A Performance Analysis of the Impact of Prior-Knowledge on Computational Thinking
Автор: Swanand K. Navandar, Arvind W. Kiwelekar, Manjushree D. Laddha
Журнал: International Journal of Modern Education and Computer Science @ijmecs
Статья в выпуске: 2 vol.15, 2023 года.
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Previously acquired knowledge plays a significant role to learn new knowledge and skills. Previously acquired knowledge consists of Short-term memory and Long-term memory. Though it is a well-accepted learning phenomenon, it is challenging to empirically analyse the impact of prior knowledge on learning. In this paper, we use two systems models for human thinking proposed by Nobel Laureate Prof. Daniel Kahneman. This is a model for human cognition which uses two systems of thinking—the first being quick and intuitively known as fast thinking and the second being slow and tedious known as slow thinking. While slow thinking uses long-term memory, fast thinking uses short-term memory. The impact of prior knowledge of programming language is analyzed to learn a new programming language. We assigned a learning task to two different groups with one having learnt a programming language i.e. senior students and the second group without any prior knowledge of programming language i.e. freshers. The impact of prior knowledge is measured and compared against the time taken to answer quizzes.
Previously acquired knowledge, performance, Kahneman two system model
Короткий адрес: https://sciup.org/15019112
IDR: 15019112 | DOI: 10.5815/ijmecs.2023.02.05
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