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It has been forcefully argued all over the world that computing must become as much a part of literacy as are reading and writing and that it must be studied from kindergarten through to higher education and beyond. It has been suggested (Booch, 2014) that literacy should not focus on coding, but rather on Computational Thinking (Wing, 2006) intended here as a way of thinking, a set of mental tools (Denning, 2016), (Tedre, 2016), guides in solving problems, designing solutions and realizing artifacts. Similar efforts have been directed in relation to Algorithm Thinking, Information Thinking and similar approaches. These efforts are united by their support for the introduction of rigorous Computer Science concepts from the beginning of the educational journey. The literature on how computing concepts have to be introduced is vast, covering technologies, pedagogies and content (Maiorana, 2017), (DeRossi, 2018), (Maiorana, 2019). A wide-ranging, comprehensive survey (Falkner, 2019a), (Falkner, 2019b), (Nistor, 2018), (Sentence, 2017) has produced a landscape of teaching practices and their relation to research. Work has been carried out to raise international awareness of the new subject’s structure and content (Maiorana, 2020 c), (Quille, 2018). Recent research work (Luxton-Reilly,2018) has reviewed aspects related to:
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Students’ learning, from models of how students learn and approach problems, to literacy on code reading, writing, and debugging, from behavior analysis of students on task activity to measurements of students’ ability.
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Students’ attitudes, engagements, and experiences for all students with a particular attention for at-risk students, women and minorities.
Following the same approach, the paper reviews research on teachers’ approaches to teaching, course design, delivery, tools, and infrastructures used. Curriculum aspects are reviewed regarding general aspects, choice of programming languages and paradigms. Review of assessment aspects related to theories, models and modes of assessment, exam questions repositories, tools, and methods to enhance feedback, automated and semi-automated assessment, and academic integrity and plagiarism completes the work.
Assessment and its relation to Computer Science concepts and expected learning outcomes has been reviewed in (Luxton-Reilly, 2017) and the relation between assessment resources and content, which couldboth crowd-sourced, has been envisaged in (Giordano, 2015), (Oates, 2016).