Building mathematical knowledge with programming: insights from the ScratchMaths project

The ScratchMaths (SM) project sets out to exploit the recent commitment to programming in schools in England for the benefit of mathematics learning and reasoning. This design research project aims to introduce students (age 9-11 years) to computational thinking as a medium for exploring mathematics following a constructionist approach. This paper outlines the project and then focuses on two tensions related to (i) the tool and learning, and (ii) direction and discovery, which can arise within constructionist learning environments and describes how these tensions were addressed through the design of the SM curriculum

Bridging Primary Programming and Mathematics: some findings of design research in England

In this paper we present the background, aims and methodology of the ScratchMaths (SM) project, which has designed curriculum materials and professional development (PD) to support mathematical learning through programming for pupils aged between 9 and 11 years. The project was framed by the particular context of computing in the English education system alongside the long history of research and development in programming and mathematics. In this paper, we present a “framework for action” (diSessa and Cobb 2004) following design research that looked to develop an evidence-based curriculum intervention around carefully chosen mathematical and computational concepts. As a first step in teasing out factors for successful implementation and addressing any gap between our design intentions and teacher delivery, we focus on two key foundational concepts within the SM curriculum: the concept of algorithm and of 360-degree total turn. We found that our intervention as a whole enabled teachers with different backgrounds and levels of confidence to tailor the delivery of the SM in ways that can make these challenging concepts more accessible for both themselves and their pupils

Development of a Sandwich ELISA to Measure Exposure to Occupational Cow Hair Allergens

Background: Cow hair and dander are important inducers of occupational allergies in cattle-exposed farmers. To estimate allergen exposure in farming environments, a sensitive enzyme immunoassay was developed to measure cow hair allergens. Methods: A sandwich ELISA was developed using polyclonal rabbit antibodies against a mixture of hair extracts from different cattle breeds. To assess the specificity of the assay, extracts from other mammalian epithelia, mites, molds and grains were tested. To validate the new assay, cow hair allergens were measured in passive airborne dust samples from the stables and homes of farmers. Dust was collected with electrostatic dust fall collectors (EDCs). Results: The sandwich ELISA was found to be very sensitive (detection limit: 0.1 ng/ml) and highly reproducible, demonstrating intra-and interassay coefficients of variation of 4 and 10%, respectively. The assay showed no reactivity with mites, molds and grains, but some cross-reactivity with other mammalian epithelia, with the strongest reaction with goat. Using EDCs for dust sampling, high concentrations of bovine allergens were measured in cow stables (4,760-559,400 mu g/m(2)). In addition, bovine allergens were detected in all areas of cattle farmer dwellings. A large variation was found between individual samples (0.3-900 mu g/m(2)) and significantly higher values were discovered in changing rooms. Conclusion: The ELISA developed for the detection of cow hair proteins is a useful tool for allergen quantification in occupational and home environments. Based on its low detection limit, this test is sensitive enough to detect allergens in passive airborne dust. Copyright (C) 2011 S. Karger AG, Base

Students’ Evolving Meaning About Tangent Line with the Mediation of a Dynamic Geometry Environment and an Instructional Example Space

In this paper I report a lengthy episode from a teaching experiment in which fifteen Year 12 Greek students negotiated their definitions of tangent line to a function graph. The experiment was designed for the purpose of introducing students to the notion of derivative and to the general case of tangent to a function graph. Its design was based on previous research results on students’ perspectives on tangency, especially in their transition from Geometry to Analysis. In this experiment an instructional example space of functions was used in an electronic environment utilising Dynamic Geometry software with Function Grapher tools. Following the Vygotskian approach according to which students’ knowledge develops in specific social and cultural contexts, students’ construction of the meaning of tangent line was observed in the classroom throughout the experiment. The analysis of the classroom data collected during the experiment focused on the evolution of students’ personal meanings about tangent line of function graph in relation to: the electronic environment; the pre-prepared as well as spontaneous examples; students’ engagement in classroom discussion; and, the role of researcher as a teacher. The analysis indicated that the evolution of students’ meanings towards a more sophisticated understanding of tangency was not linear. Also it was interrelated with the evolution of the meaning they had about the inscriptions in the electronic environment; the instructional example space; the classroom discussion; and, the role of the teacher

Peer mediation in Massachusetts public middle & high schools: Perceptions of educators

While many studies related to school violence and its prevention have focused on the perceptions of elementary students and counselors, there is a dearth of research studies that focus on the perceptions of administrators and teachers. This study examines Massachusetts public middle and high school principals, assistant principals, and teachers (n=135), from 30 schools, perceptions of their peer mediation program\u27s impact on student conflicts. Comparisons between administrators and between levels of schools were conducted to provide a finer grain for the analysis. Methodology: The method of data collection is a mixed, hybrid methodology of 41 quantitative (closed-end) and quasi-quantitative (open-ended) survey questions. The survey instrument was a 10-page, self-administered on-line questionnaire delivered through Survey Monkey, analyzed through descriptive statistics utilizing a comparison of numbers, percentages, and post hoc chi square to determine the differences between the perceptions of administrators and teachers, and differences between their responses as educators in middle school and high school. Findings: The findings indicate that administrators and teachers are concerned about student conflict and violence in their schools; administrators and teachers perceive that peer mediation programs successfully reduce conflict and increase individual student positive behaviors, while only administrators perceive that peer mediation reduces school-wide negative behaviors; similarities and differences exist between middle and high school perceptions that peer mediation successfully reduces conflict, increases positive student behavior, and provides a safe school climate; administrators and teachers perceive there is an unequal distribution of resources that contribute to peer mediation program success; and the top three barriers to successful programs are funding for mediator training, training for faculty/staff, and personnel. Keywords: student conflict, school violence prevention, peer mediation programs, principals\u27 perceptions, conflict resolution, middle schools, high schools

Beyond jam sandwiches and cups of tea: An exploration of primary pupils' algorithm‐evaluation strategies

The long-standing debate into the potential benefit of developing mathematical thinking skills through learning to program has been reignited with the widespread introduction of programming in schools across many countries, including England where it is a statutory requirement for all pupils to be taught programming from five years old. Algorithm is introduced early in the English computing curriculum, yet, there is limited knowledge of how young pupils view this concept. This paper explores pupils’ (aged 10-11) understandings of algorithm following their engagement with one year of ScratchMaths (SM), a curriculum designed to develop computational and mathematical thinking skills through learning to program. 181 pupils from six schools undertook a set of written tasks to assess their interpretations and evaluations of different algorithms that solve the same problem, with a subset of these pupils subsequently interviewed to probe their understandings in greater depth. We discuss the different approaches identified, the evaluation criteria they used and the aspects of the concept that pupils found intuitive or challenging, such as simplification and abstraction. The paper ends with some reflections on the implications of the research, concluding with a set of recommendations for pedagogy in developing primary pupils’ algorithmic thinking

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment