Dissertation and Abstract
Developing Computational Thinking Competencies and Natural Selection Understanding Through Unplugged Algorithmic Explanations
Technology and computing have permeated science, technology, engineering, and mathematics (STEM) disciplines, resulting in a need for a computationally literate STEM work force. One pathway for building computational literacy is to integrate computational thinking (CT) into core science classes. CT is the logical thought process underlying computer science that can be leveraged across science disciplines and everyday life. Most CT integrations use programming and computers, making CT difficult to implement because it requires class computers and knowledge about programming. Since CT has applications much broader than programming, there is a missed opportunity for other learning when programming is the sole context of CT learning. As such, this dissertation investigates an unplugged, or computer and programming-free, approach to integrating science content with CT. In this dissertation, students use CT to create hand-written explanations of natural selection to simultaneously learn science content and develop CT competencies. Empirical findings are presented and discussed.
Advisor: Patricia Friedrichsen
Expected graduation date: May 2019