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Computational thinking and mental models: From kodu to calypso
... The preliminary finding of this literature review revealed that AI integration in pre-college education via curricula and/or tool development is rapidly growing, especially in elementary education [14], [18], [19], [21], [22], [23]. Thus, curricula have been developed to expose students to AI skills and knowledge [17], [18], [19]. ...
... Additionally, other resources and tools developed to teach AI to elementary students include Google's Teachable Machine [20], QuickDraw, Cognimates [21], Cosmo robot, and Calypso for Cozmo [22]. Scheidt et al. [27] developed Any Cubes, a prototype toy that allows children to explore machine learning. ...
... Similar to elementary, tools have been developed for secondary education that enable students to interact with AI, such as Google's Teachable Machine and Cognimates [20], [22], [23]. These tools can be utilized by both elementary and secondary students. ...
... As our society faces major public policy issues involving AI technologies, informed citizens are required to comprehend the fundamentals of AI. To prepare children with competencies to face the rapidly transforming and tech-driven world and help them ensure their employability and career potential in the future (Preface, 2021), children must be sufficiently educated to work with and use AI (Touretzky et al., 2017). ...
... There has been a recent burst of tools and techniques that make artificial intelligence (AI) more accessible to younger students. Touretzky et al. (2017) identified a variety of tools and useful resources for both students and teachers to engage in AI education, which are listed as follows: ...
... uk/ • The Cozmo robot is a low-cost mobile manipulator with built-in computer vision that includes object and custom marker detection, face recognition, object handling, path planning, and speech production. • Calypso for Cozmo (Touretzky, 2017) is a rule-based visual programming language for Cozmo that includes speech recognition, landmark-based navigation, a visible global map, and state machine programming capabilities. https://Calypso.software ...
With the rapid technological development of society brought on by Artificial Intelligence (AI), the demand for AI-literate workers will increase in the future. It is critical to develop the next generation's AI competencies and to educate them about how to work with and use AI. Previous studies on AI were predominantly focused on secondary and university education; however, research on the Artificial Intelligence curriculum in early childhood education is scarce. Due to the lack of conformity on the standardisation of AI curriculum for early childhood education, this study examines the AI curriculum for kindergarten children using the framework which consists of four key components, including (1) aims, goals, objectives, or declarations of outcome, (2) subject matter, domains, or content, (3) methods or procedure, (4) evaluation and assessment. We recommend that AI literacy be achieved by three competencies: AI Knowledge, AI Skill, and AI Attitude. The employment of a social robot as a learning companion and programmable artifact was proven to be helpful in assisting young children in grasping AI principles. We also discovered which teaching methods had the most greatest influence on students' learning. We recommend project-based learning for future AI education based on the findings.
... Emerging research is exploring how to foster AI literacy in audiences without technical backgrounds. Within the past year, companies have pursued initiatives to broaden AI education to underrepresented audiences in an effort to increase workforce diversity [5,148], educators have published guides on how to incorporate AI into K-12 curricula [145], and researchers are exploring how to engage young learners in creative programming activities involving AI [45,79,132,146,149]. The "AI for K12" working group is currently developing a set of standards for K-12 classrooms to determine what each grade band should know about AI [130]. ...
... Most existing research on robotics education uses robotics as a context to teach design thinking [77,94] mathematics [77,131], physics [77], computational thinking [42,66,131], or software engineering [131]. Some research explores how to use robotics to teach AI concepts such as: sensors and integrating sensing, perception, and action [94,115]; representations that are used to localize and guide robot movement [42,131]; decision making, search, and planning algorithms necessary to plan robot action [81,84,105,131]; using ML (especially vision) to make sense of sensorial input [114,131,132]; understanding reactive control [115]; and using effectors and kinematic trees to control a robot's body [115,131]. ...
... Supporting References: [94,114,115,131,132] How Should AI Be Used? ...
Artificial intelligence (AI) is becoming increasingly integrated in user-facing technology, but public understanding of these technologies is often limited. There is a need for additional HCI research investigating a) what competencies users need in order to effectively interact with and critically evaluate AI and b) how to design learner-centered AI technologies that foster increased user understanding of AI. This paper takes a step towards realizing both of these goals by providing a concrete definition of AI literacy based on existing research. We synthesize a variety of interdisciplinary literature into a set of core competencies of AI literacy and suggest several design considerations to support AI developers and educators in creating learner-centered AI. These competencies and design considerations are organized in a conceptual framework thematically derived from the literature. This paper's contributions can be used to start a conversation about and guide future research on AI literacy within the HCI community.
... Many researchers (e.g., [2,14,26,28,52,64]) have expanded on Papert's "turtle. " Ackerman presents an experience where learners can use a motion detection system to draw shapes on a graph [2], Horn et al., Touretzky, and Druga et al. have developed activities using robots that learners can program to move in space [14,26,64], and Resnick taught students about complex systems by having them program parameters that affect the behavior of many "turtles" moving in space (StarLogo) [51]. ...
... Many researchers (e.g., [2,14,26,28,52,64]) have expanded on Papert's "turtle. " Ackerman presents an experience where learners can use a motion detection system to draw shapes on a graph [2], Horn et al., Touretzky, and Druga et al. have developed activities using robots that learners can program to move in space [14,26,64], and Resnick taught students about complex systems by having them program parameters that affect the behavior of many "turtles" moving in space (StarLogo) [51]. ...
... Many tool-kits that cater to non-expert audiences focus on providing learners with the tools to "tinker" with AI through the programming of robots or AI-powered cloud services such as Cozmo, Sphero, Alexa, and Google Assistant (e.g. [81,17,41]), see [80] for an exhaustive list). These tool-kits have (to date) been designed primarily for K-12 classroom activities and are accompanied by curricula and worksheets (e.g. ...
... These tool-kits have (to date) been designed primarily for K-12 classroom activities and are accompanied by curricula and worksheets (e.g. [81]). Museums and other public spaces can serve as alternative venues for AI literacy initiatives, complementing interventions in the formal education sphere and broadening access to opportunities to both interact with and learn about AI to both adults and children who may not have AI devices in their homes or schools. ...
Artificial intelligence (AI) is becoming increasingly pervasive in our everyday lives. There are consequently many common misconceptions about what AI is, what it is capable of, and how it works. Compounding the issue, opportunities to learn about AI are often limited to audiences who already have access to and knowledge about technology. Increasing access to AI in public spaces has the potential to broaden public AI literacy, and experiences involving co-creative (i.e. collaboratively creative) AI are particularly well-suited for engaging a broad range of participants. This paper explores how to design co-creative AI for public interaction spaces, drawing both on existing literature and our own experiences designing co-creative AI for public venues. It presents a set of design principles that can aid others in the development of co-creative AI for public spaces as well as guide future research agendas.
... Such visual programming environments include Scratch, code.org, and Kodu. Previous studies have investigated visual programming environments (e.g., Dohn, 2020;Kalelioğlu, 2015;Touretzky, 2017). In addition, to attract the attention of students and increase their learning motivation, many manufacturers and educational institutions have developed products combining a visual programming environment and physical robots (e.g., mBot and Dash & Dot) (Angeli et al., 2020;Sáez-López et al., 2019). ...
Education on computational thinking skills has been a focus in many countries. Previous studies have investigated educational board games based on computational thinking skills. However, there is a lack of research on the cognitive behaviors and cognitive styles promoted by these educational board games. Therefore, in this study, educational board games are adopted as a tool to explore the learning effectiveness and behavioral patterns of students with different cognitive styles. A total of 74 students participated in this study, and the educational board game used was a set of coding poker cards. The results showed that educational board games contribute to students’ computational thinking skills, especially regarding operators. For students with different cognitive styles, holist-style students experience greater learning effectiveness when using educational board games than serialist-style students. In addition, it is found that students need to analyze the choices and arrangements of cards while playing educational board games, which improves their thinking in terms of problem solving. Furthermore, through cognitive behaviors, students can use the concepts of computational thinking skills and then achieve the learning goals of computational thinking skills. Finally, suggestions for teaching and future research are proposed based on the results of this study.
... Numerous ethical concerns arise in the adoption of artificial intelligence, encompassing various aspects such as its impact on the job market, issues of prejudice and discrimination within AI systems [10], scandal [28], and data privacy breaches [29], [30]. It is evident that artificial intelligence applications can have both positive and negative ramifications for society [31]. Recognizing the significance of addressing these ethical dilemmas, efforts have been made to develop artificial intelligence ethics in education curriculum, particularly for individuals with non-technical backgrounds [21]. ...
... Informed individuals must understand the foundations of AI. In order to prepare the next generation for the quickly changing and technologically advanced world, they must acquire the AI competencies necessary to work with and utilize AI, as well as to assist in securing their future employment and professional prospects [8,9]. Researchers have presented many recent studies on educational tools that provide interaction between artificial intelligence and humans, such as language learning, systems development and application, and robot programming [10]. ...
... ogramming language functions (conceptual model). Sorva (2013) is more explicit in his review, and does not categorize notional machines as mental models or descriptions/visualizations of the computer. As expected, there are contributions in the literature that on one hand associate notional machines with mental models (Tedre, Toivonen et al., 2021;D. S. Touretzky, 2017) and on the other hand understand them as a form of conceptual model (Fincher et al., 2020). Notional machines are also relevant in the field of machine learning. Tedre et al. (2021; argued that notional machines in ML fundamentally differ from those in traditional programming, which opens research potential in this area. ...
With the increasing relevance of Artificial Intelligence (AI) in society, AI literacy is increasingly being integrated into K-12 education. However, students already form conceptions about modern technologies (especially AI) from their interaction with their everyday environment. In this paper, we conduct a systematic scoping review on the emerging research field of secondary school students' mental models and attitudes regarding AI. Our goal is to identify research approaches and uncover research gaps. To achieve this, we reviewed literature published within the timeframe of 2012–2022 found in the ACM, IEEE, ERIC, and Science Direct databases. In total, we identified 18 relevant papers, for which we collected and evaluated research objectives, approaches, results, and areas requiring further investigation. Our findings reveal that most of the literature examined focuses with students' attitudes toward AI and moderating variables. Additionally, there is preliminary research on the types of mental models students possess about AI. Notable research gaps include the absence of development of research instruments for surveying mental models and insufficient research into the impact of learning interventions on the construction of students' mental models.
... Both MachineLearningForKids (Lane 2022) and Cognimates (Druga 2022) offer speech to text and text to speech add-ins that allow students to incorporate speech recognition and generation capabilities into their Scratch projects. Speech recognition and generation are also available in Snap!, a variant of Scratch, via eCraft2Learn (Kahn et al. 2022), and in MIT App Inventor (MIT App Inventor 2020) and Calypso (Touretzky 2017). ...
Today, children of all ages interact with speech recognition systems but are largely unaware of how they work. Teaching K-12 students to investigate how these systems employ phonological, syntactic, semantic, and cultural knowledge to resolve ambiguities in the audio signal can provide them a window on complex AI decision-making and also help them appreciate the richness and complexity of human language. We describe a browser-based tool for exploring the Google Web Speech API and a series of experiments students can engage in to measure what the service knows about language and the types of biases it exhibits. Middle school students taking an introductory AI elective were able to use the tool to explore Google’s knowledge of homophones and its ability to exploit context to disambiguate them. Older students could potentially conduct more comprehensive investigations, which we lay out here. This approach to investigating the power and limitations of speech technology through carefully designed experiments can also be applied to other AI application areas, such as face detection, object recognition, machine translation, or question answering.
... al, 2019) es usado Cozmo para realizar diversas dinámicas en un entorno de Lego a pequeña escala implementado mediante ROS, lo que facilita su uso y ajuste como plataforma robótica. En (Touretzky, 2017), se presenta Calypso, un lenguaje inspirado por Kodu Game Lab de Microsoft, pero diseñado para programar robots móviles reales en lugar de personajes en un mundo virtual. La implementación inicial de Calypso utiliza el robot Cozmo de Anki (Anki1, 2020), son algunas de las aplicaciones que se han desarrollado con Cozmo, sin embargo, en ninguno de los artículos mencionados se ha llevado a cabo la aplicación de Cozmo para el control de seguimiento de trayectorias como se presenta en este artículo. ...
Este artículo presenta una aplicación novedosa con el robot Cozmo para el control de seguimiento de trayectoria mediante una experiencia práctica como un sistema embebido de control integrado. En este trabajo es presentado la instalación y la programación para el robot Cozmo, cuyo sistema embebido está disponible comercialmente para las aplicaciones, animaciones y movimientos preestablecidos. La principal contribución de este artículo es llevar a cabo un sistema de control para el seguimiento de trayectorias mediante Matlab/Simulink, Python, Visual Studio y Kinect v2 como sensor de realimentación utilizando una estrategia de control no lineal. La propuesta de solución permite validar y comprobar que mediante el robot Cozmo pueden ser desarrollados diversos tipos de resultados experimentales los cuales favorecen en gran medida la aplicación de las técnicas de control por realimentación en tiempo real.
... A recent paper outlined a set of design principles for introducing co-creative AI research projects in public spaces-while not explicitly focused on AI education, we draw on several of these principles in our work [33]. Research also suggests that having learners enact embodied simulations of algorithms (either on their own or by programming an embodied AI device [15,49]) can help them to concretize abstract concepts [15,46]. Other platforms engage learners in building machine learning (ML) models of physical gestures like dance or sports moves [8,54]. ...
Fostering public AI literacy (i.e. a high-level understanding of artificial intelligence (AI) that allows individuals to critically and effectively use AI technologies) is increasingly important as AI is integrated into individuals’ everyday lives and as concerns about AI grow. This paper investigates how to design collaborative, creative, and embodied interactions that foster AI learning and interest development. We designed three prototypes of collaborative, creative, and/or embodied learning experiences that aim to communicate AI literacy competencies. We present the design of these prototypes as well as the results from a user study that we conducted with 14 family groups (38 participants). Our data analysis explores how collaboration, creativity, and embodiment contributed to AI learning and interest development across the three prototypes. The main contributions of this paper are: 1) three designs of AI literacy learning activities and 2) insights into the role creativity, collaboration, and embodiment play in AI learning experiences.
... The device runs the PopBlocks App that is a block-based programming language using only signs. Activities are grouped in the following categories: [37] Touretzky introduced Calypso for Cozmo which is a rule-based language for the educational robot Cozmo that supports high-level concepts like speech recognition using cloud services, landmarkbased navigation and behavior programming using state machines. Benefits of this programming environment in the context of AI education K-12 are that the execution of the rules can be visualized as well as explained and that the symbol grounding is done by the system allowing kids to address object or position in an abstract way (e.g. ...
AI Education for K-12 and in particular AI literacy gained huge interest recently due to the significantly influence in daily life, society, and economy. In this paper we discuss this topic of early AI education along four dimensions: (1) formal versus informal education, (2) cooperation of researchers in AI and education, (3) the level of education, and (4) concepts and tools.
... These stories drive the imaginations and the fears of the general public. Despite the increased frequency of AI headlines in the media, there is a persistent lack of understanding of AI (West and Allen 2018). We must consider the role we play as AI researchers and educators in helping people understand the science behind our research, its limits, and its potential societal impacts. ...
The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA's national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work.
This article examines the integration of artificial intelligence (AI) into early childhood education and the noteworthy impacts it has on students' enjoyment, creativity, and development of soft skills. Artificial intelligence technology can help young pupils develop important soft skills like cooperation and communication through the use of interactive tools and individualized learning platforms. These technologies enable education to be customized to meet the needs of each student, boosting self-esteem and confidence. Additionally, they facilitate problem-solving by providing opportunities for research. Furthermore, AI encourages creativity in children by giving them new and creative ways to express themselves. This paper explores how gamified learning settings, interactive software, and creative tools that stimulate students' curiosity and foster creativity are transforming education through artificial intelligence (AI). It also highlights the challenges and ethical dilemmas surrounding the integration of AI. This essay emphasizes how important it is to employ AI ethically and cooperatively to support children's holistic development. By developing a framework based on the completed literature study, we will discuss the importance of artificial intelligence in early childhood education, the ethical conundrums raised by its use in ECE, and how it could foster children's creativity and soft skills.
This article provides an in-depth look at how K-12 students should be introduced to Machine Learning and the knowledge and skills they will develop as a result. We begin with an overview of the AI4K12 Initiative, which is developing national guidelines for teaching AI in K-12, and briefly discuss each of the “Five Big Ideas in AI” that serve as the organizing framework for the guidelines. We then discuss the general format and structure of the guidelines and grade band progression charts and provide a theoretical framework that highlights the developmental appropriateness of the knowledge and skills we want to impart to students and the learning experiences we expect them to engage in. Development of the guidelines is informed by best practices from Learning Sciences and CS Education research, and by the need for alignment with CSTA’s K-12 Computer Science Standards, Common Core standards, and Next Generation Science Standards (NGSS). The remainder of the article provides an in-depth exploration of the AI4K12 Big Idea 3 (Learning) grade band progression chart to unpack the concepts we expect students to master at each grade band. We present examples to illustrate the progressions from two perspectives: horizontal (across grade bands) and vertical (across concepts for a given grade band). Finally, we discuss how these guidelines can be used to create learning experiences that make connections across the Five Big Ideas, and free online tools that facilitate these experiences.
Block-based programming (BBP) environments have become increasingly commonplace computer science education. Despite a rapidly expanding ecosystem of BBP environments, text-based languages remain the dominant programming paradigm, motivating the transition from BBP to text-based programming (TBP). Support students in transitioning from BBP to TBP is an important and open design question. This work identifies 101 unique BBP environments, analyzes the 46 of them and identifies different design approaches used to support the transition to TBP. The contribution of this work is to provide a snapshot of the current state of BBP environments and how they support learners in transitioning to TBP.
AI is becoming increasingly integrated in common technologies, which suggests that learning experiences for audiences seeking a "casual" understanding of AI-i.e. understanding how a search engine works, not necessarily understanding how to program one-is an increasingly important design space. Informal learning spaces like museums are particularly well-suited for such public science communication efforts, but there is little research investigating how to design AI learning experiences for these spaces. This paper explores how to design museum experiences that communicate key concepts about AI, using collaboration, creativity, and embodiment as inspirations for design. We present the design of five low-fidelity AI literacy exhibit prototypes and results from a thematic analysis of participant interactions during a co-design workshop in which family groups interacted with the prototypes and designed exhibits of their own. Our findings suggest new topics and design considerations for AI-related exhibits and directions for future research.
The blocks programming community has been preoccupied with identifying syntactic obstacles that keep novices from learning to program. Unfortunately, this focus is now holding back research from systematically investigating various technological affordances that can make programming more accessible. Employing approaches from program analysis, program visualization, and real-time interfaces can push blocks programming beyond syntax towards the support of semantics and even pragmatics. Syntactic support could be compared to checking spelling and grammar in word processing. Spell checking is relatively simple to implement and immediately useful, but provides essentially no support to create meaningful text. Over the last 25 years, I have worked to empower students to create their own games, simulations, and robots. In this time I have explored, combined, and evaluated a number of programming paradigms. Every paradigm including data flow, programming by example, and programming through analogies brings its own set of affordances and obstacles. Twenty years ago, AgentSheets combined four key affordances of blocks programming, and since then has evolved into a highly accessible Computational Thinking Tool. This article describes the journey to overcome first syntactic, then semantic, and most recently pragmatic, obstacles in computer science education.
Reading, tracing, and explaining the behavior of code are strongly correlated with the ability to write code effectively. To investigate program understanding in young children, we introduced two groups of third graders to Microsoft's Kodu Game Lab; the second group was also given four semantic "Laws of Kodu" to better scaffold their reasoning and discourage some common misconceptions. Explicitly teaching semantics proved helpful with one type of misconception but not with others. During each session, students were asked to predict the behavior of short Kodu programs. We found different styles of student reasoning (analytical and analogical) that may correspond to distinct neo-Piagetian stages of development as described by Teague and Lister (2014). Kodu reasoning problems appear to be a promising tool for assessing computational thinking in young programmers.
This paper introduces reasoning about lawful behavior as an important computational thinking skill and provides examples from a novel introductory programming curriculum using Microsoft's Kodu Game Lab. We present an analysis of assessment data showing that rising 5th and 6th graders can understand the lawfulness of Kodu programs. We also discuss some misconceptions students may develop about Kodu, their causes, and potential remedies.
Our powerful computers help very little in debugging the program we have so we can change it into the program we want. We introduce Conversational Programming as a way to harness our computing power to inspect program meaning through a combination of partial program execution and semantic program annotation. A programmer in our approach interactively selects highly autonomous "agents" in a program world as conversation topics and then changes the world to explore the potential behaviors of a selected agent in different scenarios. In this way, the programmer proactively knows how their code affects program execution as they explore various contexts. This paper describes conversational programming through design principles and use cases.
This paper presents a fiducial marker system specially appropriated for camera pose estimation in applications such as augmented reality and robot localization. Three main contributions are presented. First, we propose an algorithm for generating configurable marker dictionaries (in size and number of bits) following a criterion to maximize the inter-marker distance and the number of bit transitions. In the process, we derive the maximum theoretical inter-marker distance that dictionaries of square binary markers can have. Second, a method for automatically detecting the markers and correcting possible errors is proposed. Third, a solution to the occlusion problem in augmented reality applications is shown. To that aim, multiple markers are combined with an occlusion mask calculated by color segmentation. The experiments conducted show that our proposal obtains dictionaries with higher inter-marker distances and lower false negative rates than state-of-the-art systems, and provides an effective solution to the occlusion problem.
A simple and efficient randomized algorithm is presented for
solving single-query path planning problems in high-dimensional
configuration spaces. The method works by incrementally building two
rapidly-exploring random trees (RRTs) rooted at the start and the goal
configurations. The trees each explore space around them and also
advance towards each other through, the use of a simple greedy
heuristic. Although originally designed to plan motions for a human arm
(modeled as a 7-DOF kinematic chain) for the automatic graphic animation
of collision-free grasping and manipulation tasks, the algorithm has
been successfully applied to a variety of path planning problems.
Computed examples include generating collision-free motions for rigid
objects in 2D and 3D, and collision-free manipulation motions for a
6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is
also presented
The ultimate goal of work in cognitive architecture is to provide the foundation for a system capable of general intelligent behavior. That is, the goal is to provide the underlying structure that would enable a system to perform the full range of cognitive tasks, employ the full range of problem solving methods and representations appropriate for the tasks, and learn about all aspects of the tasks and its performance on them. In this article we present SOAR, an implemented proposal for such an architecture. We describe its organizational principles, the system as currently implemented, and demonstrations of its capabilities.
Customized visual representations enable end users to achieve
their programming goals. Here, designers work with users to tailor
visual programming languages to specific problem domains. We describe a
design methodology and a tool for creating domain oriented, end user
programming languages that effectively use visualization. We first
describe a collaborative design methodology involving end users and
designers. We then present Agentsheets, a tool for creating domain
oriented visual programming languages, and illustrate how it supports
collaborative design by examining experiences from a real language
design project. Finally, we summarize the contributions of our approach
and discuss its viability in industrial design projects
The iDREAMS project aims to reinvent Computer Science education in K-12 schools, by using game design and computational science for motivating and educating students through an approach we call Scalable Game Design, starting at the middle school level. In this paper we discuss the use of Computational Thinking Patterns as the basis for our Scalable Game Design curriculum and professional development and present results from measuring student learning outcomes using our unique Computational Thinking Pattern Analysis. (Contains 7 figures and 2 footnotes.)
Educational programming environments such as Microsoft Research's Kodu Game Lab are often used to introduce novices to computer science concepts and programming. Un-like many other educational languages that rely on scripting and Java-like syntax, the Kodu language is entirely event-driven and programming takes the form of 'when – do' clauses. Despite this simplistic programing model, many computer science concepts can be expressed using Kodu. We identify and measure the frequency of these concepts in 346 Kodu programs created by users, and find that most programs exhibit sophistication through the use of complex control flow and boolean logic. Through Kodu's non-traditional lan-guage, we show that users express and explore fundamental computer science concepts.
Simplicity and visibility are two important characteristics of programming languages for novices. Novices start programming with very little idea of the properties of the notional machine implied by the language they are learning. To help them learn these properties, the notional machine should be simple. That is, it should consist of a small number of parts that interact in ways that can be easily understood, possibly by analogy to other mechanisms with which the novice is more familiar. A notional machine is the idealized model of the computer implied by the constructs of the programming language. Visibility is concerned with methods for viewing selected parts and processes of this notional machine in action. We introduce the term “commentary” which is the system's dynamic characterization of the notional machine, expressed in either text or pictures on the user's terminal. We examine the simplicity and visibility of three systems, each designed to provide programming experience to different populations of novices.
Kodu is a relatively new programming language designed specifically for young children to learn through independent exploration. Kodu seeks to lower the barrier to entry for new programmers by presenting a radically simplified programming model which nevertheless has significant expressive power. Kodu is integrated in a real-time 3D gaming environment and is designed to compete with modern console games in terms of intuitive user interface and graphical production values. In this paper we will review key tradeoffs made in the design of the programming language and illustrate how it is one of very few languages designed using user interface design principles and methodologies, to the extent that the blend of subjective and objective factors considered in the language design have succeeded in presenting a model of programming which is uniquely approachable and creatively empowering for non-technical users.
Kodu resources for teachers
- D S Touretzky