Purpose
This paper aims to present a novel pedagogical model that aims at bridging creativity with computational thinking (CT) and new media literacy skills at low-technology, information-rich learning environments. As creativity, problem solving and collaboration are among the targeted skills in twenty-first century, this model promotes the acquisition of these skills towards a holistic development of students in primary and secondary school settings. In this direction, teaching students to think like a computer scientist, an economist, a physicist or an artist can be achieved through CT practices, as well as media arts practices. The interface between these practices is imagination, a fundamental concept in the model. Imaginative teaching methods, computer science unplugged approach and low-technology prototyping method are used to develop creativity, CT, collaboration and new media literacy skills in students. Furthermore, cognitive, emotional, physical and social abilities are fostered. Principles and guidelines for the implementation of the model in classrooms are provided by following the design thinking process as a methodological tool, and a real example implemented in a primary school classroom is described. The added value of this paper is that it proposes a pedagogical model that can serve as a pool of pedagogical approaches implemented in various disciplines and grades, as CT curriculum frameworks for K-6 are still in their infancy. Further research is needed to define the point at which unplugged approach should be replaced or even combined with plugged-in approach and how this proposed model can be enriched.
Design/methodology/approach
This paper presents a pedagogical model that aims at bridging creativity with CT, collaboration and new media literacy skills.
Findings
The proposed model follows a pedagogy-driven approach rather a technology-driven one as the authors suggest its implementation in low-tech, information-rich learning environments without computers. The added value of this paper is that it proposes a novel pedagogical model that can serve as a pool of pedagogical approaches and as a framework implemented in various disciplines and grades. A CT curriculum framework for K-6 is an area of research that is still in its infancy (Angeli et al., 2016), so this model is intended to provide a holistic perspective over this area by focusing how to approach the convergence among CT, collaboration and creativity skills in practice rather than what to teach. Based on literature, the authors explained how multiple moments impact on CT, creativity and collaboration development and presented the linkages among them. Successful implementation of CT requires not only computer science and mathematics but also imaginative capacities involving innovation and curiosity (The College Board, 2012). It is necessary to understand the CT implications for teaching and learning beyond the traditional applications on computer science and mathematics (Kotsopoulos et al., 2017) and start paying more attention to CT implications on social sciences and non-cognitive skills. Though the presented example (case study) seems to exploit the proposed multiple moments model at optimal level, empirical evidence is needed to show its practical applicability in a variety of contexts and not only in primary school settings. Future studies can extend, enrich or even alter some of its elements through experimental applications on how all these macro/micromoments work in practice in terms of easiness in implementation, flexibility, social orientation and skills improvement.
Originality/value
The added value of this paper is that it joins learning theories, pedagogical methods and necessary skills acquisition in an integrated manner by proposing a pedagogical model that can orient activities and educational scenarios by giving principles and guidelines for teaching practice.
Integrating Computational Thinking into Elementary Science Curriculum: an Examination of Activities that Support Students’ Computational Thinking in the Service of Disciplinary Learning
