Being a professor at the university is associated with the acquisition of sufficient domain knowledge and skills to teach. On the other hand, there is a constant need to update and strengthen teaching skills and contribute to an effective learning process. Neuroscience research provides evidence on facilitating factors to student engagement and lasting, durable learning. Toward the improvement of the learning quality in Higher Education, the development of an innovative didactic proposal based on neuroscience was undertaken. For that reason, a mixed research method was designed with the purpose of exploring instructors’ opinions and practices. Participants were sixty academics from five Greek universities. Findings include the recognition of the validity and importance of neuroeducation training. It was also revealed that academics’ theoretical declarations are not always aligned with teaching practice. Equally important is their need to deepen the comprehension of memory function for the enhancement of students’ knowledge retention. Eventual misconceptions about the brain might be present, while not all employed pedagogical methods lead to desired student outcomes such as creativity and critical thinking. In addition, it is considered useful to apply alternative, creative and authentic assessment methods based on neuroscience evidence to increase students’ interest and engagement.

Higher education teaching staff members need to build a scientifically accurate and comprehensive understanding of the function of the brain in learning as neuroscience evidence can constitute a way to optimize teaching and achieve learning excellence. An international consortium developed a professional development six-module course on educational neuroscience and online community of practice by applying design thinking. A mixed methods research design was employed to investigate the attitudes of thirty-two (N = 32) participating academics using a survey comprising eleven closed and open questions. Data analysis methods included descriptive statistics, correlation, generalized additive model and grounded theory. The overall evaluation demonstrated a notable satisfaction level with regard to the quality of the course. Given the power of habits, mentoring and peer interactions are recommended to ensure the effective integration of theoretical neuroscientific evidence into teaching practice.

The expert system approach, although quite old, is still quite effective in scientific areas where experts are required to make diagnoses and predictions. One of those areas is fish disease diagnosis. It is an application domain that currently employs complicated processes, which require high level skills for making accurate diagnoses. On the other hand, complete datasets for full diagnosis to be able to use machine learning techniques are not available. Therefore, in aquaculture, now more than ever, fish farmers do not have the required expertise or equipment to accurately diagnose a fish disease. For that reason, expert systems that can help in the diagnosis, prevention, and treatment of diseases have been developed. In this paper, we attempt to give an overview of the expert system approaches for fish disease diagnosis developed in the last two decades. Based on the analysis of their technical and non-technical characteristics, we propose an expert system architecture and a fish disease diagnosis process aiming at improving the deficiencies of the existing systems. The proposed system can handle all types of fish diseases based on image and non-image data as well as on molecular test results and can provide explanations. The diagnosis process goes through four consecutive levels, where each next level considers an additional category of parameters and provides diagnoses with a higher certainty.

Farmed fish disease diagnosis is an important problem in the fish farming industry, affecting quality of production and financial losses. In this paper, we present a web-based intelligent system that tackles the problem of fish disease diagnosis. To this end, it uses multiple knowledge representation and reasoning methods: rule-based, case-based, weight-based, and voting. Knowledge, which concerns the diagnosis of sea bass diseases, was acquired from experts in the field and represented in the form of decision trees. The diagnostic process is performed in two stages: a general one and a specialized one. In the general stage, a level-based diagnosis is performed, where environmental parameters, external signs, and internal signs are successively examined, and the three most probable diseases are identified. In the specialized stage, which is optional, a specialized expert system is used for each of the resulting diseases, where additional parameters concerning laboratory tests (microbiological, microscopic, molecular, and chemical) are considered. The general stage is the most useful, given that it can be performed on-site in real-time, whereas the specialized one requires time-consuming lab tests. The system also provides explanations for its decisions. Evaluation of the general-stage diagnostic process showed a top-3 accuracy of 78.79% on expert test cases and 94% on an artificial dataset.

Context representation is a crucial part of a variety of robotic and other applications. Context refers to the environment, the (robotic or other) tasks as well as human-machine/environment interactions. One of the most utilized methods for representing context knowledge is ontologies. An ontology offers among others a highly descriptive structured representation and capabilities for inconsistencies checking and querying. Another well-known method is knowledge graphs, which offers a relation-based visualizable structure and querying capability, which are more convenient than those in ontologies. However, knowledge graphs cannot check inconsistencies. Therefore, we present an approach for context representation development, where an ontology is first created, checked and evaluated, and afterwards is converted into a knowledge graph. This approach assures design and implementation of more convenient and consistent context representations. The approach is applied to the creation of a robotics related ontology, where Protégé is used for ontology creation and logical consistency checking (via HermiT reasoner), OOPS! is used for ontology evaluation, and Neo4j is used for converting the ontology to a knowledge graph. Example queries in both representations show the preferability to knowledge graph, while an example shows the inability of knowledge graph to trace inconsistencies.