Improving the viability of mental models held by novice programmers

Recent research has found that many novice programmers often hold non-viable mental models of basic programming concepts such as assignment and object reference. This paper proposes a constructivist-based teaching model, integrating a cognitive conflict strategy with program visualization, with the aim of improving novice programmers’ mental models. The results of a preliminary empirical study suggest that, for the relatively straightforward concept of assignment, tight integration of program visualization with a cognitive conflict event that highlights a student’s inappropriate understanding can help improve students’ non-viable mental models. 14 out of 18 participants who held non-viable mental models of the assignment process successfully changed their model to be viable as a result of the proposed teaching model

Teaching programming at a distance: the Internet software visualization laboratory

This paper describes recent developments in our approach to teaching computer programming in the context of a part-time Masters course taught at a distance. Within our course, students are sent a pack which contains integrated text, software and video course material, using a uniform graphical representation to tell a consistent story of how the programming language works. The students communicate with their tutors over the phone and through surface mail. Through our empirical studies and experience teaching the course we have identified four current problems: (i) students' difficulty mapping between the graphical representations used in the course and the programs to which they relate, (ii) the lack of a conversational context for tutor help provided over the telephone, (iii) helping students who due to their other commitments tend to study at 'unsociable' hours, and (iv) providing software for the constantly changing and expanding range of platforms and operating systems used by students. We hope to alleviate these problems through our Internet Software Visualization Laboratory (ISVL), which supports individual exploration, and both synchronous and asynchronous communication. As a single user, students are aided by the extra mappings provided between the graphical representations used in the course and their computer programs, overcoming the problems of the original notation. ISVL can also be used as a synchronous communication medium whereby one of the users (generally the tutor) can provide an annotated demonstration of a program and its execution, a far richer alternative to technical discussions over the telephone. Finally, ISVL can be used to support asynchronous communication, helping students who work at unsociable hours by allowing the tutor to prepare short educational movies for them to view when convenient. The ISVL environment runs on a conventional web browser and is therefore platform independent, has modest hardware and bandwidth requirements, and is easy to distribute and maintain. Our planned experiments with ISVL will allow us to investigate ways in which new technology can be most appropriately applied in the service of distance education

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

A Visual Programming Paradigm for Abstract Deep Learning Model Development

Deep learning is one of the fastest growing technologies in computer science with a plethora of applications. But this unprecedented growth has so far been limited to the consumption of deep learning experts. The primary challenge being a steep learning curve for learning the programming libraries and the lack of intuitive systems enabling non-experts to consume deep learning. Towards this goal, we study the effectiveness of a no-code paradigm for designing deep learning models. Particularly, a visual drag-and-drop interface is found more efficient when compared with the traditional programming and alternative visual programming paradigms. We conduct user studies of different expertise levels to measure the entry level barrier and the developer load across different programming paradigms. We obtain a System Usability Scale (SUS) of 90 and a NASA Task Load index (TLX) score of 21 for the proposed visual programming compared to 68 and 52, respectively, for the traditional programming methods

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie