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Learning the SQL language for working with relational databases is a fundamental subject for future computer engineers. However, in distance learning contexts or unexpected situations like the COVID-19 pandemic, where students had to follow lectures remotely, they may find it hard to learn. Chatbots are software applications that aim to have conversations with people to help them solve problems or provide support in a specific domain. This paper proposes a chatbot-based learning platform to assist students in learning SQL. A case study has been conducted to evaluate the proposal, with undergraduate computer engineering students using the learning platform to perform SQL queries while being assisted by the chatbot. The results show evidence that students who used the chatbot performed better on the final SQL exam than those who did not. In addition, the research shows positive evidence of the benefits of using such learning platforms to support SQL teaching and learning for both students and lecturers: students use a platform that helps them self-regulate their learning process, while lecturers get interesting metrics on student performance.

Designing visual models to describe and conceptualize objects and systems requires abstraction skills and a predisposition for visual interactions. Readily available modeling tools rely on the users’ logical-mathematical and visual-spatial abilities to support modeling design. However, they fall short of mechanisms to tap into the users’ bodily-kinesthetic abilities. This research presents a model-driven framework to automatically develop visual editors to work with Domain Specific Languages in tangible interaction environments. The framework is illustrated through the development of an editor of entity-relationship models supported by augmented reality. The editor usability evaluation indicates good acceptance by users as well as potential to support alternative interactions and to learn database concepts.

The appropriate categorisation of written information by health professionals is very important to guarantee its accessibility. Unfortunately, the information technology tools that support professionals on that task imply a heavy workload, so that the responsibility for categorising the written content is often delegated to administrative staff. Well-known health ontologies such as SNOMED-CT or MeSH provide a representation of the clinical contents to be used by the information systems. This research proposes a computer based method to automatically extract and code the diagnostics, procedures and treatments according to health ontologies. A Knowledge Management System based on an extended version of Drupal is used to implement and evaluate this proposal. Results provide a positive evidence on the application of the method to support medical professionals.