Ever Learning, Ever Loving: Augustine on Teaching as Ministry

While most remember Augustine (354-430 AD) as theologian, exegete, and philosopher, the purpose of this essay is to consider Augustine’s legacy and ministry as teacher. After his conversion (386 AD), Augustine’s views on teaching took a turn. His theological convictions thus set the course for his views on teaching throughout the rest of his life. It is to such views on teaching that this essay seeks to examine. Therefore this essay will wrestle through Augustine’s views on the role of the teacher and the learner and the link between the two, his theological basis for teaching, and some of the methods he used in teaching. Having established these crucial points, it will be argued that Augustine’s views on the role of teacher have significant implications for Christian educators today

Approximate Decentralized Bayesian Inference

This paper presents an approximate method for performing Bayesian inference in models with conditional independence over a decentralized network of learning agents. The method first employs variational inference on each individual learning agent to generate a local approximate posterior, the agents transmit their local posteriors to other agents in the network, and finally each agent combines its set of received local posteriors. The key insight in this work is that, for many Bayesian models, approximate inference schemes destroy symmetry and dependencies in the model that are crucial to the correct application of Bayes' rule when combining the local posteriors. The proposed method addresses this issue by including an additional optimization step in the combination procedure that accounts for these broken dependencies. Experiments on synthetic and real data demonstrate that the decentralized method provides advantages in computational performance and predictive test likelihood over previous batch and distributed methods.Comment: This paper was presented at UAI 2014. Please use the following BibTeX citation: @inproceedings{Campbell14_UAI, Author = {Trevor Campbell and Jonathan P. How}, Title = {Approximate Decentralized Bayesian Inference}, Booktitle = {Uncertainty in Artificial Intelligence (UAI)}, Year = {2014}

The Impact of Elementary Mathematics Specialists

Elementary Mathematics Specialists are placed in schools to construct leadership roles and to provide on-site professional development addressing mathematical content and pedagogy in order to enhance instruction and to improve student achievement. A three-year, randomized, control study found that, over time, Specialists had a significant positive impact on student achievement in Grades 3, 4, and 5. This effect on student achievement was not evident at the conclusion of the Specialist\u27s first year of placement. It emerged as knowledgeable Specialists gained experience and as the schools\u27 instructional and administrative staffs learned and worked together. Specialists who were highly engaged with a teacher significantly impacted those teachers\u27 beliefs about mathematics teaching and learning. In addition addition, teachers in schools with a Specialist were more likely to participate in a non-coaching professional activity (attending mathematics-focused grade-level meetings, observing peers\u27 teaching, or attending schoolwide mathematics workshops). The Specialists in this study had substantial programmatic responsibilities that influenced their amount of available time for coaching teachers. Further, the Specialists in this study engaged in a high degree of professional coursework prior to and during at least their first year of placement. Findings should not be generalized to Mathematics Specialists or coaches with less expertise