Impact of Flipped Classroom in the Teaching-Learning Process on Karnaugh’s Maps
This quantitative design research analyzes flipped classroom’s effects in the teaching-learning process on Karnaugh’s maps considering data science, neural networks, and machine learning (linear regression). In machine learning, the training section used 60%, 70%, and 80% of the sample to test the research hypotheses. Karnaugh’s maps allow identifying the logical function that minimizes the number of electronic components used during the digital circuits’ construction. The general aim is to implement and analyze the use of the flipped classroom in the educational process on Karnaugh’s maps. The sample is composed of 26 students from the Executive Degree in Computational Systems. The students were enrolled in the Sequential and Combinatorial Logic course at a Mexican university. This research proposes the implementation of a flipped classroom using the consultation of YouTube videos (before the class), use of the protoboard in a collaborative mode (during the class), and use of the Crocodile Clips software (after the class). The protoboard is a test plate that allows the construction of digital circuits, and Crocodile Clips software allows performing various simulations on digital circuits’ operation. Machine learning results indicate that flipped classrooms positively influence the student’s active role and motivation during the learning process. Data science identifies six predictive models on flipped classrooms employing the decision tree technique. The information about the student’s profile (sex and age) and use of the tools (YouTube videos, breadboard, and Crocodile Clips software) allow the construction of these models. Likewise, neural networks identify the flipped classroom aspects that influence the student’s active role and motivation. Finally, flipped classrooms improve the teaching-learning conditions through the consultation of YouTube videos, the use of the protoboard in a collaborative way, and the Crocodile Clips software.
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