Abstract:

In recent decades computational thinking (CT) is increasingly perceived as a broad competency, applicable not only in computer science, but also for every scientist and, indeed, every person. Specifically, it is considered as a universally-applicable attitude and skill set, based on concepts, tools, and practices that are used in the field of computer science, but that are needed in order to solve real-world problems and to function in the modern workplace.
In this research, we:
(a) present a case study examining whether students may improve their skills in two key aspects of CT, namely problem-solving and critical-thinking, by taking part in a workshop that focuses on such skills, and
(b) propose innovative techniques for measuring such improvements.

When applying CT competencies, the person transfers and uses both domain-specific content knowledge, and meta-knowledge, system thinking and critical thinking (CRIT) about how, why, and when to apply the domain-specific knowledge. The latter include: identifying and analyzing problems, understanding and using logical inferences, judging validity and reliability of products, assumptions, and information sources, and analyzing systems by identifying their components, and understanding how the interactions of these components yield the integrated structure and behavior of the system as a whole.

Students’ CRIT can be enhanced by exposing them to complex problems in compound systems. Often, teaching computer science (CS) in high school allows students to enhance CRIT only at a basic level since the problems are usually well defined and not compound.
Hence, in this study, we aim to expose students to complex problems containing many components and interrelations, as well as certain amounts of uncertainty. We first describe a workshop in which 30 high-school CS students analyzed multiple industrial and home-assistant robots. Before and after the workshop, the students analyzed and designed robots (which they have not seen previously) and filled in questionnaires.

To measure students’ CRIT, we used a bi-dimensional method:
(a) examining the cognitive dimension of knowledge, using revised Bloom's taxonomy (remember, understand, apply, analyze, evaluate, create), and
(b) examined the system-thinking skills with a focus on applying distinct, yet interrelated, perspectives (functionality, structure, user interface, efficiency and reliability).

A preliminary analysis of the questionnaires shows that after the workshop the students demonstrated higher levels of CRIT and did a better job of designing a robot. Specifically, the results indicate that students:
(a) used higher cognitive knowledge level (including apply, analyze and evaluate);
(b) analyzed a system from more perspectives (like user experience, or robustness in dealing with unexpected conditions) and asked more critical questions; and,
(c) designed and wrote a better requirements document for a new robot;
(d) perceived the workshop as helpful for them and explained the importance of understanding the complexity of robot design.

Based on these results, we concluded that a suitable teaching model can help students to improve CRIT and problem-solving skills. The paper describes in more detail the contents and structure of the workshop, the specific problems and questionnaires used, the measurement scales and techniques, and the actual empirical results.

Keywords:

Computational thinking, critical thinking, problem-solving, system thinking, transferable knowledge, system requirements and design.