Dealing with uncertainty: challenges and possibilities for the early childhood profession

In many countries, strategies to further develop services and institutions for the education and care of young children are linked to a discourse on professionalism. Ambitious policy goals, it is argued, can only be achieved by a skilled and qualified workforce whose practice is guided by a professional body of knowledge. This article argues that the prevailing conceptualisation of the early childhood professional is constructed out of a particular, hierarchical mode of producing and applying expert knowledge that is not necessarily appropriate to professional practice in the field of early childhood education. However, it is highly effective and contributes to forming a professional habitus that contradicts the relational core of early childhood practice. Drawing on the conceptual framework of hermeneutics, the article explores an alternative paradigm of a relational, systemic professionalism that embraces openness and uncertainty, and encourages co‐construction of professional knowledges and practices. Research, in this frame of thinking, is understood as a dialogic activity of asking critical questions and creating understandings across differences, rather than producing evidence to direct practice.

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.