Artificial intelligence, computational thinking, and mathematics education

2017 ◽  
Vol 34 (2) ◽  
pp. 133-139 ◽  
Author(s):  
George Gadanidis

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.

2019 ◽  
Vol 120 (11/12) ◽  
pp. 704-722 ◽  
Author(s):  
Xanthippi Tsortanidou ◽  
Thanasis Daradoumis ◽  
Elena Barberá

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.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Raffaele Filieri ◽  
Elettra D’Amico ◽  
Alessandro Destefanis ◽  
Emilio Paolucci ◽  
Elisabetta Raguseo

Purpose The travel and tourism industry (TTI) could benefit the most from artificial intelligence (AI), which could reshape this industry. This study aims to explore the characteristics of tourism AI start-ups, the AI technological domains financed by Venture Capitalists (VCs), and the phases of the supply chain where the AI domains are in high demand. Design/methodology/approach This study developed a database of the European AI start-ups operating in the TTI from the Crunchbase database (2005–2020). The authors used start-ups as the unit of analysis as they often foster radical change. The authors complemented quantitative and qualitative methods. Findings AI start-ups have been mainly created by male Science, Technology, Engineering and Mathematics graduates between 2015 and 2017. The number of founders and previous study experience in non-start-up companies was positively related to securing a higher amount of funding. European AI start-ups are concentrated in the capital town of major tourism destinations (France, UK and Spain). The AI technological domains that received more funding from VCs were Learning, Communication and Services (i.e. big data, machine learning and natural language processing), indicating a strong interest in AI solutions enabling marketing automation, segmentation and customisation. Furthermore, VC-backed AI solutions focus on the pre-trip and post-trip. Originality/value To the best of the authors’ knowledge, this is the first study focussing on digital entrepreneurship, specifically VC-backed AI start-ups operating in the TTI. The authors apply, for the first time, a mixed-method approach in the study of tourism entrepreneurship.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hatice Beyza Sezer ◽  
Immaculate Kizito Namukasa

PurposeMany mathematical models have been shared to communicate about the COVID-19 outbreak; however, they require advanced mathematical skills. The main purpose of this study is to investigate in which way computational thinking (CT) tools and concepts are helpful to better understand the outbreak, and how the context of disease could be used as a real-world context to promote elementary and middle-grade students' mathematical and computational knowledge and skills.Design/methodology/approachIn this study, the authors used a qualitative research design, specifically content analysis, and analyzed two simulations of basic SIR models designed in a Scratch. The authors examine the extent to which they help with the understanding of the parameters, rates and the effect of variations in control measures in the mathematical models.FindingsThis paper investigated the four dimensions of sample simulations: initialization, movements, transmission, recovery process and their connections to school mathematical and computational concepts.Research limitations/implicationsA major limitation is that this study took place during the pandemic and the authors could not collect empirical data.Practical implicationsTeaching mathematical modeling and computer programming is enhanced by elaborating in a specific context. This may serve as a springboard for encouraging students to engage in real-world problems and to promote using their knowledge and skills in making well-informed decisions in future crises.Originality/valueThis research not only sheds light on the way of helping students respond to the challenges of the outbreak but also explores the opportunities it offers to motivate students by showing the value and relevance of CT and mathematics (Albrecht and Karabenick, 2018).


2019 ◽  
Vol 18 (4) ◽  
pp. 483-496
Author(s):  
Henrik Stigberg ◽  
Susanne Stigberg

Programming and computational thinking have emerged as compulsory skills in elementary school education. In 2018, Sweden has integrated programming in mathematics education with the rationale that it fosters problem solving and logical thinking skills and motivates students to learn mathematics. We investigated how teachers introduce programming in mathematics education in a Swedish primary school using an explorative case study. We followed four mathematics teachers during the first semester in which programming was mandatory. They taught second-, sixth- and ninth-grade students. Our contributions are threefold: we provide an account of how programming is taught in mathematics education; we discuss how teachers reflect on the challenge of teaching programming in mathematics; and we report on students’ understanding of programming and their view on the relationship between programming and mathematics.


Author(s):  
Penny L. Hammrich ◽  
Greer M. Richardson ◽  
Beverly D. Livingston

2019 ◽  
Vol 19 (1) ◽  
pp. 10-14
Author(s):  
Ryan Scott ◽  
Malcolm Le Lievre

Purpose The purpose of this paper is to explore insights methodology and technology by using behavioral to create a mind-set change in the way people work, especially in the age of artificial intelligence (AI). Design/methodology/approach The approach is to examine how AI is driving workplace change, introduce the idea that most organizations have untapped analytics, add the idea of what we know future work will look like and look at how greater, data-driven human behavioral insights will help prepare future human-to-human work and inform people’s work with and alongside AI. Findings Human (behavioral) intelligence will be an increasingly crucial part of behaviorally smart organizations, from hiring to placement to adaptation to team building, compliance and more. These human capability insights will, among other things, better prepare people and organizations for changing work roles, including working with and alongside AI and similar tech innovation. Research limitations/implications No doubt researchers across the private, public and nonprofit sectors will want to further study the nexus of human capability, behavioral insights technology and AI, but it is clear that such work is already underway and can prove even more valuable if adopted on a broader, deeper level. Practical implications Much “people data” inside organizations is currently not being harvested. Validated, scalable processes exist to mine that data and leverage it to help organizations of all types and sizes be ready for the future, particularly in regard to the marriage of human capability and AI. Social implications In terms of human capability and AI, individuals, teams, organizations, customers and other stakeholders will all benefit. The investment of time and other resources is minimal, but must include C-suite buy in. Originality/value Much exists on the softer aspects of the marriage of human capability and AI and other workplace advancements. What has been lacking – until now – is a 1) practical, 2) validated and 3) scalable behavioral insights tech form that quantifiably informs how people and AI will work in the future, especially side by side.


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