Bringing computational thinking to K-12 and higher education

Date

2017-05-01

Journal Title

Journal ISSN

Volume Title

Publisher

Kansas State University

Abstract

Since the introduction of new curriculum standards at K-12 schools, computational thinking has become a major research area. Creating and delivering content to enhance these skills, as well as evaluation, remain open problems. This work describes different interventions based on the Scratch programming language aimed toward improving student self-efficacy in computer science and computational thinking. These interventions were applied at a STEM outreach program for 5th-9th grade students. Previous experience in STEM-related activities and subjects, as well as student self-efficacy, were surveyed using a developed pre- and post-survey. The impact of these interventions on student performance and confidence, as well as the validity of the instrument are discussed. To complement attitude surveys, a translation of Scratch to Blockly is proposed. This will record student programming behaviors for quantitative analysis of computational thinking in support of student self-efficacy. Outreach work with Kansas Starbase, as well as the Girl Scouts of the USA, is also described and evaluated. A key goal for computational thinking in the past 10 years has been to bring computer science to other disciplines. To test the gap from computer science to STEM, computational thinking exercises were embedded in an electromagnetic fields course. Integrating computation into theory courses in physics has been a curricular need, yet there are many difficulties and obstacles to overcome in integrating with existing curricula and programs. Recommendations from this experimental study are given towards integrating CT into physics a reality. As part of a continuing collaboration with physics, a comprehensive system for automated extraction of assessment data for descriptive analytics and visualization is also described.

Description

Keywords

Computational thinking

Graduation Month

May

Degree

Doctor of Philosophy

Department

Department of Computer Science

Major Professor

William H. Hsu

Date

2017

Type

Dissertation

Citation