Learning activities for beginners almost always include programming tasks that require a student to write a program to solve a particular problem. This difference has not been observed for a group of beginners.Įvery year, millions of students learn how to write programs. Results of the analysis show that the number of completely solved modules is significantly higher in the period when achievements have been introduced to the system for users that declared having programming experience before joining the course. A set of achievements has been introduced in order to change students' behavior and achieve the effect of an increased number of completely solved modules. In order to change students' behavior, the gamification approach has been utilized. Analysis of data originating from the system provided insight into learners' behavior and allowed for identification that students often selectively solve assignments and skip tasks perceived by a lecturer as important. Such a system has been introduced as an additional tool within the university's Introduction to programming course. This feature, combined with immediate feedback after every submission may support the self-progress of students in the context of programming classes. One of the features of online learning environments is an option of an automated evaluation of programming tasks. Although Elements provides a modern, simple to program pythonic approach with Jupyter notebooks and unit-tests, its CG pipeline is not black-box, exposing for teaching for the first time unique challenging scientific, visual and neural computing concepts. It is designed to actively utilize software design patterns, under an extensible open-source approach. Taking advantage of the unique ECS in a a Scenegraph underlying system, this project aims to bridge CG curricula and modern game engines, that are based on the same approach but often present these notions in a black-box approach. This novelty allows advances in the teaching of CG: from heterogeneous directed acyclic graphs and depth-first traversals, to animation, skinning, geometric algebra and shader-based components rendered via unique systems all the way to their representation as graph neural networks for 3D scientific visualization. We present the Elements project, a computational science and computer graphics (CG) framework, that offers for the first time the advantages of an Entity-Component-System (ECS) along with the rapid prototyping convenience of a Scenegraph-based pythonic framework. In this literature study, we will identify technologies and tools that have been used in the process of teaching and learning computer graphics, we classify them, and discuss to what extend they assist learning. Technological changes over this period have affected what material is taught and how it is taught, as well as have opened new avenues to support computer graphics teaching. Over the past three decades a variety of tools and technologies have been proposed to improve computer graphics learning. Hence computer graphics is often best learned by experimenting with computer graphics concepts and interacting with the resulting renderings. There are two reasons for this: computer graphics combines a variety of skills, such as programming, mathematics, art, and spatial reasoning and computer graphics involves many 3D concepts such as geometry, transformations, illumination and shading, projections and mappings. Teaching computer graphics using traditional methods such as textbooks, whiteboards, presentation slides, websites, and so forth, can be challenging.
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