Computational thinking in the context of higher education: Analytical review

Introduction. Computational thinking is one of the categories that currently assess the quality of people’s preparedness for life, educational and professional activities in the modern world, saturated with information technologies and digital tools. Many issues related to university students’ computational thinking remain insufficiently studied as applied to general education.

Aim. The present research aims to discuss the essence of the concept of “computational thinking” and, mainly, the composition of its structural elements, methods of their formation and assessment at the level of higher education; and to compare the requirements for university students’ computational thinking and digital competencies, which have similarities and differences.

Methodology and research methods. The present review article has theoretical and applied aspects. Except for several fundamentally important works of general studies, which reveal the concept of “com­putational thinking”, the author analysed mainly review articles published in the past five years in order to identify and systematise modern solutions related to the purpose of the work.

Results and scientific novelty. An analysis of the basic concepts associated with computational think­ing showed that at the level of definitions, due to their certain abstractness, the computational think­ing of university students does not have much specificity compared to the computational thinking of schoolchildren. This specificity is manifested at the level of the list of cognitive and non-cognitive skills associated with computational thinking, requirements for the level of their development and assess­ment methods. In computational thinking, cognitive skills include abstraction, decomposition, pattern recognition, algorithmisation, visualisation, logical thinking, communicative competence, the ability to present, structure and analyse data, and some others skills. Non-cognitive skills include self-confidence, communication skills, flexibility, and others.

Methods for assessing the maturity of students’ computational thinking include the results of solv­ing problems in block programming environments such as Scrath; knowledge/skill tests, self-assessment scales/surveys; tests on knowledge of the basics of computational thinking, interviews and observations; interviews, grades for assignments/courses, surveys/questionnaires, solving problems external to the class; the use of a special software environment, the use of criteria for assessing computational thinking and/or psychometric tools; assessments based on solving robotic problems or evaluating artifacts creat­ed during the game, and others.

A comparison of computational thinking with digital competencies at the skill level leads to the conclusion that in computational thinking, skills represent a certain fixed set of meta-skills needed by a student regardless of solving specific problems (for example, abstraction skills). In digital competencies, skills are specified according to numerous types and are more specific.

Practical significance. The results of this study can be used in the design of programmes for develop­ing computational thinking and digital competencies of university students.

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