Austin Cory Bart

Transforming introductory computer science projects via real-time web data

While computing is becoming increasingly distributed, programming projects in introductory classes remain mostly divorced from the students day-to-day computing experiences. These experiences entail interacting with real-time Web-based data from sources that include weather reports, news updates, and restaurant recommendations. The disconnect between student experiences and the content of their programming projects is known to drive some students away from computing. In addition, to adequately prepare students for the realities of modern software engineering, educators should introduce issues pertaining to distributed computing early in the curriculum. To address these problems, we have created RealTimeWeb - an architectural framework that makes real-time web data accessible for introductory programming projects. The framework effectively introduces important real-time distributed computing concepts without overwhelming students with the low-level details that working with such data typically requires. Preliminary results indicate that our approach can be effective in the context of a typical CS2 course, and that real-time data is relevant to students. RealTimeWeb libraries and associated resources are publicly available for use, with multiple language bindings to many real-time data sources. A rapid-prototyping tool available through the projects website facilitates the development of client libraries with easily accessible APIs for new real-time Web-based data sources.

BlockPy: An Open Access Data-Science Environment for Introductory Programmers

Non-computer science majors often struggle to find relevance in traditional computing curricula that tend to emphasize abstract concepts, focus on nonpractical entertainment, or rely on decontextualized settings. BlockPy, a web-based, open access Python programming environment, supports introductory programmers in a data-science context through a dual block/text programming view. The web extra at https://youtu.be/RzaOPqOpMoM illustrates BlockPy features discussed in the article.

Position paper: From interest to usefulness with BlockPy, a block-based, educational environment

As block-based environments are used for more mature audiences, the environments must mature themselves. Based on holistic theories of academic motivation, this means making the environment present itself as both interesting and useful, without sacrificing pedagogical power and scaffolding. We present Data Science as a potential context that satisfies all of these constraints, and describe our new block-based programming environment for education that supports data science from day one: BlockPy, available at http://think.cs.vt.edu/blockpy/. BlockPy features a number of powerful, authentic features meant to promote transfer for students to conventional environments as they progress. This includes mutual language translation and interactive feedback, but also powerful tools for getting real-world data and visualizing it. As we have developed the tool, we have identified a number of major research questions that should be answered in order to determine the validity of our hypothesis and the potential of our approach: in particular, how can this environment and context support educators and diverse learners as they progress into conventional environments.

Design and Evaluation of a Block-based Environment with a Data Science Context

As computing becomes pervasive across fields, introductory computing curricula needs new tools to motivate and educate the influx of learners with little prior background and divergent goals. We seek to improve curricula by enriching it with authentic, real-world contexts and powerful scaffolds that can guide learners to success using automated tools, thereby reducing the strain on limited human instructional resources. To address these issues, we have created the BlockPy programming environment, a web-based, open-access, open-source platform for introductory computing students (https://www.blockpy.com). BlockPy has an embedded data science context that allows learners to connect the educational content with real-world scenarios through meaningful problems. The environment is block-based and gives guiding feedback to learners as they complete problems, but also mediates transfer to more sophisticated programming environments by supporting bidirectional, seamless transitions between block and text programming. Although it can be used as a stand-alone application, the environment has first-class support for the latest Learning Tools Interoperability standards, so that instructors can embed the environment directly within their Learning Management System. In this paper, we describe interesting design issues that we encountered during the development of BlockPy, an evaluation of the environment from fine-grained logs, and our future plans for the environment.

Authoring feedback for novice programmers in a block-based language

We present a block-based language for specifying feedback to novice learners about the programs they are constructing in a block-based programming language. In addition to feedback based on run-time and output checking, we are particularly interested in immediate feedback: corrective guidance given as the program is being constructed. Immediate feedback is a natural extension of the block-based language philosophy. Block-based languages prevent by design certain types of mistakes in all cases. Immediate feedback guides against, without fully preventing, problem-specific mistakes (i.e., constructions that are erroneous in only some cases). A feedback specification contains a block pattern and a set of actions that can be taken whenever the corresponding pattern is present or absent in the students block program for a given problem. The paper illustrates the language through several examples derived from misconceptions found in the block-based programs of students taking a university-level Computational Thinking class. The feasibility of the proposed approach is shown by the translation of a specification using an evolving programmatic interface in BlockPy, a dual block/text programming environment for a subset of Python.

Instructional Design is to Teaching as Software Engineering is to Programming

This special session will explore practical results from the educational theory of Instructional Design (ID), with particular focus on the widespread similarities between a process for creating successful courses and a process for creating successful software. We present a small set of specific practices that should be easy for CS educators to adopt. In particular, the session will cover the popular Dick & Carey model, meant for beginners to ID. This model helps instructors rigorously define who they will teach to, what they will teach, how they will assess, and (only then) how they will teach. The approach is parallel to Software Engineering techniques such as Test-Driven Development, Requirements Engineering, and Iterative Development. The session will be a blend of presentation, participation, and assessment. Participants will work in small groups both to foster discussion and to provide learning support. The content of the presentation will particularly focus on how the model can be applied practically. It is our hope that attendees, whether new to teaching or experienced, will adopt or be influenced by the model in order to approach their courses with the same rigor they apply to software development.

Preparing, Visualizing, and Using Real-world Data in Introductory Courses

Working with real-world data has increasingly become a popular context for introductory computing courses. As a valuable 21st century skill, preparing students to be able to divine meaning from data can be useful to their long-term careers. Because Data Science aligns so closely with computing, many of the topics and problems it affords as a context can support the core learning objectives in introductory computing classes. In many instances, incorporating a real-world dataset to provide concrete context for an activity or assignment can improve student engagement and understanding of the abstract educational content being presented. However, there are many problems inherent to bringing real-world data into introductory courses. How do instructors, with finite amounts of time and energy, find and prepare suitable datasets for their pedagogical needs? Once the datasets are ready, how can students conveniently interact with and draw meaning from the datasets, especially when they are used in complex projects that are typical of later introductory courses? On the other hand, how does an instructor balance the complexities of using real-world datasets in the classroom, making sure that students appreciate the meaningfulness of course activities and their connection to learning objectives? This panel brings together experts with experience in using real-world data in introductory computing courses. Each panelist provides unique perspectives and skills to the problem of preparing, interacting, visualizing, and using pedagogical datasets. This panel should be of particular interest to instructors who are considering integrating current and real-world data into their assignments and projects, and to educational developers who want to create and manage datasets for pedagogical purposes. The panel will follow a conventional format: 5 minutes of introduction, 10 minutes for each panelist to present, and then 30 minutes for audience Q&A.

BlockPy Interactive Demo: Dual Text/Block Python Programming Environment for Guided Practice and Data Science (Abstract Only)

Introductory non-major learners face the challenge of mastering programming fundamentals while remaining sufficiently motivated to engage with the computing discipline. In particular, multi-disciplinary students struggle to find relevance in traditional computing curricula that tend to either emphasize abstract concepts, focus on entertainment (e.g., game and animation design), or rely on decontextualized settings. To address these issues, this demo introduces BlockPy, a web-based environment for Python (https://blockpy.com). The most powerful feature of BlockPy is a dual text/block view that beginners can freely move between, using advanced Mutual Language Translation techniques. The environment contextualizes introductory programming with data science by integrating real-world data including weather reports, classic book statistics, and historical crime data. A fusion of Blockly and Skulpt, the entire interface runs locally with no need for server sandboxing. BlockPy is also a platform for interactive, guided practice problems with automatic feedback that scaffolds learners. This demo will walk through the novel features of BlockPys environment, including the instructors perspective of creating new problems and how BlockPy can be embedded in modern LTI-compatible learning management systems. BlockPy is available online for free and is open-sourced on GitHub. This material is based on work supported by the NSF under Grants No. DGE-0822220, DUE-1444094, and DUE-1624320.

Really pushing my buttons: Affordances in block interfaces

Block-based languages are a useful scaffold for novice programmers to create syntactically correct code. However, block environments that attempt to represent feature-rich languages, such as Python or Java, face serious usability and pedagogical challenges. How can the affordances and controls of a block be exposed, without over-complicating the blocks interface and obscuring its meaning to the user? In this paper, we visually explore a number of interface mechanisms to reconfigure blocks. We outline research questions to compare and contrast the advantages and disadvantages of these mechanisms, and call on the community to pursue these trade-offs further.

Implementing an Open-Access, Data Science Programming Environment for Learners

A key retention issue when educating computing novices is ensuring that the frustrations of mastering programming fundamentals do not demotivate and discourage students from studying the discipline. In particular, non-CS majors often struggle to find relevance in traditional computing curricula that tend to either emphasize abstract concepts, focus on non-practical entertainment (e.g., game and animation design), or rely on decontextualized settings. To address these issues, this paper introduces BlockPy, a block-based environment for Python (http://www.blockpy.com). BlockPy is a web-based, open-access programming environment that supports introductory programming with an emphasis on data science. It promotes long-term transfer by scaffolding an introduction to textual programming (Python) through a block-based programming view, ideal for beginners of any background. By supporting the latest Learning Tools Interoperability (LTI) standards, BlockPy is designed to support both informal learners and formal class settings. Specifically, it can be configured to provide guiding feedback for its interactive programming problems, so as to support learners at their own pace. The results from a pilot study of the initial deployment and utilization of BlockPy indicate the potential of the environment to address many of the problems faced by novice learners.