Exploring the Field of Computational Thinking as a 21st Century Skill

Bocconi, Stefania; Chioccariello, Augusto; Dettori, Giuliana; Ferrari, Anusca; Engelhardt, Katja; Kampylis, Panagiotis; Punie, Yves

doi:10.21125/edulearn.2016.2136

Cited by 147 publications

(226 citation statements)

References 11 publications

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“…First, by using the core capabilities of computer hardware to conceptualize digital uses and competencies, the GTCU insulates itself from the changing designs of hardware and software platforms, and environmental factors affecting technology use in particular contexts. Second, three of its four dimensions (technical, informational and social) represent a common core among major frameworks (Iordache, Mariën, & Baelden, 2017), and its computational dimension addresses competencies that are achieving prominence in the educational literature (Bocconi, Chioccariello, Dettori, Ferrari, Engelhardt, et al, 2016;Jun, Han, Kim, & Lee, 2014). Third, the GTCU's online data-collection application-the DCP-has been used repeatedly to profile the technology uses of both students and professors in higher education (Barber, DiGiuseppe, vanOostveen, Blayone, & Koroluk, 2016;Desjardins & vanOostveen, 2015;Desjardins, vanOostveen, Bullock, DiGiuseppe, & Robertson, 2010).…”

Section: A Digital Readiness Framework and Profilermentioning

confidence: 99%

“…This includes interacting with online calendar systems (E20); data-visualization tools, such as concept-mapping, diagramming and graphing applications (E21, 22 and 24); numerical and statistical-analysis packages (E23 and 25); and scripting/programming environments (E26). Indeed, it is difficult to imagine conducting research today without significant experience in some of these competencies, particularly in an age of "big data" (Bocconi et al, 2016).…”

Section: Digital Readiness For Computational Actionsmentioning

confidence: 99%

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Profiling the digital readiness of higher education students for transformative online learning in the post-soviet nations of Georgia and Ukraine

Blayone¹,

Mykhailenko²,

Kavtaradze

et al. 2018

Int J Educ Technol High Educ

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This study profiles the digital readiness of university students in Georgia and Ukraine for fully online collaborative learning, theorized as an educational pathway to democratic transformation. The Digital Competency Profiler was used to gather data from 150 students in Georgia and 129 in Ukraine about their digital competences. The analysis grouped students into high-, medium-and low-readiness segments for 52 actions in technical, communicational, informational and computational dimensions. Findings show that large percentages of Georgian and Ukrainian students are ill-prepared for many online-learning activities, and there is generally greater readiness on mobile devices than desktops/laptops. However, large percentages of Ukrainian students appear in high-readiness segments for communicating online and using social networks. In Georgia, many students report high-readiness for technical and computational interactions. Therefore, the researchers recommend using the digital-readiness data in tandem with a well-chosen, online-learning framework to align these patterns of strengths with future educational innovation.

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Section: A Digital Readiness Framework and Profilermentioning

confidence: 99%

Section: Digital Readiness For Computational Actionsmentioning

confidence: 99%

Profiling the digital readiness of higher education students for transformative online learning in the post-soviet nations of Georgia and Ukraine

Blayone¹,

Mykhailenko²,

Kavtaradze

et al. 2018

Int J Educ Technol High Educ

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“…De hecho, esta relación del PC con la robótica especialmente es intensa fuera del ámbito educativo formal. Con todo, la relación entre el PC y la robótica no es biunívoca, en el sentido de que la robótica no necesariamente garantiza el desarrollo del PC, ni el PC puede adquirirse únicamente por medio de la robótica (Bocconi, Chioccariello, Dettori, Ferrari, Engelhardt, et al, 2016a;Voogt, Fisser, Good, Mishra, & Yadav, 2015;Yadav et al, 2017). Y, de igual modo ocurre con la competencia digital, que puede ir íntimamente ligada al desarrollo del PC, especialmente si focalizamos en los elementos que añaden Barr y Stephenson (2011) y Zapata-Ros (2015), pero que no necesariamente tiene por qué incidir en el PC (del mismo modo que se puede adquirir el PC sin que la competencia digital se vea beneficiada).…”

Section: El Pensamiento Computacional En Educaciónunclassified

“…En definitiva, aunque los lenguajes de programación puramente computacionales y las tecnologías puedan ayudarnos en la adquisición del PC, esta no es automática ni tiene por qué darse implícitamente, sino que requiere de acciones específicas. Además, y como consecuencia de esto, si bien es común pensar que el PC puede constituir una materia específica en el currículum (y, de hecho, así ha sido en muchos contextos educativos nacionales que hemos mencionado antes) (Brennan & Resnick, 2012), también hay quienes abogan por su mayor efectividad cuando se integra en el resto del currículum, como una estrategia de aprendizaje y no como una materia finalista (Bocconi, Chioccariello, Dettori, Ferrari, Engelhardt, et al, 2016b;Morreale, Jimenez, Goski, & Stewart-Gardiner, 2012).…”

Section: El Pensamiento Computacional En Educaciónunclassified

¿Robots o programación? El concepto de Pensamiento Computacional y los futuros maestros

Martínez

Minguell

Peracaula

2018

Education in the Knowledge Society

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El Pensamiento Computacional, por medio de la robótica o de la programación, está siendo incluido en el currículum en todo el mundo. No en vano, se considera una de las competencias necesarias para el siglo XXI. Sin embargo, los docentes a menudo no han sido formados para enseñar Pensamiento Computacional y ni siquiera saben exactamente qué es. Por ello, intentamos conocer cuál es el concepto de Pensamiento Computacional por parte de los maestros en formación por medio de una metodología mixta, que nos permite detectar importantes faltas de conocimiento y errores en relación con el concepto. Sin embargo, por otro lado, se diagnostican buenas actitudes iniciales hacia el Pensamiento Computacional, que pueden permitir que la formación específica recibida en este ámbito sea transferible a contextos futuros de práctica docente.

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“…CT has been incorporated into curricula across the world (Bocconi, Chioccariello, Dettori, Ferrari, & Engelhardt, ). In the United States, where this study took place, CT is included in the science standards, the Next Generation Science Standards (NGSS), which have been adopted by 19 states and the District of Columbia, adapted and used by 20 states (NGSS Lead States, ).…”

Section: Introductionmentioning

confidence: 99%

Learning natural selection through computational thinking: Unplugged design of algorithmic explanations

2019

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Computational thinking (CT) is a way of making sense of the natural world and problem solving with computer science concepts and skills. Although CT and science integrations have been called for in the literature, empirical investigations of such integrations are lacking. Prior work in natural selection education indicates students struggle to explain natural selection in different contexts and natural selection misconceptions are common. In this mixed methods study, secondary honors biology students learn natural selection through CT by engaging in the design of unplugged algorithmic explanations. Students learned CT principles and practices and applied them to learn and explain the natural selection process. Algorithmic explanations were used to scaffold transfer of natural selection knowledge across contexts through investigation of three organisms and the creation of generalized natural selection algorithms. Students' pre‐ and post‐unit algorithmic explanations of natural selection were analyzed to answer the following research questions: (a) How do students' conceptions of natural selection change over the course of a CT focused unit? (b) What is the relationship between CT and natural selection in students' algorithmic explanations? (c) What are students' perspectives of learning natural selection with CT? Results indicate students' conceptions of natural selection increased and natural selection misconceptions decreased over the course of the unit. Within their post‐unit algorithmic explanations, students used specific CT principles in conjunction with natural selection concepts to explain natural selection, which helped them to learn the details of the natural selection process and correct their natural selection misconceptions. Students indicated the use of CT in unplugged algorithmic explanations in different contexts helped them learn natural selection. This study shows unplugged CT can be used to teach students science content, and it provides an example for further CT and science integrations. Implications for the field are discussed.

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Exploring the Field of Computational Thinking as a 21st Century Skill

Cited by 147 publications

References 11 publications

Profiling the digital readiness of higher education students for transformative online learning in the post-soviet nations of Georgia and Ukraine

Profiling the digital readiness of higher education students for transformative online learning in the post-soviet nations of Georgia and Ukraine

¿Robots o programación? El concepto de Pensamiento Computacional y los futuros maestros

Learning natural selection through computational thinking: Unplugged design of algorithmic explanations

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