Why Using Robots to Teach Computer Science can be Successful Theoretical Reflection to Andragogy and Minimalism

Categories and Subject Descriptors K.3.1 [Computers and Education]: Computer Uses in Education – collaborative learning; K.3.2 [Computers and Education]: Computer and Information Science Education – computer science education, self-assessmentTo help students understand subjects such as theoretical aspects of computation, algorithmic reasoning and intelligence of machines, a number of publications report experiments to teach these topics with the help of Lego Mindstorms robots. In the publications, the researchers report how they have created various ways to approach the issues either in Computer Science or in Artificial Intelligence. The reported results of the experiments are based on the learning outcomes, the feedback from the students, and the perceived informal observations (i.e. “feelings”) of the instructors. But can anyone else benefit from the reportedly positive outcomes of the experiments? To give an answer to that question, this paper analyses the reported results through two support theories. The two theories chosen for this, andragogy and minimalism, are concerned with adult learning and how teaching adults should be approached. When reflecting the results of the four teaching experiments to the suggestions drawn from the theories, a more comprehensive answer to why the experiments have been successful can be given. The four teaching experiments analysed here were in many ways similar to each other. A connection to the chosen support theories was straightforward to make. Besides describing the artefacts of teaching with the robots, a deeper discussion on this teaching approach is provided. For an instructor, all these observations offer more concrete evidence about beneficial factors of teaching with robots

A flexible and innovative platform for autonomous mobile robots

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 103-106).The development of the CREAL programming language and the STACK hardware platform by the members of the CSAIL Living Machines research group has lead to a foundation upon which roboticists may build and control autonomous mobile robots. This platform, however, has been used only within this research group on a minimal number of projects. As is, there has been no published discussion on the application of CREAL and the STACK to various types of control architectures commonly used in controlling autonomous mobile robots, or any personal accounts of the successes or failures of using such a system on a hand-built robot. In this thesis I focus on these two points. I go into depth on the use and expansion of CREAL to support multiple control architectures, as well as a personal account of the construction and use of a robot that uses these architectures and hardware to accomplish various tasks. The work to be undertaken will describe the process of design, construction, debugging and implementation of a hand-built robot that performs example tasks that are similar in nature to tasks commonly performed by autonomous mobile robots within the robotic research community. Currently, CREAL does not provide any abstract framework to facilitate implementation of neural net architectures by users. The work described in this thesis includes a set of macros that expand the CREAL language to allow user-friendly creation of neural nets. This abstraction framework is then put into use in an implementation that uses the neural net tools developed in order to achieve a fixed goal The second architecture to be discussed is that of subsumption, an architecture that is extremely well suited to be implemented in CREAL.(cont.) To demonstrate the suit- ability of CREAL, a subsumption implementation will be described that performs a complex robot behavior An account will be given of creating a subsumption base behavior and passing through nmultiple stages that increment the behavioral capabilities of the robot. This will include a description at each stage of how the subsumption architecture is expanded to bring the behavior of the robot closer toward the goal behavior. Through the implementation of the above tasks I hope to show to be true what we have claimed: that the platform consisting of the CREAL programming language and the STACK hardware is an effective, flexible, powerful and desirable platform to use in designing autonomous mobile robots.by Jessica Anne Howe.S.M

E-Learning

Technology development, mainly for telecommunications and computer systems, was a key factor for the interactivity and, thus, for the expansion of e-learning. This book is divided into two parts, presenting some proposals to deal with e-learning challenges, opening up a way of learning about and discussing new methodologies to increase the interaction level of classes and implementing technical tools for helping students to make better use of e-learning resources. In the first part, the reader may find chapters mentioning the required infrastructure for e-learning models and processes, organizational practices, suggestions, implementation of methods for assessing results, and case studies focused on pedagogical aspects that can be applied generically in different environments. The second part is related to tools that can be adopted by users such as graphical tools for engineering, mobile phone networks, and techniques to build robots, among others. Moreover, part two includes some chapters dedicated specifically to e-learning areas like engineering and architecture

Drama, a connectionist model for robot learning: experiments on grounding communication through imitation in autonomous robots

The present dissertation addresses problems related to robot learning from demonstra¬ tion. It presents the building of a connectionist architecture, which provides the robot with the necessary cognitive and behavioural mechanisms for learning a synthetic lan¬ guage taught by an external teacher agent. This thesis considers three main issues: 1) learning of spatio-temporal invariance in a dynamic noisy environment, 2) symbol grounding of a robot's actions and perceptions, 3) development of a common symbolic representation of the world by heterogeneous agents.We build our approach on the assumption that grounding of symbolic communication creates constraints not only on the cognitive capabilities of the agent but also and especially on its behavioural capacities. Behavioural skills, such as imitation, which allow the agent to co-ordinate its actionn to that of the teacher agent, are required aside to general cognitive abilities of associativity, in order to constrain the agent's attention to making relevant perceptions, onto which it grounds the teacher agent's symbolic expression. In addition, the agent should be provided with the cognitive capacity for extracting spatial and temporal invariance in the continuous flow of its perceptions. Based on this requirement, we develop a connectionist architecture for learning time series. The model is a Dynamical Recurrent Associative Memory Architecture, called DRAMA. It is a fully connected recurrent neural network using Hebbian update rules. Learning is dynamic and unsupervised. The performance of the architecture is analysed theoretically, through numerical simulations and through physical and simulated robotic experiments. Training of the network is computationally fast and inexpensive, which allows its implementation for real time computation and on-line learning in a inexpensive hardware system. Robotic experiments are carried out with different learning tasks involving recognition of spatial and temporal invariance, namely landmark recognition and prediction of perception-action sequence in maze travelling.The architecture is applied to experiments on robot learning by imitation. A learner robot is taught by a teacher agent, a human instructor and another robot, a vocabulary to describe its perceptions and actions. The experiments are based on an imitative strategy, whereby the learner robot reproduces the teacher's actions. While imitating the teacher's movements, the learner robot makes similar proprio and exteroceptions to those of the teacher. The learner robot grounds the teacher's words onto the set of common perceptions they share. We carry out experiments in simulated and physical environments, using different robotic set-ups, increasing gradually the complexity of the task. In a first set of experiments, we study transmission of a vocabulary to designate actions and perception of a robot. Further, we carry out simulation studies, in which we investigate transmission and use of the vocabulary among a group of robotic agents. In a third set of experiments, we investigate learning sequences of the robot's perceptions, while wandering in a physically constrained environment. Finally, we present the implementation of DRAMA in Robota, a doll-like robot, which can imitate the arms and head movements of a human instructor. Through this imitative game, Robota is taught to perform and label dance patterns. Further, Robota is taught a basic language, including a lexicon and syntactical rules for the combination of words of the lexicon, to describe its actions and perception of touch onto its body

Makers at School, Educational Robotics and Innovative Learning Environments

This open access book contains observations, outlines, and analyses of educational robotics methodologies and activities, and developments in the field of educational robotics emerging from the findings presented at FabLearn Italy 2019, the international conference that brought together researchers, teachers, educators and practitioners to discuss the principles of Making and educational robotics in formal, non-formal and informal education. The editors’ analysis of these extended versions of papers presented at FabLearn Italy 2019 highlight the latest findings on learning models based on Making and educational robotics. The authors investigate how innovative educational tools and methodologies can support a novel, more effective and more inclusive learner-centered approach to education. The following key topics are the focus of discussion: Makerspaces and Fab Labs in schools, a maker approach to teaching and learning; laboratory teaching and the maker approach, models, methods and instruments; curricular and non-curricular robotics in formal, non-formal and informal education; social and assistive robotics in education; the effect of innovative spaces and learning environments on the innovation of teaching, good practices and pilot projects

“My Purpose is to Assist”: How ChatGPT Can Push Liberal Arts Institutions to Think Critically About Themselves

Since its release, ChatGPT, a chatbot specialized in writing content and answering questions in response to user prompts, has posed an unclear threat to liberal arts institutions. Can it serve as an effective tool for cheating? Can its responses replace work done in the liberal arts? This thesis argues that ChatGPT’s limitations—particularly its inability to think critically—prevent it from replacing real liberal arts work, which involves questioning, critique, and re-examination. If anything, this thesis suggests, ChatGPT can push liberal arts institutions to better promote critical thinking by serving as a litmus test for liberal arts-level work

Programming Robots for Activities of Everyday Life

Text-based programming remains a challenge to novice programmers in\ua0all programming domains including robotics. The use of robots is gainingconsiderable traction in several domains since robots are capable of assisting\ua0humans in repetitive and hazardous tasks. In the near future, robots willbe used in tasks of everyday life in homes, hotels, airports, museums, etc.\ua0However, robotic missions have been either predefined or programmed usinglow-level APIs, making mission specification task-specific and error-prone.\ua0To harness the full potential of robots, it must be possible to define missionsfor specific applications domains as needed. The specification of missions of\ua0robotic applications should be performed via easy-to-use, accessible ways, and\ua0at the same time, be accurate, and unambiguous. Simplicity and flexibility in\ua0programming such robots are important, since end-users come from diverse\ua0domains, not necessarily with suffcient programming knowledge.The main objective of this licentiate thesis is to empirically understand the\ua0state-of-the-art in languages and tools used for specifying robot missions byend-users. The findings will form the basis for interventions in developing\ua0future languages for end-user robot programming.During the empirical study, DSLs for robot mission specification were\ua0analyzed through published literature, their websites, user manuals, samplemissions and using the languages to specify missions for supported robots.After extracting data from 30 environments, 133 features were identified.\ua0A feature matrix mapping the features to the environments was developedwith a feature model for robotic mission specification DSLs.Our results show that most end-user facing environments exist in the\ua0education domain for teaching novice programmers and STEM subjects. Mostof the visual languages are developed using Blockly and Scratch libraries.\ua0The end-user domain abstraction needs more work since most of the visualenvironments abstract robotic and programming language concepts but not\ua0end-user concepts. In future works, it is important to focus on the development\ua0of reusable libraries for end-user concepts; and further, explore how end-user\ua0facing environments can be adapted for novice programmers to learn\ua0general programming skills and robot programming in low resource settings\ua0in developing countries, like Uganda

How mobile robots can self-organise a vocabulary

One of the hardest problems in science is the symbol grounding problem, a question that has intrigued philosophers and linguists for more than a century. With the rise of artificial intelligence, the question has become very actual, especially within the field of robotics. The problem is that an agent, be it a robot or a human, perceives the world in analogue signals. Yet humans have the ability to categorise the world in symbols that they, for instance, may use for language.This book presents a series of experiments in which two robots try to solve the symbol grounding problem. The experiments are based on the language game paradigm, and involve real mobile robots that are able to develop a grounded lexicon about the objects that they can detect in their world. Crucially, neither the lexicon nor the ontology of the robots has been preprogrammed, so the experiments demonstrate how a population of embodied language users can develop their own vocabularies from scratch

How mobile robots can self-organise a vocabulary

One of the hardest problems in science is the symbol grounding problem, a question that has intrigued philosophers and linguists for more than a century. With the rise of artificial intelligence, the question has become very actual, especially within the field of robotics. The problem is that an agent, be it a robot or a human, perceives the world in analogue signals. Yet humans have the ability to categorise the world in symbols that they, for instance, may use for language.This book presents a series of experiments in which two robots try to solve the symbol grounding problem. The experiments are based on the language game paradigm, and involve real mobile robots that are able to develop a grounded lexicon about the objects that they can detect in their world. Crucially, neither the lexicon nor the ontology of the robots has been preprogrammed, so the experiments demonstrate how a population of embodied language users can develop their own vocabularies from scratch

How mobile robots can self-organise a vocabulary

One of the hardest problems in science is the symbol grounding problem, a question that has intrigued philosophers and linguists for more than a century. With the rise of artificial intelligence, the question has become very actual, especially within the field of robotics. The problem is that an agent, be it a robot or a human, perceives the world in analogue signals. Yet humans have the ability to categorise the world in symbols that they, for instance, may use for language.This book presents a series of experiments in which two robots try to solve the symbol grounding problem. The experiments are based on the language game paradigm, and involve real mobile robots that are able to develop a grounded lexicon about the objects that they can detect in their world. Crucially, neither the lexicon nor the ontology of the robots has been preprogrammed, so the experiments demonstrate how a population of embodied language users can develop their own vocabularies from scratch