Modeling, Measurement, and Development of Professional Competence in Industrial-Technical Professions

Ștefănică, Florina; Abele, Stephan; Walker, Felix; Nickolaus, Reinhold

doi:10.1007/978-3-319-41713-4_39

Cited by 9 publications

(1 citation statement)

References 16 publications

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“…For example, the Programme for International Student Assessment (PISA) evaluates problem-solving ability, targeting skills that enable students to successfully handle real-life problems, such as finding the shortest route between two cities without relying on domain-specific knowledge, for example, from mathematics [68]. Furthermore, vocational education prepares students for their careers by focusing on 1) the application of vocationally relevant knowledge in typical problem situations and 2) the awareness of potential problems associated with practical routines, problem-solving strategies and an encompassing set of personal and social competencies to overcome them [69]. This article explores the opportunities to scaffold the CT-Argumentation instructional design in facilitating the students' development of CT competencies through scientific argumentation.…”

Section: Discussionmentioning

confidence: 99%

Developing Computational Thinking Competencies through Constructivist Argumentation Learning: A Problem-Solving Perspective

Voon¹,

Wong²,

Wong³

et al. 2022

IJIET

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Argumentation is a scientific literacy practice focused on developing scientific thinking skills associated with problem-solving. As computing has become an integral part of our world, computational thinking skills are requisite for successful problem-solving. The significant effect of computational thinking applications on the efficacy of scientific literacy practices is increasingly acknowledged. In this article, we propose a framework that conceptualizes the constructivist argumentation as a context for problem-solving by applying five computational thinking dimensions, viz. algorithmic design, decomposition, abstraction, evaluation, and generalization. The framework emphasizes two aspects, students’ problem-solving capability and quality of argumentation. Drawing from the literature on scientific argumentation and problem-solving, we argue that the application of computational thinking dimensions in science learning is currently overlooked in the instructional environment. To nurture higher order thinking skills and to engage effective problem-solvers, our framework incorporates four Computational Thinking-Argumentation design principles to support instructional innovation in the teaching and learning of science at the secondary school level, viz. 1) developing problem-solving competencies and building capability in solving uncertainties throughout scientific inquiry; 2) developing creative thinking and cooperativity through negotiation and evaluation; 3) developing algorithmic thinking in talking and writing; 4) developing critical thinking in the processes of abstraction and generalization.

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Section: Discussionmentioning

confidence: 99%