This conceptual study aimed at refining the conceptual rigor of D. W. Sue’s tricomponential model of multicultural counselling competency, and enhancing with an addition of new attitude component. This study anchored its theoretical basis on a concept of nomological network (Cronbach & Meehi, 1955). Construct explication approach (Murphy & Davidshofer, 1998) was taken to develop full explication of four componential model of MCC, containing attitude-awareness-knowledge-skills components. Comprehensive literature review was conducted in the area of multicultural counselling competency to develop working definitions of awareness-knowledge-skills component. Another review was conducted to develop a working definition and a conceptual model of attitude. Under the four-componential framework, a total of 284 characteristic descriptions previously developed under the tricomponential model were conceptually re-examined and re-categorized. Result of the analyses revealed a total of 13 subcategories under the four components. Full construct explication of the four componential model was developed. Research implications of the new model to MCC measurement studies and practical applications to training models will be discussed.

Sequential decision making under uncertainty is a ubiquitous problem. In everyday situations we are faced with a series of decisions that aim to maximize the probability of achieving some goal. However, decision outcomes are often uncertain and it is not always immediately evident how to determine if one decision is better than another. The Reinforcement Learning framework overcomes this difficulty by learning to make optimal decisions based on interactions with the environment. One drawback of Reinforcement Learning is that it requires too much data (interactions) to learn from scratch. For this reason, current approaches attempt to incorporate prior information in order to simplify the learning process. However, this is usually accomplished by making problem-specific assumptions, which limit generalizability of the approaches to other problems. This thesis presents the first steps towards a new framework that incorporates and exploits broad prior knowledge in a principled way. It uses Constraint Satisfaction and Bayesian techniques to construct and update a belief over the environment, as well as over good decisions. This allows for incorporating broad types of prior knowledge without limiting generalizability. Preliminary experiments show that the framework's algorithms work well on toy problems in simulation and encourage further research on real-world problems.

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In activity based modelling the concept of rescheduling is very important in order to gain dynamic scheduling of activities and to adjust the effect of unexpected incidents in individual agendas to keep them realistic and valid. This report describes a new framework to investigate algorithms for rescheduling on a large scale. This framework models the information of traffic demand and results of micro simulation of traffic on a loaded network; it enables agents to adapt their schedules by providing them with information about the traffic flow. A perception filter for each agent is included in this framework. It models the concept that some agents can notice the broadcast traffic information about the incident and get their own prediction of the expected delay, while other agents who do not notice the information can become aware only by experiencing traffic jam. Initial agendas are created by means of the FEATHERS activity based schedule generator for mutually independent agents. FEATHERS has no knowledge about the actual transportation network but makes use of an impedance matrix that specifies the minimal travel time between traffic analysis zones. The matrix specifies a free-flow value for the uncongested case and correction values for the loaded network. In this new framework the network state can be changed by agent behaviour and external incidents; the effect of this change in network state is perceived differently by each agent through a perception filter, and according to the perceived value individual adaption is calculated by a ReScheduler. The modified behaviour again creates new traffic demand hence creating a new traffic state; this phenomenon continues for the complete day. Each activity in the agenda is assumed to generate some utility. Each individual is assumed to maximize the total utility over the day. The ReScheduler is implemented using a marginal utility function that monotonically decreases with activity duration. This results in a monotonically converging relaxation algorithm to efficiently determine the new activity timing when less time is available for activities due to increased travel time caused by the incident.

Sequential decision making under uncertainty is a ubiquitous problem. In everyday situations we are faced with a series of decisions that aim to maximize the probability of achieving some goal. However, decision outcomes are often uncertain and it is not always immediately evident how to determine if one decision is better than another. The Reinforcement Learning framework overcomes this difficulty by learning to make optimal decisions based on interactions with the environment. One drawback of Reinforcement Learning is that it requires too much data (interactions) to learn from scratch. For this reason, current approaches attempt to incorporate prior information in order to simplify the learning process. However, this is usually accomplished by making problem-specific assumptions, which limit generalizability of the approaches to other problems. This thesis presents the first steps towards a new framework that incorporates and exploits broad prior knowledge in a principled way. It uses Constraint Satisfaction and Bayesian techniques to construct and update a belief over the environment, as well as over good decisions. This allows for incorporating broad types of prior knowledge without limiting generalizability. Preliminary experiments show that the framework's algorithms work well on toy problems in simulation and encourage further research on real-world problems.

The domestic construction project contracts have made a great development to large-scale lump sum turnkey project, which includes engineering, procurement and construction. The turnkey contractors are responsible for the interface integration among various projects to reduce the delays caused by poor interface coordination, and to shorten the project schedule, moreover to ensure project quality. However, there are huge discrepancies between lump sum turnkey construction projects and general construction projects. Lump sum turnkey projects require a wider field of engineering project management covering the project planning and project control of engineering design, procurement and site construction. So, it is important to do further research in this area. The main purpose of this study is to develop a complete project management framework for large-scale lump sum turnkey projects from the standpoint of a project owner. A real case of the LNG (Liquified Natural Gas) receiving terminal project at Taichung Harbor was used to verify the feasibility of the proposed framework. This study is based on literature review, interview and case study of a large-scale lump sum turnkey project. The engineering project management system of a large-scale construction contractor was studied. The interviewees included top management personnel, information system personnel, and project management system users of the best practice company. The results of this study serve as guidelines of large-scale lump sum turnkey construction project management for project owners. It¡¦s also beneficial for small and median sized contractors to learn from best practice experiences.

This study has been performed with an aim to provide an insight into how Node.js is used and the Node.js technology adaptation in the open source community. This research displays the diversity of Node.js and can inspire the reader to further development or continued research. Studies into different usages of Node.js have been missing in academic research and therefore this study gives a new, important insight into this technology. The authors used the exploratory case study methodology. For data collection, the authors created a JQuery and HTML script that fetched the desired dataset from Github and that were used as a static base for the study. Based on the usage areas extracted from the literature study, the authors specified different categories of usage. The dataset was manually investigated and placed into the categories, if they were relevant. The results show that web applications is by far the most well represented category with over 50% of all usages falling into this category. Network applications and Web servers come in at second and third position with 14% and 13% respectively. This study provided further categories and the authors could generate a set of diagrams, showing a trend on how the different usage areas changed from 2010 to 2015.

The community college mission is to provide open access to higher education for all students. For that reason, accountability measures have focused primarily on student access to their institutions. However, in recent years there was a shift to include student progress and outcomes, in addition to access, as measures of institutional effectiveness. With this shift came concerns by community college leadership on a national level that they were being held accountable with measures that were inappropriate for this sector of higher education. As a result, the Voluntary Framework of Accountability (VFA) was created. This framework is unique in that it was designed for and by community colleges and is currently being piloted on a national basis by the American Association of Community Colleges at approximately 100 locations. This qualitative research case study, based upon grounded theory, chronicles the participation process of the Community College of Allegheny County (CCAC) in the development and piloting of accountability metrics using the VFA model to measure student progress and outcomes and the resultant impact on policy and practice at the CCAC. In my research, I found a number of factors shaping the implementation of the VFA pilot. The decision of the CCAC to participate in the development and pilot project of the VFA can be traced back to the increased pressure of accountability on the institution and the leadership of the current president and his philosophy about data informed decision-making. Other findings that influenced the implementation of the VFA included the limitations of the pre-VFA metric definitions and measurements that were being used to account for student progress and outcomes. In my treatise I describe the implementation issues, including the challenges and benefits of using the VFA accountability model, and then examine how it has impacted organizational policy and practices. / text

In past research we have demonstrated how methodologies used in the training of performers can both encourage whole-brain learning and answer to the demands of South Africa’s current educational paradigm, outcomes-based education (OBE). OBE is a needs-driven, outcomes-driven and competency-orientated pedagogy, which aims at incorporating learners as active agents within the learning process as opposed to the previous content-driven, teacher-orientated approach to education (Coetzee 2004). Our research was prompted by the constant need for our Drama departments to validate their existence in the light of changing funding structures for the arts, governmental and institutional demands for measured outcomes and our institutions’ emphasis on whole-brain learning as the preferred pedagogical approach to education and training. We explored the ways in which the changes in the South African educational dispensation impact on the work of educators within a Drama department in the Higher Education and Training band (HET) in South Africa. These changes include a focus on competencies and critical outcomes across learning areas and across the qualification bands identified by the new National Qualifications Framework. In our search for ways in which to implement the critical outcomes2 demanded by the OBE framework, we turned to Herrmann’s argument (1995) that optimal, deep structure learning can only take place when whole-brain modes are operative.

This conceptual study aimed at refining the conceptual rigor of D. W. Sue’s tricomponential model of multicultural counselling competency, and enhancing with an addition of new attitude component. This study anchored its theoretical basis on a concept of nomological network (Cronbach & Meehi, 1955). Construct explication approach (Murphy & Davidshofer, 1998) was taken to develop full explication of four componential model of MCC, containing attitude-awareness-knowledge-skills components. Comprehensive literature review was conducted in the area of multicultural counselling competency to develop working definitions of awareness-knowledge-skills component. Another review was conducted to develop a working definition and a conceptual model of attitude. Under the four-componential framework, a total of 284 characteristic descriptions previously developed under the tricomponential model were conceptually re-examined and re-categorized. Result of the analyses revealed a total of 13 subcategories under the four components. Full construct explication of the four componential model was developed. Research implications of the new model to MCC measurement studies and practical applications to training models will be discussed.