Entrepreneurship education constitutes a top priority in policy agendas across the globe as a means to promote economic growth, fight unemployment and create social capital. An important premise of entrepreneurship education is that it can be learned and students can be taught to formulate entrepreneurial mentality, skills and competencies, something that can result in the formulation of startups and business initiatives. Given the importance of entrepreneurship, the necessity to formulate efficient entrepreneurship education frameworks and training programs arise. In this work, we present the design of an entrepreneurship educational environment that is based on learning in 3D virtual worlds. Innovative 3D virtual reality technologies were utilized to provide immersive and efficient learning activities. Various topics of entrepreneurship education courses were designed and formulated to offer students the opportunity to obtain theoretical knowledge of entrepreneurship. The 3D virtual reality educational environment utilizes pedagogical approaches that are based on gamification principles, allowing students to study in immersive ways as well as in game-based learning activities on real challenges that can be found in business environments. The game-based learning activities can help students gain necessary skills, helping them to tackle everyday obstacles on their entrepreneurial pathways. An experimental study was performed to explore the learning efficiency of the environment and the gamified learning activities as well as assess their learning impact on student’s motivation, attitude, and overall learning experience. The evaluation study revealed that the framework offers efficient gamified learning activities that increase students’ motivation and assist in the formulation of entrepreneurship mentality, skills and competencies.

Over the last years, the successful integration of virtual reality in distance education contexts has led to the development of various frameworks related to the virtual learning approaches. 3D virtual worlds are an integral part of the landscape of education and demonstrate novel learning possibilities that can open new directions in education. An important aspect of virtual worlds relates to the intelligent, embodied pedagogical agents that are employed to enhance the interaction with students and improve their overall learning experience. The proper design and integration of embodied pedagogical agents in virtual learning environments are highly desirable. Although virtual agents constitute a vital part of virtual environments, their exact impact needs are yet to be addressed and assessed. The aim of the present study is to thoroughly examine and deeply understand the effect that embodied pedagogical agents have on the learning experience of students as well as on their performance. We examine how students perceive the role of pedagogical agents as learning companions during specific game-based activities and the effect that their assistance has on students’ learning. A concrete experimental study was conducted in AVARES, a 3D virtual world educational environment that teaches the domain of environmental engineering and energy generation. The results of the study point out that embodied pedagogical agents can improve students’ learning experience, enhance their engagement with learning activities and, most of all, improve their knowledge construction and performance.

Information about the paper titled "TPACK INSTRUCTIONAL DESIGN MODEL IN VIRTUAL REALITY FOR DEEPER LEARNING IN SCIENCE AND HIGHER EDUCATION: FROM “APATHY” TO “EMPATHY”" at IATED Digital Library

Virtual worlds constitute an important part of the educational landscape that possesses unique learning capabilities and opens up new horizons in education. A vital part of 3D virtual worlds concerns the intelligent pedagogical agents that are integrated in the environments and aim to improve the interaction with the users and enhance their learning experience. The main purpose of this study is to assess the impact that the pedagogical agents have on students' engagement and learning experiences during training activities in virtual worlds. Specifically, we examine how students perceive the role of pedagogical agents as learning companions during specific exercises and activities and the impact that the guidance offered via pedagogical agents has on students' engagement and learning. In this regard, an experimental evaluation study was designed and performed in a 3D virtual world educational environment that is used to assist students in learning aspects of environmental engineering. The results of the study show that the assistance and the help offered to students via pedagogical agents have great impact on students' engagement and improved their learning experiences.

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.

Combining Rule-based reasoning and Case-based reasoning has been widely used, exhibiting quite successful results, since they have complementary capabilities. A system that can utilize both approaches could potentially take advantage of the positive aspects of both while minimizing their negative aspects. In this paper, we propose a hybrid reasoner which combines Rule based reasoning, Case-based reasoning, as well as Process Mining. To our knowledge, this work is the first one that incorporates process modeling in a hybrid reasoner. Process Mining is used in order to take into consideration possible future facts, which are justified by the current ones through a process model extracted by the cases themselves. Each case is considered as an instance of a process and a process model is extracted by applying process mining techniques. We also present PAVEFS, an intelligent system based on the proposed hybrid reasoner, supporting personalized provision of services for individuals facing autism spectrum disorders.