An environment for learning interactive programming

Evaluations of JaguarCode: A web-based object-oriented programming environment with static and dynamic visualization

Programming environments support the creation, modification, execution and debugging of programs. The goal of integrating a programming environment is more than simply building tools that share a common data base and provide a consistent user interface. Ideally, the programming environment appears to the programmer as a single tool; there are no firewalls separating the various functions provided by the environment. This paper describes the techniques used to integrate Magpie, an interactive programming environment for Pascal. Display windows, called browsers, provide a consistent approach for interacting with the Pascal source code or the execution state of the program. Incremental compilation allows the programmer to specify debugging actions in Pascal, eliminating the need for a separate debugging language.

A critical analysis and evaluation of Web-based environments for program development

This paper describes the development of a web-based programming and assessment environment for use in supporting programming fundamentals courses (CS1, CS2) taught in Java. This environment is currently linked with WeBWorK, an open source web-based system developed at the University of Rochester that is popular for administering and assessing mathematics and physics coursework, but is designed for the potential integration with other course management system (CMS) environments. In addition to the traditional multiple-choice and short answer questions that have been augmented with the extensive randomization and customization routines of WeBWorK, this new environment (called WeBWorK-JAG where JAG stands for Java Auto Grader) can automatically collect and grade free-form program fragments written in Java. Novel pedagogy has been developed based on the capabilities of this extension and preliminary classroom results are discussed in this paper. For example, when students contributed to WeBWorK by creating WeBWorK-JAG questions for their peers, they are exposed to the reality of creating comprehensive unit tests and to the wider quality assurance aspects of formulating questions and their solution sets. This work is described in the context of an emerging commercial market for web-based programming assistants and its unique contributions are summarized.

The prominence of textual data in accounting research has increased dramatically. To assist researchers in understanding and using textual data, this monograph defines and describes common measures of textual data and then demonstrates the collection and processing of textual data using the Python programming language. The monograph is replete with sample code that replicates textual analysis tasks from recent research papers. In the first part of the monograph, we provide guidance on getting started in Python. We first describe Anaconda, a distribution of Python that provides the requisite libraries for textual analysis, and its installation. We then introduce the Jupyter notebook, a programming environment that improves research workflows and promotes replicable research. Next, we teach the basics of Python programming and demonstrate the basics of working with tabular data in the Pandas package. The second part of the monograph focuses on specific textual analysis methods and techniques commonly used in accounting research. We first introduce regular expressions, a sophisticated language for finding patterns in text. We then show how to use regular expressions to extract specific parts from text. Next, we introduce the idea of transforming text data (unstructured data) into numerical measures representing variables of interest (structured data). Specifically, we introduce dictionary-based methods of (1) measuring document sentiment, (2) computing text complexity, (3) identifying forward-looking sentences and risk disclosures, (4) collecting informative numbers in text, and (5) computing the similarity of different pieces of text. For each of these tasks, we cite relevant papers and provide code snippets to implement the relevant metrics from these papers. Finally, the third part of the monograph focuses on automating the collection of textual data. We introduce web scraping and provide code for downloading filings from EDGAR.

Alien is a generator for interactive language-specific programming environments. The system makes a language definition environment available to the language definer and supports him with an interactive generator. Using these tools every syntactic unit of a language can be made available. This can be done by a method of stepwise testing, correction and expansion. Fully defined environments can easily be restricted to subsets of a language.This paper introduces a method making a large software product executable on small UNIX machines as well as a survey of the system's components. These are designed to make the implementation on PCs effective and to develop a powerful generator for programming environments. Therefore the results are of interest to all projects in the programming environment field.ALIEN has been implemented in C running on Siemens PC-X, PC-X10, PC-MX-2 machines and Atari PC. Implementation on Apollo and SUN workstations is still in progress and on IBM PC is planned.The project is supported by the Stiftung Volkswagen-werk under grant 1/61 602.

This project focuses on creating a versatile AI desktop assistant using Python and Tkinter, designed to simplify routine tasks through intuitive voice commands. By combining speech recognition, text-to speech technologies, and a graphical user interface, the assistant delivers a seamless user experience for task automation. The project serves as an excellent starting point for those interested in artificial intelligence, human-computer interaction, and Python programming. The assistant's core functionality includes recognizing and executing voice commands, providing real-time weather updates for a specified city, announcing the current time, and automating web browsing for popular websites. A minimalistic yet effective GUI ensures accessibility for users of all skill levels. With its modular architecture, this project also demonstrates how Python's extensive library ecosystem can be leveraged to integrate diverse functionalities efficiently. This project creates a simple AI desktop assistant using Python and Tkinter. The assistant helps automate common tasks through voice commands, using speech recognition and text-to-speech technologies.

In Chapter 2, we acquainted ourselves with Python programming using Jupyter Notebook. You should now be comfortable writing interactive Python programs with Jupyter Notebook.

Teaching and learning programming is a challenge. Although several learning and programming environments have been proposed for classes, there seems to be more dissent than consensus as to which tools are preferable over others. This paper investigates teachers’ perspectives on popular learning and programming environments used in secondary computer science education in Germany. The environments investigated are: BlueJ, Scratch, Greenfoot, Eclipse, MIT App Inventor, Processing IDE, and Alice. Based on prior research, a catalogue of environment features supporting the learning processes of students was constructed. Using these criteria, an online-survey was conducted with computer science teachers in North Rhine-Westphalia, Germany. In the survey, the participating teachers evaluated the selected tools’ adequacy for teaching object-oriented programming. The findings support the results of prior research conducted with students, stressing the importance of a simple and user-friendly graphical user interface (GUI) as well as the option to visualise classes and objects. Contrary to prior studies, the results show that teachers do not see the editor as equally important, as students do, and that there is no consensus about the role of the area of application for choosing an integrated development environment (IDE). Student-friendly debugging messages as well as a step-by-step execution of programs were identified as important features. Although no tool excelled for every criterion, the clear favourite was BlueJ.

Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-point precision and algorithm stability, and extensive online resources support further study. This textbook represents a targeted package for students requiring a solid foundation in Python programming.

This paper discusses the pilot testing and evaluation of a database-driven Web-based programming environment called WIPE (Web integrated programming environment). WIPE is a teaching tool for secondary education students that are introduced to the principles of programming. The programming environment was used in secondary schools in Greece and the results of its evaluation demonstrate that it successfully deals with the difficulties novices meet.

Since Arduino is the main entry-level platform to the world of electronic circuits and systems, there are many programming environments that try to ease the burden of Arduino textual programming on young primary students. Although block-based, most of these environments retain the imperative structures of textual programming languages, which are not easily comprehensible by such students. The proposed, in this paper, Learning-Arduino-With-Rules Introductory System (LAWRIS) tries to tackle this problem by adopting a rule-based approach: an Arduino-based pre-specified system can be programmed by constructing rules with very simple and intuitive jigsaw pieces, in a visual, web-based environment. Contrary to other programming environments for Arduino, the main logic of LAWRIS is implemented on the board side and only a configuration string is downloaded to it. Thus, LAWRIS features fast responses to both circuit input changes and program modifications, a very important characteristic for young students. It also imposes minimal overhead on the host system, allows web access of the programming environment, and has very low hardware cost.

In a system audit and verification, it is important to securely collect and preserve evidence of execution environments, execution processes, and program execution results. Evidence-based verification of program processes ensures their authenticity; for example, the processes include no altered/infected program library. This paper proposes a solution for collection of evidence on program libraries based on Virtual Machine Monitor (VMM). The solution can solve semantic gap by obtaining library file path names. This paper also shows a way to obtain hash values of library files from a guest OS. Furthermore, this paper provides examples of evidence on program execution and the overhead of the solution.

Off-Line Programming Environment for Robotic Applications

The study of interaction data and learning experiences in education has the potential to allow the analysis of why students pass or fail a course or where a course can be improved. In the special case of programming courses for beginners, analyzing the behaviour of students while coding, is essential but not always possible. To avoid the need to install and setup a programming environment by the students, a web-based environment like the open source JupyterLab can be used. This paper presents Juxl, an extension of this online programming environment, allowing a modular, performant and secure way of logging user interactions and learning experiences. The logs are generated as xAPI statements and stored in a Learning Record Store to be further analyzed.