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Effective Teaching of Electric Circuit Analysis through Jigsaw Cooperative Learning Method


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1 PSG Institute of Technology and Applied Research, India
     

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In recent times, in addition to the conventional teaching methods, new pedagogical initiatives are indispensable to elongate the attention span of the present students from generation Z and generation alpha. Jigsaw is one of the Collaborative teaching practices which involves facilitators working in tandem with the students, instructing and mentoring them to solve problems. This paper presents the implementation of an enhanced Jigsaw learning method for teaching Circuit Analysis course for first-year students. Instead of randomly assigning students to groups, the proposed enhancement in the Jigsaw learning method presents diverse teams based on student personalities (Triguna levels – Sattva, Rajas, and Tamas). Such diverse groups become effective learning units and ensure team coherence. Minute feedback was obtained at the end of the session to analyze the effectiveness of the proposed enhanced jigsaw method. The feedback analysis indicates a paradigm shift in the students' level of understanding, and the assessment scores of Tamas students had a significant improvement of 20%. Also, the post-session data shows a boost in the confidence level of the students in solving different Direct Current (DC) and Alternating Current (AC) based electric circuits. A comparison between the conventional teaching method and the enhanced Jigsaw teaching-learning process is also presented. The results indicate that this method proves to be effective both from the students' learning and the faculty's teaching perspective. In addition to improving the desired learning outcome, the enhanced Jigsaw method also makes the learning process enjoyable.

Keywords

Teaching-Learning Process, Jigsaw Method, Triguna Levels, Electric Circuits, Circuit Analysis, Active Learning.
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  • Effective Teaching of Electric Circuit Analysis through Jigsaw Cooperative Learning Method

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Authors

P. Reba
PSG Institute of Technology and Applied Research, India
N. Susithra
PSG Institute of Technology and Applied Research, India
M. Deepa
PSG Institute of Technology and Applied Research, India
G. Santhanamari
PSG Institute of Technology and Applied Research, India

Abstract


In recent times, in addition to the conventional teaching methods, new pedagogical initiatives are indispensable to elongate the attention span of the present students from generation Z and generation alpha. Jigsaw is one of the Collaborative teaching practices which involves facilitators working in tandem with the students, instructing and mentoring them to solve problems. This paper presents the implementation of an enhanced Jigsaw learning method for teaching Circuit Analysis course for first-year students. Instead of randomly assigning students to groups, the proposed enhancement in the Jigsaw learning method presents diverse teams based on student personalities (Triguna levels – Sattva, Rajas, and Tamas). Such diverse groups become effective learning units and ensure team coherence. Minute feedback was obtained at the end of the session to analyze the effectiveness of the proposed enhanced jigsaw method. The feedback analysis indicates a paradigm shift in the students' level of understanding, and the assessment scores of Tamas students had a significant improvement of 20%. Also, the post-session data shows a boost in the confidence level of the students in solving different Direct Current (DC) and Alternating Current (AC) based electric circuits. A comparison between the conventional teaching method and the enhanced Jigsaw teaching-learning process is also presented. The results indicate that this method proves to be effective both from the students' learning and the faculty's teaching perspective. In addition to improving the desired learning outcome, the enhanced Jigsaw method also makes the learning process enjoyable.

Keywords


Teaching-Learning Process, Jigsaw Method, Triguna Levels, Electric Circuits, Circuit Analysis, Active Learning.

References





DOI: https://doi.org/10.16920/jeet%2F2022%2Fv36i1%2F22136