Conference Paper

Developing Computational Thinking at School with Machine Learning: An exploration

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Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have heavily irrupted in society, bringing new applications and possibilities while introducing some ethical problems. Governments and institutions around the world are working on the challenges posed by AI in all aspects, from economy to education. Therefore, introducing AI-related content at school and exploring how this kind of content can be taught becomes mandatory. In this paper we carry out a bibliographic revision of previous works done on ML, and then describe an educational resource developed by the institution of the first two authors (INTEF) aimed to teach ML in schools with Scratch and Machine Learning for Kids. The testimonials of three educators, who have implemented their own version of these resources, are depicted. More efforts should be made to introduce AI-related content in education.

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... These tools hide the ML algorithm required in the learning step in a black box [11,19,26,50], or in the best case they only allow to handle some few relevant parameters controlling the ML algorithm [9,23,43]. Many instructional units regarding this approach have been proposed [18,21,42,47,50,51], some of them make use of any of these tools [18,47,50,51]. ...
... These tools hide the ML algorithm required in the learning step in a black box [11,19,26,50], or in the best case they only allow to handle some few relevant parameters controlling the ML algorithm [9,23,43]. Many instructional units regarding this approach have been proposed [18,21,42,47,50,51], some of them make use of any of these tools [18,47,50,51]. ...
... So, one of the instruments they developed was a test intended to assess the students' knowledge on AI and ML. The questions selected were taken from other available tests, such as [16], the Machine Learning for Kids website 5 , a MOOC on AI, and previous research of the KGBL3 6 group [18,19]. ...
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The inclusion of artificial intelligence (AI) in education is increasingly highlighted by international organizations and governments around the world as a cornerstone to enable the adoption of AI in society. That is why we have developed LearningML, aiming to provide a platform that supports educators and students in the creation of hands-on AI projects, specifically based on machine learning techniques. In this investigation we explore how a workshop on AI and the creation of programming projects with LearningML impacts the knowledge on AI of students between 10 and 16 years. 135 participants completed all phases of the learning experience, which due to the COVID-19 pandemic had to be performed online. In order to assess the AI knowledge we created a test that includes different kinds of questions based on previous investigations and publications resulting in a reliable assessment instrument. Our findings show that the initiative had a positive impact on participants' AI knowledge, being the enhancement especially important for those learners who initially showed less familiarity with the topic. We observe , for instance, that while previous ideas on AI revolve around the term robot, after the experience they do around solve and problem. Based on these results we suggest that LearningML can be seen as a promising platform for the teaching and learning of AI in K-12 environments. In addition, researchers and educators can make use of the new instrument we provide to evaluate future educational interventions.
... Based on this explicit and implicit information in the reviewed records, 15 distinct pedagogical strategies could be discerned (see Table 4), of which three are predominant: active and engaged teaching, as opposed to passive listening (e.g., [52,100,125]), small group work and peer learning (e.g., [1,68,88]), and technology-mediated teaching by letting students use, modify, and/or construct technology artefacts. This last strategy includes block-based and more advanced programming activities (e.g., [34,87]), developing IoT applications (e.g., [56,99]), building or modifying and testing ML models (e.g., [88,166]), and creating AR games [78] and VR environments [26]. ...
... Most often used are extensions for Scratch (e.g., [1,35,113,157]), Snap! [73], and App Inventor [112]. García et al. [52], for instance, developed an educational resource to teach ML in schools with ML4K (Machine Learning for Kids), a web platform for children to build ML models that can be exported to Scratch or App Inventor to develop ML-powered applications [52]. ...
... Most often used are extensions for Scratch (e.g., [1,35,113,157]), Snap! [73], and App Inventor [112]. García et al. [52], for instance, developed an educational resource to teach ML in schools with ML4K (Machine Learning for Kids), a web platform for children to build ML models that can be exported to Scratch or App Inventor to develop ML-powered applications [52]. ...
Article
This systematic mapping review sheds light on how emerging technologies have been introduced and taught in various K-12 learning settings, particularly with regard to artificial intelligence (AI), machine learning (ML), the internet of things (IoT), augmented reality (AR), and virtual reality (VR). These technologies are rapidly being integrated into children's everyday lives, but their functions and implications are rarely understood due to their complex and distributed nature. The review provides a rigorous overview of the state of the art based on 107 records published across the fields of human-computer interaction, learning sciences, computing education, and child-computer interaction between 2010 and 2020. The findings show the urgent need on a global scale for inter-and transdisciplinary research that can integrate these dispersed contributions into a more coherent field of research and practice. The article presents nine discussion points for developing a shared agenda to mature the field. Based on the HCI community's expertise in human-centred approaches to technology and aspects of learning, we argue that the community is ideally positioned to take a leading role in the realisation of this future research agenda.
... These tools hide the ML algorithm required in the learning step in a black box [11,19,26,50], or in the best case they only allow to handle some few relevant parameters controlling the ML algorithm [9,23,43]. Many instructional units regarding this approach have been proposed [18,21,42,47,50,51], some of them make use of any of these tools [18,47,50,51]. ...
... These tools hide the ML algorithm required in the learning step in a black box [11,19,26,50], or in the best case they only allow to handle some few relevant parameters controlling the ML algorithm [9,23,43]. Many instructional units regarding this approach have been proposed [18,21,42,47,50,51], some of them make use of any of these tools [18,47,50,51]. ...
... So, one of the instruments they developed was a test intended to assess the students' knowledge on AI and ML. The questions selected were taken from other available tests, such as [16], the Machine Learning for Kids website 5 , a MOOC on AI, and previous research of the KGBL3 6 group [18,19]. ...
... On the one hand, educators and researchers may find other strategies to develop CT skills, as it is already the case with the use of unplugged activities (Brackmann et al., 2017). On the other hand, the intense development of artificial intelligence solutions, especially those based on machine learning, may alter dramatically the way computer programming is performed (Rodríguez-García, Moreno-León, Román-González & Robles, 2019). ...
Article
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Even though countries from all over the world are modifying their national educational curriculum in order to include computational thinking skills, there is not an agreement in the definition of this ability. This is partly caused by the myriad of definitions that has been proposed by the scholar community. In fact, there are multiple examples in educational scenarios in which coding and even robotics are considered as synonymous of computational thinking. This paper presents a text network analysis of the main definitions of this skill that have been found in the literature, aiming to offer insights on the common characteristics they share and on their relationship with computer programming. As a result, a new definition of computational thinking is proposed, which emerge from the analysed data.
... Finally, a good reason to teach AI in schools [4] is that hands-on activities based on AI can be added to programming and unplugged activities as a new and powerful instrument to foster Computational Thinking (CT) [9]. Even more, as proposed by some researchers [13], new concepts, practices and perspectives coming from AI can be added as a new dimensions to the classical Brennan-Resnick CT framework [2]: classification, prediction and generation have been proposed as concepts; training, validating and testing as new practices; and evaluating as a new perspective. ...
... Based on this rationale, we apply a similar approach to investigate the successful learning elements to develop and refine our AI curricula in K-12 settings. Second, the majority of the studies on AI were conducted in Northern America and European regions such as the United States, Finland, Spain and Brazil (Touretzky et al., 2019;Van & Lin, 2020;Williams & Breazeal, 2020;Van Brummelen, Heng, & Tabunshchyk, 2021;Tedre et al., 2020;Tedre, Toivonen, Kaihila, et al., 2021;Toivonen et al., 2020;García, León, González, & Robles, 2019;von Wangenheim et al., 2020;von Wangenheim et al., 2021) while the topic in the Asia-Pacific Region has been rarely studied, as shown in Appendix 4. In recent years, countries such as China, Japan, Korea and Singapore have also launched national curricular reforms to address the current movement in AI teaching that is aligned with the global technological trend. It is meaningful to review how existing studies in the Asian-Pacific region could bring insights for AI teaching and further advance their AI teaching policies and curricula. ...
Article
Full-text available
Artificial intelligence (AI) teaching is becoming an increasingly popular topic among educators and researchers, but the research on AI curriculum for K-12 classrooms was under-explored. Currently, most studies examine the curriculum content of the United States and the European countries. However, there has been limited research on AI learning design and activities in the Asia-Pacific region. This meta-review examined 14 research papers on AI curriculum for K-12 classrooms that were taken in the Asia-Pacific region from 2018 to 2021 by identifying the content knowledge, tools, platforms, activities, theories and models, assessment methods, and learning outcomes of the selected studies. The results indicated that AI curricula can develop students' AI knowledge and skills, learning attitudes, and interests. Furthermore, the research on AI education was conducted using both qualitative and quantitative methods which are useful for future educators and researchers to understand how they assess students’ AI learning performance. We also derive a set of implications for innovative pedagogical designs in terms of educational standards, curriculum designs, formal/informal education, student learning outcomes, teacher professional development and learning progressions to recommend how governments, researchers and educators could build a widely-accepted and age-appropriate AI curriculum for all K-12 learners.
... Existen contribuciones que tienen como fin describir el estado de la cuestión en temas vinculados a la enseñanza-aprendizaje de la IA en primaria y secundaria: Juegos y herramientas de software (Giannakos et al., 2020;Liu & Kromer, 2020;Rodríguez-García et al., 2020), Pensamiento computacional y machine learning (Rodríguez-García et al., 2019), Enseñanza inteligente con IA (Cheng, 2021), Diseño de unidades didácticas (Marques et al., 2020), Iniciativa AI4K12 en el contexto educativo , Estrategias y marcos pedagógicos (Temitayo Sanusi & Sunday Oyelere, 2020), Diseño de prototipos sobre los conceptos básicos de la IA (Woo et al., 2020). En general, estas contribuciones proporcionan una base sólida sobre la importancia de esta novel línea de investigación; sin embargo, todavía hay cuestiones que deben investigarse y, en algunos casos, siguen sin respuesta. ...
Chapter
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La presente revisión tiene como objetivo describir el estado de la cuestión de la enseñanza-aprendizaje de la Inteligencia Artificial (IA) en primaria y secundaria, desde dos enfoques: (1) literatura proveniente de bases de datos científicas de alto impacto y (2) literatura gris. Para aquello, se utilizó la metodología de Barbara Kitchenham, como también, la adaptación de esta propuesta por Pablo Torres-Carrión. Así, para el primer enfoque, el proceso de búsqueda consistió en revisar cuatro bases de datos científicas multidisciplinarias (ACM Digital Library, IEEE Xplore, Scopus y Web of Science), considerando artículos de revistas, capítulos de libros y textos de congresos, publicados, en inglés, entre los años 2005 al 2021. Así, fueron seleccionados 67 artículos según los criterios de inclusión, exclusión, duplicados, y evaluación de la calidad. En el segundo enfoque, se realizó la búsqueda de la literatura gris mediante el buscador académico Google Scholar, con los mismos criterios de calidad antes mencionados; aquí se obtuvieron 33 documentos. Los hallazgos demuestran que las iniciativas de la enseñanza-aprendizaje de la IA es un campo con mucho crecimiento y con tendencia al alza a partir del 2018. Además, el machine learning y la robótica educativa, son las dos ramas vinculadas a la IA que actualmente se usan en la enseñanza de la IA en primaria y secundaria.
... Learners' self-development is based on the skills and information achieved in online environments and communities. A tailored learning environment includes schooling instruments, services, an application that supports learner skills, an inexpensive schooling environment, and a personalised student profile [29,30]. It will offer an individualised schooling experience to students to efficiently satisfy this learner-centric criterion. ...
Article
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Similar to so many other aspects of modern life, education is gradually being automated by technological means. The software, apps, systems, platforms, and digital devices that permeate modern education are inextricably linked to these automated processes. One of the primary goals of automation has always been to improve quality and efficiency by reducing the number of human repetitive tasks based on machine learning (ML) algorithms and applications that facilitate the automation of decision-making of artificial intelligence (AI). Thus, computers and robots are predictable and do exactly what they are programmed to do. It is impossible for a computer’s memory or processing power to become “tired” because machines never rest, and now some activities can be automated, thanks to advances in artificial intelligence. Schools nowadays have software that analyses data and makes decisions based on the data rather than relying solely on human analysts regarding repetitive administrative tasks. The exploratory research within the K–12 group of teachers from LINK Educational Alliance from Serbia was performed on 109 persons to identify the genuine knowledge about AI and the potential for automatisation of work processes. Based on the teachers’ opinions regarding opportunities brought about by AI in K–12 schools, we analysed their implications in implementing AI in the educational process in K–12 education.
Technical Report
La Escuela de Pensamiento Computacional e Inteligencia Artificial (EPCIA) es un proyecto del Ministerio de Educación y Formación Profesional, que se desarrolla en colaboración con las Consejerías y Departamentos de Educación de las comunidades y ciudades autónomas. El objetivo del proyecto es ofrecer recursos educativos abiertos, formación, acompañamiento y evidencias de impacto en las prácticas educativas y en el aprendizaje del alumnado, a fin de impulsar la incorporación del pensamiento computacional en la práctica docente a través de actividades de programación y robótica. Este proyecto, que está dirigido a docentes de todas las etapas educativas no universitarias y de cualquier materia o especialidad, lanzó su primera edición en el curso 18/19 en la que se inscribieron más de 700 docentes y durante el curso 19/20 en la que se inscribieron más de un millar de docentes de la práctica totalidad del país para participar en el proyecto. En este caso, la temática se centró en la Inteligencia Artificial. Uno de los objetivos de este proyecto es que la formación de los docentes se traslade a las aulas. Por ello, las tareas prácticas con las que el profesorado participante se familiarizó durante la fase de formación estaban diseñadas para ser utilizadas directamente en el aula. De este modo, los docentes de esta edición de la EPCIA han llevado a la práctica, con su alumnado, al menos 5 sesiones de trabajo relacionado con el pensamiento computacional y la Inteligencia Artificial. Por último, y en paralelo con la Fase 2, de puesta en práctica, se realizó una investigación para medir el impacto del proyecto en el aprendizaje y en la práctica docente. Esta investigación se ha desarrollado de forma independiente, pero coordinada, en las tres propuestas de la EPCIA: las actividades desconectadas, la programación con bloques (Scratch) y el desarrollo de apps con App Inventor, estas dos últimas combinadas con Machine Learning for Kids. Son los resultados de esta investigación los que se presentan en este informe.
Chapter
New theories often emerge from seemingly contradictory empirical evidences. This is precisely the starting point of this chapter. Recent computational thinking (CT) research in K-12 shows different results depending on whether the computational concepts involved are used to solve visuospatial (Román-González, Pérez-González, and Jiménez-Fernández 2017) or linguistic-narrative problems (Howland and Good 2015). Furthermore, the former study empirically demonstrates that CT is mainly a problem-solving ability linked with fluid intelligence, which is characterized by adapting to the context demands. All of the above suggests that CT could be manifested in multiple and different ways depending on the type of problems to be solved. In other words, we hypothesize the existence not of a single, but of multiple computational thinkings; analogous to the existence of multiple intelligences postulated by Howard Gardner (1983, 1999). In this vein, this chapter aims to address a triple goal. Firstly, we intend to ground our theory through a complete and comprehensive review of K-12 educational interventions, along which CT has been developed, mostly by means of computer programming, in order to solve different kinds of problems: verbal-linguistic, logical-mathematical, musical, bodily-kinesthetic, visual-spatial, interpersonal, intrapersonal or naturalistic problems. Secondly, we anticipate how to empirically contrast the theory through a proof-of-concept design of several items that will be part of a battery of CT assessment tests, which will allow to check the hypothesized multifactorial structure of CT. Thirdly, we speculate about some relevant implications that would arise in case of confirming the theory, for example: the possibility of establishing a personalized CT profile for each student; the subsequent design of multiple CT interventions and curricula that may include all types of problems and, therefore, may be more equitable and inclusive; ultimately, CT might serve as the anchor that Gardner’s theory needs to be finally contrasted.
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Mitchel Resnick (mres@media.mit.edu) MIT Media Lab Brennan, K., & Resnick, M. (2012). Using artifact-based interviews to study the development of computational thinking in interactive media design. Paper presented at annual American Educational Research Association meeting, Abstract Computational thinking is a phrase that has received considerable attention over the past several years – but there is little agreement about what computational thinking encompasses, and even less agreement about strategies for assessing the development of computational thinking in young people. We are interested in the ways that design-based learning activities – in particular, programming interactive media – support the development of computational thinking in young people. Over the past several years, we have developed a computational thinking framework that emerged from our studies of the activities of interactive media designers. Our context is Scratch – a programming environment that enables young people to create their own interactive stories, games, and simulations, and then share those creations in an online community with other young programmers from around the world. The first part of the paper describes the key dimensions of our computational thinking framework: computational concepts (the concepts designers engage with as they program, such as iteration, parallelism, etc.), computational practices (the practices designers develop as they engage with the concepts, such as debugging projects or remixing others' work), and computational perspectives (the perspectives designers form about the world around them and about themselves). The second part of the paper describes our evolving approach to assessing these dimensions, including project portfolio analysis, artifact-based interviews, and design scenarios. We end with a set of suggestions for assessing the learning that takes place when young people engage in programming.
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"Digital fluency" should mean designing, creating, and remixing, not just browsing, chatting, and interacting.
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As the world becomes increasingly saturated with artificial intelligence (AI) technology, computational thinking (CT) frameworks must be updated to incorporate AI concepts. In this paper, we propose five AI-related computation concepts, practice, and perspective: classification, prediction, generation, training/validating/testing, and evaluation. We propose adding them to a widely-used CT framework and present an MIT App Inventor extension that explores this framework through project-based learning. Full text available here: https://www.eduhk.hk/cte2019/doc/CTE2019_Proceedings%20(ISSN%202664-035X%20and%202664-5661).pdf
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Purpose The purpose of this paper is to examine the intersection of artificial intelligence (AI), computational thinking (CT), and mathematics education (ME) for young students (K-8). Specifically, it focuses on three key elements that are common to AI, CT and ME: agency, modeling of phenomena and abstracting concepts beyond specific instances. Design/methodology/approach The theoretical framework of this paper adopts a sociocultural perspective where knowledge is constructed in interactions with others (Vygotsky, 1978). Others also refers to the multiplicity of technologies that surround us, including both the digital artefacts of our new media world, and the human methods and specialized processes acting in the world. Technology is not simply a tool for human intention. It is an actor in the cognitive ecology of immersive humans-with-technology environments (Levy, 1993, 1998) that supports but also disrupts and reorganizes human thinking (Borba and Villarreal, 2005). Findings There is fruitful overlap between AI, CT and ME that is of value to consider in mathematics education. Originality/value Seeing ME through the lenses of other disciplines and recognizing that there is a significant overlap of key elements reinforces the importance of agency, modeling and abstraction in ME and provides new contexts and tools for incorporating them in classroom practice.
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At the core of every computing related discipline and impacting everyone everywhere, computational thinking or CT has increasingly emerged as its own subject in all levels of education. How to effectively teach CT skills poses real challenges and creates opportunities. Focusing on engineering and computer science undergraduates, we resourcefully integrated elements of Artificial Intelligence (AI) into introductory computing courses. In addition to a comprehension of the essence of CT, AI enabled inspirations of collaborative problem solving beyond abstraction, logical reasoning, critical, and analytical thinking. It fostered the study of basic data structures and algorithms. Students enjoyed active learning classrooms. Learning to learn, they constructed essential knowledge from solving exciting AI puzzles, competing in strategic AI games, and participating in intellectual discussion. Every activity is designed to allow students to fully engage their mental tools. Neither coding nor programming was required.
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Programming is more than just coding, for, it exposes students to computational thinking which involves problem-solving using computer science concepts like abstraction and decomposition. Even for non-computing majors, computational thinking is applicable and useful in their daily lives. The three dimensions of computational thinking are computational concepts, computational practices and computational perspectives. In recent years, the availability of free and user-friendly programming languages has fuelled the interest of researchers and educators to explore how computational thinking can be introduced in K-12 contexts. Through an analysis of 27 available intervention studies, this paper presents the current trends of empirical research in the development of computational thinking through programming and suggests possible research and instructional implications. From the review, we propose that more K-12 intervention studies centering on computational practices and computational perspectives could be conducted in the regular classroom. To better examine these two dimensions, students could be asked to verbalize their thought process using think aloud protocol while programming and their on-screen programming activity could be captured and analyzed. Predetermined categories based on both past and recent programming studies could be used to guide the analysis of the qualitative data. As for the instructional implication, it is proposed that a constructionism-based problem-solving learning environment, with information processing, scaffolding and reflection activities, could be designed to foster computational practices and computational perspectives.
Artificial intelligence in education: challenges and opportunities for sustainable development -UNESCO Biblioteca Digital
  • Pedró Francesc
  • Subosa Miguel
  • Rivas Axel
  • Valverde Paula
Pedró Francesc, Subosa Miguel, Rivas Axel, and Valverde Paula, 'Artificial intelligence in education: challenges and opportunities for sustainable development -UNESCO Biblioteca Digital', UNESCO.
Developing a Framework for Computational Thinking from a Disciplinary Perspective
  • J Malyn-Smith
  • I A Lee
  • F Martin
  • S Grover
  • M A Evans
  • S Pillai
J. Malyn-Smith, I. A. Lee, F. Martin, S. Grover, M. A. Evans, and S. Pillai, 'Developing a Framework for Computational Thinking from a Disciplinary Perspective', Proceedings of the International Conference on Computational Thinking Education 2018. Hong Kong: The Education University of Hong Kong, p. 5.
Building the foundational skills needed for success in work at the human-technology frontier
  • J Malyn-Smith
J. Malyn-Smith, et al. 'Building the foundational skills needed for success in work at the human-technology frontier'. En Conference proceedings. libreriauniversitaria. it Edizioni, 2018. p. 345.
Classroom Activities for Teaching Artificial Intelligence to Primary School Students
  • J W K Ho
  • M Scadding
J. W. K. Ho and M. Scadding, 'Classroom Activities for Teaching Artificial Intelligence to Primary School Students', Proceedings of International Conference on Computational Thinking Education 2019. Hong Kong: The Education University of Hong Kong., p. 3.