Abstract
The lazy nature of learning is an obstacle in teaching and learning activities. The higher level of laziness will reduce the students learning achievement. This problem has motivated the author to make a mathematical model of student learning's lazy nature and analyze it. The lazy nature of learning is modeled to a discrete-time system with , denotes a state vector of the lazy nature, represents an input vector system in time, and denotes a coefficient matrix of and . The optimal quadratic control method is applied in the model to analyze the system's stability and give the interpretation of the model based on the graphics info obtained during the simulation process. The feedback controller is obtained as a result of optimizing the quadratic objective function. a feedback controller's existence makes the lazy nature of learning physics students decrease from time to time, and the system becomes stable.
Highlights
The lazy nature of learning is an obstacle in teaching and learning activities (Kessels and Heyder 2020)
Based on the observation the lazy nature of learning students is influenced by several factors such as the temptation to use social media, the density of activities on campus, dating with boy or girlfriend, uncomfortable classroom environment, and inadequate infrastructure
In ten times period the results showed the percentages of the lazy nature of learning physics students has decreased and attended to zero
Summary
The lazy nature of learning is an obstacle in teaching and learning activities (Kessels and Heyder 2020). Some of the negative effects of social media are student lose the habit of communicating face to face (Siddiqui and Singh 2016), the students lose concentration while studying because they open social media frequently (Raut and Patil 2016), and student need more time to complete assignments (Flanigan and Babchuck 2015) These three examples show that social media has negative impacts and fosters a lazy nature of learning reflected in various student behaviors. The Discrete-time system has been widely used to model daily events such as a traffic model (Rachim 2017), an economic model in Hu and Tu (2015), (Canto et al 2008), population growth model (Haberman 1998), and several other models Based on these studies, a discrete-time system is considered appropriate to model the lazy nature of student learning
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