Values and Beliefs Held About Parenting and Education by School Staff and Parents of Pupils with Special Educational Needs in the Context of Home- School Collaboration

Effective collaboration between school staff and parents of children identified as having special educational needs is considered to be an essential component of the child’s successful education. Differences in beliefs and perspectives adopted by the school staff and parents play an important role in the process of collaboration. However, little is known about the precise relationship between the beliefs and the process of collaboration. The purpose of this study was to explore the values and beliefs held by the school staff and parents in the areas of parenting and education. The study also explored the link between these beliefs and the process of collaboration within four parent-teacher dyads from mainstream primary schools. Focus groups and semi-structured interviews based on repertory grid technique were used. The findings highlighted an overall similarity in the participants’ views on collaboration and in their important beliefs about parenting and education. At the same time, differences in perspectives adopted by parents and teachers were also identified. The author discusses how these differences in perspectives are manifested in the process of collaboration from the point of Cultural Capital Theory. The factors such as power differentials, trust between parents and teachers, and limited resources and constraints of educational system are highlighted. Implication for practice for teachers and educational psychologists are discussed.

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance.