regression analysis; Small Classes; small private online courses (SPOCs); Springs; student assessment; Student Behavior; Student Centered Learning; student performance; Students; Teachers; Teaching methods; Videos
• ISSN: 0018-9359; 1557-9638
• DOI: 10.1109/TE.2020.3008751

Contribution: This article explored blended learning by implementing a student-centered teaching method based on the flipped classroom and small private online course (SPOC). The impact of general online learning behavior on student performance was analyzed. This work is practical and provides enlightenment for learning analysis and individualized teaching in blended learning. Background: Providing individualized teaching in a large class is an effective way to improve teaching quality, but the traditional teaching method makes it difficult for teachers to learn about each student's learning situation. Blended learning offers the possibility of individualized teaching for teachers. The combination of flipped classroom and SPOC is a good way to implement blended learning, but few studies have verified the predictability of learning performance in such a scenario to explore individualized teaching. Intended Outcomes: Students' behavior in blended learning can be used to predict their learning outcomes, and the implementation method is reproducible. Teachers can implement individualized teaching in blended learning. Application Design: The learning activities were designed and reconstructed to create a blended learning scenario, data that depict students' learning behavior were collected and used to predict their performance by a multiple regression model. Student performance was measured by the final offline exam, and its predictability in the 1/4, 1/2, and 3/4 semester was tested for early intervention. Findings: The results show that students' online behavior can be predictors of their performance, and with the advance of the course, the predicted results are more stable and reliable.