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Enabling the Creation of Intelligent Things: Bringing Artificial Intelligence and Robotics to Schools
... Until 2016, the term "AI literacy" was first defined as the ability to understand the basic techniques and concepts behind AI products (Kandlhofer et al., 2019). Recent researchers associate AI literacy with other skills including communication and collaboration using AI (e.g., Long & Magerko, 2020;Ng et al., 2021a, b). ...
... • Lego robot (Kandlhofer et al. (2019) Appendix 3 ...
... • Real robots (Kandlhofer et al., 2019;Michaud, 2014;Narahara & Kobayashi, 2018;Swoboda et al., 2011;Wong et al., 2010) • Robot simulators (Dodds, 2008;Fernandes, 2016;McKee, 2002;Swoboda et al., 2011;Wallace et al., 2010) ...
In recent years, with the popularity of AI technologies in our everyday life, researchers have begun to discuss an emerging term “AI literacy”. However, there is a lack of review to understand how AI teaching and learning (AITL) research looks like over the past two decades to provide the research basis for AI literacy education. To summarize the empirical findings from the literature, this systematic literature review conducts a thematic and content analysis of 49 publications from 2000 to 2020 to pave the way for recent AI literacy education. The related pedagogical models, teaching tools and challenges identified help set the stage for today’s AI literacy. The results show that AITL focused more on computer science education at the university level before 2021. Teaching AI had not become popular in K-12 classrooms at that time due to a lack of age-appropriate teaching tools for scaffolding support. However, the pedagogies learnt from the review are valuable for educators to reflect how they should develop students’ AI literacy today. Educators have adopted collaborative project-based learning approaches, featuring activities like software development, problem-solving, tinkering with robots, and using game elements. However, most of the activities require programming prerequisites and are not ready to scaffold students’ AI understandings. With suitable teaching tools and pedagogical support in recent years, teaching AI shifts from technology-oriented to interdisciplinary design. Moreover, global initiatives have started to include AI literacy in the latest educational standards and strategic initiatives. These findings provide a research foundation to inform educators and researchers the growth of AI literacy education that can help them to design pedagogical strategies and curricula that use suitable technologies to better prepare students to become responsible educated citizens for today’s growing AI economy.
... A course for different educational levels (including High School) to teach fundamental AI/computer science topics (automatas, intelligent agents, graphs and data structure, problem-solving and ML). (Kandlhofer et al., 2019) Enabling the Creation of Intelligent Things: ...
... (Grillenberger and Romeike, 2019) Unplugged computer science material or card-based material turned out to be effective for students to describe ML systems. (Kandlhofer et al., 2019;Kandlhofer et al., 2016;Bilstrup et al., 2020) Students agreed that course material in the form of code, multimedia, and realworld examples helped in their learning. (Chua et al., 2019) Difficulties Certain instructional materials/tools need to be adapted to reduce the complexity and extent of certain contents in order to present AI topics in a target groupspecific manner. ...
... (Chua et al., 2019) Difficulties Certain instructional materials/tools need to be adapted to reduce the complexity and extent of certain contents in order to present AI topics in a target groupspecific manner. (Kandlhofer et al., 2019) Information in text form may take students a long time to read and understand, indicating a need for the adoption of other formats. (Wan et al., 2020) Longer lectures are challenging to maintain students' focus, requiring more breaks or breaking up the lecture with more hands-on activities. ...
Machine Learning (ML) is becoming increasingly present in our lives. Thus, it is important to introduce ML already in High School, enabling young people to become conscious users and creators of intelligent solutions. Yet, as typically ML is taught only in higher education, there is still a lack of knowledge on how to properly teach younger students. Therefore, in this systematic literature review, we analyze findings on teaching ML in High School with regard to content, pedagogical strategy, and technology. Results show that High School students were able to understand and apply basic ML concepts, algorithms and tasks. Pedagogical strategies focusing on active problem/project-based hands-on approaches were successful in engaging students and demonstrated positive learning effects. Visual as well as text-based programming environments supported students to build ML models in an effective way. Yet, the review also identified the need for more rigorous evaluations on how to teach ML.
... In order to meet these challenges, the European Driving License for Robots and Intelligent Systems (EDLRIS) was developed [39,41,49] 1 . This novel educational project aimed at the development and implementation of a professional, standardized, and internationally accepted system for training and certifying teachers and educators (in this context referred to as trainers) and school students, apprentices and young people (referred to as trainees, learners respectively) in AI and Robotics. ...
... In general, the project comprises four stages [41]: Stage 1-foundation: The EDLRIS project consortium is composed of two technical universities in Austria (AT) and Hungary (HU) which have a strong research background in AI and Robotics ensuring sound technological preparation, one university of teacher education (AT) ensuring the sound didactical preparation as well as two computer societies (AT, HU) with long-term experience in conducting computer certifications across Europe. As a first step, the educational needs of society and economy as well as the expected Robotics and AI skills of graduates were assessed by conducting a survey among various stakeholders from educational institutions and industry. ...
... It turned out that certain teaching methods, and teaching materials/ tools had to be adapted, and the complexity and extend of certain contents had to be reduced in order to impart AI/ Robotics topics in a target group specific manner. Based on the insights of this pilot implementation, the further AI and Robotics modules were developed and adapted accordingly [41]. The thesis of Lassnig [44] describes in detail the development, realization, and evaluation as well as results and conclusions of this first pilot implementation. ...
This article presents a novel educational project aiming at the development and implementation of a professional, standardized, internationally accepted system for training and certifying teachers, school students and young people in Artificial Intelligence (AI) and Robotics. In recent years, AI and Robotics have become major topics with a huge impact not only on our everyday life but also on the working environment. Hence, sound knowledge about principles and concepts of AI and Robotics are key skills for this century. Nonetheless, hardly any systematic approaches exist that focus on teaching principles of intelligent systems at K-12 level, addressing students as well as teachers who act as multipliers. In order to meet this challenge, the European Driving License for Robots and Intelligent Systems—EDLRIS was developed. It is based on a number of previously implemented and evaluated projects and comprises teaching curricula and training modules for AI and Robotics, following a competency-based, blended learning approach. Additionally, a certification system proves peoples’ acquired competencies. After developing the training and certification system, the first 32 trainer and trainee courses with a total of 445 participants have been implemented and evaluated. By applying this innovative approach—a standardized and widely recognized training and certification system for AI and Robotics at K-12 level for both high school teachers and students—we envision to foster AI/Robotics literacy on a broad basis.
... Eight studies focused on primary schoolers (Mariescu-Istodor and Jormanainen, 2019; Lee et al., 2020;Ho & Scadding, 2019;Toivonen, et al., 2020;Chai, et al., 2020;Druga et al., 2019;Tedre, et al., 2020;Hitron, et al., 2018) while only two studies were found that targets Kindergarten (Williams et al., 2019a(Williams et al., , 2019b. Four studies focused each on elementary, middle (Sabuncuoglu, 2020;Rodríguez-García et al., 2020aSakulkueakulsuk et al., 2018), middle/high (Opel et al., 2019;Zimmermann-Niefield et al., 2019aZimmermann-Niefield, et al., 2020) and teachers Kandlhofer et al., 2019;Zhou, et al., 2021) and only one that covers all levels from elementary to high school. ...
... Since the co-design was online, Zoom, Slack, and Miro were used to facilitate the sessions.Chiu and Chai (2020) also explore teachers with and without AI teaching experience views on key factors for designing AI curriculum for K-12. Relatedly,Kandlhofer et al. (2019) developed a professional training and certifying system for teachers in AI and robotics. Only an article ...
The increasing attention to Machine Learning (ML) in K-12 levels and studies exploring a different aspect of research on K-12 ML has necessitated the need to synthesize this existing research. This study systematically reviewed how research on ML teaching and learning in K-12 has fared, including the current area of focus, and the gaps that need to be addressed in the literature in future studies. We reviewed 43 conference and journal articles to analyze specific focus areas of ML learning and teaching in K-12 from four perspectives as derived from the data: curriculum development, technology development, pedagogical development, and teacher training/professional development. The findings of our study reveal that (a) additional ML resources are needed for kindergarten to middle school and informal settings, (b) further studies need to be conducted on how ML can be integrated into subject domains other than computing, (c) most of the studies focus on pedagogical development with a dearth of teacher professional development programs, and (d) more evidence of societal and ethical implications of ML should be considered in future research. While this study recognizes the present gaps and direction for future research, these findings provide insight for educators, practitioners, instructional designers, and researchers into K-12 ML research trends to advance the quality of the emerging field.
... Line follower basic block diagram [17] A lot of research had explore the effect of ER on STEM performance. Kandlhofer et al. [18] used 179 students as research subjects from nine elementary schools and found that there was no significant difference in learning outcomes of ''robot assembly and programming'' according to student gender, age, and background. The research method is in the form of a quasi-experimental design using a sample of 148 students (with an estimated age of 14.9) on the significant effect of the intervention in three subscales (mathematics and scientific inquiry, teamwork, social skills) and in two main categories (technical skills and soft skills/social aspects) found a very strong relationship significantly between the various sub-scales [19]. ...
The aim of this study was to determine the motivation of science teachers and students towards science after participating in the activity of assembling, simulating, and recording line follower robots as an effort to motivate middle school students and teachers towards science in Bengkulu Province. The research was done by direct practicing, where 60 students and 15 teachers of three junior high school (SMP): SMP Negeri 06 Seluma, SMP Negeri 02 Kota Bengkulu, and SMP Negeri 8 Rejang Lebong, were involved as the research subjects. The research activity concluded that the schools are ready to prepare simple electronics/robot laboratories for the three research subjects and the science teachers and students were motivated to learn science. It was seen from the score of 3.95 (scale of 1 to 5) for students, and for the science teacher, the score was 3.83 (scale of 1 to 5). The science teachers will follow up on robotics activities so that students will be interested in learning science at home and school.
... However, the PD course did not include AIrelated TPACK, with only a focus on teachers' fundamental programming concepts. Kandlhofer et al. (2019) presented an educational project for training and certifying teachers and school students in AI and robotics and described the four stages and the detailed content of the training modules and curricula in this project. Although the appropriateness of the teaching methods and teaching materials of the developing system were explored among 16 teachers, the impact on teachers' learning outcomes has not yet been examined. ...
With the rapid development of artificial intelligence (AI), the demand for K-12 computer science (CS) education continues to grow. However, there has long been a lack of trained CS teachers. To promote the AI teaching competency of CS teachers, a professional development (PD) program based on the technological pedagogical content knowledge (TPACK) framework was intentionally designed in this research. A quasi-experimental design with a 25-day (75-h) intervention was conducted among 40 in-service CS teachers to examine its impact on AI teaching competency, including AI knowledge, AI teaching skills, and AI teaching self-efficacy. The quantitative data were collected via a pretest and posttest, and qualitative data were collected via artifact analysis and semistructured interviews. The results indicated that the TPACK-based PD program a) significantly improved CS teachers’ AI knowledge, especially in representation and reasoning, interaction, and social impact; b) developed CS teachers’ AI teaching skills, including their AI lesson plan ability and AI programming skills; and c) significantly improved CS teachers’ AI teaching self-efficacy, both in AI teaching efficacy beliefs and AI teaching outcome expectancy. These findings revealed the effectiveness of the TPACK-based PD program in improving the AI teaching competency of K-12 CS teachers and could help to expand the design of effective PD for CS teachers.
... One special method to highlight is the in-application ask questions of Microsoft Power BI [43] which allows the users to ask a question related to the data set they are currently working on. Tool-specific: For the scientific publications we identified three onboarding approaches which can be categorized as tool-specific [45,69,32]. The remaining six are non-tool-specific [2,40,62,52,8,19]. ...
The aim of visualization is to support people in dealing with large and complex information structures, to make these structures more comprehensible, facilitate exploration, and enable knowledge discovery. However, users often have problems reading and interpreting data from visualizations, in particular when they experience them for the first time. A lack of visualization literacy, i.e., knowledge in terms of domain, data, visual encoding, interaction, and also analytical methods can be observed. To support users in learning how to use new digital technologies, the concept of onboarding has been successfully applied in other domains. However, it has not received much attention from the visualization community so far. This chapter aims to fill this gap by defining the concept and systematically laying out the design space of onboarding in the context of visualization as a descriptive design space. On this basis, we present a survey of approaches from the academic community as well as from commercial products, especially surveying educational theories that inform the onboarding strategies. Additionally, we derived design considerations based on previous publications and present some guidelines for the design of visualization onboarding concepts.
Around the world, the prevalence of artificial intelligence (AI) in global citizens’ work and life has been recognized by government and intergovernmental agencies. Efforts by researchers, practitioners, and policy-makers are underway to develop guidelines, curriculum and best practices to help the future workforce who are youth in today’s classrooms develop basic AI competencies. While there is growing attention to broadening AI educational opportunities and, especially, to providing learning experiences for younger students, relatively little is currently known about how to most effectively provide AI education to K-12 (kindergarten through 12th grade) students. In this paper, we discuss the design and evaluation of an educational game for high-school AI education called ARIN-561. Drawing on feedback from over 1,200 students, we conducted analysis on student performances in the game, particularly student errors. Results show the relationship and dependencies of different activities within the game and shed light on the design instructional support to help students build AI knowledge to succeed in the game.Keywordseducational gameK-12 AI education
This article presents the Robostudio space for collaborative learning (co-learning) around and with social robots, and two novel co-learning concepts. The first concept, Robocamp, is a home-based one-month learning model for family members’ co-learning with a robot. In this model, a social robot is borrowed for families, and weekly hands-on tasks to be conducted with the robot are provided. The second concept, a co-learning workshop called Robotour, takes place in Robostudio and there, university students and primary school pupils together gain an understanding of different aspects of social robots. Both concepts aim for the cooperation between different learner groups and increasing their knowledge about social robots, interaction with them, and how to operate or program them. We present the current state-of-the-art in the area of educational robots, as well as initial evaluations of our concepts with the authentic target groups. We also reflect on our concepts in light of their benefits and potential. According to the evaluation findings, Robocamp provided an encouraging environment that allowed all the participating family members to participate in the collaborative activity, as well as think critically about the limitations of the robots. Robotour successfully raised different learner groups’ curiosity towards robots, which further resulted in the enhancement of their creativity. The knowledge from this article can be utilized by researchers, designers, and teachers, who are interested in the development and implementation of co-learning activities around and with social robots.KeywordsSocial RobotsEducational RobotsCo-learningCo-learning Space
Artificial Intelligence (AI) is a foundational technology that is permeating every aspect of our daily lives. It is also profoundly transforming our workforce around the globe. It is critical to prepare future generations with basic knowledge of AI, not just through higher education, but beginning with childhood learning. There is a growing body of research into how students, especially K-12 students, construct an understanding of and engage with core ideas in the field, that can ground the design of learning experiences in evidence-based accounts of how youth learn AI concepts, how understanding progresses across concepts, or what concepts are most appropriate for what age-levels. On July 7, 2023, we held a workshop on AI Education for K-12 at the 24th International Conference on Artificial Intelligence in Education, in Tokyo, Japan, in a synchronous hybrid format, allowing both in-person and virtual contributions. The workshop brought together researchers in K-12 AI education to discuss the state-of-the-art and identify research gaps and future directions to bring evidence-based AI education in and out of the classrooms.KeywordsK-12 AI educationyouth AI educationworkshop
Although Machine Learning (ML) is found practically everywhere, few understand the technology behind it. This presents new challenges to extend computing education by including ML concepts in order to help students to understand its potential and limits and empowering them to become creators of intelligent solutions. Therefore, we developed an introductory course to teach basic ML concepts, such as fundamentals of neural networks as well as limitations and ethical concerns in alignment with the K-12 Guidelines for Artificial Intelligence. It also teaches the application of these concepts, by guiding the students to develop a first classification model of recycling trash images using Google Teachable Machine. In order to promote ML education, the interactive course is available online in Brazilian Portuguese to be used as an extracurricular course or in an interdisciplinary way as part of science classes covering recycling topics. The course was applied and evaluated through a series of exploratory case studies with a total of 108 middle and high school students. The results of the evaluation show that middle and high school students were able to understand ML concepts and develop an image classification model. No substantial difference with regard to the educational stage, gender and instructional mode was observed. The course was also perceived as an enjoyable learning experience motivating students to learn more about ML.
Background
The recent discussion of introducing artificial intelligence (AI) knowledge to K–12 students, like many engineering and technology education topics, has attracted a wide range of stakeholders and resources for school curriculum development. While teachers often have to directly interact with external stakeholders out of the public schooling system, few studies have scrutinized their negotiation process, especially teachers' responses to external influences, in such complex environments.
Purpose
Guided by an integrated theoretical framework of social constructionism, this research examined the process of how a teacher‐initiated AI curriculum was constructed with external influences. The research focused on teachers' perspectives and responses in mediating external influences into local schools and classrooms.
Methods
A 3‐year ethnographic study was conducted in relation to an AI curriculum project among 23 Computer Science (CS) teachers from primary schools. Data collected from ethnographic observation, teacher interviews, and artifacts, were analyzed using open coding and triangulation rooted in the ethnographic, interpretivist approach.
Results
Three sets of external influences were found salient for teachers' curriculum decisions, including the orientation of state‐level educational policies, AI faculty at a partner university, and students' media and technology environments. The teachers' situational logics and strategic actions were reconstructed with thick descriptions to uncover how they navigated and negotiated the external influences to fulfill local challenges and expectations in classrooms and schools.
Conclusions
The ethnographic study uncovered the dynamic and multifaceted negotiation involved in the collaborative curriculum development, and offers insights to inform policymaking, teacher education, and student support in engineering education.
Artificial Intelligence (AI) is successfully intervening in areas such as education, health and industry. In education, AI is being used to improve educational processes. UNESCO considers as a new vision to involve AI not only as a didactic medium, but also as a science in which children can develop their intellect, through workshops, courses and curricula focused on the fundamentals of AI, allowing them to develop skills such as computational thinking and critical thinking. The present Systematic Literature Review was developed according to the methodological guidelines proposed by Barbara Kitchenham, with the objective of identifying how AI teaching-learning processes are carried out from early ages. Sixty-four studies were obtained between 2016-2021, where eleven countries are identified with AI teaching initiatives, through workshops, courses and projects to update their curricula; machine learning and educational robotics are the two branches linked to AI that are currently taught to children and adolescents.
Profound knowledge about Artificial Intelligence (AI) will become increasingly important for careers in science and engineering. Therefore an innovative educational project teaching fundamental concepts of AI at high school level will be presented in this paper. We developed an AI-course covering major topics (problem solving, search, planning, graphs, datastructures, automata, agent systems, machine learning) which comprises both theoretical and hands-on components. A pilot project was conducted and empirically evaluated. Results of the evaluation show that the participating pupils have become familiar with those concepts and the various topics addressed. Results and lessons learned from this project form the basis for further projects in different schools which intend to integrate AI in future secondary science education.
PopBots is a hands-on toolkit and curriculum designed to help young children learn about artificial intelligence (AI) by building, programming, training, and interacting with a social robot. Today’s children encounter AI in the forms of smart toys and computationally curated educational and entertainment content. However, children have not yet been empowered to understand or create with this technology. Existing computational thinking platforms have made ideas like sequencing and conditionals accessible to young learners. Going beyond this, we seek to make AI concepts accessible. We designed PopBots to address the specific learning needs of children ages four to seven by adapting constructionist ideas into an AI curriculum. This paper describes how we designed the curriculum and evaluated its effectiveness with 80 Pre-K and Kindergarten children. We found that the use of a social robot as a learning companion and programmable artifact was effective in helping young children grasp AI concepts. We also identified teaching approaches that had the greatest impact on student’s learning. Based on these, we make recommendations for future modules and iterations for the PopBots platform.
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.
Artificial Intelligence (AI) is becoming increasingly important. Thus, sound knowledge about the principles of AI will be a crucial factor for future careers of young people as
well as for the development of novel, innovative products. Addressing this challenge, we present an ambitious 3-year project focusing on developing and implementing a professional, internationally accepted, standardized training and certification system for AI which will also be recognized by the industry and educational institutions. The approach is based on already implemented and evaluated pilot projects in the area of AI education. The project’s main goal is to train and certify teachers and mentors as well as students and young people in basic and advanced AI topics, fostering AI literacy among this target audience.
This eBook emphasises the theory to practice of curriculum design in higher education. The book focuses on programme level of design. It incorporates face-to-face, blended and online curricula and attempts to link theory to practice by giving some practical resources and/or exercises. The content draws on the author's experiences of working and researching into curriculum design in the Irish higher education sector. It is aimed at all staff involved in curriculum design, including academic staff (faculty), institutional managers, educational developers and technologists, support staff, library staff and curriculum researchers. Although it is primarily drawn from literature and experiences in the higher education sector, those in adult and further education may also find it useful. The structure of this book is based on a curriculum design process that the author has developed as part of her experience and research on curriculum design.
The authors make the case that implementation of a successful blended learning program requires alignment of institutional, faculty, and student goals. Reliable and robust infrastructure must be in place to support students and faculty. Continuous evaluation can effectively track the impact of blended learning on students, faculty, and the institution. These data are used to inform stakeholders and impact policy to improve faculty development and other support structures necessary for success. This iterative loop of continuous quality improvement is augmented by faculty scholarship of teaching and learning research. The evolution of blended learning at the University of Central Florida is used as a model and research collected over sixteen years illustrates that with proper support and planning, blended learning can result in positive institutional transformation.
Here, truly, is the essential Piaget_a distillation of the eminent Genevan's extraordinary legacy to modern psychological knowledge. This generous selection of the most important of Piaget's writings, spanning a period of some seventy years, organizes the core of his remarkable contribution in a way that clarifies and illuminates his aims, ideas, and underlying themes.
Reviews 55 studies in which self-evaluations of ability were compared with measures of performance to show a low mean validity coefficient (mean
r = .29) with high variability (
SD = .25). A meta-analysis by the procedures of J. E. Hunter et al (1982) calculated sample-size weighted estimates of
–- r and
SDr and estimated the appropriate adjustments of these values for sampling error and unreliability. Among person variables, high intelligence, high achievement status, and internal locus of control were associated with more accurate evaluations. Much of the variability in the validity coefficients (
R = .64) could be accounted for by 9 specific conditions of measurement, notably (a) the rater's expectation that the self-evaluation would be compared with criterion measures, (b) the rater's previous experience with self-evaluation, (c) instructions guaranteeing anonymity of the self-evaluation, and (d) self-evaluation instructions emphasizing comparison with others. It is hypothesized that conditions increasing self-awareness would increase the validity of self-evaluation. (84 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This paper gives an overview and summary of the Roberta Initiative – an approach to raise especially but not only girlsínterest in STEM (science, technology, engineering and math). Roberta comes with a didactic concept that uses robot construction kits in combination with a gender-balanced didactic material and course concept. Roberta teacher trainings delivered by certified Roberta coaches and a dissemination network are the approach of the Roberta Initiative to establish a sustainable activity to raise girlsínterest in technical topics and in the end the portion of female engineers in Europe. Robot competitions turned out to be an attractive next step for Roberta course participants to further follow their interest in STEM.
The projects that we have developed for UC Berkeley's intro-ductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI al-gorithms and then to inject domain knowledge about the Pac-Man environment using search heuristics, evaluation func-tions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students' excitement about video games.
This article describes a series of research designs for combining qualitative and quantitative methods, using a Priority-Sequence Model that relies on the principle of complementarity. First, a decision about the priority of the two methods selects either a qualitative or a quantitative approach to be the principal method. Second, a decision about sequencing determines whether the complementary method will serve as either a preliminary or a follow-up to the principal method. These two decisions yield four basic research designs: (a) preliminary qualitative methods in a quantitative study, (b) preliminary quantitative methods in a qualitative study, (c) follow-up qualitative methods in a quantitative study, and (d) follow-up quantitative methods in a qualitative study. The conclusions consider further research designs and the expertise necessary for multiple-methods research.
Many phenomena related to software development are qualitative in nature. Relevant measures of such phenomena are often collected using semi-structured interviews. Such interviews involve high costs, and the quality of the collected data is related to how the interviews are conducted. Careful planning and conducting of the interviews are therefore necessary, and experiences from interview studies in software engineering should consequently be collected and analyzed to provide advice to other researchers. We have brought together experiences from 12 software engineering studies, in which a total of 280 interviews were conducted. Four areas were particularly challenging when planning and conducting these interviews; estimating the necessary effort, ensuring that the interviewer had the needed skills, ensuring good interaction between interviewer and interviewees, and using the appropriate tools and project artifacts. The paper gives advice on how to handle these areas and suggests what information about the interviews should be included when reporting studies where interviews have been used in data collection. Knowledge from other disciplines is included. By sharing experience, knowledge about the accomplishments of software engineering interviews is increased and hence, measures of high quality can be achieved.
Die Künstliche Intelligenz (KI) beschäftigt sich seit über 60 Jahren mit der Frage, ob und wie Computer und Roboter Dinge tun können, die wir Menschen noch besser können. Viele für uns Menschen kognitiv schwierige Aufgaben wie etwa das Schach spielen oder große Welche technischen Innovationen durch KI gibt es und welche sind künftig zu erwarten? Welche Auswirkungen werden sie haben und wie kann man damit umgehen? Sollte man steuernd eingreifen, um kommenden Generationen eine lebenswerte Welt zu hinterlassen? Bereiche der numerischen Mathematik, der Logik und der Mustererkennung können heute von Computern besser gelöst werden als von Menschen. Auch Dank der Methoden der KI. Andere Aufgaben, wie etwa das Greifen eines Schlüssels in einer Tasche oder das Finden einer Tür in einem Raum fallen uns Menschen leicht, den Robotern aber schwer. Aber heute wird mit Hochdruck an Aufgaben wie etwa lernfähiges Greifen von Objekten oder Objektwahrnehmung in komplexen Umgebungen geforscht
This paper discusses the design of an introductory computer science course for high school students using declarative programming. Though not often taught at the K-12 level, declarative programming is a viable paradigm for teaching computer science due to its importance in artificial intelligence and in helping student explore and understand problem spaces. This paper describes the authors' implementation of a declarative programming course for high school students during a 4-week summer session.
Introductory artificial intelligence undergraduate classes often introduce different search methods using different search algorithms. In this context one of algorithms that is often taught, is the minimax algorithm which is used in adversarial games where you want to minimize your opponent's chance of winning while maximizing your chance of winning. Different instructors use different games to make the students implement the minimax algorithm such as Checkers, Othello or Chess. However, one common problem with this assignment is that the students often spend more time implementing the game itself rather than the artificial intelligence techniques in the game. For this reason, in this paper we present a java-based open source Othello framework that was designed to be used in artificial intelligence undergraduate classes. Our framework has several features that help the students to focus on the development of the artificial intelligence aspects of the game, rather than developing the game itself. One particular feature of the framework is that it has a method that returns the list of valid moves given the current state of the game board and which player is going to make the next move. With this method, the students can focus on how to evaluate the different states using several heuristic functions and implementing the minimax algorithm. Another feature of the framework is the graphical user interface and the HumanPlayer class that allows the students to play against their own code. This feature is important as it allows the students to not only debug their codes but also to evaluate the effectiveness of their implemented heuristics. Another aspect of the framework is that it allows to set up a tournament of the codes developed by the students. The tournament can be organized in two modes. In the first mode every AI developed by one student plays against the AI developed by every other student. In the second mode, each student developed code is paired against another student developed code and only the winner plays against the winner of another pairing until there is only one winner left. An analysis of the framework in our artificial intelligence undergraduate computer engineering classes shows that it properly supports the student learning and the tournament mode also challenges them to create the best AI for Othello as they can.
Artificial Intelligence (AI) already plays a major role in our daily life (e.g. intelligent household appliances like robotic vacuum cleaners or AI-based applications like Google Maps, Google Now, Siri, Cortana, …). Sound knowledge about AI and the principles of computer science will be of vast importance for future careers in science and engineering. Looking towards the near future, jobs will largely be related to AI. In this context literacy in AI and computer science will become as important as classic literacy (reading/writing). By using an analogy with this process we developed a novel AI education concept aiming at fostering AI literacy. The concept comprises modules for different age groups on different educational levels. Fundamental AI/computer science topics addressed in each module are, amongst others, problem solving by search, sorting, graphs and data structures. We developed, conducted and evaluated four proof-of-concepts modules focusing on kindergarten/primary school as well as middle school, high school and university. Preliminary results of the pilot implementations indicate that the proposed AI education concept aiming at fostering AI literacy works.
Systems engineering activities provide an opportunity for pre-college students to engage in sophisticated engineering design practices beyond commonly-accepted limits of their capability. This research qualitatively investigates how pre-college students engaged in a systems engineering activity using LEGO EV3 robotics with three learning objectives: 1) a system can be decomposed into subsystems, 2) designers coordinate activities by communicating requirements and interfaces, and 3) testing reveals problems and changes propagate to other components. The analysis was based on methods of qualitative content analysis and employed video data produced by students to describe their robot designs as well as video data collected by the researchers. While students did not use the language of systems engineering, examples show students productively engaging in systems engineering methods that relate to each learning objective. Results suggest pre-college students are capable of understanding and implementing basic systems engineering concepts representing a more sophisticated approach to engineering design.
Investigating the meanings of human existence as they are constructed and enacted by people in everyday life situations and settings presents serious challenges for all forms of human studies. Participant observation, whereby the researcher interacts with people in everyday life while collecting information, is a unique method for investigating the enormously rich, complex, conflictual, problematic, and diverse experiences, thoughts, feelings, and activities of human beings and the meanings of their existence. Use of this distinctive method emerged with the professionalization of anthropology and sociology where it gradually was formalized and later spread to a full range of human studies fields. Its practice nevertheless remains artful, requiring creative decision making about problems and questions to be studied, appropriate settings and situations for gathering information, the performance of membership roles, establishing and sustaining trusting relationships, ethics, values, and politics, as well as record making, data analysis and interpretation, and reporting results. This essay provides a brief sketch of the method of participant observation and an overview of a few of the more central issues of its practice, including its location historically within the framework of different views of social scientific methodology.
In Australia, the Scientists-in-Schools program partners prof essional scientists with teachers from K-12 schools to improve early engagement and educational outcomes in the sciences and mathematics. An overview of the developing syllabus of a K-6 course resulting from the pairing of a senior AI researcher with teachers from a K-6 (primary) school is presented. Now entering its third year, the course introduces the basic concepts, vocabulary and history of science generally and AI specifically in a manner that emphasises student engagement and provides a challenging but age appropriate syllabus. Reflecting on the course at this time provides an action research basis for ongoing maturation of the syllabus, and the paper is presented in that light. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
The methodology and ideas behind educational robotics arise from the 1960s, when the first hardware platforms together with computers were used in research studies in schools. Since the 1990s, the market for educational robotics has grown, and there are many solutions available to use in schools. Despite a wide variety of platform approaches for using robots in education, they are still based on ideas that are decades old. This study evaluates different approaches used nowadays to teach with robots. Problembased, constructionist, and competitionbased learning are identified as the most common uses of robots under observation. Each approach is analysed qualitatively based on the published literature. Each has positive and negative properties; though none have been studied thoroughly using quantitative methods. Results indicate that all these approaches are used in schools with robots interdisciplinary. The current reasons for using robots are based mostly on teachers' and students' impressions. However, robotics can be seen as a "tool" to create many approaches to science education, such as inquiry learning and problem solving.
Dorothy is an integrated 3D/robotics educational tool created by augmenting the Alice programming environment for teaching core computing skills to students without prior programming experience. The tool provides a drag and drop interface to create graphical routines in virtual worlds; these routines are automatically translated into code to provide a real-time or offline enactment on mobile robots in the real world. This paper summarizes the key capabilities of Dorothy, and describes the contributions made to: (a) enhance the bidirectional communication between the virtual interface and robots; and (b) support multirobot collaboration. Specifically, we describe the ability to automatically revise the virtual world based on sensor data obtained from robots, creating or deleting objects in the virtual world based on their observed presence or absence in the real world. Furthermore, we describe the use of visually observed behavior of teammates for collaboration between robots when they cannot communicate with each other. Dorothy thus helps illustrate sophisticated algorithms for fundamental challenges in robotics and AI to teach advanced computing concepts, and to emphasize the importance of computing in real world applications, to beginning programmers. Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
The second edition of this handbook provides a state-of-the-art cover view on the various aspects in the rapidly developing field of robotics. Reaching for the human frontier, robotics is vigorously engaged in the growing challenges of new emerging domains. Interacting, exploring, and working with humans, the new generation of robots will increasingly touch people and their lives. The credible prospect of practical robots among humans is the result of the scientific endeavour of a half a century of robotic developments that established robotics as a modern scientific discipline. The ongoing vibrant expansion and strong growth of the field during the last decade has fueled this second edition of the Springer Handbook of Robotics. The first edition of the handbook soon became a landmark in robotics publishing and won the American Association of Publishers PROSE Award for Excellence in Physical Sciences & Mathematics as well as the organizations Award for Engineering & Technology. The second edition of the handbook, edited by two internationally renowned scientists with the support of an outstanding team of seven part editors and more than 200 authors, continues to be an authoritative reference for robotics researchers, newcomers to the field, and scholars from related disciplines. The contents have been restructured to achieve four main objectives: the enlargement of foundational topics for robotics, the enlightenment of design of various types of robotic systems, the extension of the treatment on robots moving in the environment, and the enrichment of advanced robotics applications. Further to an extensive update, fifteen new chapters have been introduced on emerging topics, and a new generation of authors have joined the handbooks team. A novel addition to the second edition is a comprehensive collection of multimedia references to more than 700 videos, which bring valuable insight into the contents. The videos can be viewed directly augmented into the text with a smartphone or tablet using a unique and specially designed app.
We report on the experience of teaching an industry-validated course on Artificial Intelligence in Computer Games within the Simulation and Game Design department at a two-year community college during a 16-week semester. The course format used a blended learning just-in-time teaching approach, which included active learning programming exercises and one-on-one student interactions. Moskal's Attitudes Toward Computer Science survey showed a positive and significant increase in students in both interest (W(10) = 25, p = 0.011) and professional (W(10) = 49.5, p = 0.037) constructs. The Felder-Soloman Index of Learning Styles (n = 14) failed to identify any statistically significant differences in learning styles when compared to a four-year CS1 class. In the final class evaluation, 8 out of 13 students (62%) strongly or very strongly preferred the blended learning approach. We validated this course through four semi-structured interviews with game companies. The interview results suggest that companies are strongly favorable to the course content and structure. The results of this work serve as a template that community colleges can adopt for their curriculum.
During the summer of 2011, twenty-four high school students participated in an intense, three-week computer science course at the University of Virginia. The course met for twenty-one three-hour sessions, thus encompassing more contact time than a standard college-level course. The course was structured in an “Inform, Experience, Implement” active-learning format: students were exposed to the history of a particular problem in context and participated in an active learning lesson regarding the topic before learning how to address the examined problem through programming. This structure helped integrate into the course best practices from experiential learning, kinesthetic outreach activities, and active learning pedagogy. Utilizing this three-part rotation curriculum achieved some important goals, including holding the interest of students during the summer for six hours a day and successfully motivating students who had no programming background.
This paper describes a newly designed upper-level undergraduate and graduate course, Autonomous Mobile Robots. The course employs active, cooperative, problem-based learning and is grounded in the fundamental computational problems in mobile robotics defined by Dudek and Jenkin. Students receive a broad survey of robotics through lectures, weekly assignments, and a final capstone project that includes a community outreach element and collaboration with artists. Students were assessed on several metrics from the ASEE literature; overall, they performed well in the course. The outreach event was also a success and was enjoyed by both the community attendees and student participants.
Explains the International Computer Driving License (ICDL), an internationally recognized computer literacy training and certification program. Topics include standards, goals, and guidelines that have been developed at national, state, and local levels for elementary, secondary, and higher education; use in business; and the seven modules in the ICDL certification process. (LRW)
In this paper, we report on the efforts at the University of Southern California to teach computer science and artificial intelligence with games because games mo-tivate students, which we believe increases enrollment and retention and helps us to educate better computer scientists. The Department of Computer Science is now in its second year of operating its Bachelor's Program in Computer Science (Games), which provides students with all the necessary computer science knowledge and skills for working anywhere in industry or pursuing advanced degrees but also enables them to be imme-diately productive in the game development industry. It consists of regular computer science classes, game engineering classes, game design classes, game cross-disciplinary classes and a final game project. The Intro-duction to Artificial Intelligence class is a regular com-puter science class that is part of the curriculum. We are now converting the class to use games as a motivat-ing topic in lectures and as the domain for projects. We describe both the new bachelor's program and some of our current efforts to teach the Introduction to Artificial Intelligence class with games.
In this paper, we describe the development of an artificial neural network strategy for an industrial robot to play the game of Tic-Tac-Toe. This project was undertaken by two high school students during the university's technology and engineering research programme. The strategy is based on the feedforward multi-layered neural networks with backpropagation of error training. The performance of the strategy is evaluated by its accomplishment against human opponents. The results indicate that the neural network strategy developed will almost always win if given the opportunity and at most draw if not. The neural network strategy developed has been successfully interfaced with a Scorbot-ER VII robot via an in-house designed electronic game board.
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