Leveraging Data Analytics to Investigate the Effectiveness of Flipped Classroom Models: A Case Study of Practical Programming Teaching
DOI:
https://doi.org/10.34190/ejel.22.9.3597Keywords:
Flipped Classroom, Practical programming course, Learning analytics, Correlation analysis, Naive Bayes classificationAbstract
Educators face multiple challenges when teaching programming, such as the intricate nature of programming knowledge, the choice of effective teaching methods, and the diverse abilities of learners. Traditional teaching methods often fail to address these challenges, leading to higher dropout rates and lower student grades. This paper explores a study on the effectiveness of the flipped classroom model as a strategy to enhance student engagement in a practical programming course. In addition, learning data was analyzed to examine the relationship between pre-class preparation and in-class learning outcomes. The study's results indicate that the flipped classroom model significantly enhances student engagement and performance. Students who diligently completed pre-class assignments and dedicated more time to study demonstrated improved performance in subsequent in-class exercises. The results emphasize the potential of the flipped classroom model as a successful teaching method for increasing student involvement, encouraging self-directed learning, and ultimately improving the overall educational experience in programming courses.
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Copyright (c) 2024 Huy Tran, Tien Vu-Van, Thanh-Van Le, Hoang-Anh Pham, Nguyen Huynh-Tuong
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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