What a good training proposal for working on computational thinking should look like

At what age should we start teaching it? How do we do it? How do we evaluate the tools and progress? Now that we know what it is and how it can help us to improve the education of future generations, let’s see what characteristics a good didactic proposal should have in order to develop computational thinking in the classroom.

What a good training proposal for working on computational thinking should look like

In recent articles we’ve analysed computational thinking from multiple perspectives. For example, we’ve seen, among other things, what it is and how it came into being, how it can be used to bridge the digital divide, adapting it to highly vulnerable environments, how to introduce it into different educational curricula and how to train teachers to teach computational thinking. We’ve also talked to a number of experts who’ve told us about the key issues, challenges and experiences they’ve faced when it comes to implementing it at their schools. Taking all this information into account, this article explains what we think the characteristics of a training proposal that incorporates this thinking in different educational contexts should be.

Computational thinking from an early age

Until now, computational thinking has been chiefly focused on the stages of secondary education, but, over the last decade, thanks to the findings of different research projects and good educational practices, the benefits of developing and working on it from an early age have become evident. Working on computational thinking from the early years of childhood facilitates the design of a progressive proposal that brings Computational Science and problem-solving closer to Compulsory Basic Education in an orderly, planned manner and within the curricular framework.

Progressive and suited to cognitive processes

In order to design a coherent proposal, it’s vital to take the evolutionary stages of the students as a reference, working progressively on the cognitive processes required for the development of computational thinking in accordance with each phase and offering itineraries ranging from an introductory to an advanced level. Similarly, the contents should be designed while taking into account the formats and practices that the students are used to working with, proposing a progressive transformation towards active pedagogical models.

Modular educational resources with varying degrees of digitisation

We’ve already seen how, within some contexts, a lack of equipment, connectivity or access to additional digital resources (electronic boards, robots or digital fabrication tools) may limit the development of computational thinking. Therefore, in order not to leave anyone behind, it’s crucial to take this into consideration when designing our pedagogical proposals. This proposal envisages the use of digital resources in a progressive manner, proposing activities that don’t require the use of computers while including maker projects that are carried out with the support of FabLab resources.

Didactic proposals with a gender-based perspective and attention to diversity

Working with an equitable approach at the school is extremely important, bearing in mind that the proposals made must be inclusive and flexible and open enough to make the necessary changes to cater for the diversity of the classroom. This includes, among other factors, selecting programmes and devices appropriate to the special educational needs of the students and creating proposals that are suited to their potential. Similarly, these educational proposals must incorporate the gender equality approach in a cross-cutting manner. The aim is to eliminate gender stereotypes and traditional roles and prevent situations of sexist discrimination in the future.

Articulation of monitoring and evaluation tools

One of the biggest challenges facing computational thinking is the problem of designing and articulating a set of tools that allow the learning achievements to be proven. A review of the existing literature reveals that there are very few evaluation instruments and systems that cover the complexity of the cognitive processes implemented by means of computational thinking. It’s therefore vital to prioritise the design of tools and instruments for evaluation and optimal support within the framework of a cross-disciplinary computational thinking programme. Throughout this process there’s a need for mixed practices and approaches, which in turn can be integrated into digital platforms that enable the implementation of Learning Analytics proposals. With the application of the latter, teachers can personalise their students’ learning while evaluating their own performance, besides learning more about their strengths and the areas they can improve on. These data can also be used to identify educational shortcomings and design processes for continuous improvement.

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