Diogene-CT: tools and methodologies for teaching and learning coding

Abstract

Computational thinking is the capacity of undertaking a problem-solving process in various disciplines (including STEM, i.e. science, technology, engineering and mathematics) using distinctive techniques that are typical of computer science. It is nowadays considered a fundamental skill for students and citizens, that has the potential to affect future generations. At the roots of computational-thinking abilities stands the knowledge of computer programming, i.e. coding. With the goal of fostering computational thinking in young students, we address the challenging and open problem of using methods, tools and techniques to support teaching and learning of computer-programming skills in school curricula of the secondary grade and university courses. This problem is made complex by several factors. In fact, coding requires abstraction capabilities and complex cognitive skills such as procedural and conditional reasoning, planning, and analogical reasoning. In this paper, we introduce a new paradigm called ACME (“Code Animation by Evolved Metaphors”) that stands at the foundation of the Diogene-CT code visualization environment and methodology. We develop consistent visual metaphors for both procedural and object-oriented programming. Based on the metaphors, we introduce a playground architecture to support teaching and learning of the principles of coding. To the best of our knowledge, this is the first scalable code visualization tool using consistent metaphors in the field of the Computing Education Research (CER). It might be considered as a new kind of tools named as code visualization environments.

Computational thinking in mathematics education: A systematic review

“…There were four studies performed in ASEAN countries (Cheng et al, 2017;Fang et al, 2017;Matere et al, 2021;Shorey et al, 2021) and 12 studies conducted in non-ASEAN countries (Çoban & Korkmaz, 2021;Critten et al, 2021;Jeng et al, 2020;Marcelino et al, 2018;Mecca et al, 2021;Menolli & Neto, 2021;Mouza et al, 2017;Quitério Figueiredo, 2017;Relkin et al, 2021;Rich et al, 2021;Sung & Black, 2020;Tucker-Raymond et al, 2021). This systematic review explains various types of teaching methods used in developing CT in mathematics, such as instant communication (IM) teaching method (Cheng et al, 2017), innovative curriculum design relying on an Internet-of-Things (IoT) programming course (Jeng et al, 2020), project-based learning and problem-solving learning method (Menolli & Neto, 2021), BootUp's model teaching method (Rich et al, 2021), technological pedagogical content knowledge (TPACK) educational technology course (Mouza et al, 2017), pre-programming (CS0) course (Quitério Figueiredo, 2017), elearning course employing Moodle as a learning management system (Marcelino et al, 2018), designbased learning (Matere et al, 2021), blending learning flipped class (Fang et al, 2017), physical body movement practice (Sung & Black, 2020), coding as another language (CAL) curriculum (Relkin et al, 2021), guided play activities (Critten et al, 2021), online performance-based assessment (Çoban & Korkmaz, 2021) and procedural programming course (Mecca et al, 2021). This includes delegating responsibility to students, encouraging independent problem-solving among students, co-learning with students, fostering interdependence among students, offering a variety of additional resources…”

“…Relying on the literature review, researchers discovered that six types of tools were used in developing CT in mathematics education. The tools are teaching method (Cheng et al, 2017;Çoban & Korkmaz, 2021;Critten et al, 2021;Fang et al, 2017;Jeng et al, 2020;Marcelino et al, 2018;Matere et al, 2021;Menolli & Neto, 2021;Mouza et al, 2017;Quitério Figueiredo, 2017;Relkin et al, 2021;Rich et al, 2021;Sung & Black, 2020;Tucker-Raymond et al, 2021;Yildiz Durak, 2020), game-based learning (Agbo et al, 2021;Croff, 2017;Hooshyar et al, 2021;Menolli & Neto, 2021;Ng & Cui, 2021), coding programming (Critten et al, 2021;Deng et al, 2020;Fanchamps et al, 2019;Marcelino et al, 2018;Mecca et al, 2021;Ng & Cui, 2021;Özmutlu et al, 2021;Piedade et al, 2020;Quitério Figueiredo, 2017;Relkin et al, 2021;Rich et al, 2021;Ríos Félix et al, 2020;Shorey et al, 2021;Silva et al, 2021;Sun et al, 2021a;Sung & Black, 2020;Yi Wu & Sheng Su, 2021;Yildiz Durak, 2020), robotic activities (Critten et al, 2021;…”

“…However, in mathematics education, insufficient attention has been made to the systematic evaluation with regards to the efficiency of programming instructional design in facilitating the CT abilities acquisition (Grover & Pea, 2017;Lye & Koh, 2014). As a result, researchers have conducted a number of studies in an attempt to identify tools for developing CT abilities in mathematics education (Mecca et al, 2021;Pei et al, 2018;Siong & Kamisah, 2018). However, many people are still ignorant of the most popular method for developing CT abilities in mathematics.…”

Mapeamento Sistemático dos Artigos do Estado da Arte sobre Experimentos com Pensamento Computacional no Ensino Básico

“…Além disso, o PC tem um importante papel nos estudos que utilizam disciplinas diretamente ligadas a área de Science, Technology, Engineering and Mathematics (STEM) no ensino básico [Mecca et al 2021, Rowe et al 2021, mas que também poderia ser usado em outras áreas do conhecimento.…”

Applying the design-based learning model to foster undergraduates’ web design skills: the role of knowledge integration

“…Programming education can not only promote learners' computational thinking, but can also improve their logical thinking and problem-solving skills (de Vries, 2006;Jamil & Isiaq, 2019;Ke, 2014;Mecca et al, 2021;Wang & Wang, 2011). The improvement of these abilities is an important issue for cultivating manpower, and should be strongly emphasized in higher education.…”