An Investigation of the Data Collection Instruments Developed to Measure Computational Thinking

Haseski, Halil İbrahim; İliç, Ulaş

doi:10.15388/infedu.2019.14

Cited by 22 publications

(31 citation statements)

References 59 publications

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“…In studies measuring and assessing computational thinking, the reliability of data collection tools and the number of scales confirmed by statistical analysis are important aspects. We support the view of Haseski and Ilic (2019) on the reliability and variety of measurement tools for evaluating computational thinking. Tests with open and closed questions can be effective in measuring and evaluating levels of development of computational thinking in primary school students.…”

Section: Discussionsupporting

confidence: 60%

Computational thinking assessment at primary school in the context of learning programming

Mukasheva¹,

Omirzakova

2021

WJET

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The study was carried out from 2018 to 2020 with the challenge - how to assess the level of computational thinking. The research design is mixed since the disclosure of mutual influence of the components of the chain ‘learning programming – computational thinking – evaluating computational thinking’ requires the use of both qualitative and quantitative research methods. The conceptualisation of the ‘computational thinking’ idea is based on the premise of the impact of abstract thinking and computers on human thinking evolution. The structural interpretation of ‘computational thinking’, consisting of nine components, reflects the presence of a semantic link between teaching programming and the development of abstract computational thinking. Four levels (phenomenological, analytic–synthetic, set–prognostic and axiomatic) of computational thinking have been identified for each of these nine components. The study involved 102 elementary school students who are learning programming in Scratch. The guiding questions and problems we have developed for elementary school students are designed following the characteristics of the four levels of computational thinking. The results of the study showed that the ratio of ‘structural components’ to different levels of computational thinking, with the corresponding characteristics, allows one to determine the degree of its development or its individual components. Keywords: Computational thinking, educational taxonomies, evaluation methodologies, levels of development;

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Section: Discussionsupporting

confidence: 60%

Computational thinking assessment at primary school in the context of learning programming

Mukasheva¹,

Omirzakova

2021

WJET

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“…A survey is helpful for examining the relationships between CT and other variables using a large sample. Ilıc¸et al (2018) reviewed the trends of CT research and suggested further studies to develop valid and reliable scales for more effectively measuring CT. Haseski_ and I_li_c (2019) investigated the instruments developed to measure CT and concluded that the number of instruments revealed an increasing trend over time; however, the validity and reliability were not specified for more than half of the designed instruments.…”

Section: Assessment Tools Of Ct: Outcomes Versus Processmentioning

confidence: 99%

The Computational Thinking Scale for Computer Literacy Education

Tsai

Liang

Hsu

2020

Journal of Educational Computing Research

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Computational thinking has received tremendous attention from computer science educators and educational researchers in the last decade. However, most prior literature defines computational thinking as thinking outcomes rather than thinking processes. Based on Selby and Woodland’s framework, this study developed and validated the Computational Thinking Scale (CTS) to assess all students’ thought processes of computational thinking for both general and specific problem-solving contexts in five dimensions: abstraction, decomposition, algorithmic thinking, evaluation and generalization. A survey including 25 candidate items for CTS as well as demographic variables was administered to 388 junior high school students in Taiwan. An explorative factor analysis using the principal axis method with the oblimin rotation was used to validate the scale. Finally, 19 items were extracted successfully under the designed five dimensions, with a total explained variance of 64.03% and an overall reliability of 0.91. Results of the demographic comparisons showed that boys had a greater disposition than girls in decomposition thinking when solving problems using computer programming. In addition, programming learning experience, especially self-directed learning and after-school learning, had significant positive effects on all dimensions of CTS. Several future studies are suggested using this tool.

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“…Notwithstanding, there is a persistent desire in all fields to distinguish between various conceptualizations of CT measurement. Psychometric scales are one of the most often employed instruments for evaluating computational thinking (49)(50)(51)(52)(53). However, psychometric instruments are predicated on the notion that an individual provides accurate and comprehensive information (54).…”

Section: Computational Thinkingmentioning

confidence: 99%

“…It also helps create content for the most common components in modern literature. In many studies, limited tools such as perception-attitude scales, multiple choice tests, or just coding have been used to measure computational thinking (49,51,52,(64)(65)(66). This study established the Computational Thinking Disposition Instrument (CTDI) by considering several aspects of computational thinking.…”

Section: Computational Thinkingmentioning

confidence: 99%

Gender differential item functioning analysis in measuring computational thinking disposition among secondary school students

Sovey¹,

Osman²,

Matore³

2022

Front. Psychiatry

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Computational thinking refers to the cognitive processes underpinning the application of computer science concepts and methodologies to the methodical approach and creation of a solution to a problem. The study aims to determine how students’ cognitive, affective, and conative dispositions in using computational thinking are influenced by a gender. This study used a survey research design with quantitative approach. Five hundred thirty-five secondary school students were sampled using probability sampling with the Computational Thinking Disposition Instrument (CTDI). WINSTEPS version 3.71.0 software was subsequently employed to assess the Gender Differential item functioning (GDIF) including reliability and validity with descriptive statistics were employed to assess students’ disposition toward practicing computational thinking. In addition to providing implications for the theory, the data give verifiable research that the CT disposition profile consists of three constructs. In addition, the demonstrated CTDI has good GDIF features, which may be employed to evaluate the efficacy of the application of CT in the Malaysian curriculum by measuring the level of CT in terms of the disposition profile of students.

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An Investigation of the Data Collection Instruments Developed to Measure Computational Thinking

Cited by 22 publications

References 59 publications

Computational thinking assessment at primary school in the context of learning programming

Computational thinking assessment at primary school in the context of learning programming

The Computational Thinking Scale for Computer Literacy Education

Gender differential item functioning analysis in measuring computational thinking disposition among secondary school students

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