A Learning Trajectory for Developing Computational Thinking and Programming
Author(s) -
Sean Brophy,
Tony Lowe
Publication year - 2018
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--27472
Subject(s) - computational thinking , computer science , programmer , mathematics education , discipline , software engineering , programming language , artificial intelligence , psychology , social science , sociology
This research study identifies the relationship between students’ prior experiences with programming and their development of computational thinking and programming during their first year engineering experience. Many first year programs teach students basic programming concepts using languages like MATLAB or LABView. These languages are used because many of the disciplinary schools expect students to use computational models to analyze systems of interest. Some undergraduate engineering students are entering college with strong computational backgrounds, while others have no experience at all. This study is the first in a series to better identify students’ transition into developing and reasoning with analytical tools. The learning progression across two programming languages is critical to developing a student’s ability to generalize across various computational tools. The goal of this study is to identify how students progress in their ability to engage in computational thinking and programming relative to other learning outcomes for the course. This initial investigation uses students’ prior background in programming and their exam scores to evaluate their interdependence of prior knowledge on learning programming across their first semester in university. As anticipated, learning a new language is difficult compared to the other course objectives. However, students with some prior knowledge of programming demonstrate a balanced performance between computational thinking and the other course objectives. Students who have limited programming experience do demonstrate a higher variance in their performance in problems related to computational thinking compared to their other course objectives. One of the leading factors is the time spent practicing programming. This paper will be of interest to instructors with the objective of developing computational thinking and programming in classrooms with a large variance in students’ backgrounds with programming.
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