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

Interviews With College Students: Evaluating Computer Programming Environments For Introductory Courses

Different methods, strategies, or tools have been proposed for teaching Object Oriented Programming (OOP). However, it is still difficult to introduce OOP to novice learners. The problem may be not only adopting a method or language, but also use of an appropriate integrated development environment (IDE). Therefore, the focus should be on the needs of learners when selecting an IDE and the evaluations for instructional purposes may allow making objective decisions for an introductory course design. There are different methods or frameworks for evaluating IDEs and the majority focuses on the experts’ needs. Unfortunately, studies done on instructional appropriateness of IDEs are insufficient. In this study, an evaluation framework is initially proposed, then the candidate IDEs are evaluated, and finally, the perceptions of college students are explored by conducting semi-structured interviews. The data are analyzed by the Verbal Analysis technique, and the results are discussed in view of the evaluation criteria. The results imply that the learners view one of the criteria relatively more supportive for learning

A model driven component agent framework for domain experts

Industrial software systems are becoming more complex with a large number of interacting parts distributed over networks. Due to the inherent complexity in the problem domains, most such systems are modified over time to incorporate emerging requirements, making incremental development a suitable approach for building complex systems. In domain specific systems it is the domain experts as end users who identify improvements that better suit their needs. Examples include meteorologists who use weather modeling software, engineers who use control systems and business analysts in business process modeling. Most domain experts are not fluent in systems programming and changes are realised through software engineers. This process hinders the evolution of the system, making it time consuming and costly. We hypothesise that if domain experts are empowered to make some of the system cha nges, it would greatly ease the evolutionary process, thereby making the systems more effective. Agent Oriented Software Engineering (AOSE) is seen as a natural fit for modeling and implementing distributed complex systems. With concepts such as goals and plans, agent systems support easy extension of functionality that facilitates incremental development. Further agents provide an intuitive metaphor that works at a higher level of abstraction compared to the object oriented model. However agent programming is not at a level accessible to domain experts to capitalise on its intuitiveness and appropriateness in building complex systems. We propose a model driven development approach for domain experts that uses visual modeling and automated code generation to simplify the development and evolution of agent systems. Our approach is called the Component Agent Framework for domain-Experts (CAFnE), which builds upon the concepts from Model Driven Development and the Prometheus agent software engineering methodolo gy. CAFnE enables domain experts to work with a graphical representation of the system, which is easier to understand and work with than textual code. The model of the system, updated by domain experts, is then transformed to executable code using a transformation function. CAFnE is supported by a proof-of-concept toolkit that implements the visual modeling, model driven development and code generation. We used the CAFnE toolkit in a user study where five domain experts (weather forecasters) with no prior experience in agent programming were asked to make changes to an existing weather alerting system. Participants were able to rapidly become familiar with CAFnE concepts, comprehend the system's design, make design changes and implement them using the CAFnE toolkit

Code Park: A New 3D Code Visualization Tool

We introduce Code Park, a novel tool for visualizing codebases in a 3D game-like environment. Code Park aims to improve a programmer's understanding of an existing codebase in a manner that is both engaging and intuitive, appealing to novice users such as students. It achieves these goals by laying out the codebase in a 3D park-like environment. Each class in the codebase is represented as a 3D room-like structure. Constituent parts of the class (variable, member functions, etc.) are laid out on the walls, resembling a syntax-aware "wallpaper". The users can interact with the codebase using an overview, and a first-person viewer mode. We conducted two user studies to evaluate Code Park's usability and suitability for organizing an existing project. Our results indicate that Code Park is easy to get familiar with and significantly helps in code understanding compared to a traditional IDE. Further, the users unanimously believed that Code Park was a fun tool to work with.Comment: Accepted for publication in 2017 IEEE Working Conference on Software Visualization (VISSOFT 2017); Supplementary video: https://www.youtube.com/watch?v=LUiy1M9hUK

The Effects of Objects-First and Objects-Late Methods on Achievements of OOP Learners

Our research explored the effects of objects-first and objects-late methods on achievements of object-oriented programming (OOP) learners during a graduate course. The course’s scope was virtually identical for two groups, but the structure of the contents differed in sequence. The objects-first method emphasized the design and discussion of the object-oriented concepts from the very beginning while the objects-late deferred these concepts to the late lectures. The objects-first learners used all visual functionalities of BlueJ IDE. However, the objects-late learners started with only the text-based interfaces of BlueJ and they benefited its visual support in the last lectures. At the end of the study, we found that there was a statistically significant difference between OOP learner groups

The Role of Artificial Intelligence in Educating Novice Programmers

Programming is an inherently difficult skill to acquire and develop. Those who attempt to learn programming may be easily discouraged. The current landscape for computer science education does not address the needs of every novice programmer. Literature reports a discrepancy between student misconceptions and instructors’ perceptions of those misconceptions. Those who can afford a one-on-one human tutor perform on average two standard deviations better than those who learn via conventional methods, suggesting there is a need for a comparable, cheaper replacement. As a result, a number of intelligent tutoring systems have been developed for the purpose of teaching introductory programming concepts and replicating the benefits of one-on-one human tutoring. In this thesis, we analyze and discuss the literature pertaining to student misconceptions, selecting five fundamental misconception categories for introductory programming to demonstrate the effectiveness of existing intelligent tutoring systems. The features of existing intelligent tutoring systems are discussed and analyzed with respect to their effectiveness in addressing student misconceptions. Finally, we highlight the current gap in research on intelligent tutoring systems, hypothesizing the architecture and features of an ideal intelligent tutoring system for introductory programming.Electrical and Computer Engineerin