Analysis of the relation between computational thinking skills and various variables with the structural equation model

Durak, Hatice; Sarıtepeci, Mustafa

doi:10.1016/j.compedu.2017.09.004

Cited by 168 publications

(107 citation statements)

References 30 publications

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“…A model is created using the AMOS 22.0 Graphic program. The compatibility level of relation patterns in the recommended model is determined by several fit indexes (Durak & Saritepeci, 2018). 1) (chi-square value), RMSEA (root-mean-square error of approximation), GFI (goodnessof-fit-index), CFI (comparative fit index), AGFI (adjusted goodness-of-fit index).…”

Section: Resultsmentioning

confidence: 99%

Exploring the relationship between students’ interaction in smart learning environments and various variables through the structural equation model

2019

JALT

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For the functions of enhancing students' learning performance by integrating advanced technologies and equipment, the smart classroom has become a worldwide research issue recently. This study aims to produce a model to determine what variables affect students' interaction in a smart learning environment. Participants consist of 254 undergraduate students and master students who have experience of learning in a smart classroom for more than one semester. Two scales were adopted in this study as a data instrument. The first one is the "Smart Classroom Inventory". The second one is "Interaction and Student Satisfaction Scales". The Structural Equation Model (SEM) was used in this study for data analysis. A model is built to explain the relations between students' interaction and variables related to the smart classroom. According to the research results, we find that students' interaction can be positively affected by variables of Spatial physical design, Learning data, Differentiation, and Cooperation related to the smart classroom.

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Section: Resultsmentioning

confidence: 99%

Exploring the relationship between students’ interaction in smart learning environments and various variables through the structural equation model

2019

JALT

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“…Unfortunately, teachers' knowledge of CT still lows because they did not have proper guidelines to teach them. This also one of the main effect that students still weak in application computation thinking skills (Durak, et. al., 2018).…”

Section: Literature Reviewmentioning

confidence: 97%

“…Computer Science subjects have a profound impact on social and economic life. Today, as we know that almost all generations, regardless of age and gender, are expected to have some tasks that require basic computer skills in parallel with emerging technology development (Sengupta, 2013;Durak & Saritepeci, 2018). Computer science has an important position in other areas.…”

Section: Introductionmentioning

confidence: 99%

Computer Science Education in Malaysia Schools: The Challenges of Enhancing Computational Thinking Skills

Puteh

2019

IJEAT

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The aim of this paper is to discuss the challenges of computational thinking in computer science subject at school in Ministry of Education Malaysia. The study focuses on teaching and learning of computational thinking skills and computer science at secondary school to cope with the expected situation towards Education 4.0. The contribution study addresses the problem of improving computational skills among students through computer science education. The survey was conducted using the interview method and involved 3 experienced teacher in computer science. Qualitative data were analyzed using thematic analyzed using thematic analysis to extract meaning to understand and explain the subject matter of the study. The findings show that there is a gap based on technology exposure between the generation of teachers and students, therefore computer science requires a module and teaching materials that feature the latest technology. The study also found that the existing curriculum was in line with the efforts of improving the student's computational thinking skills. Therefore, teacher training, modular facilities, and technological materials still need to be improved in parallel with the development of technological knowledge for the generation of students and towards education 4.0.

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“…• CT perceptions-attitudes scales: They are aimed at assessing the perceptions (e.g., self-efficacy perceptions) and attitudes of the subjects not only about CT, but also about related issues such as computers, computer science, computer programming, or even digital literacy. Among scales targeted to students, we can name the Computational Thinking Scales (CTS) (Korkmaz, Çakir, & Özden, 2017), the Computational Thinking Skills Scale (CTSS) (Durak & Saritepeci, 2018), or the Computer Programming Self -Efficacy Scale (CPSES) (Kukul, Gökçearslan, & Günbatar, 2017). When we are interested in assessing the perceptions and attitudes of teachers, the research work of Yadav, Mayfield, Zhou, Hambrusch, and Korb (2014) can be highlighted.…”

Section: Computational Thinking Assessment Toolsmentioning

confidence: 99%

Combining Assessment Tools for a Comprehensive Evaluation of Computational Thinking Interventions

González

Moreno-León²,

Robles

2019

Computational Thinking Education

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Given that computational thinking (CT) is still a blurry psychological construct, its assessment remains as a thorny, unresolved issue. Hence, in recent years, several assessment tools have been developed from different approaches and operational definitions of CT. However, very little research has been conducted to study whether these instruments provide convergent measurements, and how to combine them properly in educational settings. In response, we first review a myriad of CT assessment tools and classify them according to their evaluative approach. Second, we report the results of two convergent validity studies that involve three of these CT assessment tools, which come from different perspectives: the Computational Thinking Test, the Bebras Tasks, and Dr. Scratch. Finally, we propose a comprehensive model to evaluate the development of CT within educational scenarios and interventions, which includes the aforementioned and other reviewed assessment tools. Our comprehensive model intends to assess CT along every cognitive level of Bloom's taxonomy and throughout the various stages of typical educational interventions. Furthermore, the model explicitly indicates how to harmoniously combine the different types of CT assessment tools in order to give answer to the most common research questions in the field of CT Education. Thus, this contribution may lead scholars and policy-makers to perform accurate evaluation designs of CT according to their inquiry goals.

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Analysis of the relation between computational thinking skills and various variables with the structural equation model

Cited by 168 publications

References 30 publications

Exploring the relationship between students’ interaction in smart learning environments and various variables through the structural equation model

Exploring the relationship between students’ interaction in smart learning environments and various variables through the structural equation model

Computer Science Education in Malaysia Schools: The Challenges of Enhancing Computational Thinking Skills

Combining Assessment Tools for a Comprehensive Evaluation of Computational Thinking Interventions

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