Renaldas Gudauskas

Life cycle process model of a market-oriented and student centered higher education

There is not a single definition/explanation about market-orientation education. Two opposite definitions/explanations of pure market-oriented education ( Cato , 2010) and social-market-oriented education (Amaratunga, 2009) are provided in the paper. Integrated multiple criteria analysis at the micro-, meso- and macro-levels are needed to increase efficiency of the market-oriented higher education reforms. Market-oriented higher education reforms management involves numerous aspects that should be considered in addition to making educational, pedagogical, didactical, economic, political and legal/regulatory decisions. These must include social, culture, ethical, psychological, environmental, technological, technical, organizational and managerial aspects. This article presents a Life Cycle Process Model of a Market-Oriented and Student Centered Higher Education (developed during BELL-CURVE (Built Environment Lifelong Learning Challenging University Responses to Vocational Education) projects activities) for such considerations and discusses certain composite parts of it. To demonstrate the application of this research, two Case Studies from Lithuania are submitted for consideration.

Integrated model and system for passive houses multiple criteria analysis

In order to design and implement a high-quality passive house project, it is neces sary to take care of its efficiency from the brief stage to the end of its lifes service. The entire process should be planned and executed with a consideration of the goals that are aspired to by the participating and interested parties and the micro, meso and macro environment levels. In order to realize the above purposes, an original Model for an Integrated analysis of Passive Houses and Passive Houses Design Multiple Criteria Decision Support System was developedby the authors, enabling one to analyze a passive houses life cycle, the parties involved in the project and its micro, meso and macro environment as one integrated entity.

Student progress assessment with the help of an intelligent pupil analysis system

Students and lecturers would like to know how well students have learned the study materials being taught. A formal test or exam would cause needless stress for students. To resolve this problem, the authors of this article have developed an Intelligent Pupil Analysis (IPA) System. A sufficient amount of studies worldwide prove an interrelation between pupil size and a persons cognitive load. The obtained research results are comparable with the results from other similar studies. The original contribution of this article, compared to the research results published earlier, is as follows: the IPA System developed by the authors is superior to the traditional pupil analysis research due to the integration of pupil analysis with subsystems of decision support, recommender and intelligent tutoring systems and innovative Models of the Model-base, which permit a more detailed analysis of the knowledge attained by a student. This article ends with a case study to demonstrate the practical operation of the IPA System.

Multiple-Criteria Analysis of Life Cycle of Energy-Efficient Built Environment

For a broader application of the life cycle of energy-efficient built environment in the practice of various countries, more attention needs to be paid not only on the selected most rational processes and solutions, the interest level of the stakeholders, but also on the micro-, meso- and macro-level factors. The authors of this article developed the life cycle of energy-efficient built environment model and different decision support systems over the course of two international projects (IDES-EDU and LEAN CC). Based on this model, professionals involved in design and realization of life cycle of energy-efficient built environment can develop a lot of the alternatives as well as assessing them and making the final choice of the most efficient variant. The model and two systems (Energy Efficient House DSS for cooling and decision support system for assessment of energy generation technologies) are briefly described in this chapter.