Arvind W. Kiwelekar

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

E-mail: awk@dbatu.ac.in

Website: http://orcid.org/0000-0002-3407-0221

Research Interests: Artificial Intelligence, Computational Learning Theory, Computer Architecture and Organization, Data Structures and Algorithms

Biography

Arvind W Kiwelekar is a Professor in the Department of Computer Engineering at Dr Babasaheb Ambedkar Technological University-Lonere, India. He is the founding head of the department. He has thirty years of teaching and research experience. His areas of interest include diverse topics, including Artificial Intelligence, Blockchain Technology, Information and Communication Technologies for Sustainable Development (ICT4SD), Learning Analytics, Ontology, and Software Architecture. He published several papers at leading venues such as ECSA, IJCAI, ICSE, CompSAC and SAC. He graduated with a PhD from Indian Institute of Technology Bombay (2012), M E from Veermata Jijabai Technical Institute (VJTI) Mumbai(1997) and B E from Marathwada University (1991). He received a research fellowship from the Indian Academy of Sciences Bangalore, Ministry of Education Govt. of India and IBM. Alumni of the Department of Computer Engineering. at DBATU has constituted an award to honour his dedication towards teaching

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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