Physics as a Mechanism for Including ELLs in Classroom Discourse

English Language Learners (ELLs) are frequently left on the periphery of classroom interactions. Due to misalignment of language skills, teachers and peers communicate with these students less often, decreasing the number of opportunities to engage. Exclusion can be avoided with learning activities that invite all students to participate and contribute ideas. We argue that environments and activities that privilege scientific inductive reasoning increase possibilities for emerging bilingual students to engage. This study investigated first-grade students' discussions about factors that affect how objects float. Students came from a variety of language backgrounds; all were considered beginner/intermediate ELLs. Results show that the goal of inducing principles from actual phenomena encouraged students to communicate their ideas and reasoning, boosting students' confidence in expressing themselves. Following the hybrid space argument of Vygotsky's theory of concept formation, we illustrate that physics can be particularly suitable context for the co-development of concepts and English language skills.Comment: 4 Pages; Physics Education Research Conference Proceedings - 2013, Portland O

3rd grade English language learners making sense of sound

Despite the extensive body of research that supports scientific inquiry and argumentation as cornerstones of physics learning, these strategies continue to be virtually absent in most classrooms, especially those that involve students who are learning English as a second language. This study presents results from an investigation of 3rd grade students' discourse about how length and tension affect the sound produced by a string. These students came from a variety of language backgrounds, and all were learning English as a second language. Our results demonstrate varying levels, and uses, of experiential, imaginative, and mechanistic reasoning strategies. Using specific examples from students' discourse, we will demonstrate some of the productive aspects of working within multiple language frameworks for making sense of physics. Conjectures will be made about how to utilize physics as a context for English Language Learners to further conceptual understanding, while developing their competence in the English language.Comment: 4 pages, PERC 201

Using attribute construction to improve the predictability of a GP financial forecasting algorithm

Financial forecasting is an important area in computational finance. EDDIE 8 is an established Genetic Programming financial forecasting algorithm, which has successfully been applied to a number of international datasets. The purpose of this paper is to further increase the algorithm’s predictive performance, by improving its data space representation. In order to achieve this, we use attribute construction to create new (high-level) attributes from the original (low-level) attributes. To examine the effectiveness of the above method, we test the extended EDDIE’s predictive performance across 25 datasets and compare it to the performance of two previous EDDIE algorithms. Results show that the introduction of attribute construction benefits the algorithm, allowing EDDIE to explore the use of new attributes to improve its predictive accuracy

Changing Roles and Identities in a Teacher Driven Professional Development Community

In a climate where teachers feel deprofessionalized at the hands of regulations, testing, and politics, it is vital that teachers become empowered both in their own teaching and as agents of change. This physics education research study investigates the Streamline to Mastery professional development program, in which the teachers design professional development opportunities for themselves and for fellow teachers. The research reported here describes the process of teacher professional growth through changes in roles and identities. Videos, emails, and interviews were analyzed to glean insight into practice and participation shifts as these physical science teachers formed a community and engaged in their own classroom research. Implications for the role of PER in teacher professional development and teacher preparation will be discussed.Comment: 4 pages, 3 figures, Physics Education Research Conference 2011 Proceedings, Finalist in the PERC 2011 proceedings paper awar

Morphological analysis of CDC2 and glycogen synthase kinase 3β phosphorylation as markers of g2 → m transition in glioma.

G2 → M transition is a strategic target for glioma chemotherapy. Key players in G2 → M transition include CDC2 and glycogen synthase kinase 3β (GSK3β), which are highly regulated by posttranslational phosphorylation. This report is a morphological analysis of CDC2 and GSK3β phosphorylation using immunohistochemistry in gliomas with different biological properties. GBM showed a 2.8-fold and 5.6-fold increase in number of cells positive for pThr161CDC2 and a 4.2- and 6.9-fold increase in number of cells positive for pTyr15CDC2 relative to oligodendroglioma and ependymoma, respectively. Elevated labeling for inhibited phospho-CDC2 (pTyr15CDC) correlates with elevated levels of phosphorylated glycogen synthase kinase 3β (GSK3β). 71% of the GBM cases showed intermediate to high intensity staining for pSer9SGK3β 53% of oligodendroglioma, and 73% of ependymoma showed low intensity staining. CDC2 gene amplification correlates with increased survival in glioblastoma multiforme (GBM) and astrocytoma WHO grades II-III, but not in oligodendroglioma WHO grades II-III

Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

Ant-Miner is a classification rule discovery algorithm that is based on Ant Colony Optimization (ACO) meta-heuristic. cAnt-Miner is the extended version of the algorithm that handles continuous attributes on-the-fly during the rule construction process, while ?Ant-Miner is an extension of the algorithm that selects the rule class prior to its construction, and utilizes multiple pheromone types, one for each permitted rule class. In this paper, we combine these two algorithms to derive a new approach for learning classification rules using ACO. The proposed approach is based on using the measure function for 1) computing the heuristics for rule term selection, 2) a criteria for discretizing continuous attributes, and 3) evaluating the quality of the constructed rule for pheromone update as well. We explore the effect of using different measure functions for on the output model in terms of predictive accuracy and model size. Empirical evaluations found that hypothesis of different functions produce different results are acceptable according to Friedman’s statistical test

Learning Multi-Tree Classification Models with Ant Colony Optimization

Ant Colony Optimization (ACO) is a meta-heuristic for solving combinatorial optimization problems, inspired by the behaviour of biological ant colonies. One of the successful applications of ACO is learning classification models (classifiers). A classifier encodes the relationships between the input attribute values and the values of a class attribute in a given set of labelled cases and it can be used to predict the class value of new unlabelled cases. Decision trees have been widely used as a type of classification model that represent comprehensible knowledge to the user. In this paper, we propose the use of ACO-based algorithms for learning an extended multi-tree classification model, which consists of multiple decision trees, one for each class value. Each class-based decision trees is responsible for discriminating between its class value and all other values available in the class domain. Our proposed algorithms are empirically evaluated against well-known decision trees induction algorithms, as well as the ACO-based Ant-Tree-Miner algorithm. The results show an overall improvement in predictive accuracy over 32 benchmark datasets. We also discuss how the new multi-tree models can provide the user with more understanding and knowledge-interpretability in a given domain