THE ROLE OF SCRATCH VISUAL PROGRAMMING IN THE DEVELOPMENT OF COMPUTATIONAL THINKING OF NON-IS MAJORS

The study explored the role of Scratch in developing the computational thinking (CT) abilities of Non-IS majors. Literature shows that abstraction, parallelism, logical thinking, data representation, flow control, pattern generalization and systematic processing of information produce computational thinking. Using a survey (n = 92) analyzed through PLS-SEM, the study explored and validated computational thinking definitions and constructs based on the other constructs. A final conceptual model shows the relationships between the constructs. The results of the survey indicated that Scratch played a significant role in abstraction for developing computational thinking. Further analysis concluded that Scratch also played a role in developing logical thinking by acting through abstraction and the other CT constructs. Nevertheless, these were not observed to influence computation thinking significantly. Further research is required to link logical thinking to computational thinking and to determine if flow control has a mediating or moderating impact on computational thinking

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Clickstream for learning analytics to assess students’ behavior with Scratch

The construction of knowledge through computational practice requires to teachers a substantial amount of time and effort to evaluate programming skills, to understand and to glimpse the evolution of the students and finally to state a quantitative judgment in learning assessment. The field of learning analytics has been a common practice in research since last years due to their great possibilities in terms of learning improvement. Both, Big and Small data techniques support the analysis cycle of learning analytics and risk of students’ failure prediction. Such possibilities can be a strong positive contribution to the field of computational practice such as programming. Our main objective was to help teachers in their assessments through to make those possibilities effective. Thus, we have developed a functional solution to categorize and understand students’ behavior in programming activities based in Scratch. Through collection and analysis of data generated by students’ clicks in Scratch, we proceed to execute both exploratory and predictive analytics to detect patterns in students’ behavior when developing solutions for assignments. We concluded that resultant taxonomy could help teachers to better support their students by giving real-time quality feedback and act before students deliver incorrectly or at least incomplete tasks.Peer ReviewedPostprint (author's final draft

Evaluating a Course for Teaching Advanced Programming Concepts with Scratch to Preservice Kindergarten Teachers: A Case Study in Greece

Coding is a new literacy for the twenty-first century, and as a literacy, coding enables new ways of thinking and new ways of communicating and expressing ideas, as well as new ways of civic participation. A growing number of countries, in Europe and beyond, have established clear policies and frameworks for introducing computational thinking (CT) and computer programming to young children. In this chapter, we discuss a game-based approach to coding education for preservice kindergarten teachers using Scratch. The aim of using Scratch was to excite students’ interest and familiarize them with the basics of programming in an open-ended, project-based, and personally meaningful environment for a semester course in the Department of Preschool Education in the University of Crete. For 13 weeks, students were introduced to the main Scratch concepts and, afterward, were asked to prepare their projects. For the projects, they were required to design their own interactive stories to teach certain concepts about mathematics or physical science to preschool-age students. The results we obtained were more satisfactory than expected and, in some regards, encouraging if one considers the fact that the research participants had no prior experiences with computational thinking

Computational Thinking in Education: Where does it fit? A systematic literary review

Computational Thinking (CT) has been described as an essential skill which everyone should learn and can therefore include in their skill set. Seymour Papert is credited as concretising Computational Thinking in 1980 but since Wing popularised the term in 2006 and brought it to the international community's attention, more and more research has been conducted on CT in education. The aim of this systematic literary review is to give educators and education researchers an overview of what work has been carried out in the domain, as well as potential gaps and opportunities that still exist. Overall it was found in this review that, although there is a lot of work currently being done around the world in many different educational contexts, the work relating to CT is still in its infancy. Along with the need to create an agreed-upon definition of CT lots of countries are still in the process of, or have not yet started, introducing CT into curriculums in all levels of education. It was also found that Computer Science/Computing, which could be the most obvious place to teach CT, has yet to become a mainstream subject in some countries, although this is improving. Of encouragement to educators is the wealth of tools and resources being developed to help teach CT as well as more and more work relating to curriculum development. For those teachers looking to incorporate CT into their schools or classes then there are bountiful options which include programming, hands-on exercises and more. The need for more detailed lesson plans and curriculum structure however, is something that could be of benefit to teachers

In Search of a “Fair Explanation”: Helping Young People to Consider the Possibilities, Limitations, and Risks of Computer- and Data-Mediated Systems

Significant resources have been directed towards K-12 computing and data education over the past ten years, as part of what has come to be known as the CSforAll initiative. This initiative has focused on raising awareness of computing education among parents and students, developing situated learning progressions that resonate with many different interests and pursuits, training teachers, and addressing issues of underrepresentation in computing among females and racial minorities. In this dissertation, I argue that as the CSforAll initiative continues to expand, it is important for the education community to also reflect on the forms of knowledge that are believed to be essential, and the presumed benefits of computing and data education. Specifically, how might the goal of producing citizens with robust computing and data literacies change what is considered to be fundamental to a computing education; as well as the kinds of contexts in which computing and data science are situated?I use the term sociotechnical literacy to name this vision for computing education, which I define as a broad set of social and technical practices, strategies, ideas, and dispositions that can help people to reason about the computer-mediated systems that shape their everyday lives. As the term suggests, I argue that it is important for learners to engage with technical ideas as well as their social applications and implications. To examine what this might mean for teaching and learning, I describe two design experiments that I conducted with young people (ages 14 – 22). Each approach aimed to make the applications of computing primary (rather than treating applications as the backdrop from which the abstractions of computation are motivated), so that learners could examine some of the specific ways in which data and computing might be directed to particular goals, subject to real possibilities and constraints, and in relation to alternative forms of participation. I examine the possibilities and limitations of each approach. I also analyze some of the assumptions that framed the design experiments – which were naïve, but also reflective of a broader ethos that pervades CSforAll. I reflect on what these studies collectively reveal about the possibilities, limitations, and risks of data and computing, as situated in the lives of young people; as well as what this might mean for helping young people develop a robust sociotechnical literacy. There are very real limits to what can be accomplished with computing and data alone. There are also significant benefits and risks associated with the many sociotechnical systems that shape our lives. As such, I argue that rather than positioning computing education as a remedy to various social ills, we instead offer young people a fair explanation of what computing is and is not capable of, grounded within specific contexts involving real people. I conclude with what this fair explanation might include, and how it might be fostered

Measuring the Usability and Capability of App Inventor to Create Mobile Applications

MIT App Inventor is a web service that enables users with little to no previous programming experience to create mobile applications using a visual blocks language. We analyze a sample of 5,228 random projects from the corpus of 9.7 million and group projects by functionality. We then use the number of unique blocks in projects as a metric to better understand the usability and realized capability of using App Inventor to implement specific functionalities. We introduce the notion of a usability score and our results indicate that introductory tutorials heavily influence the usability of App Inventor to implement particular functionalities. Our findings suggest that the sequential nature of App Inventor’s learning resources results in users realizing only a portion of App Inventor’s capabilities and propose improvements to these learning resources that are transferable to other programming environments and tools.Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Google Research and Innovation Scholarship