Building Logic Gate Circuits in Python | Chapter 7 | Mathematics and Computer Science: Contemporary Developments Vol. 5

This paper presents the development of a Python module designed for various Logic Gates. Three circuits were built using the functions defined in Python for the logic gates, and the outcomes were found to align completely with those reported in the literature. The circuits were tested with different inputs obtaining the expected outputs.

 

Author (s) Details

Pankaj Dumka
Department of Mechanical Engineering, Jaypee University of Engineering and Technology, A. B. Road, Raghogarh-473226, Guna, Madhya Pradesh, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v5/2500

Assessing the Effectiveness of Intentional Learning Instruments in Teaching Circuits: A Mixed Method Analysis | Chapter 1 | An Overview of Literature, Language and Education Research Vol. 6

Intentional learning is defined as learning that is motivated by intentions and is goal-directed. The aim of this study is to assess the effectiveness of Intentional learning Instruments in teaching and learning circuits. An intentional learning instrument for the electric circuit concept was developed based on seven existing instruments found in Self-Determination Theory. The instruments have a bearing on the assessment of motivation and learning strategy scales and their corresponding subscales. In this study, the researcher analyzed data gathered from the implementation of the Intentional Learning Questionnaire in Electric Circuit (ILQ-EC) and Intentional Learning Module (ILM) as the intentional learning instruments. The overall study involves the implementation of ILQ-EC, development of ILM and testing its effectiveness using quantitative and qualitative research analysis. Participants were 12 engineering students in two electricity and magnetism classes at Central Mindanao University, Bukidnon, Philippines. Previous research used ILQ-EC in identifying motivation and learning strategies factors which were highly correlated and predictive to conceptual understanding of student respondents. These factors served as the benchmark in the development and testing of ILM for this present study. Results for both quantitative and qualitative analysis showed that high gainers tend to be more intentional, specifically, more critical thinkers and with higher perceived competence than low gainers. Moreover, the average normal gains of the student participants after using ILM were higher compared to previous research results using different learning approaches. ILM evaluation based on student responses also confirmed the influence of ILM on their learning gains. Thus, enhancing intentionality proved a significant effect on the teaching and learning process. Future research may consider ILQ-EC to explore motivation and learning strategy factors in other topics or subject areas not only in physics.

 

Author (s) Details

 

Cecilia O. Bucayong
Central Mindanao University, Musuan, Bukidnon, Philippines.

 

Please see the book here:- https://doi.org/10.9734/bpi/aoller/v6/484