Percorrer por autor "Freitas, Tiago Carvalho"
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- Goliath and the cognitive load theoryPublication . Freitas, Tiago Carvalho; Costa Neto, Alvaro; Pereira, Maria João; Henriques, Pedro RangelA successful teaching effort is usually dependant on several factors. From the right environment, to a precisely worded exercise statement, it rests on the teacher's shoulders the concoction of the most effective learning assets to their students. A significant part of this process lies on practise: students commonly solidify their knowledge by solving exercises. Creating new programming exercises, specially in high-demand environments such as large classrooms, is a repetitive and error-prone process, specially when stacked with other typical affairs that educators are required to attend to. Goliath, one of the two main contributions of this article, is a template-based, Artificial Intelligence (AI) supported exercise generator, that aims to facilitate the creation of exercise repositories. By using a Domain-Specific Language (DSL) to define exercise templates, combined with the automatic generation of different exercise types, educators can use Goliath's features to improve their exercise repositories, both in size and variety. This systematic approach allows for greater control and automatisation than using a Large Language Model (LLM) directly, as the exercises’ main components can be pre-defined and pre-configured via their templates. Goliath, which is available online for free access, has been tested and its usability assessed. Combined with these functionalities, the content of the exercises themselves, the manner in which they are presented, and how they are rated for difficulty should also be considered in high regard when designing programming exercises. The Cognitive Load Theory (CLT) provides a conceptual foundation to understand problem-solving mechanisms that are commonly found in several aspects and situations of daily life, such as solving programming exercises. This foundation has been explored and systematically structured to construct the second main contribution of this article: guides to create exercise templates in Goliath founded on the Cognitive Load Theory, aiming to improve both teaching and learning computer programming.
- Goliath, a Programming Exercises Generator Supported by AIPublication . Freitas, Tiago Carvalho; Costa Neto, Alvaro; Pereira, Maria João; Henriques, Pedro RangelThe teaching-learning process is complex in nature, requiring many tasks and skills to achieve success in the construction of knowledge. As per any particular kind of cognitive development, teaching and learning Computer Programming is no different in this regard: tasks must be executed, sometimes repeatedly, and skills must be developed. Despite different approaches and methodologies, exercising what has been studied is proven to be effective in pretty much any teaching-learning process. Many tools have been developed throughout time to aid in the execution of this important task, sometimes approaching the problem from the students’ perspective, sometimes from the teachers’. This paper presents Goliath, a semi-automatic generator of Computer Programming exercises, whose functionality is based on Artificial Intelligence (AI) models, a Domain-Specific Language (DSL), and an online application that binds them together. Goliath’s goals are directed towards teachers (and indirectly, students) by aiming to lower the burden of repeatedly constructing exercises. This is achieved through the use of templates that allow for automatic variations of an exercise to be created instantly, while relying on a common foundation. Goliath is meant to be a facilitator, raising availability of exercise lists, while avoiding repetition and the common mistakes that accompany their construction.