Research Interests

My Path...

When I started trying to apply distinctive topics with my work, someone called me an inventor. Then I realised, I needed to do research more seriously and starting to focus on the essential and leave the accessory behind.

Having this in mind, I started doing research, looking at several specific topics related with Education and Artificial Intelligence:

- E-Learning, Learning Theories and Environments;

-E-learning;

- Knowledge Representation and Semantic Web;

A first approach, in 2009, of a mashable solution gave place, in 2012, to a tailored application, which can be better described in some publications.

However, that approach did not seem the right one. I decided to design an assessment tool looking at other solutions but also finding the real needs people have when using an assessment tool.

This led my research to the fields of:

- Instructional Design;

- Human Computer Interaction;

- Adaptive Assessment Environments;

- Visual Feedback.

Unfortunately I drop out my research in 2012 and only recently, 2021, I embarrassed again this research.

However, since then I embraced Agent-Based Simulation, Social Networks Analysis with machine learning for my research and dropped out Instructional Design and Human Computer Interaction.

So, my current research interests are:

-Machine Learning;

- Social Graph and Networks Analysis;

- Adaptive Assessment Environments

- Visual Feedback;

- Agent Based Simulation;

Introduction to the research field

Considering that knowledge transfer is ubiquitous, happens anywhere and anytime, and is a social process, learning environments such as schools should be contextualised with this characteristic of ubiquity. In addition, knowledge transfer must have core domains and expert mentors.

There is no doubt that schools and more specific classrooms are excellent places to promote knowledge transfer, but this is not a closed environment. The digital age breaks down the walls of the classroom and expands its accessibility.

Our hypothesis is that students desire immediate results during the learning process and quantitative measures to differentiate themselves. There is also an appealing interest in image representations, in line with visual thinkers, who use the emotional and creative part of the brain to organise information or thoughts in an intuitive way. Our research is focused on how to use technology for formative assessment in formal learning contexts, to infer individually what knowledge each student is acquiring and identify learning strengths and weaknesses.

When I grasped the focus of my real interest: Adaptive Assessment became the thesis topic. I manage to understand what I was looking for and aimed my effort to adaptive assessment, based on visual feedback, trying to motivate students with visual representations of the acquiring knowledge. Also learning is a social process, where the interactions are undoubtedly essential for learner's success.

I am working with this idea for a while now. At present my research is related with neural networks, coloured petri nets, within adaptive assessment, and time-oriented visualization for visual feedback. Also, mobile learning is a research interest, based on the idea of delivering the formative assessment tool for teachers and students work anywhere. In order to develop the prototype my interest relies on Python, with Flask for the Web development and Kivy for the mobile app.