Item Response Theory for Peer Assessment

As an assessment method based on a constructivist approach, peer assessment has become popular in recent years. However, in peer assessment, a problem remains that reliability depends on the rater characteristics. For this reason, some item response models that incorporate rater parameters have been proposed. Those models are expected to improve the reliability if the model parameters can be estimated accurately. However, when applying them to actual peer assessment, the parameter estimation accuracy would be reduced for the following reasons. 1) The number of rater parameters increases with two or more times the number of raters because the models include higher-dimensional rater parameters. 2) The accuracy of parameter estimation from sparse peer assessment data depends strongly on hand-tuning parameters, called hyperparameters. To solve these problems, this article presents a proposal of a new item response model for peer assessment that incorporates rater parameters to maintain as few rater parameters as possible. Furthermore, this article presents a proposal of a parameter estimation method using a hierarchical Bayes model for the proposed model that can learn the hyperparameters from data. Finally, this article describes the effectiveness of the proposed method using results obtained from a simulation and actual data experiments

evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R

Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. This paper describes the "evtree" package, which implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. Computationally intensive tasks are fully computed in C++ while the "partykit" (Hothorn and Zeileis 2011) package is leveraged for representing the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions. "evtree" is compared to "rpart" (Therneau and Atkinson 1997), the open-source CART implementation, and conditional inference trees ("ctree", Hothorn, Hornik, and Zeileis 2006). The usefulness of "evtree" is illustrated in a textbook customer classification task and a benchmark study of predictive accuracy in which "evtree" achieved at least similar and most of the time better results compared to the recursive algorithms "rpart" and "ctree".machine learning, classification trees, regression trees, evolutionary algorithms, R

COVNET : A cooperative coevolutionary model for evolving artificial neural networks

This paper presents COVNET, a new cooperative coevolutionary model for evolving artificial neural networks. This model is based on the idea of coevolving subnetworks. that must cooperate to form a solution for a specific problem, instead of evolving complete networks. The combination of this subnetwork is part of a coevolutionary process. The best combinations of subnetworks must be evolved together with the coevolution of the subnetworks. Several subpopulations of subnetworks coevolve cooperatively and genetically isolated. The individual of every subpopulation are combined to form whole networks. This is a different approach from most current models of evolutionary neural networks which try to develop whole networks. COVNET places as few restrictions as possible over the network structure, allowing the model to reach a wide variety of architectures during the evolution and to be easily extensible to other kind of neural networks. The performance of the model in solving three real problems of classification is compared with a modular network, the adaptive mixture of experts and with the results presented in the bibliography. COVNET has shown better generalization and produced smaller networks than the adaptive mixture of experts and has also achieved results, at least, comparable with the results in the bibliography

A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems

Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite

Teaching evolution as an interdisciplinary science: concepts, theory, and network infrastructure for educational design research

Evolution is an interdisciplinary science. Evolutionary theory is routinely employed across the overlapping domains of the natural, social, and computational sciences, as a high level generalization of processes of change within complex adaptive systems. Despite this interdisciplinary character of evolutionary science, evolution education remains almost exclusively the purview of the biology classroom within general education curricula around the world. This thesis engages conceptual clarification and educational design research to map and explore the educational potential of teaching evolution as the interdisciplinary science that it is. Beginning with a foray into student conceptions of the capacities for and causes of cooperation in chimpanzees and human children, it is argued that research in comparative psychology provides a fertile entry point for engaging the interdisciplinarity of evolutionary sciences. A considered analysis of persistent challenges within traditional approaches to biological evolution education then outlines core conceptual issues and pedagogical strategies for an interdisciplinary approach. This conceptual work supports the exploratory development of two novel directions in evolution education. First, in human evolution, a new toolkit is presented to engage students in causal mapping of the many processes and information streams that have shaped human origins. Second, an interdisciplinary approach to community-based school improvement has been developed that empowers youth to become drivers of valued change within their school community, while challenging them to reflect on the evolutionary theoretical context for such cultural change. Future directions in research are discussed within the context of the OpenEvo learning hub, an online educational innovation and design research lab to drive continued development in this space

An Expression of Faith that Fits (Chapter 8 of Starting Missional Churches)

With a wealth of dynamic information regarding the people of his city, Pastor AJ Swoboda, of the Foursquare Church tradition, writes a compelling chapter on planting a church in the unique place of Portland, Oregon. Beginning in their living room, AJ\u27s family pursued God\u27s initiatives by meeting people in a local coffee shop. In three years they have missionally joined God in creating an expression of faith that fits the unique people of Portland. If you are interested in bivocational ministry or how your academic background can equip you for church planting, please continue to read

An Empirical Study of Meta- and Hyper-Heuristic Search for Multi-Objective Release Planning

A variety of meta-heuristic search algorithms have been introduced for optimising software release planning. However, there has been no comprehensive empirical study of different search algorithms across multiple different real-world datasets. In this article, we present an empirical study of global, local, and hybrid meta- and hyper-heuristic search-based algorithms on 10 real-world datasets. We find that the hyper-heuristics are particularly effective. For example, the hyper-heuristic genetic algorithm significantly outperformed the other six approaches (and with high effect size) for solution quality 85% of the time, and was also faster than all others 70% of the time. Furthermore, correlation analysis reveals that it scales well as the number of requirements increases

Econometric Methods for Causal Evaluation of Education Policies and Practices: A Non-Technical Guide

Education policy-makers and practitioners want to know which policies and practices can best achieve their goals. But research that can inform evidence-based policy often requires complex methods to distinguish causation from accidental association. Avoiding econometric jargon and technical detail, this paper explains the main idea and intuition of leading empirical strategies devised to identify causal impacts and illustrates their use with real-world examples. It covers six evaluation methods: controlled experiments, lotteries of oversubscribed programs, instrumental variables, regression discontinuities, differences-indifferences, and panel-data techniques. Illustrating applications include evaluations of early-childhood interventions, voucher lotteries, funding programs for disadvantaged, and compulsory-school and tracking reforms.causal effects, education, policy evaluation, non-technical guide