Computational Thinking Frameworks used in Computational Thinking Assessment in Higher Education. A Systematized Literature Review.

Castro, Laura M.; Shoaib, Huma; Douglas, Kerrie A.

doi:10.18260/1-2--36824

Cited by 2 publications

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References 6 publications

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“…Обзор L. M. C. Castro и др. [10] посвящен в основном существующим практикам оценивания уровня сформированности вычислительного мышления у студентов вузов. На основе анализа 19 статей авторы ищут ответы на вопросы: что эти статьи сообщают нам о практиках и типах оценок, используемых для оценки вычислительного мышления в высшем образовании, и какие концепции вычислительного мышления используются для поддержки указанных оценок.…”

Section: обзор работ по вычислительному мышлению студентов вузовunclassified

Computational thinking in the context of higher education: Analytical review

Khenner

2024

Obraz. nauka

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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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Section: обзор работ по вычислительному мышлению студентов вузовunclassified

Computational thinking in the context of higher education: Analytical review

Khenner

2024

Obraz. nauka

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“…Computational thinking is defined as a technique to solve a problem effectively with a machine or computational tools (i.e., algorithmic thinking, decomposition, abstraction, pattern recognition) [7]. Computational thinking is an essential skill for programmers and a concept that can be difficult but essential to instill during an introductory programming course [8].…”

Section: Computational Thinkingmentioning

confidence: 99%

Work-in-Progress: Using a Scavenger Hunt to Tackle Challenges of CS1: Computational Thinking, Analyzing Code, and Debugging

Coffman-Wolph

2022 ASEE Annual Conference &Amp; Exposition Proceedings

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WVU Tech). While at WVU Tech she was a founding member and faculty advisor of AWESOME (Association of Women Engineers, Scientists, Or Mathematicians Empowerment) at WVU Tech. She is actively involved in community outreach with a goal of increasing the number of women in STEM and creating effective methods for introducing young children to CS concepts and topics. Dr. Coffman-Wolph's research interests include: Artificial Intelligence, Fuzzy Logic, Software Engineering, STEM Education, and Diversity and Inclusion within STEM. While growing up in Michigan, she was a dancer (ballet, tap, and jazz) until she graduated high school and a competitive figure skating through her senior year of college. Her hobbies include: reading mystery and fantasy books, knitting, shopping (especially to add to her shoe collection), yoga, and disc golf.

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Computational Thinking Frameworks used in Computational Thinking Assessment in Higher Education. A Systematized Literature Review.

Cited by 2 publications

References 6 publications

Computational thinking in the context of higher education: Analytical review

Computational thinking in the context of higher education: Analytical review

Work-in-Progress: Using a Scavenger Hunt to Tackle Challenges of CS1: Computational Thinking, Analyzing Code, and Debugging

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