Manjushree D. Laddha

Work place: Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, 402103, India

E-mail: mdladdha@dbatu.ac.in

Website: http://orcid.org/0000-0002-6265-1728

Research Interests: Computational Science and Engineering, Software Construction, Software Development Process, Software Engineering, Computational Learning Theory

Biography

Manjushree D. Laddha received the Ph.D degree (Doctor of Philosophy) in Computer Engineering from the Dr. Babasaheb Ambedkar Technological University, Lonere, India, with the Dissertation “A Software Architecture Perspective of Learning Analytics”. She is a Assistant Professor of Computer Engineering at Dr. Babasaheb Ambedkar Technological University, Lonere, India. In addition, she is serving as researcher in the research group on Cognitive Science and Learning Analytics. She has published journals papers, books chapters, and papers in the International Conferences. Her research interests are in Machine Learning, Cognitive Science, Learning Analytics, and Software Engineering

Author Articles
A Performance Analysis of the Impact of Prior-Knowledge on Computational Thinking

By Swanand K. Navandar Arvind W. Kiwelekar Manjushree D. Laddha

DOI: https://doi.org/10.5815/ijmecs.2023.02.05, Pub. Date: 8 Apr. 2023

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.

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