3/21/2023 0 Comments Coderunner![]() ![]() Rapidly altering our teaching tools to suit the current blended To aid students in grasping concepts quickly. Indeed many courses use graphs and other visualisations Purely on programming, provide an additional challenge to students. Methods of teaching path finding algorithms, based Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. ![]() This work surveys the state-of-the-art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. The outcome of an evaluation evolved from the primordial boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. Program efficiency, behavior, readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deeply, and fairly fashion. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. Practical programming competencies are critical to the success in computer science education and go-to-market of fresh graduates. We further discuss the implications of OpenAI Codex and similar tools for introductory programming education and highlight future research streams that have the potential to improve the quality of the educational experience for both teachers and students alike. Our analysis suggests that there is significant value in massive generative machine learning models as a tool for instructors, although there remains a need for some oversight to ensure the quality of the generated content before it is delivered to students. When creating exercises we find that it is remarkably easy to influence both the programming concepts and the contextual themes they contain, simply by supplying keywords as input to the model. Our results suggest that the majority of the automatically generated content is both novel and sensible, and in some cases ready to use as is. Using OpenAI Codex as the large language model, we create programming exercises (including sample solutions and test cases) and code explanations, assessing these qualitatively and quantitatively. This article explores the natural language generation capabilities of large language models with application to the production of two types of learning resources common in programming courses. A user study with 44 participants shows that the introduction was perceived well by the students, although improvements are still possible, especially in the area of feedback to the students. We describe the different experiences and lessons learned through the introduction and conduction of these exercises. To compensate for the new set of tasks, the workload of assignments on theoretical aspect was reduced. The programming assignments were given in regular intervals during lecture period with a thematic alignment between assignments and lectures. With these assignments, the students should improve their understanding of the theoretical aspects as well as their programming skills. While still maintaining the primary focus of a theoretical computer science course, we introduce a secondary objective of enhancing programming competence by giving practical programming exercises based on select topics from the course. The course used to mostly focus on theoretical and formal aspects of selected algorithms and data structures. In this paper, we describe our lessons learned during the introduction of automatically assessed programming exercises to a Bachelor's level course on algorithms and data structures in the Winter semester 2019/2020, which is yearly taken by around 300 students.
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