Studying Professional Learning from the Perspective of Wittgenstein's Picture of Language and Meaning

In professional learning, both practice and research have tended to limit focus on linear features of dissemination, the development of programmatic change approaches, and scientism. The aim of this dissertation is to find a grounding way to open up differently thinking about professionals learning in situ. By turning to Wittgenstein I shift the fundamentals underlying our talk about professional learning towards a picture of language and meaning. Reacting to the representationalist approach to language initiated by Frege, Wittgenstein sketches a picture of language and meaning consisting of the interrelated parts of language-games, grammar, and rules, focused around the use of signs. My view of Wittgenstein emphasizes language-games, and thus I emphasize moves and move-making (as per Sudnows picture of language as a moving between places). Professional learning, then, can be viewed as a matter of being able to play more relevant language-games and to have more and better moves to make and places to go. Understanding, in this picture, is a matter of being able to go on correctly in the contexts of community and the institution of language; thus I view professional learning not in terms of knowledge but rather in terms of meaning, i.e., mastery of the use of signs. I apply this picture of professional learning by exploring a species of the classic learning paradox, and then by considering the discourse of educators in actual learning sessions. A professional learning paradox emerges through application of Wittgensteins ideas concerning novices training into a practice, a paradox which oscillates throughout the thought of other theorists of education as well. Next, by applying this picture of professional learning in the case of educators peer-group learning discussions, I show how to view the learning efforts of professionals on the basis of their use of relevant signs. Insights drawn from taking up this perspective have to do with the ways in which the professional learners attempt to forge for themselves new connexions between signs. In sum, by turning to Wittgenstein and his picture of language and meaning, one finds the extraordinary in the ordinary

Applying artificial neural networks to top-down construction cost estimating of highway projects at the conceptual stage

Conceptual cost estimating (CCE) is a challenging task for highway agencies due to the limited design information available at early stages of project development. As a result, agencies frequently experience large variance from the initial construction estimate to the final cost. Despite the initial estimate’s low level of confidence, it is required for all highway projects as an input to feasibility studies and to establish the project’s budget. Many authors have explored the use of artificial intelligence and multiple-regression analysis with promising findings to aide CCE. Unfortunately, at this writing, no highway agencies are known to have implemented these data-driven techniques in practice. One of many reasons for this situation is related to a belief that accurate quantities of work are required to produce an accurate estimate. This approach is termed ‘bottom-up’ estimating and is clearly impossible at the initial stage of project development. A second reason relates to the investment necessary to create a reliable database structure that permits high-level statistical analysis. Therefore, this thesis seeks to investigate improvements to data-driven, ‘top-down’ CCE methods to enable practical application. Firstly, a method to rationally select data used in the model is investigated. The analysis reported in this thesis found that random sampling does not test the true performance of a model for its future application. Secondly, a method to select input variables that have the largest impact on predicting the construction cost but require the least amount of effort is proposed. The models reached a point whereby expending additional effort to include more input variables did not yield an increased performance and debunked the notion that ‘bottom-up’ estimating approaches are intuitively more accurate. This finding is significant for practitioners as resources expended to collect and store additional data points than required is wasted at the conceptual stage. Finally, a method to express the conceptual estimate stochastically is proposed. The traditional deterministic approach of relying on a specific number communicates false precision. This thesis proposes combining artificial neural networks with bootstrap sampling to create an empirical distribution of the construction costs and better communicate a likely range of project costs