Four approaches to teaching programming

Based on a survey of literature, four different approaches to teaching introductory programming are identified and described. Examples of the practice of each approach are identified representing procedural, visual, and object-oriented programming language paradigms. Each approach is then further analysed, identifying advantages and disadvantages for the student and the teacher. The first approach, code analysis, is analogous to reading before writing, that is, recognising the parts and what they mean. It requires learners to analyse and understand existing code prior to producing their own. An alternative is the building blocks approach, analogous to learning vocabulary, nouns and verbs, before constructing sentences. A third approach is identified as simple units in which learners master solutions to small problems before applying the learned logic to more complex problems. The final approach, full systems, is analogous to learning a foreign language by immersion whereby learners design a solution to a non-trivial problem and the programming concepts and language constructs are introduced only when the solution to the problem requires their application. The conclusion asserts that competency in programming cannot be achieved without mastering each of the approaches, at least to some extent. Use of the approaches in combination could provide novice programmers with the opportunities to acquire a full range of knowledge, understanding, and skills. Several orders for presenting the approaches in the classroom are proposed and analysed reflecting the needs of the learners and teachers. Further research is needed to better understand these and other approaches to teaching programming, not in terms of learner outcomes, but in terms of teachers’ actions and techniques employed to facilitate the construction of new knowledge by the learners. Effective classroom teaching practices could be informed by further investigations into the effect on progression of different toolset choices and combinations of teaching approache

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Visual iconic object-oriented programming to advance computer science education and novice programming

Learning how to program a computer is difficult for most people. Computer programming is a cognitively challenging, time consuming, labor intensive, and frustrating endeavor. Years of formal study and training are required to learn a programming language\u27s world of algorithms and data structures. Instructions are coded in advance before the computer demonstrates the desired behavior. Seeing all the programming steps and instruction code is complicated. There exists a tremendous gap between the representations the human brain uses when thinking about a problem and the representations used in programming a computer. Often people are much better at dealing with specific, concrete objects than working with abstract ideas. Concrete and specific programming examples and demonstrations can be very useful. When cleverly chosen and properly used, programming examples and demonstrations help people understand the abstract concepts. Programming by example or demonstration attempts to extend these novel ideas to novice programming

The Stores Model of Code Cognition

Program comprehension is perhaps one of the oldest topics within the psychology of programming. It addresses a central issue: how programmers work with and manipulate source code to construct effective software systems. Models can play an important role in understanding the challenges developers and engineers contend with. This paper presents a model of program comprehension, or code cognition, which has been derived from literature found within the disciplines of computing and psychology. Drawing on direct experimentation, this paper argues that a model of code cognition should take account of the visual, spatial and linguistic abilities of developers. The strengths and weaknesses of this model are discussed and further research directions presented

PenDraw - A Language for Improving Take-Up of SVG

The case is presented for preferring a dedicated Computer Graphics (CG) language to the traditional conventional language plus add-on. PenDraw is presented as an existing language that overcomes many problems of CG add-ons, providing compile-time checking and reduced need for run-time debugging. PenDraw produces well-formed SVG. Evidence is given that PenDraw decreases development costs. Evidence is presented that PenDraw has brought CG programming to a wider ability range of potential users than professional programmers. It is argued that, given its qualities, PenDraw should be able to improve the take-up of CG programming. Such take-up is expected to be slow at first, given the market-place focus on interactivity and 3D. But it is believed that PenDraw's expressive power and cost benefits should lead to growth in its use, and in use of SVG

Designing and evaluating the usability of a machine learning API for rapid prototyping music technology

To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from the questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable

The Example Guru: Suggesting Examples to Novice Programmers in an Artifact-Based Context

Programmers in artifact-based contexts could likely benefit from skills that they do not realize exist. We define artifact-based contexts as contexts where programmers have a goal project, like an application or game, which they must figure out how to accomplish and can change along the way. Artifact-based contexts do not have quantifiable goal states, like the solution to a puzzle or the resolution of a bug in task-based contexts. Currently, programmers in artifact-based contexts have to seek out information, but may be unaware of useful information or choose not to seek out new skills. This is especially problematic for young novice programmers in blocks programming environments. Blocks programming environments often lack even minimal in-context support, such as auto-complete or in-context documentation. Novices programming independently in these blocks-based programming environments often plateau in the programming skills and API methods they use. This work aims to encourage novices in artifact-based programming contexts to explore new API methods and skills. One way to support novices may be with examples, as examples are effective for learning and highly available. In order to better understand how to use examples for supporting novice programmers, I first ran two studies exploring novices\u27 use and focus on example code. I used those results to design a system called the Example Guru. The Example Guru suggests example snippets to novice programmers that contain previously unused API methods or code concepts. Finally, I present an approach for semi-automatically generating content for this type of suggestion system. This approach reduces the amount of expert effort required to create suggestions. This work contains three contributions: 1) a better understanding of difficulties novices have using example code, 2) a system that encourages exploration and use of new programming skills, and 3) an approach for generating content for a suggestion system with less expert effort